explore_predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generic end to end flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data_processing.numerai import NumeraiDataLoader\n",
    "from executors.numerai import Numerai\n",
    "from executors.sagemaker import Sagemaker\n",
    "from models.aws_linear_learner import LinearAwsLinearLearner\n",
    "from models.aws_xgboost import LinearAwsXGBooost\n",
    "from models.combiner import NaiveCombiner\n",
    "from models.meta_model import MetaModel\n",
    "from models.pytorch.aws_models import AwsTwoLayerLinearNeuralNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(\n",
    "    format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-24 20:09:48,488 - executors.numerai - INFO - Loading Numerai credentials from environment.\n",
      "2019-09-24 20:09:48,835 - numerapi.numerapi - INFO - target file already exists\n"
     ]
    }
   ],
   "source": [
    "# Loading Numerai data\n",
    "numerai = Numerai()\n",
    "data_location = numerai.download_latest_data(\"data/temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-24 20:09:56,840 - executors.sagemaker - INFO - Initializing Sagemaker executor\n"
     ]
    }
   ],
   "source": [
    "# Initialising the data and sagemaker\n",
    "sagemaker = Sagemaker()\n",
    "data_loader = NumeraiDataLoader(\n",
    "    local_data_location=os.path.join(data_location, \"numerai_training_data.csv\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 18:34:38,098 - models.aws_base - INFO - Initializing AWS Estimator linear_xgboost model\n",
      "2019-09-23 18:34:38,101 - models.aws_base - INFO - Initializing AWS Estimator linear_learner model\n"
     ]
    }
   ],
   "source": [
    "# Loading models\n",
    "xgboost = LinearAwsXGBooost(data=data_loader, aws_executor=sagemaker)\n",
    "linear_learner = LinearAwsLinearLearner(data=data_loader, aws_executor=sagemaker)\n",
    "tl_nn = AwsTwoLayerLinearNeuralNetwork(data=data_loader, aws_executor=sagemaker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the meta model to combine them\n",
    "combiner = NaiveCombiner(data_loader.execute_scoring, 10)\n",
    "meta_model = MetaModel(\n",
    "    data=data_loader, models=[xgboost, linear_learner, tl_nn], combiner=combiner\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 18:35:08,673 - models.meta_model - INFO - Start tuning the meta model\n",
      "2019-09-23 18:35:08,687 - models.aws_base - INFO - Starting the hyperparameter tuning.\n",
      "2019-09-23 18:35:08,712 - data_processing.data_loader - INFO - Splitting the train, validation and test data\n",
      "2019-09-23 18:35:50,227 - data_processing.data_loader - INFO - Done splitting train vs validation-test\n",
      "2019-09-23 18:35:50,650 - data_processing.data_loader - INFO - Done splitting validation and test\n",
      "2019-09-23 18:35:51,765 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 18:35:54,638 - models.aws_base - INFO - Writing data to local machine\n",
      "2019-09-23 18:39:10,126 - executors.sagemaker - INFO - Uploading data to S3\n",
      "2019-09-23 18:43:34,774 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 18:43:44,368 - models.aws_base - INFO - Writing data to local machine\n",
      "2019-09-23 18:44:01,198 - executors.sagemaker - INFO - Uploading data to S3\n",
      "2019-09-23 18:44:32,719 - models.aws_base - INFO - Starting to tune hyperparameters\n",
      "2019-09-23 18:44:33,142 - sagemaker - INFO - Creating hyperparameter tuning job with name: xgboost-190923-1844\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "........................................................................................................!"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 18:53:37,478 - models.aws_base - INFO - Done with the tuning job\n",
      "2019-09-23 18:53:37,479 - models.aws_base - INFO - Starting the hyperparameter tuning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 18:54:04,501 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 18:54:04,511 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 18:54:04,803 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 18:54:04,803 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 18:54:04,815 - models.aws_base - INFO - Starting to tune hyperparameters\n",
      "2019-09-23 18:54:04,817 - sagemaker - INFO - Creating hyperparameter tuning job with name: linear-learner-190923-1854\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................!"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 19:53:46,843 - models.aws_base - INFO - Done with the tuning job\n",
      "2019-09-23 19:53:46,950 - models.aws_pytorch - INFO - Starting to train model.\n",
      "2019-09-23 19:53:48,086 - models.aws_base - INFO - Preparing data for usage\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 19:53:48,087 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 19:53:49,630 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 19:53:49,631 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 19:53:49,631 - models.aws_pytorch - INFO - Starting to fit model\n",
      "2019-09-23 19:53:51,850 - sagemaker - INFO - Creating training-job with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-09-23 17:53:54 Starting - Starting the training job...\n",
      "2019-09-23 17:53:55 Starting - Launching requested ML instances......\n",
      "2019-09-23 17:54:56 Starting - Preparing the instances for training...\n",
      "2019-09-23 17:55:34 Downloading - Downloading input data...\n",
      "2019-09-23 17:56:21 Training - Training image download completed. Training in progress..\u001b[31mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[31mbash: no job control in this shell\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:22,897 sagemaker-containers INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:22,900 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:22,914 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:25,939 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:26,192 sagemaker-containers INFO     Module two_layer_linear does not provide a setup.py. \u001b[0m\n",
      "\u001b[31mGenerating setup.py\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:26,192 sagemaker-containers INFO     Generating setup.cfg\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:26,193 sagemaker-containers INFO     Generating MANIFEST.in\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:26,193 sagemaker-containers INFO     Installing module with the following command:\u001b[0m\n",
      "\u001b[31m/usr/bin/python -m pip install -U . \u001b[0m\n",
      "\u001b[31mProcessing /opt/ml/code\u001b[0m\n",
      "\u001b[31mBuilding wheels for collected packages: two-layer-linear\n",
      "  Running setup.py bdist_wheel for two-layer-linear: started\u001b[0m\n",
      "\u001b[31m  Running setup.py bdist_wheel for two-layer-linear: finished with status 'done'\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-zbdyqkaj/wheels/35/24/16/37574d11bf9bde50616c67372a334f94fa8356bc7164af8ca3\u001b[0m\n",
      "\u001b[31mSuccessfully built two-layer-linear\u001b[0m\n",
      "\u001b[31mInstalling collected packages: two-layer-linear\u001b[0m\n",
      "\u001b[31mSuccessfully installed two-layer-linear-1.0.0\u001b[0m\n",
      "\u001b[31mYou are using pip version 18.1, however version 19.2.3 is available.\u001b[0m\n",
      "\u001b[31mYou should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:28,089 sagemaker-containers INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[31m2019-09-23 17:56:28,103 sagemaker-containers INFO     Invoking user script\n",
      "\u001b[0m\n",
      "\u001b[31mTraining Env:\n",
      "\u001b[0m\n",
      "\u001b[31m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"validation\": \"/opt/ml/input/data/validation\",\n",
      "        \"train\": \"/opt/ml/input/data/train\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"D_in\": 310,\n",
      "        \"lr\": 0.001,\n",
      "        \"D_out\": 1,\n",
      "        \"Hidden\": 150,\n",
      "        \"epochs\": 10\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"validation\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        },\n",
      "        \"train\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"is_master\": true,\n",
      "    \"job_name\": \"sagemaker-pytorch-2019-09-23-17-53-49-735\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"s3://sagemaker-eu-west-1-729071960169/sagemaker-pytorch-2019-09-23-17-53-49-735/source/sourcedir.tar.gz\",\n",
      "    \"module_name\": \"two_layer_linear\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 4,\n",
      "    \"num_gpus\": 0,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"two_layer_linear.py\"\u001b[0m\n",
      "\u001b[31m}\n",
      "\u001b[0m\n",
      "\u001b[31mEnvironment variables:\n",
      "\u001b[0m\n",
      "\u001b[31mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[31mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[31mSM_HPS={\"D_in\":310,\"D_out\":1,\"Hidden\":150,\"epochs\":10,\"lr\":0.001}\u001b[0m\n",
      "\u001b[31mSM_USER_ENTRY_POINT=two_layer_linear.py\u001b[0m\n",
      "\u001b[31mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[31mSM_RESOURCE_CONFIG={\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[31mSM_INPUT_DATA_CONFIG={\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validation\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n",
      "\u001b[31mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[31mSM_CHANNELS=[\"train\",\"validation\"]\u001b[0m\n",
      "\u001b[31mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[31mSM_MODULE_NAME=two_layer_linear\u001b[0m\n",
      "\u001b[31mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[31mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n",
      "\u001b[31mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[31mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[31mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[31mSM_NUM_CPUS=4\u001b[0m\n",
      "\u001b[31mSM_NUM_GPUS=0\u001b[0m\n",
      "\u001b[31mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[31mSM_MODULE_DIR=s3://sagemaker-eu-west-1-729071960169/sagemaker-pytorch-2019-09-23-17-53-49-735/source/sourcedir.tar.gz\u001b[0m\n",
      "\u001b[31mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"train\":\"/opt/ml/input/data/train\",\"validation\":\"/opt/ml/input/data/validation\"},\"current_host\":\"algo-1\",\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"D_in\":310,\"D_out\":1,\"Hidden\":150,\"epochs\":10,\"lr\":0.001},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validation\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"sagemaker-pytorch-2019-09-23-17-53-49-735\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-eu-west-1-729071960169/sagemaker-pytorch-2019-09-23-17-53-49-735/source/sourcedir.tar.gz\",\"module_name\":\"two_layer_linear\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"two_layer_linear.py\"}\u001b[0m\n",
      "\u001b[31mSM_USER_ARGS=[\"--D_in\",\"310\",\"--D_out\",\"1\",\"--Hidden\",\"150\",\"--epochs\",\"10\",\"--lr\",\"0.001\"]\u001b[0m\n",
      "\u001b[31mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[31mSM_CHANNEL_VALIDATION=/opt/ml/input/data/validation\u001b[0m\n",
      "\u001b[31mSM_CHANNEL_TRAIN=/opt/ml/input/data/train\u001b[0m\n",
      "\u001b[31mSM_HP_D_IN=310\u001b[0m\n",
      "\u001b[31mSM_HP_LR=0.001\u001b[0m\n",
      "\u001b[31mSM_HP_D_OUT=1\u001b[0m\n",
      "\u001b[31mSM_HP_HIDDEN=150\u001b[0m\n",
      "\u001b[31mSM_HP_EPOCHS=10\u001b[0m\n",
      "\u001b[31mPYTHONPATH=/usr/local/bin:/usr/lib/python36.zip:/usr/lib/python3.6:/usr/lib/python3.6/lib-dynload:/usr/local/lib/python3.6/dist-packages:/usr/lib/python3/dist-packages\n",
      "\u001b[0m\n",
      "\u001b[31mInvoking script with the following command:\n",
      "\u001b[0m\n",
      "\u001b[31m/usr/bin/python -m two_layer_linear --D_in 310 --D_out 1 --Hidden 150 --epochs 10 --lr 0.001\n",
      "\n",
      "\u001b[0m\n",
      "\u001b[31mGet data loader.\u001b[0m\n",
      "\u001b[31mEpoch: 1, Loss: 0.1258542706345744\u001b[0m\n",
      "\u001b[31mEpoch: 2, Loss: 0.12481397560566175\u001b[0m\n",
      "\u001b[31mEpoch: 3, Loss: 0.12462716007558577\u001b[0m\n",
      "\u001b[31mEpoch: 4, Loss: 0.12455940043244011\u001b[0m\n",
      "\u001b[31mEpoch: 5, Loss: 0.12453974208325533\u001b[0m\n",
      "\u001b[31mEpoch: 6, Loss: 0.12451778971776425\u001b[0m\n",
      "\u001b[31mEpoch: 7, Loss: 0.12450622964728188\u001b[0m\n",
      "\u001b[31mEpoch: 8, Loss: 0.12448968173674414\u001b[0m\n",
      "\u001b[31mEpoch: 9, Loss: 0.12447208993370865\u001b[0m\n",
      "\u001b[31mEpoch: 10, Loss: 0.12445622201306425\u001b[0m\n",
      "\u001b[31mSaving the model.\u001b[0m\n",
      "\u001b[31m2019-09-23 18:01:49,173 sagemaker-containers INFO     Reporting training SUCCESS\u001b[0m\n",
      "\n",
      "2019-09-23 18:01:55 Uploading - Uploading generated training model\n",
      "2019-09-23 18:01:55 Completed - Training job completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:02:24,424 - models.aws_pytorch - INFO - Done with fitting model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training seconds: 381\n",
      "Billable seconds: 381\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:02:25,349 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 20:02:25,350 - models.aws_base - INFO - Writing data to local machine\n",
      "2019-09-23 20:02:38,959 - executors.sagemaker - INFO - Uploading data to S3\n",
      "2019-09-23 20:03:08,794 - models.aws_base - INFO - Creating the transformer job\n",
      "2019-09-23 20:03:08,794 - models.aws_base - INFO - Loading best model from tuning job\n",
      "2019-09-23 20:03:09,004 - models.aws_base - INFO - Loading already trained model xgboost-190923-1844-002-c4b5f3de\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-09-23 16:50:07 Starting - Preparing the instances for training\n",
      "2019-09-23 16:50:07 Downloading - Downloading input data\n",
      "2019-09-23 16:50:07 Training - Training image download completed. Training in progress.\n",
      "2019-09-23 16:50:07 Uploading - Uploading generated training model\n",
      "2019-09-23 16:50:07 Completed - Training job completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:03:09,722 - sagemaker - INFO - Creating model with name: xgboost-190923-1844-002-c4b5f3de\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mArguments: train\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] Running standalone xgboost training.\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] Setting up HPO optimized metric to be : rmse\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] File size need to be processed in the node: 586.01mb. Available memory size in the node: 8587.27mb\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[16:47:23] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[31m[16:47:27] 401446x310 matrix with 124448260 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:27:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[16:47:27] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[31m[16:47:27] 50181x310 matrix with 15556110 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[31m[16:47:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 1002 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[0]#011train-rmse:0.35308#011validation-rmse:0.351838\u001b[0m\n",
      "\u001b[31mMultiple eval metrics have been passed: 'validation-rmse' will be used for early stopping.\n",
      "\u001b[0m\n",
      "\u001b[31mWill train until validation-rmse hasn't improved in 10 rounds.\u001b[0m\n",
      "\u001b[31m[16:47:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 1014 pruned nodes, max_depth=2\u001b[0m\n",
      "\u001b[31m[1]#011train-rmse:0.353045#011validation-rmse:0.351824\u001b[0m\n",
      "\u001b[31m[16:47:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 1016 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[2]#011train-rmse:0.353005#011validation-rmse:0.351786\u001b[0m\n",
      "\u001b[31m[16:47:50] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 984 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[3]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:47:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[4]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:47:59] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[5]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:48:03] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1014 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[6]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:48:08] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[7]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:48:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 1016 pruned nodes, max_depth=2\u001b[0m\n",
      "\u001b[31m[8]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1014 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[9]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:21] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[10]#011train-rmse:0.352942#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:26] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[11]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:30] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[12]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 1016 pruned nodes, max_depth=2\u001b[0m\n",
      "\u001b[31m[13]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[14]#011train-rmse:0.352919#011validation-rmse:0.351707\u001b[0m\n",
      "\u001b[31m[16:48:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[15]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[16]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:53] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[17]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:57] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1016 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[18]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:02] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[19]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:06] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1016 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[20]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:11] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[21]#011train-rmse:0.352919#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:15] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 1018 pruned nodes, max_depth=1\u001b[0m\n",
      "\u001b[31m[22]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:19] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[23]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:24] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1008 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[24]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:28] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[25]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:33] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[26]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[27]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[28]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:46] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1012 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[29]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:51] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1016 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[30]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:55] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[31]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31mStopping. Best iteration:\u001b[0m\n",
      "\u001b[31m[21]#011train-rmse:0.352919#011validation-rmse:0.351705\n",
      "\u001b[0m\n",
      "Training seconds: 208\n",
      "Billable seconds: 208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:03:09,966 - sagemaker - INFO - Creating transform job with name: xgboost-190923-1844-002-c4b5f3de-2019-09-23-18-03-09-966\n",
      "2019-09-23 20:03:10,361 - models.aws_base - INFO - Waiting for the transformer job\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...................\u001b[31mArguments: serve\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:06:10 +0000] [1] [INFO] Starting gunicorn 19.7.1\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:06:10 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:06:10 +0000] [1] [INFO] Using worker: gevent\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:06:10 +0000] [37] [INFO] Booting worker with pid: 37\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:06:10 +0000] [38] [INFO] Booting worker with pid: 38\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:06:10 +0000] [39] [INFO] Booting worker with pid: 39\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:10:INFO] Model loaded successfully for worker : 37\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:10:INFO] Model loaded successfully for worker : 38\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:06:10 +0000] [40] [INFO] Booting worker with pid: 40\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:10:INFO] Model loaded successfully for worker : 39\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:10:INFO] Model loaded successfully for worker : 40\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:24:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:24:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:24:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:24:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:24:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:24:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:24:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:24:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:25:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:25:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:25:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:25:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:25:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:25:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:25:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:25:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:26:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:26:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:26:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:26:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:26:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:18:06:26:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:06:56,823 - models.aws_base - INFO - Loading the results of the transformer job\n",
      "2019-09-23 20:06:56,842 - executors.sagemaker - INFO - Downloading data from s3: from s3://sagemaker-eu-west-1-729071960169/data/linear_xgboost/output_data/test.csv.out to data/temp/linear_xgboost/test.csv.out\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.csv.out\n",
      "data/temp/linear_xgboost\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:06:57,851 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 20:06:57,851 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 20:06:57,852 - models.aws_base - INFO - Creating the transformer job\n",
      "2019-09-23 20:06:57,852 - models.aws_base - INFO - Loading best model from tuning job\n",
      "2019-09-23 20:06:58,073 - models.aws_base - INFO - Loading already trained model linear-learner-190923-1854-009-f7872322\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-09-23 17:19:45 Starting - Preparing the instances for training\n",
      "2019-09-23 17:19:45 Downloading - Downloading input data\n",
      "2019-09-23 17:19:45 Training - Training image download completed. Training in progress.\n",
      "2019-09-23 17:19:45 Uploading - Uploading generated training model\n",
      "2019-09-23 17:19:45 Completed - Training job completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:06:58,700 - sagemaker - INFO - Creating model with name: linear-learner-190923-1854-009-f7872322\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-input.json: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'1000', u'huber_delta': u'1.0', u'num_classes': u'1', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'loss': u'auto', u'mini_batch_size': u'255', u'optimizer': u'auto', u'predictor_type': u'regressor', u'_tuning_objective_metric': u'validation:objective_loss', u'learning_rate': u'0.03719555404301278', u'epochs': u'10', u'feature_dim': u'310', u'use_bias': u'True'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Final configuration: {u'loss_insensitivity': u'0.01', u'epochs': u'10', u'feature_dim': u'310', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'True', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'validation:objective_loss', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'0.03719555404301278', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'255', u'huber_delta': u'1.0', u'num_classes': u'1', u'predictor_type': u'regressor', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 WARNING 140514251040576] Loggers have already been setup.\u001b[0m\n",
      "\u001b[31mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Using default worker.\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Create Store: local\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7fcbb4aa08d0>\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] Scaling model computed with parameters:\n",
      " {'stdev_weight': \u001b[0m\n",
      "\u001b[31m[0.3537611  0.35095778 0.35166723 0.35195994 0.35307026 0.35241228\n",
      " 0.3514709  0.3531433  0.35244408 0.35103846 0.35305503 0.3533745\n",
      " 0.35036698 0.33419597 0.33837804 0.33732823 0.31661114 0.35302675\n",
      " 0.32048059 0.29292822 0.35306332 0.34554133 0.34256738 0.34457472\n",
      " 0.34219706 0.33734596 0.29251617 0.3357016  0.3380498  0.354191\n",
      " 0.35095403 0.34213048 0.34198478 0.28873786 0.33441877 0.33442068\n",
      " 0.26527917 0.33953795 0.28715953 0.34540525 0.34145442 0.3418308\n",
      " 0.35205427 0.3048389  0.33812737 0.35354832 0.335374   0.34203467\n",
      " 0.34926325 0.34260625 0.33443332 0.351621   0.3030377  0.33788905\n",
      " 0.3526646  0.34807053 0.3374744  0.3536615  0.265267   0.3035835\n",
      " 0.29048425 0.34059906 0.35073793 0.3399737  0.34549683 0.35311547\n",
      " 0.35487202 0.2895118  0.35271186 0.34348562 0.30198482 0.28891417\n",
      " 0.35036567 0.33964518 0.3530212  0.34225366 0.29127002 0.35360232\n",
      " 0.34012598 0.33247417 0.3520331  0.3521761  0.34117463 0.32154047\n",
      " 0.29039764 0.34708267 0.33905157 0.35256395 0.28637356 0.34654653\n",
      " 0.31584507 0.34440574 0.34920245 0.3317727  0.3532545  0.34593198\n",
      " 0.35230166 0.34620667 0.35197338 0.33754084 0.35360718 0.35329992\n",
      " 0.33683082 0.28342342 0.29841852 0.35228258 0.35181993 0.3538608\n",
      " 0.3513658  0.3396064  0.33868167 0.35104877 0.35331288 0.35112768\n",
      " 0.28377047 0.3249551  0.3538427  0.17767234 0.35226056 0.35156608\n",
      " 0.35085157 0.3310198  0.33817074 0.30524072 0.3534917  0.35303426\n",
      " 0.3313532  0.3399587  0.34005126 0.33656815 0.30603644 0.35211098\n",
      " 0.29991305 0.35371295 0.3262256  0.17681037 0.35163218 0.35192516\n",
      " 0.35059574 0.3550061  0.35402036 0.35112074 0.3530585  0.351962\n",
      " 0.3515507  0.35268438 0.3520931  0.3539431  0.35291067 0.35377726\n",
      " 0.341418   0.34554633 0.34607992 0.35304102 0.33980057 0.33981326\n",
      " 0.35219172 0.34040475 0.35396844 0.33941206 0.34656715 0.35223365\n",
      " 0.3515711  0.3406446  0.34477025 0.35316995 0.34048668 0.33700305\n",
      " 0.33933833 0.34320885 0.3333512  0.3528724  0.3460018  0.35629132\n",
      " 0.35376993 0.34339944 0.35239097 0.34780326 0.3536386  0.336963\n",
      " 0.33734035 0.35306883 0.35205635 0.35382834 0.35373518 0.35556582\n",
      " 0.3455203  0.35095152 0.33670777 0.35207164 0.33653432 0.3361133\n",
      " 0.35298866 0.33381426 0.35439444 0.35152832 0.35448724 0.35434037\n",
      " 0.35301417 0.35451335 0.34149125 0.35133028 0.3526697  0.35626987\n",
      " 0.33485204 0.3541649  0.35379592 0.3549642  0.35189438 0.3457919\n",
      " 0.33941442 0.34137413 0.35275692 0.35517013 0.35310125 0.3317456\n",
      " 0.35177937 0.3559507  0.3517823  0.3330517  0.3521959  0.3396734\n",
      " 0.344751   0.33971256 0.35308772 0.35053355 0.3532979  0.33916107\n",
      " 0.35174635 0.34174645 0.35213804 0.3563388  0.3510854  0.3408226\n",
      " 0.35277402 0.34890404 0.3445582  0.33885193 0.35224056 0.339613\n",
      " 0.3515716  0.35395107 0.3332635  0.3484597  0.35597005 0.33757678\n",
      " 0.33877686 0.3552606  0.3543929  0.3523425  0.35404626 0.35188335\n",
      " 0.33853662 0.3375497  0.3534921  0.35468644 0.33685172 0.3377131\n",
      " 0.3499684  0.35410747 0.35129902 0.35362667 0.34990337 0.35143253\n",
      " 0.35384825 0.35299143 0.3515194  0.34804285 0.3507869  0.3330553\n",
      " 0.35250968 0.35244545 0.35260624 0.34955972 0.33284464 0.3540037\n",
      " 0.34492967 0.35433874 0.35231876 0.34759888 0.35430488 0.3518586\n",
      " 0.34808582 0.35560977 0.34717858 0.35280612 0.30060324 0.33975565\n",
      " 0.35188562 0.35235375 0.352591   0.34797797 0.35434967 0.34656346\n",
      " 0.35149682 0.30187145 0.33985385 0.34720308 0.3562008  0.35141775\n",
      " 0.3510822  0.35224736 0.35173705 0.34994438 0.34995598 0.3515191\n",
      " 0.35295492 0.35066628 0.34610516 0.35386616]\u001b[0m\n",
      "\u001b[31m<NDArray 310 @cpu(0)>, 'stdev_label': \u001b[0m\n",
      "\u001b[31m[0.3544432]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_label': \u001b[0m\n",
      "\u001b[31m[0.5010538]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_weight': \u001b[0m\n",
      "\u001b[31m[0.50039214 0.5029657  0.50071067 0.49830884 0.49676472 0.49830878\n",
      " 0.4980637  0.49865195 0.4977696  0.49730393 0.49830884 0.49598038\n",
      " 0.49941176 0.5020588  0.49411765 0.503848   0.5051961  0.4952206\n",
      " 0.49926472 0.49862745 0.5039217  0.5008333  0.5007353  0.502451\n",
      " 0.49830884 0.49539214 0.4979657  0.49742648 0.5064951  0.50147057\n",
      " 0.49769607 0.502353   0.49634805 0.49926472 0.49848038 0.5010294\n",
      " 0.5008578  0.498603   0.5016176  0.4970098  0.49857843 0.50453436\n",
      " 0.5019607  0.4954657  0.4954902  0.49688727 0.49757352 0.4960049\n",
      " 0.5006862  0.5021324  0.49779412 0.5038236  0.5005148  0.49468136\n",
      " 0.50296557 0.4977696  0.4954902  0.5017892  0.5010294  0.4944608\n",
      " 0.50007343 0.49605393 0.5019364  0.5000736  0.49909315 0.49536765\n",
      " 0.49816176 0.49943617 0.5000245  0.5012991  0.50002444 0.49830884\n",
      " 0.50367653 0.50164217 0.50289214 0.5009559  0.49843138 0.50147057\n",
      " 0.4992157  0.4996078  0.50355387 0.5019608  0.4982598  0.49953437\n",
      " 0.49884805 0.49767157 0.49436274 0.50034314 0.50247544 0.5039706\n",
      " 0.5038971  0.5034803  0.50022054 0.5006373  0.50171566 0.50044125\n",
      " 0.5010784  0.50007355 0.50237745 0.50014716 0.4922549  0.503701\n",
      " 0.5009314  0.5014216  0.5038726  0.5029166  0.5058332  0.4982353\n",
      " 0.5040441  0.50409317 0.50078434 0.5001961  0.50213236 0.50492656\n",
      " 0.5011274  0.502157   0.49414214 0.5010784  0.5054901  0.5059068\n",
      " 0.50585777 0.4969608  0.49818623 0.5028676  0.5050246  0.5041912\n",
      " 0.49830884 0.50534314 0.49708334 0.4997303  0.5042646  0.49916667\n",
      " 0.5039216  0.49811274 0.5021323  0.5005882  0.49919116 0.49830884\n",
      " 0.5023284  0.49970594 0.4993137  0.4967157  0.49933815 0.5027451\n",
      " 0.50166655 0.4990196  0.49732843 0.4998775  0.4992157  0.5009805\n",
      " 0.50382346 0.49816176 0.4954902  0.50071067 0.5036764  0.50220585\n",
      " 0.50225496 0.5009069  0.505      0.502794   0.5012255  0.4997549\n",
      " 0.49916667 0.5017158  0.50605404 0.49882352 0.49955878 0.5050736\n",
      " 0.5016421  0.50794125 0.50210786 0.5039216  0.49438727 0.5073529\n",
      " 0.50553906 0.5065197  0.50237745 0.5026716  0.49732843 0.5031373\n",
      " 0.49926472 0.50737745 0.50291675 0.50276965 0.49968138 0.502598\n",
      " 0.5052451  0.5009559  0.504044   0.50316167 0.5021078  0.50240195\n",
      " 0.5050244  0.4986275  0.50416666 0.49963236 0.50357836 0.50928915\n",
      " 0.50102943 0.49933824 0.5011765  0.5059314  0.4977206  0.5045098\n",
      " 0.49943638 0.4985049  0.5082107  0.5023039  0.5000246  0.50485295\n",
      " 0.49977937 0.5062009  0.4977696  0.50002444 0.49561274 0.5000733\n",
      " 0.5011274  0.5015686  0.5054657  0.501348   0.4985294  0.4986274\n",
      " 0.49904412 0.50242645 0.50536764 0.49992648 0.50676477 0.50061274\n",
      " 0.49990192 0.5048039  0.49992648 0.507549   0.5022304  0.50321066\n",
      " 0.5059803  0.50017154 0.50416666 0.501299   0.50115186 0.5001226\n",
      " 0.49941176 0.49931368 0.5012746  0.5018872  0.505049   0.4995098\n",
      " 0.5063481  0.50176466 0.49992648 0.50174016 0.4992892  0.5012745\n",
      " 0.49911764 0.4975     0.49933824 0.5065687  0.50485295 0.50031865\n",
      " 0.50313723 0.5001961  0.501397   0.49605393 0.5004657  0.5002941\n",
      " 0.50242645 0.50220597 0.4994608  0.50698537 0.49990195 0.5042158\n",
      " 0.5037255  0.4968137  0.49735293 0.5012746  0.5041422  0.50247556\n",
      " 0.49953422 0.5027942  0.49882352 0.5067157  0.49894607 0.49919116\n",
      " 0.50068617 0.49985296 0.49968138 0.49906862 0.49769607 0.502696\n",
      " 0.49980402 0.49632353 0.49764705 0.50142163 0.50014704 0.505343\n",
      " 0.5031618  0.4989706  0.5028186  0.5012255  0.49855393 0.50208324\n",
      " 0.50102925 0.5029658  0.5035538  0.50147057 0.49958333 0.5025736\n",
      " 0.5000736  0.50223047 0.5072059  0.50232846]\u001b[0m\n",
      "\u001b[31m<NDArray 310 @cpu(0)>}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] nvidia-smi took: 0.0251808166504 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 40, \"sum\": 40.0, \"min\": 40}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 41, \"sum\": 41.0, \"min\": 41}, \"Total Records Seen\": {\"count\": 1, \"max\": 10455, \"sum\": 10455.0, \"min\": 10455}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 10200, \"sum\": 10200.0, \"min\": 10200}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1569258879.422365, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1569258879.422326}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.139855016040277, \"sum\": 1.139855016040277, \"min\": 1.139855016040277}}, \"EndTime\": 1569258909.898884, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569258909.898845}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:09 INFO 140514251040576] #quality_metric: host=algo-1, epoch=0, train mse_objective <loss>=1.13985501604\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12951701730908427, \"sum\": 0.12951701730908427, \"min\": 0.12951701730908427}}, \"EndTime\": 1569258914.345346, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569258914.345303}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #quality_metric: host=algo-1, epoch=0, validation mse_objective <loss>=0.129517017309\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=mse_objective, value=0.129517017309\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1616, \"sum\": 1616.0, \"min\": 1616}, \"Total Records Seen\": {\"count\": 1, \"max\": 411901, \"sum\": 411901.0, \"min\": 411901}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1569258914.347123, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569258879.422564}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11494.6234769 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0223224015182084, \"sum\": 1.0223224015182084, \"min\": 1.0223224015182084}}, \"EndTime\": 1569258944.16863, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569258944.168593}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:44 INFO 140514251040576] #quality_metric: host=algo-1, epoch=1, train mse_objective <loss>=1.02232240152\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12585277085676658, \"sum\": 0.12585277085676658, \"min\": 0.12585277085676658}}, \"EndTime\": 1569258947.800369, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569258947.800326}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #quality_metric: host=algo-1, epoch=1, validation mse_objective <loss>=0.125852770857\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=mse_objective, value=0.125852770857\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] Epoch 1: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 3191, \"sum\": 3191.0, \"min\": 3191}, \"Total Records Seen\": {\"count\": 1, \"max\": 813347, \"sum\": 813347.0, \"min\": 813347}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1569258947.802053, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569258914.347374}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11999.649369 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9981346370066798, \"sum\": 0.9981346370066798, \"min\": 0.9981346370066798}}, \"EndTime\": 1569258978.132421, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569258978.132387}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:18 INFO 140514251040576] #quality_metric: host=algo-1, epoch=2, train mse_objective <loss>=0.998134637007\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12425399832422306, \"sum\": 0.12425399832422306, \"min\": 0.12425399832422306}}, \"EndTime\": 1569258981.708207, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569258981.708159}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #quality_metric: host=algo-1, epoch=2, validation mse_objective <loss>=0.124253998324\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=mse_objective, value=0.124253998324\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] Epoch 2: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 4766, \"sum\": 4766.0, \"min\": 4766}, \"Total Records Seen\": {\"count\": 1, \"max\": 1214793, \"sum\": 1214793.0, \"min\": 1214793}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1569258981.710058, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569258947.802305}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11839.3085984 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9918992249138863, \"sum\": 0.9918992249138863, \"min\": 0.9918992249138863}}, \"EndTime\": 1569259011.548878, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569259011.548841}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:51 INFO 140514251040576] #quality_metric: host=algo-1, epoch=3, train mse_objective <loss>=0.991899224914\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12368582337468151, \"sum\": 0.12368582337468151, \"min\": 0.12368582337468151}}, \"EndTime\": 1569259015.203788, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569259015.203744}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #quality_metric: host=algo-1, epoch=3, validation mse_objective <loss>=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=mse_objective, value=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] Epoch 3: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 6341, \"sum\": 6341.0, \"min\": 6341}, \"Total Records Seen\": {\"count\": 1, \"max\": 1616239, \"sum\": 1616239.0, \"min\": 1616239}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1569259015.205427, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569258981.710352}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11985.1745353 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9902387193278305, \"sum\": 0.9902387193278305, \"min\": 0.9902387193278305}}, \"EndTime\": 1569259044.998423, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569259044.998389}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:24 INFO 140514251040576] #quality_metric: host=algo-1, epoch=4, train mse_objective <loss>=0.990238719328\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364221331402393, \"sum\": 0.12364221331402393, \"min\": 0.12364221331402393}}, \"EndTime\": 1569259048.599141, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569259048.599098}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #quality_metric: host=algo-1, epoch=4, validation mse_objective <loss>=0.123642213314\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=mse_objective, value=0.123642213314\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #progress_metric: host=algo-1, completed 50 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 7916, \"sum\": 7916.0, \"min\": 7916}, \"Total Records Seen\": {\"count\": 1, \"max\": 2017685, \"sum\": 2017685.0, \"min\": 2017685}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1569259048.600234, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569259015.205676}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12021.2539263 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9898242556490723, \"sum\": 0.9898242556490723, \"min\": 0.9898242556490723}}, \"EndTime\": 1569259078.239435, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1569259078.2394}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:58 INFO 140514251040576] #quality_metric: host=algo-1, epoch=5, train mse_objective <loss>=0.989824255649\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364073699174565, \"sum\": 0.12364073699174565, \"min\": 0.12364073699174565}}, \"EndTime\": 1569259081.825879, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1569259081.825844}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #quality_metric: host=algo-1, epoch=5, validation mse_objective <loss>=0.123640736992\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=mse_objective, value=0.123640736992\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 9491, \"sum\": 9491.0, \"min\": 9491}, \"Total Records Seen\": {\"count\": 1, \"max\": 2419131, \"sum\": 2419131.0, \"min\": 2419131}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1569259081.826992, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1569259048.600468}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12082.0442619 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9898115187021317, \"sum\": 0.9898115187021317, \"min\": 0.9898115187021317}}, \"EndTime\": 1569259111.664931, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1569259111.664896}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:31 INFO 140514251040576] #quality_metric: host=algo-1, epoch=6, train mse_objective <loss>=0.989811518702\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364087580199667, \"sum\": 0.12364087580199667, \"min\": 0.12364087580199667}}, \"EndTime\": 1569259115.256418, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1569259115.256376}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #quality_metric: host=algo-1, epoch=6, validation mse_objective <loss>=0.123640875802\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=mse_objective, value=0.123640875802\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #progress_metric: host=algo-1, completed 70 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 11066, \"sum\": 11066.0, \"min\": 11066}, \"Total Records Seen\": {\"count\": 1, \"max\": 2820577, \"sum\": 2820577.0, \"min\": 2820577}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}}, \"EndTime\": 1569259115.257311, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1569259081.827278}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12008.4952158 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9898111228715721, \"sum\": 0.9898111228715721, \"min\": 0.9898111228715721}}, \"EndTime\": 1569259145.095741, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1569259145.095707}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:05 INFO 140514251040576] #quality_metric: host=algo-1, epoch=7, train mse_objective <loss>=0.989811122872\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364088864916668, \"sum\": 0.12364088864916668, \"min\": 0.12364088864916668}}, \"EndTime\": 1569259148.633992, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1569259148.633956}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #quality_metric: host=algo-1, epoch=7, validation mse_objective <loss>=0.123640888649\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=0.123640888649\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] Early stop condition met. Stopping training.\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #progress_metric: host=algo-1, completed 100 % epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 12641, \"sum\": 12641.0, \"min\": 12641}, \"Total Records Seen\": {\"count\": 1, \"max\": 3222023, \"sum\": 3222023.0, \"min\": 3222023}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1569259148.63497, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1569259115.257547}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12027.4209355 records/second\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 WARNING 140514251040576] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 WARNING 140514251040576] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:12 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=0.123640888649\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #validation_score (algo-1) : ('mse_objective', 0.12368582337468151)\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #validation_score (algo-1) : ('mse', 0.12368582337468151)\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #validation_score (algo-1) : ('absolute_loss', 0.30015450932553356)\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #quality_metric: host=algo-1, validation mse_objective <loss>=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #quality_metric: host=algo-1, validation mse <loss>=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #quality_metric: host=algo-1, validation absolute_loss <loss>=0.300154509326\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] Best model found for hyperparameters: {\"lr_scheduler_step\": 10, \"wd\": 0.0001, \"optimizer\": \"adam\", \"lr_scheduler_factor\": 0.99, \"l1\": 0.0, \"learning_rate\": 0.03719555404301278, \"lr_scheduler_minimum_lr\": 1e-05}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] Saved checkpoint to \"/tmp/tmpwM7FyB/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] Test data is not provided.\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 277520.5268859863, \"sum\": 277520.5268859863, \"min\": 277520.5268859863}, \"finalize.time\": {\"count\": 1, \"max\": 7273.230791091919, \"sum\": 7273.230791091919, \"min\": 7273.230791091919}, \"initialize.time\": {\"count\": 1, \"max\": 934.1781139373779, \"sum\": 934.1781139373779, \"min\": 934.1781139373779}, \"check_early_stopping.time\": {\"count\": 9, \"max\": 1.1758804321289062, \"sum\": 5.347013473510742, \"min\": 0.17595291137695312}, \"setuptime\": {\"count\": 1, \"max\": 29.300928115844727, \"sum\": 29.300928115844727, \"min\": 29.300928115844727}, \"update.time\": {\"count\": 8, \"max\": 34924.36408996582, \"sum\": 269209.0108394623, \"min\": 33226.33981704712}, \"epochs\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1569259155.915844, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1569258878.487937}\n",
      "\u001b[0m\n",
      "Training seconds: 368\n",
      "Billable seconds: 368\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:06:58,929 - sagemaker - INFO - Creating transform job with name: linear-learner-190923-1854-009-f7872322-2019-09-23-18-06-58-929\n",
      "2019-09-23 20:06:59,328 - models.aws_base - INFO - Waiting for the transformer job\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...................\u001b[31mDocker entrypoint called with argument(s): serve\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded entry point class algorithm.serve.server_config:config_api\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loading entry points\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded request iterator text/csv\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded request iterator application/x-recordio-protobuf\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded request iterator application/json\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded request iterator application/jsonlines\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded response encoder text/csv\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded response encoder application/x-recordio-protobuf\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded response encoder application/json\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded response encoder application/jsonlines\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loaded entry point class algorithm:model\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] loading model...\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] ...model loaded.\u001b[0m\n",
      "\u001b[31m[09/23/2019 18:09:59 INFO 140611990759232] Number of server workers: 4\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:09:59 +0000] [1] [INFO] Starting gunicorn 19.7.1\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:09:59 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:09:59 +0000] [1] [INFO] Using worker: sync\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:09:59 +0000] [71] [INFO] Booting worker with pid: 71\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:09:59 +0000] [80] [INFO] Booting worker with pid: 80\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:09:59 +0000] [89] [INFO] Booting worker with pid: 89\u001b[0m\n",
      "\u001b[31m[2019-09-23 18:09:59 +0000] [98] [INFO] Booting worker with pid: 98\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"execution_parameters.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262214.027621, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262199.405298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 65.11306762695312, \"sum\": 65.11306762695312, \"min\": 65.11306762695312}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262215.662753, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262199.405298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 55.55009841918945, \"sum\": 55.55009841918945, \"min\": 55.55009841918945}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262215.712285, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262214.027735}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 52.809953689575195, \"sum\": 52.809953689575195, \"min\": 52.809953689575195}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262215.760277, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262199.405298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 29.80494499206543, \"sum\": 29.80494499206543, \"min\": 29.80494499206543}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262215.862287, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262199.405298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.170003890991211, \"sum\": 13.170003890991211, \"min\": 13.170003890991211}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262216.380796, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262215.712396}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.222000122070312, \"sum\": 16.222000122070312, \"min\": 16.222000122070312}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262216.447238, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262215.760378}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.365030288696289, \"sum\": 13.365030288696289, \"min\": 13.365030288696289}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262216.65405, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262215.662896}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 21.730899810791016, \"sum\": 21.730899810791016, \"min\": 21.730899810791016}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262216.701029, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262215.862473}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 9.547948837280273, \"sum\": 9.547948837280273, \"min\": 9.547948837280273}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262216.966534, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262216.380874}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 8.094072341918945, \"sum\": 8.094072341918945, \"min\": 8.094072341918945}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262217.122415, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262216.447755}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 12.588977813720703, \"sum\": 12.588977813720703, \"min\": 12.588977813720703}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569262217.149841, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569262216.701125}\n",
      "\u001b[0m\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:10:46,455 - models.aws_base - INFO - Loading the results of the transformer job\n",
      "2019-09-23 20:10:46,485 - executors.sagemaker - INFO - Downloading data from s3: from s3://sagemaker-eu-west-1-729071960169/data/linear_learner/output_data/test.csv.out to data/temp/linear_learner/test.csv.out\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.csv.out\n",
      "data/temp/linear_learner\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:10:52,579 - models.aws_pytorch - INFO - Starting the PyTorch predictions\n",
      "2019-09-23 20:10:52,580 - models.aws_pytorch - INFO - Deploying the predictor\n",
      "2019-09-23 20:10:52,782 - sagemaker - INFO - Creating model with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 20:10:53,312 - sagemaker - INFO - Creating endpoint with name sagemaker-pytorch-2019-09-23-17-53-49-735\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------------!"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 20:18:09,247 - models.aws_pytorch - WARNING - Don't forget to delete the predicion endpoint\n",
      "2019-09-23 20:18:09,268 - models.aws_pytorch - INFO - Splitting the data in batches\n",
      "2019-09-23 20:18:09,277 - models.aws_pytorch - INFO - Done splitting the data in batches\n",
      "2019-09-23 20:18:09,278 - models.aws_pytorch - INFO - Sending the data to predict to the model\n",
      "2019-09-23 20:19:50,788 - models.aws_pytorch - INFO - Got the predictions\n",
      "2019-09-23 20:19:50,789 - models.aws_pytorch - INFO - Deleting the pytorch endpoint\n",
      "2019-09-23 20:19:50,789 - sagemaker - INFO - Deleting endpoint configuration with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 20:19:51,022 - sagemaker - INFO - Deleting endpoint with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 20:19:51,099 - models.aws_pytorch - INFO - Deleting the endpoint\n",
      "2019-09-23 20:19:51,100 - models.combiner - INFO - Starting to combine\n",
      "2019-09-23 20:19:51,464 - models.combiner - INFO - Got a new best score for a combination: 0.04587268243679211 with weights [1.0, 0.0, 0.0]\n",
      "2019-09-23 20:19:51,578 - models.combiner - INFO - Got a new best score for a combination: 0.04837428787564842 with weights [0.0, 1.0, 0.0]\n",
      "2019-09-23 20:19:51,592 - models.combiner - INFO - Got a new best score for a combination: 0.054399282945416134 with weights [0.5, 0.5, 0.0]\n",
      "2019-09-23 20:19:51,606 - models.combiner - INFO - Got a new best score for a combination: 0.05581770684818853 with weights [0.6666666666666667, 0.33333333333333337, 0.0]\n",
      "2019-09-23 20:19:51,762 - models.combiner - INFO - Got a new best score for a combination: 0.0558221698053331 with weights [0.7142857142857143, 0.28571428571428575, 0.0]\n",
      "2019-09-23 20:19:51,909 - models.combiner - INFO - Got a new best score for a combination: 0.05585596271631148 with weights [0.7000000000000001, 0.3, 0.0]\n",
      "2019-09-23 20:19:52,056 - models.combiner - INFO - Got a new best score for a combination: 0.05585856659541435 with weights [0.6923076923076923, 0.3076923076923077, 0.0]\n",
      "2019-09-23 20:19:52,829 - models.combiner - INFO - Got a new best score for a combination: 0.057360371171968794 with weights [0.5, 0.0, 0.5]\n",
      "2019-09-23 20:19:52,965 - models.combiner - INFO - Got a new best score for a combination: 0.05821952941711658 with weights [0.5, 0.25, 0.25]\n",
      "2019-09-23 20:19:52,980 - models.combiner - INFO - Got a new best score for a combination: 0.05832446740224368 with weights [0.5999999999999999, 0.19999999999999998, 0.19999999999999998]\n",
      "2019-09-23 20:19:54,325 - models.combiner - INFO - Got a new best score for a combination: 0.058394320772981255 with weights [0.5, 0.16666666666666669, 0.33333333333333337]\n",
      "2019-09-23 20:19:54,338 - models.combiner - INFO - Got a new best score for a combination: 0.058422408741799284 with weights [0.5714285714285714, 0.14285714285714285, 0.2857142857142857]\n",
      "2019-09-23 20:19:55,836 - models.combiner - INFO - Got a new best score for a combination: 0.058457993464754894 with weights [0.5454545454545454, 0.18181818181818182, 0.2727272727272727]\n",
      "2019-09-23 20:19:58,702 - models.combiner - INFO - Got a new best score for a combination: 0.058459308565058064 with weights [0.5555555555555556, 0.16666666666666666, 0.2777777777777778]\n",
      "2019-09-23 20:20:06,328 - models.combiner - INFO - Ended the combination job: score 0.058459308565058064 with weights [0.5555555555555556, 0.16666666666666666, 0.2777777777777778]\n",
      "2019-09-23 20:20:06,330 - models.meta_model - INFO - Done with tuning the meta model\n"
     ]
    }
   ],
   "source": [
    "# Train/tune and set the weights\n",
    "meta_model.tune()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict Numerai production data\n",
    "production_data = NumeraiDataLoader(\n",
    "    local_data_location=os.path.join(data_location, \"numerai_tournament_data.csv\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:12:00,959 - models.meta_model - INFO - Predicting the data of the meta model\n",
      "2019-09-23 22:12:00,960 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 22:12:00,961 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 22:12:00,961 - models.aws_base - INFO - Creating the transformer job\n",
      "2019-09-23 22:12:00,962 - models.aws_base - INFO - Loading best model from tuning job\n",
      "2019-09-23 22:12:01,242 - models.aws_base - INFO - Loading already trained model xgboost-190923-1844-002-c4b5f3de\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-09-23 16:50:07 Starting - Preparing the instances for training\n",
      "2019-09-23 16:50:07 Downloading - Downloading input data\n",
      "2019-09-23 16:50:07 Training - Training image download completed. Training in progress.\n",
      "2019-09-23 16:50:07 Uploading - Uploading generated training model\n",
      "2019-09-23 16:50:07 Completed - Training job completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:12:02,084 - sagemaker - INFO - Creating model with name: xgboost-190923-1844-002-c4b5f3de\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mArguments: train\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] Running standalone xgboost training.\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] Setting up HPO optimized metric to be : rmse\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] File size need to be processed in the node: 586.01mb. Available memory size in the node: 8587.27mb\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[16:47:23] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[31m[16:47:27] 401446x310 matrix with 124448260 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:27:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[16:47:27] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[31m[16:47:27] 50181x310 matrix with 15556110 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[31m[16:47:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 1002 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[0]#011train-rmse:0.35308#011validation-rmse:0.351838\u001b[0m\n",
      "\u001b[31mMultiple eval metrics have been passed: 'validation-rmse' will be used for early stopping.\n",
      "\u001b[0m\n",
      "\u001b[31mWill train until validation-rmse hasn't improved in 10 rounds.\u001b[0m\n",
      "\u001b[31m[16:47:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 1014 pruned nodes, max_depth=2\u001b[0m\n",
      "\u001b[31m[1]#011train-rmse:0.353045#011validation-rmse:0.351824\u001b[0m\n",
      "\u001b[31m[16:47:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 1016 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[2]#011train-rmse:0.353005#011validation-rmse:0.351786\u001b[0m\n",
      "\u001b[31m[16:47:50] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 984 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[3]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:47:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[4]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:47:59] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[5]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:48:03] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1014 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[6]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:48:08] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[7]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:48:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 1016 pruned nodes, max_depth=2\u001b[0m\n",
      "\u001b[31m[8]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1014 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[9]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:21] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[10]#011train-rmse:0.352942#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:26] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[11]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:30] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[12]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 1016 pruned nodes, max_depth=2\u001b[0m\n",
      "\u001b[31m[13]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[14]#011train-rmse:0.352919#011validation-rmse:0.351707\u001b[0m\n",
      "\u001b[31m[16:48:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[15]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[16]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:53] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[17]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:57] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1016 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[18]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:02] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[19]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:06] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1016 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[20]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:11] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[21]#011train-rmse:0.352919#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:15] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 1018 pruned nodes, max_depth=1\u001b[0m\n",
      "\u001b[31m[22]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:19] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[23]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:24] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1008 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[24]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:28] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[25]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:33] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[26]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[27]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[28]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:46] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1012 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[29]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:51] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1016 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[30]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:55] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[31]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31mStopping. Best iteration:\u001b[0m\n",
      "\u001b[31m[21]#011train-rmse:0.352919#011validation-rmse:0.351705\n",
      "\u001b[0m\n",
      "Training seconds: 208\n",
      "Billable seconds: 208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:12:02,371 - sagemaker - WARNING - Using already existing model: xgboost-190923-1844-002-c4b5f3de\n",
      "2019-09-23 22:12:02,372 - sagemaker - INFO - Creating transform job with name: xgboost-190923-1844-002-c4b5f3de-2019-09-23-20-12-02-372\n",
      "2019-09-23 22:12:03,039 - models.aws_base - INFO - Waiting for the transformer job\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".................\u001b[31mArguments: serve\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:14:52 +0000] [1] [INFO] Starting gunicorn 19.7.1\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:14:52 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:14:52 +0000] [1] [INFO] Using worker: gevent\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:14:52 +0000] [38] [INFO] Booting worker with pid: 38\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:14:52 +0000] [39] [INFO] Booting worker with pid: 39\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:14:52:INFO] Model loaded successfully for worker : 38\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:14:52 +0000] [40] [INFO] Booting worker with pid: 40\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:14:52:INFO] Model loaded successfully for worker : 39\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:14:52 +0000] [41] [INFO] Booting worker with pid: 41\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:14:52:INFO] Model loaded successfully for worker : 40\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:14:52:INFO] Model loaded successfully for worker : 41\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:10:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:10:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:10:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:10:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:10:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:10:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:11:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:11:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:12:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:12:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:12:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:12:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:12:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:12:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:12:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:12:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:13:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:13:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:13:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:13:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:14:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:14:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:14:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:14:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:14:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:14:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:15:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:15:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:15:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:15:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:15:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:15:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:16:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:16:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:16:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:16:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:16:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:16:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:17:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:17:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:17:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:17:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:17:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:17:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:18:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:18:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:18:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:18:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:18:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:18:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:19:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:19:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:19:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:19:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:20:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:20:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:20:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:20:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:20:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:20:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:20:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:20:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:21:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:21:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:21:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:21:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:21:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:21:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:22:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:22:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:22:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:22:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:23:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:23:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:23:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:23:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:23:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:23:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:24:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:24:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:24:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:24:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:24:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:24:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:25:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:25:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:25:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:25:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:25:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:25:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:26:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:26:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:26:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:26:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:27:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:27:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:27:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:27:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:27:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:27:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:27:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:27:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:28:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:28:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:28:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:28:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:28:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:28:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:29:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:29:INFO] Determined delimiter of CSV input is ','\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[31m[2019-09-23:20:15:29:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:29:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:29:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:29:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:30:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:30:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:30:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:30:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:30:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:30:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:31:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:31:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:31:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:31:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:31:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:31:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:32:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:32:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:32:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:32:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:32:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:32:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:32:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:32:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:33:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:33:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:34:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:34:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:34:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:34:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:34:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:34:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:35:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:35:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:35:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:35:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:35:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:35:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:36:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:36:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:36:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:36:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:36:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:36:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:36:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:36:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:37:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:37:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:37:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:37:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:38:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:38:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:38:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:20:15:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:15:51,113 - models.aws_base - INFO - Loading the results of the transformer job\n",
      "2019-09-23 22:15:51,137 - executors.sagemaker - INFO - Downloading data from s3: from s3://sagemaker-eu-west-1-729071960169/data/linear_xgboost/output_data/test.csv.out to data/temp/linear_xgboost/test.csv.out\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.csv.out\n",
      "data/temp/linear_xgboost\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:15:55,017 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 22:15:55,018 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 22:15:55,020 - models.aws_base - INFO - Creating the transformer job\n",
      "2019-09-23 22:15:55,020 - models.aws_base - INFO - Loading best model from tuning job\n",
      "2019-09-23 22:15:55,253 - models.aws_base - INFO - Loading already trained model linear-learner-190923-1854-009-f7872322\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-09-23 17:19:45 Starting - Preparing the instances for training\n",
      "2019-09-23 17:19:45 Downloading - Downloading input data\n",
      "2019-09-23 17:19:45 Training - Training image download completed. Training in progress.\n",
      "2019-09-23 17:19:45 Uploading - Uploading generated training model\n",
      "2019-09-23 17:19:45 Completed - Training job completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:15:55,950 - sagemaker - INFO - Creating model with name: linear-learner-190923-1854-009-f7872322\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-input.json: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'1000', u'huber_delta': u'1.0', u'num_classes': u'1', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'loss': u'auto', u'mini_batch_size': u'255', u'optimizer': u'auto', u'predictor_type': u'regressor', u'_tuning_objective_metric': u'validation:objective_loss', u'learning_rate': u'0.03719555404301278', u'epochs': u'10', u'feature_dim': u'310', u'use_bias': u'True'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Final configuration: {u'loss_insensitivity': u'0.01', u'epochs': u'10', u'feature_dim': u'310', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'True', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'validation:objective_loss', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'0.03719555404301278', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'255', u'huber_delta': u'1.0', u'num_classes': u'1', u'predictor_type': u'regressor', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 WARNING 140514251040576] Loggers have already been setup.\u001b[0m\n",
      "\u001b[31mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Using default worker.\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Create Store: local\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7fcbb4aa08d0>\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] Scaling model computed with parameters:\n",
      " {'stdev_weight': \u001b[0m\n",
      "\u001b[31m[0.3537611  0.35095778 0.35166723 0.35195994 0.35307026 0.35241228\n",
      " 0.3514709  0.3531433  0.35244408 0.35103846 0.35305503 0.3533745\n",
      " 0.35036698 0.33419597 0.33837804 0.33732823 0.31661114 0.35302675\n",
      " 0.32048059 0.29292822 0.35306332 0.34554133 0.34256738 0.34457472\n",
      " 0.34219706 0.33734596 0.29251617 0.3357016  0.3380498  0.354191\n",
      " 0.35095403 0.34213048 0.34198478 0.28873786 0.33441877 0.33442068\n",
      " 0.26527917 0.33953795 0.28715953 0.34540525 0.34145442 0.3418308\n",
      " 0.35205427 0.3048389  0.33812737 0.35354832 0.335374   0.34203467\n",
      " 0.34926325 0.34260625 0.33443332 0.351621   0.3030377  0.33788905\n",
      " 0.3526646  0.34807053 0.3374744  0.3536615  0.265267   0.3035835\n",
      " 0.29048425 0.34059906 0.35073793 0.3399737  0.34549683 0.35311547\n",
      " 0.35487202 0.2895118  0.35271186 0.34348562 0.30198482 0.28891417\n",
      " 0.35036567 0.33964518 0.3530212  0.34225366 0.29127002 0.35360232\n",
      " 0.34012598 0.33247417 0.3520331  0.3521761  0.34117463 0.32154047\n",
      " 0.29039764 0.34708267 0.33905157 0.35256395 0.28637356 0.34654653\n",
      " 0.31584507 0.34440574 0.34920245 0.3317727  0.3532545  0.34593198\n",
      " 0.35230166 0.34620667 0.35197338 0.33754084 0.35360718 0.35329992\n",
      " 0.33683082 0.28342342 0.29841852 0.35228258 0.35181993 0.3538608\n",
      " 0.3513658  0.3396064  0.33868167 0.35104877 0.35331288 0.35112768\n",
      " 0.28377047 0.3249551  0.3538427  0.17767234 0.35226056 0.35156608\n",
      " 0.35085157 0.3310198  0.33817074 0.30524072 0.3534917  0.35303426\n",
      " 0.3313532  0.3399587  0.34005126 0.33656815 0.30603644 0.35211098\n",
      " 0.29991305 0.35371295 0.3262256  0.17681037 0.35163218 0.35192516\n",
      " 0.35059574 0.3550061  0.35402036 0.35112074 0.3530585  0.351962\n",
      " 0.3515507  0.35268438 0.3520931  0.3539431  0.35291067 0.35377726\n",
      " 0.341418   0.34554633 0.34607992 0.35304102 0.33980057 0.33981326\n",
      " 0.35219172 0.34040475 0.35396844 0.33941206 0.34656715 0.35223365\n",
      " 0.3515711  0.3406446  0.34477025 0.35316995 0.34048668 0.33700305\n",
      " 0.33933833 0.34320885 0.3333512  0.3528724  0.3460018  0.35629132\n",
      " 0.35376993 0.34339944 0.35239097 0.34780326 0.3536386  0.336963\n",
      " 0.33734035 0.35306883 0.35205635 0.35382834 0.35373518 0.35556582\n",
      " 0.3455203  0.35095152 0.33670777 0.35207164 0.33653432 0.3361133\n",
      " 0.35298866 0.33381426 0.35439444 0.35152832 0.35448724 0.35434037\n",
      " 0.35301417 0.35451335 0.34149125 0.35133028 0.3526697  0.35626987\n",
      " 0.33485204 0.3541649  0.35379592 0.3549642  0.35189438 0.3457919\n",
      " 0.33941442 0.34137413 0.35275692 0.35517013 0.35310125 0.3317456\n",
      " 0.35177937 0.3559507  0.3517823  0.3330517  0.3521959  0.3396734\n",
      " 0.344751   0.33971256 0.35308772 0.35053355 0.3532979  0.33916107\n",
      " 0.35174635 0.34174645 0.35213804 0.3563388  0.3510854  0.3408226\n",
      " 0.35277402 0.34890404 0.3445582  0.33885193 0.35224056 0.339613\n",
      " 0.3515716  0.35395107 0.3332635  0.3484597  0.35597005 0.33757678\n",
      " 0.33877686 0.3552606  0.3543929  0.3523425  0.35404626 0.35188335\n",
      " 0.33853662 0.3375497  0.3534921  0.35468644 0.33685172 0.3377131\n",
      " 0.3499684  0.35410747 0.35129902 0.35362667 0.34990337 0.35143253\n",
      " 0.35384825 0.35299143 0.3515194  0.34804285 0.3507869  0.3330553\n",
      " 0.35250968 0.35244545 0.35260624 0.34955972 0.33284464 0.3540037\n",
      " 0.34492967 0.35433874 0.35231876 0.34759888 0.35430488 0.3518586\n",
      " 0.34808582 0.35560977 0.34717858 0.35280612 0.30060324 0.33975565\n",
      " 0.35188562 0.35235375 0.352591   0.34797797 0.35434967 0.34656346\n",
      " 0.35149682 0.30187145 0.33985385 0.34720308 0.3562008  0.35141775\n",
      " 0.3510822  0.35224736 0.35173705 0.34994438 0.34995598 0.3515191\n",
      " 0.35295492 0.35066628 0.34610516 0.35386616]\u001b[0m\n",
      "\u001b[31m<NDArray 310 @cpu(0)>, 'stdev_label': \u001b[0m\n",
      "\u001b[31m[0.3544432]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_label': \u001b[0m\n",
      "\u001b[31m[0.5010538]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_weight': \u001b[0m\n",
      "\u001b[31m[0.50039214 0.5029657  0.50071067 0.49830884 0.49676472 0.49830878\n",
      " 0.4980637  0.49865195 0.4977696  0.49730393 0.49830884 0.49598038\n",
      " 0.49941176 0.5020588  0.49411765 0.503848   0.5051961  0.4952206\n",
      " 0.49926472 0.49862745 0.5039217  0.5008333  0.5007353  0.502451\n",
      " 0.49830884 0.49539214 0.4979657  0.49742648 0.5064951  0.50147057\n",
      " 0.49769607 0.502353   0.49634805 0.49926472 0.49848038 0.5010294\n",
      " 0.5008578  0.498603   0.5016176  0.4970098  0.49857843 0.50453436\n",
      " 0.5019607  0.4954657  0.4954902  0.49688727 0.49757352 0.4960049\n",
      " 0.5006862  0.5021324  0.49779412 0.5038236  0.5005148  0.49468136\n",
      " 0.50296557 0.4977696  0.4954902  0.5017892  0.5010294  0.4944608\n",
      " 0.50007343 0.49605393 0.5019364  0.5000736  0.49909315 0.49536765\n",
      " 0.49816176 0.49943617 0.5000245  0.5012991  0.50002444 0.49830884\n",
      " 0.50367653 0.50164217 0.50289214 0.5009559  0.49843138 0.50147057\n",
      " 0.4992157  0.4996078  0.50355387 0.5019608  0.4982598  0.49953437\n",
      " 0.49884805 0.49767157 0.49436274 0.50034314 0.50247544 0.5039706\n",
      " 0.5038971  0.5034803  0.50022054 0.5006373  0.50171566 0.50044125\n",
      " 0.5010784  0.50007355 0.50237745 0.50014716 0.4922549  0.503701\n",
      " 0.5009314  0.5014216  0.5038726  0.5029166  0.5058332  0.4982353\n",
      " 0.5040441  0.50409317 0.50078434 0.5001961  0.50213236 0.50492656\n",
      " 0.5011274  0.502157   0.49414214 0.5010784  0.5054901  0.5059068\n",
      " 0.50585777 0.4969608  0.49818623 0.5028676  0.5050246  0.5041912\n",
      " 0.49830884 0.50534314 0.49708334 0.4997303  0.5042646  0.49916667\n",
      " 0.5039216  0.49811274 0.5021323  0.5005882  0.49919116 0.49830884\n",
      " 0.5023284  0.49970594 0.4993137  0.4967157  0.49933815 0.5027451\n",
      " 0.50166655 0.4990196  0.49732843 0.4998775  0.4992157  0.5009805\n",
      " 0.50382346 0.49816176 0.4954902  0.50071067 0.5036764  0.50220585\n",
      " 0.50225496 0.5009069  0.505      0.502794   0.5012255  0.4997549\n",
      " 0.49916667 0.5017158  0.50605404 0.49882352 0.49955878 0.5050736\n",
      " 0.5016421  0.50794125 0.50210786 0.5039216  0.49438727 0.5073529\n",
      " 0.50553906 0.5065197  0.50237745 0.5026716  0.49732843 0.5031373\n",
      " 0.49926472 0.50737745 0.50291675 0.50276965 0.49968138 0.502598\n",
      " 0.5052451  0.5009559  0.504044   0.50316167 0.5021078  0.50240195\n",
      " 0.5050244  0.4986275  0.50416666 0.49963236 0.50357836 0.50928915\n",
      " 0.50102943 0.49933824 0.5011765  0.5059314  0.4977206  0.5045098\n",
      " 0.49943638 0.4985049  0.5082107  0.5023039  0.5000246  0.50485295\n",
      " 0.49977937 0.5062009  0.4977696  0.50002444 0.49561274 0.5000733\n",
      " 0.5011274  0.5015686  0.5054657  0.501348   0.4985294  0.4986274\n",
      " 0.49904412 0.50242645 0.50536764 0.49992648 0.50676477 0.50061274\n",
      " 0.49990192 0.5048039  0.49992648 0.507549   0.5022304  0.50321066\n",
      " 0.5059803  0.50017154 0.50416666 0.501299   0.50115186 0.5001226\n",
      " 0.49941176 0.49931368 0.5012746  0.5018872  0.505049   0.4995098\n",
      " 0.5063481  0.50176466 0.49992648 0.50174016 0.4992892  0.5012745\n",
      " 0.49911764 0.4975     0.49933824 0.5065687  0.50485295 0.50031865\n",
      " 0.50313723 0.5001961  0.501397   0.49605393 0.5004657  0.5002941\n",
      " 0.50242645 0.50220597 0.4994608  0.50698537 0.49990195 0.5042158\n",
      " 0.5037255  0.4968137  0.49735293 0.5012746  0.5041422  0.50247556\n",
      " 0.49953422 0.5027942  0.49882352 0.5067157  0.49894607 0.49919116\n",
      " 0.50068617 0.49985296 0.49968138 0.49906862 0.49769607 0.502696\n",
      " 0.49980402 0.49632353 0.49764705 0.50142163 0.50014704 0.505343\n",
      " 0.5031618  0.4989706  0.5028186  0.5012255  0.49855393 0.50208324\n",
      " 0.50102925 0.5029658  0.5035538  0.50147057 0.49958333 0.5025736\n",
      " 0.5000736  0.50223047 0.5072059  0.50232846]\u001b[0m\n",
      "\u001b[31m<NDArray 310 @cpu(0)>}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] nvidia-smi took: 0.0251808166504 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 40, \"sum\": 40.0, \"min\": 40}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 41, \"sum\": 41.0, \"min\": 41}, \"Total Records Seen\": {\"count\": 1, \"max\": 10455, \"sum\": 10455.0, \"min\": 10455}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 10200, \"sum\": 10200.0, \"min\": 10200}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1569258879.422365, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1569258879.422326}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.139855016040277, \"sum\": 1.139855016040277, \"min\": 1.139855016040277}}, \"EndTime\": 1569258909.898884, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569258909.898845}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:09 INFO 140514251040576] #quality_metric: host=algo-1, epoch=0, train mse_objective <loss>=1.13985501604\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12951701730908427, \"sum\": 0.12951701730908427, \"min\": 0.12951701730908427}}, \"EndTime\": 1569258914.345346, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569258914.345303}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #quality_metric: host=algo-1, epoch=0, validation mse_objective <loss>=0.129517017309\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=mse_objective, value=0.129517017309\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1616, \"sum\": 1616.0, \"min\": 1616}, \"Total Records Seen\": {\"count\": 1, \"max\": 411901, \"sum\": 411901.0, \"min\": 411901}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1569258914.347123, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569258879.422564}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11494.6234769 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0223224015182084, \"sum\": 1.0223224015182084, \"min\": 1.0223224015182084}}, \"EndTime\": 1569258944.16863, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569258944.168593}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:44 INFO 140514251040576] #quality_metric: host=algo-1, epoch=1, train mse_objective <loss>=1.02232240152\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12585277085676658, \"sum\": 0.12585277085676658, \"min\": 0.12585277085676658}}, \"EndTime\": 1569258947.800369, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569258947.800326}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #quality_metric: host=algo-1, epoch=1, validation mse_objective <loss>=0.125852770857\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=mse_objective, value=0.125852770857\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] Epoch 1: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 3191, \"sum\": 3191.0, \"min\": 3191}, \"Total Records Seen\": {\"count\": 1, \"max\": 813347, \"sum\": 813347.0, \"min\": 813347}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1569258947.802053, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569258914.347374}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11999.649369 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9981346370066798, \"sum\": 0.9981346370066798, \"min\": 0.9981346370066798}}, \"EndTime\": 1569258978.132421, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569258978.132387}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:18 INFO 140514251040576] #quality_metric: host=algo-1, epoch=2, train mse_objective <loss>=0.998134637007\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12425399832422306, \"sum\": 0.12425399832422306, \"min\": 0.12425399832422306}}, \"EndTime\": 1569258981.708207, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569258981.708159}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #quality_metric: host=algo-1, epoch=2, validation mse_objective <loss>=0.124253998324\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=mse_objective, value=0.124253998324\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] Epoch 2: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 4766, \"sum\": 4766.0, \"min\": 4766}, \"Total Records Seen\": {\"count\": 1, \"max\": 1214793, \"sum\": 1214793.0, \"min\": 1214793}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1569258981.710058, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569258947.802305}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11839.3085984 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9918992249138863, \"sum\": 0.9918992249138863, \"min\": 0.9918992249138863}}, \"EndTime\": 1569259011.548878, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569259011.548841}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:51 INFO 140514251040576] #quality_metric: host=algo-1, epoch=3, train mse_objective <loss>=0.991899224914\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12368582337468151, \"sum\": 0.12368582337468151, \"min\": 0.12368582337468151}}, \"EndTime\": 1569259015.203788, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569259015.203744}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #quality_metric: host=algo-1, epoch=3, validation mse_objective <loss>=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=mse_objective, value=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] Epoch 3: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 6341, \"sum\": 6341.0, \"min\": 6341}, \"Total Records Seen\": {\"count\": 1, \"max\": 1616239, \"sum\": 1616239.0, \"min\": 1616239}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1569259015.205427, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569258981.710352}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11985.1745353 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9902387193278305, \"sum\": 0.9902387193278305, \"min\": 0.9902387193278305}}, \"EndTime\": 1569259044.998423, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569259044.998389}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:24 INFO 140514251040576] #quality_metric: host=algo-1, epoch=4, train mse_objective <loss>=0.990238719328\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364221331402393, \"sum\": 0.12364221331402393, \"min\": 0.12364221331402393}}, \"EndTime\": 1569259048.599141, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569259048.599098}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #quality_metric: host=algo-1, epoch=4, validation mse_objective <loss>=0.123642213314\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=mse_objective, value=0.123642213314\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #progress_metric: host=algo-1, completed 50 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 7916, \"sum\": 7916.0, \"min\": 7916}, \"Total Records Seen\": {\"count\": 1, \"max\": 2017685, \"sum\": 2017685.0, \"min\": 2017685}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1569259048.600234, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569259015.205676}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12021.2539263 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9898242556490723, \"sum\": 0.9898242556490723, \"min\": 0.9898242556490723}}, \"EndTime\": 1569259078.239435, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1569259078.2394}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:58 INFO 140514251040576] #quality_metric: host=algo-1, epoch=5, train mse_objective <loss>=0.989824255649\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364073699174565, \"sum\": 0.12364073699174565, \"min\": 0.12364073699174565}}, \"EndTime\": 1569259081.825879, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1569259081.825844}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #quality_metric: host=algo-1, epoch=5, validation mse_objective <loss>=0.123640736992\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=mse_objective, value=0.123640736992\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 9491, \"sum\": 9491.0, \"min\": 9491}, \"Total Records Seen\": {\"count\": 1, \"max\": 2419131, \"sum\": 2419131.0, \"min\": 2419131}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1569259081.826992, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1569259048.600468}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12082.0442619 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9898115187021317, \"sum\": 0.9898115187021317, \"min\": 0.9898115187021317}}, \"EndTime\": 1569259111.664931, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1569259111.664896}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:31 INFO 140514251040576] #quality_metric: host=algo-1, epoch=6, train mse_objective <loss>=0.989811518702\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364087580199667, \"sum\": 0.12364087580199667, \"min\": 0.12364087580199667}}, \"EndTime\": 1569259115.256418, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1569259115.256376}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #quality_metric: host=algo-1, epoch=6, validation mse_objective <loss>=0.123640875802\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=mse_objective, value=0.123640875802\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #progress_metric: host=algo-1, completed 70 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 11066, \"sum\": 11066.0, \"min\": 11066}, \"Total Records Seen\": {\"count\": 1, \"max\": 2820577, \"sum\": 2820577.0, \"min\": 2820577}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}}, \"EndTime\": 1569259115.257311, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1569259081.827278}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12008.4952158 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9898111228715721, \"sum\": 0.9898111228715721, \"min\": 0.9898111228715721}}, \"EndTime\": 1569259145.095741, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1569259145.095707}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:05 INFO 140514251040576] #quality_metric: host=algo-1, epoch=7, train mse_objective <loss>=0.989811122872\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364088864916668, \"sum\": 0.12364088864916668, \"min\": 0.12364088864916668}}, \"EndTime\": 1569259148.633992, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1569259148.633956}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #quality_metric: host=algo-1, epoch=7, validation mse_objective <loss>=0.123640888649\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=0.123640888649\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] Early stop condition met. Stopping training.\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #progress_metric: host=algo-1, completed 100 % epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 12641, \"sum\": 12641.0, \"min\": 12641}, \"Total Records Seen\": {\"count\": 1, \"max\": 3222023, \"sum\": 3222023.0, \"min\": 3222023}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1569259148.63497, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1569259115.257547}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12027.4209355 records/second\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 WARNING 140514251040576] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 WARNING 140514251040576] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:12 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=0.123640888649\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #validation_score (algo-1) : ('mse_objective', 0.12368582337468151)\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #validation_score (algo-1) : ('mse', 0.12368582337468151)\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #validation_score (algo-1) : ('absolute_loss', 0.30015450932553356)\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #quality_metric: host=algo-1, validation mse_objective <loss>=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #quality_metric: host=algo-1, validation mse <loss>=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #quality_metric: host=algo-1, validation absolute_loss <loss>=0.300154509326\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] Best model found for hyperparameters: {\"lr_scheduler_step\": 10, \"wd\": 0.0001, \"optimizer\": \"adam\", \"lr_scheduler_factor\": 0.99, \"l1\": 0.0, \"learning_rate\": 0.03719555404301278, \"lr_scheduler_minimum_lr\": 1e-05}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] Saved checkpoint to \"/tmp/tmpwM7FyB/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] Test data is not provided.\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 277520.5268859863, \"sum\": 277520.5268859863, \"min\": 277520.5268859863}, \"finalize.time\": {\"count\": 1, \"max\": 7273.230791091919, \"sum\": 7273.230791091919, \"min\": 7273.230791091919}, \"initialize.time\": {\"count\": 1, \"max\": 934.1781139373779, \"sum\": 934.1781139373779, \"min\": 934.1781139373779}, \"check_early_stopping.time\": {\"count\": 9, \"max\": 1.1758804321289062, \"sum\": 5.347013473510742, \"min\": 0.17595291137695312}, \"setuptime\": {\"count\": 1, \"max\": 29.300928115844727, \"sum\": 29.300928115844727, \"min\": 29.300928115844727}, \"update.time\": {\"count\": 8, \"max\": 34924.36408996582, \"sum\": 269209.0108394623, \"min\": 33226.33981704712}, \"epochs\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1569259155.915844, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1569258878.487937}\n",
      "\u001b[0m\n",
      "Training seconds: 368\n",
      "Billable seconds: 368\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:15:56,198 - sagemaker - WARNING - Using already existing model: linear-learner-190923-1854-009-f7872322\n",
      "2019-09-23 22:15:56,199 - sagemaker - INFO - Creating transform job with name: linear-learner-190923-1854-009-f7872322-2019-09-23-20-15-56-199\n",
      "2019-09-23 22:15:56,694 - models.aws_base - INFO - Waiting for the transformer job\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..................\u001b[31mDocker entrypoint called with argument(s): serve\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded entry point class algorithm.serve.server_config:config_api\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loading entry points\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded request iterator text/csv\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded request iterator application/x-recordio-protobuf\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded request iterator application/json\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded request iterator application/jsonlines\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded response encoder text/csv\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded response encoder application/x-recordio-protobuf\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded response encoder application/json\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded response encoder application/jsonlines\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loaded entry point class algorithm:model\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] loading model...\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] ...model loaded.\u001b[0m\n",
      "\u001b[31m[09/23/2019 20:18:59 INFO 139764540991296] Number of server workers: 4\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:18:59 +0000] [1] [INFO] Starting gunicorn 19.7.1\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:18:59 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:18:59 +0000] [1] [INFO] Using worker: sync\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:18:59 +0000] [71] [INFO] Booting worker with pid: 71\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:18:59 +0000] [80] [INFO] Booting worker with pid: 80\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:18:59 +0000] [89] [INFO] Booting worker with pid: 89\u001b[0m\n",
      "\u001b[31m[2019-09-23 20:18:59 +0000] [98] [INFO] Booting worker with pid: 98\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"execution_parameters.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269956.87144, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269939.636531}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 68.85886192321777, \"sum\": 68.85886192321777, \"min\": 68.85886192321777}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269960.229101, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269939.636531}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 38.88893127441406, \"sum\": 38.88893127441406, \"min\": 38.88893127441406}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269960.352599, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269939.636531}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 40.36116600036621, \"sum\": 40.36116600036621, \"min\": 40.36116600036621}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269960.454639, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269956.871564}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 31.657934188842773, \"sum\": 31.657934188842773, \"min\": 31.657934188842773}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269960.559378, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269939.636531}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 24.155855178833008, \"sum\": 24.155855178833008, \"min\": 24.155855178833008}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269961.093574, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269960.352735}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 18.227815628051758, \"sum\": 18.227815628051758, \"min\": 18.227815628051758}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269961.153764, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269960.229228}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 18.91493797302246, \"sum\": 18.91493797302246, \"min\": 18.91493797302246}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269961.29705, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269960.559515}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 12.614965438842773, \"sum\": 12.614965438842773, \"min\": 12.614965438842773}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269961.390764, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269960.454779}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.018131256103516, \"sum\": 13.018131256103516, \"min\": 13.018131256103516}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269961.919769, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269961.093675}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 22.867918014526367, \"sum\": 22.867918014526367, \"min\": 22.867918014526367}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269961.990815, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269961.297228}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 17.60101318359375, \"sum\": 17.60101318359375, \"min\": 17.60101318359375}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269962.042988, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269961.390871}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.755083084106445, \"sum\": 13.755083084106445, \"min\": 13.755083084106445}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269962.381091, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269961.153866}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.858007431030273, \"sum\": 14.858007431030273, \"min\": 14.858007431030273}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269962.61773, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269961.991257}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 41.3970947265625, \"sum\": 41.3970947265625, \"min\": 41.3970947265625}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269962.828911, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269961.919867}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.171932220458984, \"sum\": 16.171932220458984, \"min\": 16.171932220458984}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269962.970672, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269962.043091}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 50.248146057128906, \"sum\": 50.248146057128906, \"min\": 50.248146057128906}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269963.170025, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269962.381192}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 42.298078536987305, \"sum\": 42.298078536987305, \"min\": 42.298078536987305}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269963.423375, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269962.617813}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.192892074584961, \"sum\": 13.192892074584961, \"min\": 13.192892074584961}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269963.549524, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269962.829019}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 19.69313621520996, \"sum\": 19.69313621520996, \"min\": 19.69313621520996}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269963.693464, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269962.970755}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 15.462875366210938, \"sum\": 15.462875366210938, \"min\": 15.462875366210938}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269963.905565, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269963.170118}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 12.001991271972656, \"sum\": 12.001991271972656, \"min\": 12.001991271972656}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269964.190664, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269963.423461}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.364076614379883, \"sum\": 13.364076614379883, \"min\": 13.364076614379883}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269964.295695, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269963.549616}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 32.479047775268555, \"sum\": 32.479047775268555, \"min\": 32.479047775268555}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269964.469702, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269963.693555}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.466119766235352, \"sum\": 13.466119766235352, \"min\": 13.466119766235352}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269964.565001, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269963.905657}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.916969299316406, \"sum\": 13.916969299316406, \"min\": 13.916969299316406}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269964.939986, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269964.190756}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.409065246582031, \"sum\": 14.409065246582031, \"min\": 14.409065246582031}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269965.044271, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269964.296316}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 34.353017807006836, \"sum\": 34.353017807006836, \"min\": 34.353017807006836}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269965.175288, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269964.469806}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 25.823116302490234, \"sum\": 25.823116302490234, \"min\": 25.823116302490234}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269965.279472, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269964.565091}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.045093536376953, \"sum\": 16.045093536376953, \"min\": 16.045093536376953}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269965.764801, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269965.04497}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 17.750978469848633, \"sum\": 17.750978469848633, \"min\": 17.750978469848633}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269965.835244, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269964.940069}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 20.25008201599121, \"sum\": 20.25008201599121, \"min\": 20.25008201599121}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269965.836085, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269965.279566}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.494035720825195, \"sum\": 16.494035720825195, \"min\": 16.494035720825195}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269966.092216, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269965.175432}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 23.648977279663086, \"sum\": 23.648977279663086, \"min\": 23.648977279663086}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269966.441797, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269965.764892}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.61396026611328, \"sum\": 16.61396026611328, \"min\": 16.61396026611328}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269966.526821, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269965.836168}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 37.824153900146484, \"sum\": 37.824153900146484, \"min\": 37.824153900146484}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269966.600519, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269965.835914}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 21.852970123291016, \"sum\": 21.852970123291016, \"min\": 21.852970123291016}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269966.855017, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269966.092318}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 17.31109619140625, \"sum\": 17.31109619140625, \"min\": 17.31109619140625}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269967.155739, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269966.441899}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.5250301361084, \"sum\": 16.5250301361084, \"min\": 16.5250301361084}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269967.261153, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269966.600618}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.809940338134766, \"sum\": 16.809940338134766, \"min\": 16.809940338134766}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269967.402746, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269966.52749}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.247013092041016, \"sum\": 13.247013092041016, \"min\": 13.247013092041016}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269967.596365, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269966.855105}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 40.65203666687012, \"sum\": 40.65203666687012, \"min\": 40.65203666687012}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269967.814952, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269967.155836}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 19.178152084350586, \"sum\": 19.178152084350586, \"min\": 19.178152084350586}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269967.943912, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269967.261272}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.516923904418945, \"sum\": 16.516923904418945, \"min\": 16.516923904418945}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269968.121883, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269967.402848}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 12.73202896118164, \"sum\": 12.73202896118164, \"min\": 12.73202896118164}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269968.374246, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269967.597113}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 15.661001205444336, \"sum\": 15.661001205444336, \"min\": 15.661001205444336}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269968.473333, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269967.815043}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 20.401954650878906, \"sum\": 20.401954650878906, \"min\": 20.401954650878906}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269968.638774, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269967.944006}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.278173446655273, \"sum\": 14.278173446655273, \"min\": 14.278173446655273}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269968.788492, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269968.121971}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.779090881347656, \"sum\": 14.779090881347656, \"min\": 14.779090881347656}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269968.998888, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269968.374355}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.075113296508789, \"sum\": 13.075113296508789, \"min\": 13.075113296508789}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269969.110981, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269968.473415}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 24.492979049682617, \"sum\": 24.492979049682617, \"min\": 24.492979049682617}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269969.290072, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269968.638859}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.219999313354492, \"sum\": 14.219999313354492, \"min\": 14.219999313354492}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269969.467669, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269968.788579}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 22.027015686035156, \"sum\": 22.027015686035156, \"min\": 22.027015686035156}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269969.660592, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269968.998971}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.405820846557617, \"sum\": 16.405820846557617, \"min\": 16.405820846557617}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269969.737484, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269969.111583}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 17.210006713867188, \"sum\": 17.210006713867188, \"min\": 17.210006713867188}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269969.953511, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269969.290159}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.32712173461914, \"sum\": 13.32712173461914, \"min\": 13.32712173461914}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269970.159246, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269969.467756}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.77193832397461, \"sum\": 14.77193832397461, \"min\": 14.77193832397461}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269970.286008, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269969.660687}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.885995864868164, \"sum\": 16.885995864868164, \"min\": 16.885995864868164}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269970.398008, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269969.737595}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 19.030094146728516, \"sum\": 19.030094146728516, \"min\": 19.030094146728516}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269970.621912, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269969.953599}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 15.465021133422852, \"sum\": 15.465021133422852, \"min\": 15.465021133422852}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269970.823127, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269970.159339}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.922929763793945, \"sum\": 13.922929763793945, \"min\": 13.922929763793945}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269970.914933, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269970.286101}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.494180679321289, \"sum\": 14.494180679321289, \"min\": 14.494180679321289}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269971.059706, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269970.398546}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 19.407987594604492, \"sum\": 19.407987594604492, \"min\": 19.407987594604492}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269971.293426, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269970.62199}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 23.38099479675293, \"sum\": 23.38099479675293, \"min\": 23.38099479675293}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269971.477138, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269970.823211}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 35.63880920410156, \"sum\": 35.63880920410156, \"min\": 35.63880920410156}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269971.550242, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269970.91502}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.557123184204102, \"sum\": 14.557123184204102, \"min\": 14.557123184204102}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269971.753401, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269971.06037}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.083955764770508, \"sum\": 16.083955764770508, \"min\": 16.083955764770508}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269971.970903, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269971.294057}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.059139251708984, \"sum\": 13.059139251708984, \"min\": 13.059139251708984}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269972.165106, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269971.477231}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.576911926269531, \"sum\": 14.576911926269531, \"min\": 14.576911926269531}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269972.252201, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269971.550345}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 17.38595962524414, \"sum\": 17.38595962524414, \"min\": 17.38595962524414}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269972.388847, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269971.754078}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.208866119384766, \"sum\": 13.208866119384766, \"min\": 13.208866119384766}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269972.647867, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269971.971537}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.586997985839844, \"sum\": 13.586997985839844, \"min\": 13.586997985839844}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269972.868228, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269972.165208}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 12.88604736328125, \"sum\": 12.88604736328125, \"min\": 12.88604736328125}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269972.883015, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269972.2523}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 23.324966430664062, \"sum\": 23.324966430664062, \"min\": 23.324966430664062}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269973.031594, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269972.388938}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.173030853271484, \"sum\": 14.173030853271484, \"min\": 14.173030853271484}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269973.28157, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269972.648567}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.198064804077148, \"sum\": 14.198064804077148, \"min\": 14.198064804077148}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269973.509343, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269972.883146}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 24.567127227783203, \"sum\": 24.567127227783203, \"min\": 24.567127227783203}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269973.615171, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269972.868312}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.446020126342773, \"sum\": 14.446020126342773, \"min\": 14.446020126342773}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269973.701411, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269973.031684}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.575077056884766, \"sum\": 13.575077056884766, \"min\": 13.575077056884766}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269973.946288, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269973.28167}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 15.594959259033203, \"sum\": 15.594959259033203, \"min\": 15.594959259033203}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269974.145055, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269973.509436}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.83497428894043, \"sum\": 16.83497428894043, \"min\": 16.83497428894043}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269974.273676, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269973.615249}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.323949813842773, \"sum\": 14.323949813842773, \"min\": 14.323949813842773}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269974.352728, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269973.701507}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 4.445075988769531, \"sum\": 4.445075988769531, \"min\": 4.445075988769531}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269974.431478, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269974.273758}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 8.991003036499023, \"sum\": 8.991003036499023, \"min\": 8.991003036499023}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269974.54577, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269973.946979}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 8.941888809204102, \"sum\": 8.941888809204102, \"min\": 8.941888809204102}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569269974.592496, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569269974.145133}\n",
      "\u001b[0m\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:20:15,120 - models.aws_base - INFO - Loading the results of the transformer job\n",
      "2019-09-23 22:20:15,127 - executors.sagemaker - INFO - Downloading data from s3: from s3://sagemaker-eu-west-1-729071960169/data/linear_learner/output_data/test.csv.out to data/temp/linear_learner/test.csv.out\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.csv.out\n",
      "data/temp/linear_learner\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:21:04,335 - models.aws_pytorch - INFO - Starting the PyTorch predictions\n",
      "2019-09-23 22:21:04,336 - models.aws_pytorch - INFO - Deploying the predictor\n",
      "2019-09-23 22:21:04,659 - sagemaker - INFO - Creating model with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 22:21:05,012 - sagemaker - WARNING - Using already existing model: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 22:21:05,518 - sagemaker - INFO - Creating endpoint with name sagemaker-pytorch-2019-09-23-17-53-49-735\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------------!"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:29:17,624 - models.aws_pytorch - WARNING - Don't forget to delete the predicion endpoint\n",
      "2019-09-23 22:29:17,625 - models.aws_pytorch - INFO - Splitting the data in batches\n",
      "2019-09-23 22:29:17,670 - models.aws_pytorch - INFO - Done splitting the data in batches\n",
      "2019-09-23 22:29:17,671 - models.aws_pytorch - INFO - Sending the data to predict to the model\n",
      "2019-09-23 22:41:08,076 - models.aws_pytorch - INFO - Got the predictions\n",
      "2019-09-23 22:41:08,077 - models.aws_pytorch - INFO - Deleting the pytorch endpoint\n",
      "2019-09-23 22:41:08,078 - sagemaker - INFO - Deleting endpoint configuration with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 22:41:08,332 - sagemaker - INFO - Deleting endpoint with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 22:41:08,412 - models.aws_pytorch - INFO - Deleting the endpoint\n",
      "2019-09-23 22:41:08,423 - models.meta_model - INFO - Done with the predictions of the meta model\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction_kazutsugi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>n0003aa52cab36c2</td>\n",
       "      <td>0.487833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>n000920ed083903f</td>\n",
       "      <td>0.491079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>n0038e640522c4a6</td>\n",
       "      <td>0.539819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>n004ac94a87dc54b</td>\n",
       "      <td>0.504252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>n0052fe97ea0c05f</td>\n",
       "      <td>0.490443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nffa68f2e76d7348</td>\n",
       "      <td>0.505428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nffb33fb3e9902f0</td>\n",
       "      <td>0.496655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nffc5c731616a3e7</td>\n",
       "      <td>0.496323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nffd1243713ec75a</td>\n",
       "      <td>0.502624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfff2b0dbc73ef6c</td>\n",
       "      <td>0.501492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>385257 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prediction_kazutsugi\n",
       "id                                    \n",
       "n0003aa52cab36c2              0.487833\n",
       "n000920ed083903f              0.491079\n",
       "n0038e640522c4a6              0.539819\n",
       "n004ac94a87dc54b              0.504252\n",
       "n0052fe97ea0c05f              0.490443\n",
       "...                                ...\n",
       "nffa68f2e76d7348              0.505428\n",
       "nffb33fb3e9902f0              0.496655\n",
       "nffc5c731616a3e7              0.496323\n",
       "nffd1243713ec75a              0.502624\n",
       "nfff2b0dbc73ef6c              0.501492\n",
       "\n",
       "[385257 rows x 1 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "predictions meta_model.predict(data_loader=production_data, all_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 22:42:44,950 - executors.numerai - INFO - Saving predictions to data/temp/predictions/predictions.csv\n",
      "2019-09-23 22:42:45,876 - executors.numerai - INFO - Uploading predictions to Numerai\n",
      "2019-09-23 22:42:45,877 - numerapi.numerapi - INFO - uploading predictions...\n",
      "2019-09-23 22:43:17,073 - executors.numerai - INFO - Done with the upload to Numerai\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-c5b8177a6162>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Format and upload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproduction_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnumerai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"data/temp/predictions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/code/udacity-numerai/executors/numerai.py\u001b[0m in \u001b[0;36mupload_predictions\u001b[0;34m(self, predictions, local_folder, name)\u001b[0m\n\u001b[1;32m     98\u001b[0m         \"\"\"\n\u001b[1;32m     99\u001b[0m         \u001b[0mlocal_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload_predictions_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mupload_predictions_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_location\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/udacity-numerai/executors/numerai.py\u001b[0m in \u001b[0;36mupload_predictions_csv\u001b[0;34m(self, file_location)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0msubmission_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_location\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mLOGGER\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Done with the upload to Numerai\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m         \u001b[0msuccess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_submission_successful\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubmission_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/udacity-numerai-zNHiffNx/lib/python3.7/site-packages/numerapi/numerapi.py\u001b[0m in \u001b[0;36mcheck_submission_successful\u001b[0;34m(self, submission_id)\u001b[0m\n\u001b[1;32m   1445\u001b[0m         \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubmission_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubmission_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1446\u001b[0m         \u001b[0;31m# need to cast to bool to not return None in some cases.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m         \u001b[0msuccess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"concordance\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"value\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1448\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "# Format and upload\n",
    "predictions = production_data.format_predictions(predictions, all_data=True)\n",
    "numerai.upload_predictions(predictions, local_folder=\"data/temp/predictions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We already loaded the data, but we'll get it out for easier access\n",
    "df = data_loader.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# See how much the features are correlated\n",
    "correlations = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_intelligence1</th>\n",
       "      <th>feature_intelligence2</th>\n",
       "      <th>feature_intelligence3</th>\n",
       "      <th>feature_intelligence4</th>\n",
       "      <th>feature_intelligence5</th>\n",
       "      <th>feature_intelligence6</th>\n",
       "      <th>feature_intelligence7</th>\n",
       "      <th>feature_intelligence8</th>\n",
       "      <th>feature_intelligence9</th>\n",
       "      <th>feature_intelligence10</th>\n",
       "      <th>...</th>\n",
       "      <th>feature_wisdom38</th>\n",
       "      <th>feature_wisdom39</th>\n",
       "      <th>feature_wisdom40</th>\n",
       "      <th>feature_wisdom41</th>\n",
       "      <th>feature_wisdom42</th>\n",
       "      <th>feature_wisdom43</th>\n",
       "      <th>feature_wisdom44</th>\n",
       "      <th>feature_wisdom45</th>\n",
       "      <th>feature_wisdom46</th>\n",
       "      <th>target_kazutsugi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>feature_intelligence1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014157</td>\n",
       "      <td>-0.024404</td>\n",
       "      <td>0.652596</td>\n",
       "      <td>0.069868</td>\n",
       "      <td>0.151265</td>\n",
       "      <td>0.188467</td>\n",
       "      <td>0.760617</td>\n",
       "      <td>-0.017661</td>\n",
       "      <td>-0.029344</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.088465</td>\n",
       "      <td>-0.061247</td>\n",
       "      <td>-0.120785</td>\n",
       "      <td>-0.120960</td>\n",
       "      <td>0.021476</td>\n",
       "      <td>0.037546</td>\n",
       "      <td>-0.125501</td>\n",
       "      <td>-0.032981</td>\n",
       "      <td>-0.168257</td>\n",
       "      <td>0.001904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>feature_intelligence2</td>\n",
       "      <td>-0.014157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905315</td>\n",
       "      <td>-0.028097</td>\n",
       "      <td>0.184372</td>\n",
       "      <td>-0.048150</td>\n",
       "      <td>-0.031199</td>\n",
       "      <td>-0.014185</td>\n",
       "      <td>0.483491</td>\n",
       "      <td>0.465704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.127448</td>\n",
       "      <td>-0.141152</td>\n",
       "      <td>0.049052</td>\n",
       "      <td>-0.009823</td>\n",
       "      <td>-0.156142</td>\n",
       "      <td>-0.222864</td>\n",
       "      <td>0.025692</td>\n",
       "      <td>-0.044577</td>\n",
       "      <td>-0.109628</td>\n",
       "      <td>-0.007274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>feature_intelligence3</td>\n",
       "      <td>-0.024404</td>\n",
       "      <td>0.905315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.041086</td>\n",
       "      <td>0.173870</td>\n",
       "      <td>-0.053716</td>\n",
       "      <td>-0.027043</td>\n",
       "      <td>-0.027653</td>\n",
       "      <td>0.467432</td>\n",
       "      <td>0.503837</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.105961</td>\n",
       "      <td>-0.134758</td>\n",
       "      <td>0.055136</td>\n",
       "      <td>0.001594</td>\n",
       "      <td>-0.152337</td>\n",
       "      <td>-0.232693</td>\n",
       "      <td>0.029589</td>\n",
       "      <td>-0.034833</td>\n",
       "      <td>-0.107264</td>\n",
       "      <td>-0.006729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>feature_intelligence4</td>\n",
       "      <td>0.652596</td>\n",
       "      <td>-0.028097</td>\n",
       "      <td>-0.041086</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054492</td>\n",
       "      <td>0.103287</td>\n",
       "      <td>0.110541</td>\n",
       "      <td>0.815162</td>\n",
       "      <td>-0.030180</td>\n",
       "      <td>-0.043915</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.096097</td>\n",
       "      <td>-0.114118</td>\n",
       "      <td>-0.110214</td>\n",
       "      <td>-0.107755</td>\n",
       "      <td>-0.007601</td>\n",
       "      <td>-0.000272</td>\n",
       "      <td>-0.130211</td>\n",
       "      <td>-0.036535</td>\n",
       "      <td>-0.209641</td>\n",
       "      <td>-0.002600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>feature_intelligence5</td>\n",
       "      <td>0.069868</td>\n",
       "      <td>0.184372</td>\n",
       "      <td>0.173870</td>\n",
       "      <td>0.054492</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.145064</td>\n",
       "      <td>0.154358</td>\n",
       "      <td>0.067219</td>\n",
       "      <td>0.190649</td>\n",
       "      <td>0.188392</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.101133</td>\n",
       "      <td>-0.104007</td>\n",
       "      <td>-0.016642</td>\n",
       "      <td>-0.046638</td>\n",
       "      <td>-0.100245</td>\n",
       "      <td>-0.071022</td>\n",
       "      <td>-0.027973</td>\n",
       "      <td>-0.050636</td>\n",
       "      <td>-0.079726</td>\n",
       "      <td>0.000502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>feature_wisdom43</td>\n",
       "      <td>0.037546</td>\n",
       "      <td>-0.222864</td>\n",
       "      <td>-0.232693</td>\n",
       "      <td>-0.000272</td>\n",
       "      <td>-0.071022</td>\n",
       "      <td>-0.009765</td>\n",
       "      <td>0.030643</td>\n",
       "      <td>-0.004125</td>\n",
       "      <td>-0.276340</td>\n",
       "      <td>-0.288251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196352</td>\n",
       "      <td>0.265656</td>\n",
       "      <td>-0.023005</td>\n",
       "      <td>0.081989</td>\n",
       "      <td>0.311838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004611</td>\n",
       "      <td>0.002951</td>\n",
       "      <td>0.197166</td>\n",
       "      <td>-0.000656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>feature_wisdom44</td>\n",
       "      <td>-0.125501</td>\n",
       "      <td>0.025692</td>\n",
       "      <td>0.029589</td>\n",
       "      <td>-0.130211</td>\n",
       "      <td>-0.027973</td>\n",
       "      <td>0.015334</td>\n",
       "      <td>0.010520</td>\n",
       "      <td>-0.139625</td>\n",
       "      <td>0.051455</td>\n",
       "      <td>0.032859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161515</td>\n",
       "      <td>0.168145</td>\n",
       "      <td>0.905125</td>\n",
       "      <td>0.779072</td>\n",
       "      <td>0.120180</td>\n",
       "      <td>0.004611</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086558</td>\n",
       "      <td>0.172650</td>\n",
       "      <td>0.008732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>feature_wisdom45</td>\n",
       "      <td>-0.032981</td>\n",
       "      <td>-0.044577</td>\n",
       "      <td>-0.034833</td>\n",
       "      <td>-0.036535</td>\n",
       "      <td>-0.050636</td>\n",
       "      <td>-0.017144</td>\n",
       "      <td>-0.002331</td>\n",
       "      <td>-0.034896</td>\n",
       "      <td>0.080112</td>\n",
       "      <td>0.064880</td>\n",
       "      <td>...</td>\n",
       "      <td>0.346918</td>\n",
       "      <td>0.299520</td>\n",
       "      <td>0.085849</td>\n",
       "      <td>0.100799</td>\n",
       "      <td>0.263568</td>\n",
       "      <td>0.002951</td>\n",
       "      <td>0.086558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506321</td>\n",
       "      <td>0.000435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>feature_wisdom46</td>\n",
       "      <td>-0.168257</td>\n",
       "      <td>-0.109628</td>\n",
       "      <td>-0.107264</td>\n",
       "      <td>-0.209641</td>\n",
       "      <td>-0.079726</td>\n",
       "      <td>-0.069061</td>\n",
       "      <td>-0.028651</td>\n",
       "      <td>-0.227540</td>\n",
       "      <td>-0.000731</td>\n",
       "      <td>-0.024170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.568304</td>\n",
       "      <td>0.607140</td>\n",
       "      <td>0.159357</td>\n",
       "      <td>0.199589</td>\n",
       "      <td>0.505521</td>\n",
       "      <td>0.197166</td>\n",
       "      <td>0.172650</td>\n",
       "      <td>0.506321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>target_kazutsugi</td>\n",
       "      <td>0.001904</td>\n",
       "      <td>-0.007274</td>\n",
       "      <td>-0.006729</td>\n",
       "      <td>-0.002600</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.003360</td>\n",
       "      <td>0.004150</td>\n",
       "      <td>-0.000306</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.005048</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.009217</td>\n",
       "      <td>-0.000656</td>\n",
       "      <td>0.008732</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311 rows × 311 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       feature_intelligence1  feature_intelligence2  \\\n",
       "feature_intelligence1               1.000000              -0.014157   \n",
       "feature_intelligence2              -0.014157               1.000000   \n",
       "feature_intelligence3              -0.024404               0.905315   \n",
       "feature_intelligence4               0.652596              -0.028097   \n",
       "feature_intelligence5               0.069868               0.184372   \n",
       "...                                      ...                    ...   \n",
       "feature_wisdom43                    0.037546              -0.222864   \n",
       "feature_wisdom44                   -0.125501               0.025692   \n",
       "feature_wisdom45                   -0.032981              -0.044577   \n",
       "feature_wisdom46                   -0.168257              -0.109628   \n",
       "target_kazutsugi                    0.001904              -0.007274   \n",
       "\n",
       "                       feature_intelligence3  feature_intelligence4  \\\n",
       "feature_intelligence1              -0.024404               0.652596   \n",
       "feature_intelligence2               0.905315              -0.028097   \n",
       "feature_intelligence3               1.000000              -0.041086   \n",
       "feature_intelligence4              -0.041086               1.000000   \n",
       "feature_intelligence5               0.173870               0.054492   \n",
       "...                                      ...                    ...   \n",
       "feature_wisdom43                   -0.232693              -0.000272   \n",
       "feature_wisdom44                    0.029589              -0.130211   \n",
       "feature_wisdom45                   -0.034833              -0.036535   \n",
       "feature_wisdom46                   -0.107264              -0.209641   \n",
       "target_kazutsugi                   -0.006729              -0.002600   \n",
       "\n",
       "                       feature_intelligence5  feature_intelligence6  \\\n",
       "feature_intelligence1               0.069868               0.151265   \n",
       "feature_intelligence2               0.184372              -0.048150   \n",
       "feature_intelligence3               0.173870              -0.053716   \n",
       "feature_intelligence4               0.054492               0.103287   \n",
       "feature_intelligence5               1.000000               0.145064   \n",
       "...                                      ...                    ...   \n",
       "feature_wisdom43                   -0.071022              -0.009765   \n",
       "feature_wisdom44                   -0.027973               0.015334   \n",
       "feature_wisdom45                   -0.050636              -0.017144   \n",
       "feature_wisdom46                   -0.079726              -0.069061   \n",
       "target_kazutsugi                    0.000502               0.003360   \n",
       "\n",
       "                       feature_intelligence7  feature_intelligence8  \\\n",
       "feature_intelligence1               0.188467               0.760617   \n",
       "feature_intelligence2              -0.031199              -0.014185   \n",
       "feature_intelligence3              -0.027043              -0.027653   \n",
       "feature_intelligence4               0.110541               0.815162   \n",
       "feature_intelligence5               0.154358               0.067219   \n",
       "...                                      ...                    ...   \n",
       "feature_wisdom43                    0.030643              -0.004125   \n",
       "feature_wisdom44                    0.010520              -0.139625   \n",
       "feature_wisdom45                   -0.002331              -0.034896   \n",
       "feature_wisdom46                   -0.028651              -0.227540   \n",
       "target_kazutsugi                    0.004150              -0.000306   \n",
       "\n",
       "                       feature_intelligence9  feature_intelligence10  ...  \\\n",
       "feature_intelligence1              -0.017661               -0.029344  ...   \n",
       "feature_intelligence2               0.483491                0.465704  ...   \n",
       "feature_intelligence3               0.467432                0.503837  ...   \n",
       "feature_intelligence4              -0.030180               -0.043915  ...   \n",
       "feature_intelligence5               0.190649                0.188392  ...   \n",
       "...                                      ...                     ...  ...   \n",
       "feature_wisdom43                   -0.276340               -0.288251  ...   \n",
       "feature_wisdom44                    0.051455                0.032859  ...   \n",
       "feature_wisdom45                    0.080112                0.064880  ...   \n",
       "feature_wisdom46                   -0.000731               -0.024170  ...   \n",
       "target_kazutsugi                    0.000768                0.001677  ...   \n",
       "\n",
       "                       feature_wisdom38  feature_wisdom39  feature_wisdom40  \\\n",
       "feature_intelligence1         -0.088465         -0.061247         -0.120785   \n",
       "feature_intelligence2         -0.127448         -0.141152          0.049052   \n",
       "feature_intelligence3         -0.105961         -0.134758          0.055136   \n",
       "feature_intelligence4         -0.096097         -0.114118         -0.110214   \n",
       "feature_intelligence5         -0.101133         -0.104007         -0.016642   \n",
       "...                                 ...               ...               ...   \n",
       "feature_wisdom43               0.196352          0.265656         -0.023005   \n",
       "feature_wisdom44               0.161515          0.168145          0.905125   \n",
       "feature_wisdom45               0.346918          0.299520          0.085849   \n",
       "feature_wisdom46               0.568304          0.607140          0.159357   \n",
       "target_kazutsugi               0.004310          0.005048          0.008775   \n",
       "\n",
       "                       feature_wisdom41  feature_wisdom42  feature_wisdom43  \\\n",
       "feature_intelligence1         -0.120960          0.021476          0.037546   \n",
       "feature_intelligence2         -0.009823         -0.156142         -0.222864   \n",
       "feature_intelligence3          0.001594         -0.152337         -0.232693   \n",
       "feature_intelligence4         -0.107755         -0.007601         -0.000272   \n",
       "feature_intelligence5         -0.046638         -0.100245         -0.071022   \n",
       "...                                 ...               ...               ...   \n",
       "feature_wisdom43               0.081989          0.311838          1.000000   \n",
       "feature_wisdom44               0.779072          0.120180          0.004611   \n",
       "feature_wisdom45               0.100799          0.263568          0.002951   \n",
       "feature_wisdom46               0.199589          0.505521          0.197166   \n",
       "target_kazutsugi               0.008000          0.009217         -0.000656   \n",
       "\n",
       "                       feature_wisdom44  feature_wisdom45  feature_wisdom46  \\\n",
       "feature_intelligence1         -0.125501         -0.032981         -0.168257   \n",
       "feature_intelligence2          0.025692         -0.044577         -0.109628   \n",
       "feature_intelligence3          0.029589         -0.034833         -0.107264   \n",
       "feature_intelligence4         -0.130211         -0.036535         -0.209641   \n",
       "feature_intelligence5         -0.027973         -0.050636         -0.079726   \n",
       "...                                 ...               ...               ...   \n",
       "feature_wisdom43               0.004611          0.002951          0.197166   \n",
       "feature_wisdom44               1.000000          0.086558          0.172650   \n",
       "feature_wisdom45               0.086558          1.000000          0.506321   \n",
       "feature_wisdom46               0.172650          0.506321          1.000000   \n",
       "target_kazutsugi               0.008732          0.000435          0.000358   \n",
       "\n",
       "                       target_kazutsugi  \n",
       "feature_intelligence1          0.001904  \n",
       "feature_intelligence2         -0.007274  \n",
       "feature_intelligence3         -0.006729  \n",
       "feature_intelligence4         -0.002600  \n",
       "feature_intelligence5          0.000502  \n",
       "...                                 ...  \n",
       "feature_wisdom43              -0.000656  \n",
       "feature_wisdom44               0.008732  \n",
       "feature_wisdom45               0.000435  \n",
       "feature_wisdom46               0.000358  \n",
       "target_kazutsugi               1.000000  \n",
       "\n",
       "[311 rows x 311 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "era\n",
      "['era1' 'era2' 'era3' 'era4' 'era5' 'era6' 'era7' 'era8' 'era9' 'era10'\n",
      " 'era11' 'era12' 'era13' 'era14' 'era15' 'era16' 'era17' 'era18' 'era19'\n",
      " 'era20' 'era21' 'era22' 'era23' 'era24' 'era25' 'era26' 'era27' 'era28'\n",
      " 'era29' 'era30' 'era31' 'era32' 'era33' 'era34' 'era35' 'era36' 'era37'\n",
      " 'era38' 'era39' 'era40' 'era41' 'era42' 'era43' 'era44' 'era45' 'era46'\n",
      " 'era47' 'era48' 'era49' 'era50' 'era51' 'era52' 'era53' 'era54' 'era55'\n",
      " 'era56' 'era57' 'era58' 'era59' 'era60' 'era61' 'era62' 'era63' 'era64'\n",
      " 'era65' 'era66' 'era67' 'era68' 'era69' 'era70' 'era71' 'era72' 'era73'\n",
      " 'era74' 'era75' 'era76' 'era77' 'era78' 'era79' 'era80' 'era81' 'era82'\n",
      " 'era83' 'era84' 'era85' 'era86' 'era87' 'era88' 'era89' 'era90' 'era91'\n",
      " 'era92' 'era93' 'era94' 'era95' 'era96' 'era97' 'era98' 'era99' 'era100'\n",
      " 'era101' 'era102' 'era103' 'era104' 'era105' 'era106' 'era107' 'era108'\n",
      " 'era109' 'era110' 'era111' 'era112' 'era113' 'era114' 'era115' 'era116'\n",
      " 'era117' 'era118' 'era119' 'era120']\n",
      "data_type\n",
      "['train']\n",
      "feature_intelligence1\n",
      "[0.   0.25 1.   0.5  0.75]\n",
      "feature_intelligence2\n",
      "[0.5  0.   0.25 1.   0.75]\n",
      "feature_intelligence3\n",
      "[0.25 0.   1.   0.5  0.75]\n",
      "feature_intelligence4\n",
      "[0.   0.25 0.5  0.75 1.  ]\n",
      "feature_intelligence5\n",
      "[0.5  1.   0.   0.75 0.25]\n",
      "feature_intelligence6\n",
      "[0.25 0.   0.75 1.   0.5 ]\n",
      "feature_intelligence7\n",
      "[0.25 0.   0.75 0.5  1.  ]\n",
      "feature_intelligence8\n",
      "[0.25 0.75 0.5  1.   0.  ]\n",
      "feature_intelligence9\n",
      "[0.75 0.5  0.   0.25 1.  ]\n",
      "feature_intelligence10\n",
      "[0.75 0.5  0.25 0.   1.  ]\n",
      "feature_intelligence11\n",
      "[0.25 0.   0.5  0.75 1.  ]\n",
      "feature_intelligence12\n",
      "[0.25 0.5  1.   0.75 0.  ]\n",
      "feature_charisma1\n",
      "[1.   0.   0.5  0.75 0.25]\n",
      "feature_charisma2\n",
      "[0.75 0.5  0.25 0.   1.  ]\n",
      "feature_charisma3\n",
      "[0.5  1.   0.   0.25 0.75]\n",
      "feature_charisma4\n",
      "[1.   0.5  0.75 0.25 0.  ]\n",
      "feature_charisma5\n",
      "[0.5  1.   0.25 0.   0.75]\n",
      "feature_charisma6\n",
      "[0.   0.5  0.25 0.75 1.  ]\n",
      "feature_charisma7\n",
      "[0.5  0.25 1.   0.   0.75]\n",
      "feature_charisma8\n",
      "[0.5  0.25 0.   1.   0.75]\n",
      "feature_charisma9\n",
      "[0.   0.5  0.25 0.75 1.  ]\n",
      "feature_charisma10\n",
      "[0.   0.5  0.75 1.   0.25]\n",
      "feature_charisma11\n",
      "[0.   1.   0.25 0.5  0.75]\n",
      "feature_charisma12\n",
      "[1.   0.5  0.75 0.   0.25]\n",
      "feature_charisma13\n",
      "[0.25 0.5  0.75 0.   1.  ]\n",
      "feature_charisma14\n",
      "[0.   0.5  0.75 0.25 1.  ]\n",
      "feature_charisma15\n",
      "[0.5  0.75 0.   1.   0.25]\n",
      "feature_charisma16\n",
      "[0.25 0.5  0.75 0.   1.  ]\n",
      "feature_charisma17\n",
      "[0.75 0.5  1.   0.25 0.  ]\n",
      "feature_charisma18\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_charisma19\n",
      "[1.   0.25 0.5  0.75 0.  ]\n",
      "feature_charisma20\n",
      "[0.75 0.5  0.25 0.   1.  ]\n",
      "feature_charisma21\n",
      "[0.75 0.25 1.   0.5  0.  ]\n",
      "feature_charisma22\n",
      "[0.5  0.   1.   0.75 0.25]\n",
      "feature_charisma23\n",
      "[0.5  0.25 1.   0.75 0.  ]\n",
      "feature_charisma24\n",
      "[0.75 0.5  0.25 0.   1.  ]\n",
      "feature_charisma25\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_charisma26\n",
      "[0.25 0.5  1.   0.   0.75]\n",
      "feature_charisma27\n",
      "[0.75 0.5  0.25 1.   0.  ]\n",
      "feature_charisma28\n",
      "[0.75 0.5  0.25 1.   0.  ]\n",
      "feature_charisma29\n",
      "[0.5  0.75 0.   0.25 1.  ]\n",
      "feature_charisma30\n",
      "[0.25 0.5  1.   0.75 0.  ]\n",
      "feature_charisma31\n",
      "[0.25 0.75 0.5  1.   0.  ]\n",
      "feature_charisma32\n",
      "[0.5  0.25 0.   1.   0.75]\n",
      "feature_charisma33\n",
      "[0.25 0.5  0.75 0.   1.  ]\n",
      "feature_charisma34\n",
      "[0.5  0.25 0.   0.75 1.  ]\n",
      "feature_charisma35\n",
      "[0.25 0.5  0.75 0.   1.  ]\n",
      "feature_charisma36\n",
      "[0.75 0.25 0.5  0.   1.  ]\n",
      "feature_charisma37\n",
      "[0.25 0.75 0.5  1.   0.  ]\n",
      "feature_charisma38\n",
      "[0.25 0.5  0.75 1.   0.  ]\n",
      "feature_charisma39\n",
      "[0.5  0.25 1.   0.   0.75]\n",
      "feature_charisma40\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_charisma41\n",
      "[0.75 0.5  1.   0.25 0.  ]\n",
      "feature_charisma42\n",
      "[0.25 0.5  0.75 1.   0.  ]\n",
      "feature_charisma43\n",
      "[0.5  0.   0.25 0.75 1.  ]\n",
      "feature_charisma44\n",
      "[0.25 1.   0.   0.75 0.5 ]\n",
      "feature_charisma45\n",
      "[0.5  0.25 1.   0.   0.75]\n",
      "feature_charisma46\n",
      "[0.25 0.   0.75 1.   0.5 ]\n",
      "feature_charisma47\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_charisma48\n",
      "[0.5  0.25 0.   1.   0.75]\n",
      "feature_charisma49\n",
      "[0.5  0.75 0.25 0.   1.  ]\n",
      "feature_charisma50\n",
      "[0.25 0.5  0.75 0.   1.  ]\n",
      "feature_charisma51\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_charisma52\n",
      "[0.25 0.5  0.75 0.   1.  ]\n",
      "feature_charisma53\n",
      "[0.75 1.   0.5  0.25 0.  ]\n",
      "feature_charisma54\n",
      "[0.   0.5  0.25 0.75 1.  ]\n",
      "feature_charisma55\n",
      "[0.25 0.   0.5  0.75 1.  ]\n",
      "feature_charisma56\n",
      "[0.5  0.25 0.75 0.   1.  ]\n",
      "feature_charisma57\n",
      "[0.   1.   0.25 0.75 0.5 ]\n",
      "feature_charisma58\n",
      "[0.   1.   0.75 0.5  0.25]\n",
      "feature_charisma59\n",
      "[0.75 0.5  1.   0.25 0.  ]\n",
      "feature_charisma60\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_charisma61\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_charisma62\n",
      "[0.25 0.5  0.75 0.   1.  ]\n",
      "feature_charisma63\n",
      "[0.5  0.25 1.   0.75 0.  ]\n",
      "feature_charisma64\n",
      "[0.5  0.75 0.   0.25 1.  ]\n",
      "feature_charisma65\n",
      "[0.5  0.25 0.75 0.   1.  ]\n",
      "feature_charisma66\n",
      "[0.25 0.   0.5  1.   0.75]\n",
      "feature_charisma67\n",
      "[0.25 0.5  0.   0.75 1.  ]\n",
      "feature_charisma68\n",
      "[0.25 0.5  0.75 1.   0.  ]\n",
      "feature_charisma69\n",
      "[0.   0.75 0.25 0.5  1.  ]\n",
      "feature_charisma70\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_charisma71\n",
      "[0.5  0.75 0.   0.25 1.  ]\n",
      "feature_charisma72\n",
      "[0.5  0.   1.   0.25 0.75]\n",
      "feature_charisma73\n",
      "[0.5  0.75 0.25 0.   1.  ]\n",
      "feature_charisma74\n",
      "[0.   0.25 0.75 0.5  1.  ]\n",
      "feature_charisma75\n",
      "[0.5  0.75 1.   0.   0.25]\n",
      "feature_charisma76\n",
      "[0.5  1.   0.25 0.75 0.  ]\n",
      "feature_charisma77\n",
      "[0.75 0.5  0.   1.   0.25]\n",
      "feature_charisma78\n",
      "[0.   0.25 0.75 0.5  1.  ]\n",
      "feature_charisma79\n",
      "[0.5  0.75 0.25 0.   1.  ]\n",
      "feature_charisma80\n",
      "[0.75 0.5  0.   0.25 1.  ]\n",
      "feature_charisma81\n",
      "[0.5  1.   0.25 0.75 0.  ]\n",
      "feature_charisma82\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_charisma83\n",
      "[0.   1.   0.5  0.75 0.25]\n",
      "feature_charisma84\n",
      "[0.75 0.5  1.   0.25 0.  ]\n",
      "feature_charisma85\n",
      "[0.5  0.25 0.75 1.   0.  ]\n",
      "feature_charisma86\n",
      "[0.5  0.75 1.   0.   0.25]\n",
      "feature_strength1\n",
      "[0.   0.5  0.25 0.75 1.  ]\n",
      "feature_strength2\n",
      "[0.75 0.5  0.   0.25 1.  ]\n",
      "feature_strength3\n",
      "[0.   0.25 0.5  0.75 1.  ]\n",
      "feature_strength4\n",
      "[0.25 0.5  0.   0.75 1.  ]\n",
      "feature_strength5\n",
      "[0.75 0.   0.25 0.5  1.  ]\n",
      "feature_strength6\n",
      "[0.25 0.   0.5  1.   0.75]\n",
      "feature_strength7\n",
      "[0.5  1.   0.25 0.75 0.  ]\n",
      "feature_strength8\n",
      "[1.   0.5  0.   0.75 0.25]\n",
      "feature_strength9\n",
      "[0.75 0.   0.5  1.   0.25]\n",
      "feature_strength10\n",
      "[0.5  0.   0.25 1.   0.75]\n",
      "feature_strength11\n",
      "[0.   0.75 0.25 0.5  1.  ]\n",
      "feature_strength12\n",
      "[0.5  0.   0.75 0.25 1.  ]\n",
      "feature_strength13\n",
      "[0.75 0.5  0.   1.   0.25]\n",
      "feature_strength14\n",
      "[0.5  0.25 0.   1.   0.75]\n",
      "feature_strength15\n",
      "[0.   0.25 0.5  0.75 1.  ]\n",
      "feature_strength16\n",
      "[0.75 1.   0.25 0.   0.5 ]\n",
      "feature_strength17\n",
      "[0.5  0.25 0.   1.   0.75]\n",
      "feature_strength18\n",
      "[0.75 0.25 1.   0.   0.5 ]\n",
      "feature_strength19\n",
      "[0.   0.5  0.25 1.   0.75]\n",
      "feature_strength20\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_strength21\n",
      "[0.   0.5  0.25 1.   0.75]\n",
      "feature_strength22\n",
      "[0.75 0.   0.25 1.   0.5 ]\n",
      "feature_strength23\n",
      "[0.75 1.   0.25 0.5  0.  ]\n",
      "feature_strength24\n",
      "[0.25 0.   0.5  0.75 1.  ]\n",
      "feature_strength25\n",
      "[0.5  0.   0.25 0.75 1.  ]\n",
      "feature_strength26\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_strength27\n",
      "[0.   0.5  0.25 0.75 1.  ]\n",
      "feature_strength28\n",
      "[1.   0.25 0.   0.75 0.5 ]\n",
      "feature_strength29\n",
      "[0.25 0.   0.5  1.   0.75]\n",
      "feature_strength30\n",
      "[0.5  0.   0.75 0.25 1.  ]\n",
      "feature_strength31\n",
      "[0.75 0.   0.25 0.5  1.  ]\n",
      "feature_strength32\n",
      "[0.75 0.5  0.25 0.   1.  ]\n",
      "feature_strength33\n",
      "[0.5  0.75 0.25 0.   1.  ]\n",
      "feature_strength34\n",
      "[0.75 0.5  0.   0.25 1.  ]\n",
      "feature_strength35\n",
      "[0.5  1.   0.25 0.75 0.  ]\n",
      "feature_strength36\n",
      "[0.5  0.   0.25 1.   0.75]\n",
      "feature_strength37\n",
      "[0.5  0.25 1.   0.   0.75]\n",
      "feature_strength38\n",
      "[0.5  0.75 1.   0.25 0.  ]\n",
      "feature_dexterity1\n",
      "[0.75 0.   0.25 0.5  1.  ]\n",
      "feature_dexterity2\n",
      "[0.75 0.5  0.25 0.   1.  ]\n",
      "feature_dexterity3\n",
      "[0.5  0.   1.   0.25 0.75]\n",
      "feature_dexterity4\n",
      "[1.   0.5  0.   0.75 0.25]\n",
      "feature_dexterity5\n",
      "[0.75 0.25 0.   1.   0.5 ]\n",
      "feature_dexterity6\n",
      "[0.75 0.25 0.5  0.   1.  ]\n",
      "feature_dexterity7\n",
      "[1.   0.25 0.75 0.   0.5 ]\n",
      "feature_dexterity8\n",
      "[0.5  0.   0.75 0.25 1.  ]\n",
      "feature_dexterity9\n",
      "[0.75 0.5  0.   0.25 1.  ]\n",
      "feature_dexterity10\n",
      "[0.75 0.25 0.5  0.   1.  ]\n",
      "feature_dexterity11\n",
      "[0.75 0.5  0.25 0.   1.  ]\n",
      "feature_dexterity12\n",
      "[0.25 0.   0.5  1.   0.75]\n",
      "feature_dexterity13\n",
      "[0.75 0.   0.25 1.   0.5 ]\n",
      "feature_dexterity14\n",
      "[0.5  0.   0.25 1.   0.75]\n",
      "feature_constitution1\n",
      "[0.25 1.   0.   0.75 0.5 ]\n",
      "feature_constitution2\n",
      "[0.5  0.25 0.75 1.   0.  ]\n",
      "feature_constitution3\n",
      "[0.5  0.   1.   0.25 0.75]\n",
      "feature_constitution4\n",
      "[0.75 0.25 1.   0.5  0.  ]\n",
      "feature_constitution5\n",
      "[0.   0.75 0.25 0.5  1.  ]\n",
      "feature_constitution6\n",
      "[0.   0.5  0.75 0.25 1.  ]\n",
      "feature_constitution7\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_constitution8\n",
      "[0.75 1.   0.25 0.5  0.  ]\n",
      "feature_constitution9\n",
      "[0.25 1.   0.5  0.   0.75]\n",
      "feature_constitution10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.25 0.75 1.   0.   0.5 ]\n",
      "feature_constitution11\n",
      "[0.75 0.5  0.25 0.   1.  ]\n",
      "feature_constitution12\n",
      "[0.5  1.   0.   0.25 0.75]\n",
      "feature_constitution13\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_constitution14\n",
      "[0.5  1.   0.   0.25 0.75]\n",
      "feature_constitution15\n",
      "[0.   0.75 0.5  0.25 1.  ]\n",
      "feature_constitution16\n",
      "[1.   0.   0.25 0.5  0.75]\n",
      "feature_constitution17\n",
      "[0.75 1.   0.5  0.   0.25]\n",
      "feature_constitution18\n",
      "[0.25 1.   0.   0.5  0.75]\n",
      "feature_constitution19\n",
      "[0.75 0.5  0.25 1.   0.  ]\n",
      "feature_constitution20\n",
      "[0.25 1.   0.   0.75 0.5 ]\n",
      "feature_constitution21\n",
      "[0.75 1.   0.   0.5  0.25]\n",
      "feature_constitution22\n",
      "[0.   1.   0.75 0.25 0.5 ]\n",
      "feature_constitution23\n",
      "[0.5  0.   0.25 1.   0.75]\n",
      "feature_constitution24\n",
      "[0.   0.75 0.25 0.5  1.  ]\n",
      "feature_constitution25\n",
      "[0.25 1.   0.5  0.   0.75]\n",
      "feature_constitution26\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_constitution27\n",
      "[0.25 0.   0.75 1.   0.5 ]\n",
      "feature_constitution28\n",
      "[0.5  0.25 0.75 0.   1.  ]\n",
      "feature_constitution29\n",
      "[0.25 1.   0.   0.5  0.75]\n",
      "feature_constitution30\n",
      "[0.5  1.   0.   0.75 0.25]\n",
      "feature_constitution31\n",
      "[1.   0.5  0.   0.75 0.25]\n",
      "feature_constitution32\n",
      "[0.   0.75 0.5  0.25 1.  ]\n",
      "feature_constitution33\n",
      "[0.25 1.   0.5  0.   0.75]\n",
      "feature_constitution34\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_constitution35\n",
      "[0.75 1.   0.5  0.25 0.  ]\n",
      "feature_constitution36\n",
      "[0.5  1.   0.25 0.   0.75]\n",
      "feature_constitution37\n",
      "[0.25 0.75 0.5  0.   1.  ]\n",
      "feature_constitution38\n",
      "[0.   0.75 0.25 0.5  1.  ]\n",
      "feature_constitution39\n",
      "[0.25 1.   0.   0.5  0.75]\n",
      "feature_constitution40\n",
      "[0.75 0.5  0.   0.25 1.  ]\n",
      "feature_constitution41\n",
      "[0.5  0.   0.75 0.25 1.  ]\n",
      "feature_constitution42\n",
      "[0.25 1.   0.5  0.   0.75]\n",
      "feature_constitution43\n",
      "[0.25 1.   0.75 0.   0.5 ]\n",
      "feature_constitution44\n",
      "[0.5  0.75 1.   0.   0.25]\n",
      "feature_constitution45\n",
      "[0.5  1.   0.75 0.25 0.  ]\n",
      "feature_constitution46\n",
      "[0.75 0.   0.25 0.5  1.  ]\n",
      "feature_constitution47\n",
      "[0.75 1.   0.25 0.   0.5 ]\n",
      "feature_constitution48\n",
      "[0.   0.75 1.   0.25 0.5 ]\n",
      "feature_constitution49\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_constitution50\n",
      "[0.25 0.5  0.   1.   0.75]\n",
      "feature_constitution51\n",
      "[0.5  1.   0.   0.75 0.25]\n",
      "feature_constitution52\n",
      "[0.   0.5  1.   0.75 0.25]\n",
      "feature_constitution53\n",
      "[0.5  0.   1.   0.75 0.25]\n",
      "feature_constitution54\n",
      "[1.   0.75 0.25 0.   0.5 ]\n",
      "feature_constitution55\n",
      "[0.5  0.75 1.   0.   0.25]\n",
      "feature_constitution56\n",
      "[0.5  1.   0.75 0.   0.25]\n",
      "feature_constitution57\n",
      "[0.   0.5  0.25 0.75 1.  ]\n",
      "feature_constitution58\n",
      "[1.   0.5  0.   0.75 0.25]\n",
      "feature_constitution59\n",
      "[0.25 1.   0.   0.75 0.5 ]\n",
      "feature_constitution60\n",
      "[0.5  1.   0.25 0.   0.75]\n",
      "feature_constitution61\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_constitution62\n",
      "[0.25 0.75 0.   1.   0.5 ]\n",
      "feature_constitution63\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_constitution64\n",
      "[0.75 1.   0.25 0.   0.5 ]\n",
      "feature_constitution65\n",
      "[0.   0.5  0.75 1.   0.25]\n",
      "feature_constitution66\n",
      "[0.5  0.25 0.75 1.   0.  ]\n",
      "feature_constitution67\n",
      "[0.25 0.75 1.   0.   0.5 ]\n",
      "feature_constitution68\n",
      "[1.   0.   0.5  0.25 0.75]\n",
      "feature_constitution69\n",
      "[0.25 0.   0.5  0.75 1.  ]\n",
      "feature_constitution70\n",
      "[0.75 1.   0.25 0.5  0.  ]\n",
      "feature_constitution71\n",
      "[0.5  0.   1.   0.75 0.25]\n",
      "feature_constitution72\n",
      "[0.25 0.5  0.   0.75 1.  ]\n",
      "feature_constitution73\n",
      "[0.25 0.5  0.75 1.   0.  ]\n",
      "feature_constitution74\n",
      "[0.   0.75 0.5  0.25 1.  ]\n",
      "feature_constitution75\n",
      "[0.   0.75 0.25 0.5  1.  ]\n",
      "feature_constitution76\n",
      "[0.75 0.   0.25 1.   0.5 ]\n",
      "feature_constitution77\n",
      "[0.   0.75 0.25 0.5  1.  ]\n",
      "feature_constitution78\n",
      "[0.5  0.25 0.75 1.   0.  ]\n",
      "feature_constitution79\n",
      "[0.25 0.   1.   0.75 0.5 ]\n",
      "feature_constitution80\n",
      "[0.25 0.75 1.   0.5  0.  ]\n",
      "feature_constitution81\n",
      "[0.25 1.   0.   0.5  0.75]\n",
      "feature_constitution82\n",
      "[0.25 1.   0.75 0.   0.5 ]\n",
      "feature_constitution83\n",
      "[1.   0.   0.75 0.5  0.25]\n",
      "feature_constitution84\n",
      "[0.25 0.75 1.   0.5  0.  ]\n",
      "feature_constitution85\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_constitution86\n",
      "[0.   0.75 0.25 0.5  1.  ]\n",
      "feature_constitution87\n",
      "[0.5  1.   0.25 0.   0.75]\n",
      "feature_constitution88\n",
      "[0.75 0.5  0.   0.25 1.  ]\n",
      "feature_constitution89\n",
      "[0.5  0.   0.75 0.25 1.  ]\n",
      "feature_constitution90\n",
      "[0.75 0.5  0.   0.25 1.  ]\n",
      "feature_constitution91\n",
      "[0.25 0.5  1.   0.75 0.  ]\n",
      "feature_constitution92\n",
      "[0.5  0.75 0.   1.   0.25]\n",
      "feature_constitution93\n",
      "[0.5  1.   0.   0.75 0.25]\n",
      "feature_constitution94\n",
      "[0.   0.75 0.25 0.5  1.  ]\n",
      "feature_constitution95\n",
      "[1.   0.75 0.25 0.   0.5 ]\n",
      "feature_constitution96\n",
      "[0.25 1.   0.   0.5  0.75]\n",
      "feature_constitution97\n",
      "[0.25 1.   0.5  0.   0.75]\n",
      "feature_constitution98\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_constitution99\n",
      "[0.5  0.75 0.   0.25 1.  ]\n",
      "feature_constitution100\n",
      "[0.75 0.25 0.   0.5  1.  ]\n",
      "feature_constitution101\n",
      "[0.5  0.75 0.   1.   0.25]\n",
      "feature_constitution102\n",
      "[0.5  1.   0.75 0.   0.25]\n",
      "feature_constitution103\n",
      "[0.25 0.75 0.5  0.   1.  ]\n",
      "feature_constitution104\n",
      "[1.   0.75 0.5  0.   0.25]\n",
      "feature_constitution105\n",
      "[0.75 0.5  0.25 1.   0.  ]\n",
      "feature_constitution106\n",
      "[0.25 0.75 0.5  0.   1.  ]\n",
      "feature_constitution107\n",
      "[0.25 1.   0.   0.5  0.75]\n",
      "feature_constitution108\n",
      "[0.5  0.75 0.   0.25 1.  ]\n",
      "feature_constitution109\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_constitution110\n",
      "[0.25 0.5  0.75 1.   0.  ]\n",
      "feature_constitution111\n",
      "[0.25 0.75 1.   0.5  0.  ]\n",
      "feature_constitution112\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_constitution113\n",
      "[0.5  1.   0.25 0.   0.75]\n",
      "feature_constitution114\n",
      "[0.25 0.75 0.   0.5  1.  ]\n",
      "feature_wisdom1\n",
      "[0.25 0.5  1.   0.75 0.  ]\n",
      "feature_wisdom2\n",
      "[1.   0.5  0.75 0.25 0.  ]\n",
      "feature_wisdom3\n",
      "[0.75 0.   1.   0.5  0.25]\n",
      "feature_wisdom4\n",
      "[0.5  0.25 0.75 1.   0.  ]\n",
      "feature_wisdom5\n",
      "[0.75 0.   0.5  1.   0.25]\n",
      "feature_wisdom6\n",
      "[0.75 1.   0.25 0.5  0.  ]\n",
      "feature_wisdom7\n",
      "[0.75 1.   0.   0.25 0.5 ]\n",
      "feature_wisdom8\n",
      "[0.75 0.25 1.   0.   0.5 ]\n",
      "feature_wisdom9\n",
      "[0.75 0.25 1.   0.5  0.  ]\n",
      "feature_wisdom10\n",
      "[0.75 0.25 1.   0.5  0.  ]\n",
      "feature_wisdom11\n",
      "[0.5  1.   0.75 0.25 0.  ]\n",
      "feature_wisdom12\n",
      "[1.   0.5  0.75 0.   0.25]\n",
      "feature_wisdom13\n",
      "[1.   0.5  0.75 0.   0.25]\n",
      "feature_wisdom14\n",
      "[0.5  1.   0.75 0.   0.25]\n",
      "feature_wisdom15\n",
      "[0.75 0.5  1.   0.25 0.  ]\n",
      "feature_wisdom16\n",
      "[0.5  0.   1.   0.25 0.75]\n",
      "feature_wisdom17\n",
      "[0.25 1.   0.75 0.5  0.  ]\n",
      "feature_wisdom18\n",
      "[0.25 1.   0.   0.5  0.75]\n",
      "feature_wisdom19\n",
      "[0.75 0.5  1.   0.   0.25]\n",
      "feature_wisdom20\n",
      "[0.5  1.   0.25 0.75 0.  ]\n",
      "feature_wisdom21\n",
      "[1.   0.5  0.75 0.25 0.  ]\n",
      "feature_wisdom22\n",
      "[0.5  1.   0.   0.25 0.75]\n",
      "feature_wisdom23\n",
      "[0.75 0.5  0.   1.   0.25]\n",
      "feature_wisdom24\n",
      "[0.75 0.5  1.   0.25 0.  ]\n",
      "feature_wisdom25\n",
      "[0.25 1.   0.   0.75 0.5 ]\n",
      "feature_wisdom26\n",
      "[0.5  0.25 1.   0.75 0.  ]\n",
      "feature_wisdom27\n",
      "[1.   0.75 0.5  0.   0.25]\n",
      "feature_wisdom28\n",
      "[0.75 1.   0.   0.5  0.25]\n",
      "feature_wisdom29\n",
      "[0.5  0.25 1.   0.75 0.  ]\n",
      "feature_wisdom30\n",
      "[0.5  0.25 1.   0.75 0.  ]\n",
      "feature_wisdom31\n",
      "[1.   0.5  0.75 0.   0.25]\n",
      "feature_wisdom32\n",
      "[0.25 0.5  0.75 0.   1.  ]\n",
      "feature_wisdom33\n",
      "[0.5  1.   0.75 0.25 0.  ]\n",
      "feature_wisdom34\n",
      "[0.5  0.75 0.25 1.   0.  ]\n",
      "feature_wisdom35\n",
      "[0.5  0.75 1.   0.25 0.  ]\n",
      "feature_wisdom36\n",
      "[0.75 0.25 1.   0.5  0.  ]\n",
      "feature_wisdom37\n",
      "[1.   0.75 0.5  0.   0.25]\n",
      "feature_wisdom38\n",
      "[1.   0.25 0.75 0.5  0.  ]\n",
      "feature_wisdom39\n",
      "[1.   0.5  0.75 0.25 0.  ]\n",
      "feature_wisdom40\n",
      "[0.75 0.   0.25 0.5  1.  ]\n",
      "feature_wisdom41\n",
      "[0.5  0.   0.75 1.   0.25]\n",
      "feature_wisdom42\n",
      "[0.75 0.5  1.   0.   0.25]\n",
      "feature_wisdom43\n",
      "[0.5  0.25 1.   0.   0.75]\n",
      "feature_wisdom44\n",
      "[1.   0.   0.75 0.5  0.25]\n",
      "feature_wisdom45\n",
      "[0.5  0.25 1.   0.   0.75]\n",
      "feature_wisdom46\n",
      "[0.75 1.   0.   0.25 0.5 ]\n",
      "target_kazutsugi\n",
      "[0.75 0.25 0.   0.5  1.  ]\n"
     ]
    }
   ],
   "source": [
    "# See which values are present for each column\n",
    "for col in df:\n",
    "    if col not in ('id', ): # Ignore id's bceause, they are unique\n",
    "        print(col)\n",
    "        print(df[col].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:22:52,155 - models.aws_base - INFO - Initializing AWS Estimator linear_learner model\n",
      "2019-09-23 21:22:52,157 - models.aws_base - INFO - Starting to train model.\n",
      "2019-09-23 21:23:09,282 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 21:23:09,283 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 21:23:11,622 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 21:23:11,623 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 21:23:11,624 - models.aws_base - INFO - Starting to fit model\n",
      "2019-09-23 21:23:11,739 - sagemaker - INFO - Creating training-job with name: linear-learner-2019-09-23-19-23-11-675\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-09-23 19:23:14 Starting - Starting the training job...\n",
      "2019-09-23 19:23:16 Starting - Launching requested ML instances...\n",
      "2019-09-23 19:24:11 Starting - Preparing the instances for training......\n",
      "2019-09-23 19:24:57 Downloading - Downloading input data...\n",
      "2019-09-23 19:25:47 Training - Downloading the training image...\n",
      "2019-09-23 19:26:05 Training - Training image download completed. Training in progress.\u001b[31mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:08 INFO 140156479870784] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-input.json: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'1000', u'huber_delta': u'1.0', u'num_classes': u'1', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:08 INFO 140156479870784] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'epochs': u'10', u'loss': u'auto', u'feature_dim': u'310', u'optimizer': u'auto', u'predictor_type': u'regressor'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:08 INFO 140156479870784] Final configuration: {u'loss_insensitivity': u'0.01', u'epochs': u'10', u'feature_dim': u'310', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'1000', u'huber_delta': u'1.0', u'num_classes': u'1', u'predictor_type': u'regressor', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:08 WARNING 140156479870784] Loggers have already been setup.\u001b[0m\n",
      "\u001b[31mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:08 INFO 140156479870784] Using default worker.\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:08 INFO 140156479870784] Create Store: local\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:08 INFO 140156479870784] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7f785bd8f8d0>\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:09 INFO 140156479870784] Scaling model computed with parameters:\n",
      " {'stdev_weight': \u001b[0m\n",
      "\u001b[31m[0.35382628 0.35199285 0.35225543 0.35266927 0.3529467  0.35237288\n",
      " 0.35113704 0.35397646 0.35270768 0.35148695 0.35344845 0.3528342\n",
      " 0.35097262 0.33394215 0.33786827 0.3374128  0.3160197  0.3536588\n",
      " 0.3203075  0.2932473  0.3532288  0.34575298 0.34223518 0.34468514\n",
      " 0.34217227 0.33708912 0.2928341  0.33577582 0.33844122 0.3544362\n",
      " 0.3515257  0.34292683 0.34176594 0.28862482 0.33453378 0.33428675\n",
      " 0.2654325  0.34010357 0.2867219  0.34460863 0.34163916 0.34203988\n",
      " 0.35160467 0.30518773 0.3382351  0.3542455  0.33548418 0.34176165\n",
      " 0.3485601  0.3430173  0.33460653 0.3521111  0.3028067  0.33783445\n",
      " 0.35307467 0.34830102 0.3371323  0.35350004 0.2650145  0.30436888\n",
      " 0.29033658 0.34077558 0.35065076 0.33969158 0.34486416 0.3535313\n",
      " 0.35507485 0.28941467 0.35334814 0.34346122 0.30171815 0.28919587\n",
      " 0.35085362 0.3391919  0.3532716  0.3431054  0.29112187 0.35367703\n",
      " 0.33989662 0.33272168 0.35214007 0.35255587 0.341771   0.3210503\n",
      " 0.29070428 0.34739733 0.33867434 0.3527889  0.28557634 0.34664023\n",
      " 0.31541598 0.34434673 0.34926763 0.33188504 0.3533087  0.3462046\n",
      " 0.35245806 0.34636    0.3527048  0.33797526 0.35363317 0.35398474\n",
      " 0.33642372 0.2834176  0.29877254 0.35199365 0.3521466  0.35468066\n",
      " 0.35174435 0.33968833 0.33853742 0.3515304  0.35315067 0.35130587\n",
      " 0.2837512  0.32525277 0.35464633 0.1779425  0.35233635 0.35207382\n",
      " 0.35098872 0.33136904 0.33759457 0.30500895 0.35310048 0.35281745\n",
      " 0.33166146 0.34017527 0.33964235 0.33692217 0.3060122  0.3524342\n",
      " 0.30027625 0.35369423 0.32648668 0.17698516 0.35205537 0.352305\n",
      " 0.3513394  0.3552686  0.3546523  0.35182625 0.35336855 0.35238028\n",
      " 0.35206506 0.3529019  0.35270262 0.35452422 0.35360134 0.35409138\n",
      " 0.3416181  0.34577602 0.34580326 0.3535854  0.3396356  0.3390842\n",
      " 0.3523206  0.34006625 0.3546793  0.33887908 0.34724447 0.3519978\n",
      " 0.35161582 0.3407789  0.3450431  0.35286164 0.34053758 0.33670762\n",
      " 0.3397019  0.3434841  0.33347243 0.35246637 0.3458358  0.3558963\n",
      " 0.3533128  0.34370303 0.35270676 0.34803686 0.35402188 0.3378043\n",
      " 0.3375628  0.3529925  0.35289663 0.35389405 0.35394675 0.35590306\n",
      " 0.34558314 0.35041237 0.33661315 0.35194442 0.3365669  0.33679348\n",
      " 0.352877   0.33418286 0.3545546  0.35228825 0.35471    0.35420048\n",
      " 0.3531662  0.3545083  0.3417951  0.35168076 0.3523777  0.3566452\n",
      " 0.334825   0.3547323  0.35362983 0.3546076  0.35207865 0.34618807\n",
      " 0.3396672  0.3415772  0.3530576  0.35532385 0.3530451  0.332483\n",
      " 0.35172457 0.35572624 0.3520736  0.3333744  0.35171658 0.338695\n",
      " 0.3451033  0.3394274  0.35322872 0.35085097 0.35330877 0.3397358\n",
      " 0.35168323 0.3419726  0.35252994 0.35568324 0.35069227 0.3410195\n",
      " 0.35261226 0.34974018 0.34572238 0.33852038 0.35248134 0.3388812\n",
      " 0.35211954 0.35372522 0.3336429  0.3492664  0.35624003 0.33814698\n",
      " 0.33838496 0.3553081  0.35460258 0.35197297 0.35356945 0.3526526\n",
      " 0.3378729  0.3376073  0.3537297  0.354698   0.33635935 0.3373355\n",
      " 0.3501253  0.35503447 0.3511262  0.3543504  0.3499919  0.35102785\n",
      " 0.35338414 0.35317194 0.3521119  0.3485625  0.35151455 0.33298397\n",
      " 0.3532925  0.35211775 0.35252103 0.3502236  0.33246133 0.353934\n",
      " 0.34454313 0.35384083 0.3527891  0.34810865 0.35473764 0.35206997\n",
      " 0.3489174  0.35496438 0.3470812  0.3536728  0.30076417 0.34026122\n",
      " 0.3521359  0.35250235 0.3527631  0.3474699  0.3548121  0.34708184\n",
      " 0.35222313 0.30212298 0.34033746 0.34676662 0.3561074  0.35132924\n",
      " 0.35100985 0.35247377 0.3525525  0.3503929  0.35044608 0.35192275\n",
      " 0.35335213 0.3514274  0.34668827 0.35439065]\u001b[0m\n",
      "\u001b[31m<NDArray 310 @cpu(0)>, 'stdev_label': \u001b[0m\n",
      "\u001b[31m[0.35457224]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_label': \u001b[0m\n",
      "\u001b[31m[0.4996591]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_weight': \u001b[0m\n",
      "\u001b[31m[0.50036365 0.5031818  0.5011591  0.49775    0.49713635 0.49804544\n",
      " 0.49802274 0.4986591  0.4991591  0.49845454 0.49754545 0.49629545\n",
      " 0.5001818  0.50231814 0.4938182  0.50325    0.50540906 0.49595454\n",
      " 0.49768183 0.49754545 0.50279546 0.500591   0.5003182  0.50215906\n",
      " 0.49815908 0.49568182 0.49690908 0.49679545 0.5060909  0.4998636\n",
      " 0.49811363 0.50186366 0.49622726 0.4984091  0.49861363 0.5015454\n",
      " 0.50036365 0.49761364 0.5008864  0.4975     0.4979318  0.50379544\n",
      " 0.50220454 0.49497727 0.49581817 0.49681818 0.49731818 0.49661362\n",
      " 0.4995682  0.50197726 0.4979091  0.5034773  0.5011365  0.49538636\n",
      " 0.50295454 0.49786365 0.49513635 0.50190914 0.50052273 0.49402273\n",
      " 0.4996364  0.49625    0.50206816 0.49870455 0.49927267 0.49604544\n",
      " 0.49734092 0.49927273 0.50174993 0.50054544 0.5005     0.49720454\n",
      " 0.5031136  0.5000227  0.5024545  0.50052273 0.49806818 0.50186354\n",
      " 0.4979091  0.4990909  0.50295454 0.50234085 0.49775    0.4975\n",
      " 0.49845454 0.49838635 0.49538636 0.4995227  0.5017273  0.5033182\n",
      " 0.5042954  0.50325006 0.49913636 0.50022733 0.50372726 0.5005682\n",
      " 0.5007727  0.5008182  0.50165915 0.49986377 0.49206817 0.50270456\n",
      " 0.50095457 0.50200003 0.5036591  0.503091   0.50618184 0.4970909\n",
      " 0.50297725 0.5030909  0.50061363 0.5004773  0.5007273  0.50400007\n",
      " 0.50152266 0.5034318  0.49379545 0.50111365 0.5048864  0.5066364\n",
      " 0.50534093 0.49570453 0.49731818 0.5019091  0.50363636 0.50445455\n",
      " 0.49747726 0.5044091  0.49647728 0.4992954  0.5039545  0.49938637\n",
      " 0.504      0.49711365 0.50379544 0.50034094 0.50045455 0.49929544\n",
      " 0.5019545  0.50036365 0.4995682  0.4971591  0.5000682  0.50206816\n",
      " 0.50218177 0.49984092 0.4979318  0.50029546 0.4995909  0.50011367\n",
      " 0.5027045  0.49879545 0.4966818  0.50031817 0.5033636  0.50220454\n",
      " 0.5009318  0.5016363  0.50388634 0.5027499  0.5009091  0.5009773\n",
      " 0.49836364 0.5006591  0.5055227  0.49981818 0.50027275 0.5044772\n",
      " 0.5005909  0.5059772  0.5025227  0.5034318  0.49588636 0.50693184\n",
      " 0.50472724 0.50522727 0.5011591  0.50259095 0.49806818 0.50275004\n",
      " 0.50047725 0.50663644 0.50193185 0.50299996 0.50034094 0.50302273\n",
      " 0.50518185 0.49902272 0.50393176 0.5031819  0.5010682  0.5014091\n",
      " 0.50345457 0.49809092 0.5026364  0.5009773  0.50215906 0.50804543\n",
      " 0.50095457 0.5000227  0.49997726 0.50604546 0.49936363 0.5029545\n",
      " 0.4995682  0.49952272 0.5073409  0.5030228  0.49906817 0.5034318\n",
      " 0.4989091  0.5048864  0.49806818 0.49920455 0.49572727 0.49927276\n",
      " 0.49975    0.50315905 0.5062045  0.50188637 0.49995455 0.49872726\n",
      " 0.49943182 0.50222725 0.5043863  0.4995682  0.50604546 0.5002273\n",
      " 0.49913636 0.5045455  0.49897727 0.5075909  0.50386363 0.50261366\n",
      " 0.50475    0.49995455 0.5031818  0.50074995 0.50111365 0.49931812\n",
      " 0.5006818  0.49813637 0.5007728  0.50127274 0.50279546 0.49820456\n",
      " 0.5064091  0.5007045  0.4988409  0.502659   0.5000909  0.502341\n",
      " 0.499      0.4979318  0.4994546  0.5055     0.50463635 0.49954545\n",
      " 0.50297725 0.5012955  0.4995682  0.498      0.4999318  0.49897727\n",
      " 0.5024546  0.50286365 0.49961364 0.50704545 0.50011367 0.5037046\n",
      " 0.50443184 0.4986818  0.49729547 0.50159097 0.5038637  0.50302273\n",
      " 0.49947733 0.5035455  0.49975    0.5064773  0.49854547 0.49886364\n",
      " 0.5009773  0.50054544 0.5007273  0.49911365 0.49893183 0.5034773\n",
      " 0.49945456 0.49813637 0.4988182  0.5018182  0.49934092 0.50534093\n",
      " 0.50356823 0.5003409  0.50336367 0.50293183 0.5000682  0.50229543\n",
      " 0.50152266 0.5035     0.50368184 0.5017955  0.5002046  0.5028863\n",
      " 0.50047725 0.50211364 0.5067728  0.5030909 ]\u001b[0m\n",
      "\u001b[31m<NDArray 310 @cpu(0)>}\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:09 INFO 140156479870784] nvidia-smi took: 0.0251848697662 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:09 INFO 140156479870784] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}, \"Total Records Seen\": {\"count\": 1, \"max\": 12000, \"sum\": 12000.0, \"min\": 12000}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 11000, \"sum\": 11000.0, \"min\": 11000}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1569266769.119971, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1569266769.119925}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0174350500701372, \"sum\": 1.0174350500701372, \"min\": 1.0174350500701372}}, \"EndTime\": 1569266800.64654, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.646445}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0189870087963684, \"sum\": 1.0189870087963684, \"min\": 1.0189870087963684}}, \"EndTime\": 1569266800.646647, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.646627}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0161547050951723, \"sum\": 1.0161547050951723, \"min\": 1.0161547050951723}}, \"EndTime\": 1569266800.646709, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.646693}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0178917488992363, \"sum\": 1.0178917488992363, \"min\": 1.0178917488992363}}, \"EndTime\": 1569266800.646775, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.646757}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.5011600571630006, \"sum\": 1.5011600571630006, \"min\": 1.5011600571630006}}, \"EndTime\": 1569266800.646832, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.646816}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3880049576985272, \"sum\": 1.3880049576985272, \"min\": 1.3880049576985272}}, \"EndTime\": 1569266800.646901, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.646885}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3992903844210276, \"sum\": 1.3992903844210276, \"min\": 1.3992903844210276}}, \"EndTime\": 1569266800.646957, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.646941}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3584495761329098, \"sum\": 1.3584495761329098, \"min\": 1.3584495761329098}}, \"EndTime\": 1569266800.647019, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647001}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.01654925095708, \"sum\": 1.01654925095708, \"min\": 1.01654925095708}}, \"EndTime\": 1569266800.647088, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.64707}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.018726426731024, \"sum\": 1.018726426731024, \"min\": 1.018726426731024}}, \"EndTime\": 1569266800.647141, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647125}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0135400720914998, \"sum\": 1.0135400720914998, \"min\": 1.0135400720914998}}, \"EndTime\": 1569266800.647196, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.64718}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0183278036902372, \"sum\": 1.0183278036902372, \"min\": 1.0183278036902372}}, \"EndTime\": 1569266800.647256, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647239}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.5278640751636534, \"sum\": 1.5278640751636534, \"min\": 1.5278640751636534}}, \"EndTime\": 1569266800.647318, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647301}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.516472855793865, \"sum\": 1.516472855793865, \"min\": 1.516472855793865}}, \"EndTime\": 1569266800.64739, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647371}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3928647663373306, \"sum\": 1.3928647663373306, \"min\": 1.3928647663373306}}, \"EndTime\": 1569266800.647453, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647435}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.523938852362502, \"sum\": 1.523938852362502, \"min\": 1.523938852362502}}, \"EndTime\": 1569266800.647514, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647498}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0078449618750973, \"sum\": 1.0078449618750973, \"min\": 1.0078449618750973}}, \"EndTime\": 1569266800.647575, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647559}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.008285543922177, \"sum\": 1.008285543922177, \"min\": 1.008285543922177}}, \"EndTime\": 1569266800.647637, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647626}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0074980497669401, \"sum\": 1.0074980497669401, \"min\": 1.0074980497669401}}, \"EndTime\": 1569266800.647676, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647668}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0096885662126422, \"sum\": 1.0096885662126422, \"min\": 1.0096885662126422}}, \"EndTime\": 1569266800.64772, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647705}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.340624241398457, \"sum\": 1.340624241398457, \"min\": 1.340624241398457}}, \"EndTime\": 1569266800.647783, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647768}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3068416260983284, \"sum\": 1.3068416260983284, \"min\": 1.3068416260983284}}, \"EndTime\": 1569266800.64783, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.64782}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.4851640341894288, \"sum\": 1.4851640341894288, \"min\": 1.4851640341894288}}, \"EndTime\": 1569266800.647867, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647858}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3729274897777528, \"sum\": 1.3729274897777528, \"min\": 1.3729274897777528}}, \"EndTime\": 1569266800.647903, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647895}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0001436723437986, \"sum\": 1.0001436723437986, \"min\": 1.0001436723437986}}, \"EndTime\": 1569266800.647957, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647943}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9990447594697339, \"sum\": 0.9990447594697339, \"min\": 0.9990447594697339}}, \"EndTime\": 1569266800.647996, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.647987}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9984751737599361, \"sum\": 0.9984751737599361, \"min\": 0.9984751737599361}}, \"EndTime\": 1569266800.648033, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.648024}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9988787190349322, \"sum\": 0.9988787190349322, \"min\": 0.9988787190349322}}, \"EndTime\": 1569266800.648068, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.64806}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0282364296473172, \"sum\": 1.0282364296473172, \"min\": 1.0282364296473172}}, \"EndTime\": 1569266800.648112, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.648097}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.02141355336634, \"sum\": 1.02141355336634, \"min\": 1.02141355336634}}, \"EndTime\": 1569266800.648162, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.648152}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0333105703149352, \"sum\": 1.0333105703149352, \"min\": 1.0333105703149352}}, \"EndTime\": 1569266800.648195, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.648186}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.025635007330307, \"sum\": 1.025635007330307, \"min\": 1.025635007330307}}, \"EndTime\": 1569266800.64823, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266800.648221}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:40 INFO 140156479870784] #quality_metric: host=algo-1, epoch=0, train mse_objective <loss>=1.01743505007\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12697957405422838, \"sum\": 0.12697957405422838, \"min\": 0.12697957405422838}}, \"EndTime\": 1569266804.708855, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.708787}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12791327801500657, \"sum\": 0.12791327801500657, \"min\": 0.12791327801500657}}, \"EndTime\": 1569266804.708963, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.708941}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12697966569484043, \"sum\": 0.12697966569484043, \"min\": 0.12697966569484043}}, \"EndTime\": 1569266804.709027, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709009}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12786591829113034, \"sum\": 0.12786591829113034, \"min\": 0.12786591829113034}}, \"EndTime\": 1569266804.709095, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709077}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13896874006860546, \"sum\": 0.13896874006860546, \"min\": 0.13896874006860546}}, \"EndTime\": 1569266804.70916, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709142}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1412644486921334, \"sum\": 0.1412644486921334, \"min\": 0.1412644486921334}}, \"EndTime\": 1569266804.70923, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709213}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13676581572133195, \"sum\": 0.13676581572133195, \"min\": 0.13676581572133195}}, \"EndTime\": 1569266804.709287, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709272}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.17165795462667574, \"sum\": 0.17165795462667574, \"min\": 0.17165795462667574}}, \"EndTime\": 1569266804.709349, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709333}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12691375823036233, \"sum\": 0.12691375823036233, \"min\": 0.12691375823036233}}, \"EndTime\": 1569266804.709395, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709385}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12782209819064605, \"sum\": 0.12782209819064605, \"min\": 0.12782209819064605}}, \"EndTime\": 1569266804.709427, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709419}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1269547526756243, \"sum\": 0.1269547526756243, \"min\": 0.1269547526756243}}, \"EndTime\": 1569266804.709457, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709449}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12787070458413444, \"sum\": 0.12787070458413444, \"min\": 0.12787070458413444}}, \"EndTime\": 1569266804.709495, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709486}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13769640705607858, \"sum\": 0.13769640705607858, \"min\": 0.13769640705607858}}, \"EndTime\": 1569266804.709543, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709528}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14121244376606684, \"sum\": 0.14121244376606684, \"min\": 0.14121244376606684}}, \"EndTime\": 1569266804.709604, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709587}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13664079865572906, \"sum\": 0.13664079865572906, \"min\": 0.13664079865572906}}, \"EndTime\": 1569266804.709662, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709646}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14123865130870292, \"sum\": 0.14123865130870292, \"min\": 0.14123865130870292}}, \"EndTime\": 1569266804.709729, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709712}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12614804690528586, \"sum\": 0.12614804690528586, \"min\": 0.12614804690528586}}, \"EndTime\": 1569266804.709791, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709773}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12666615624328115, \"sum\": 0.12666615624328115, \"min\": 0.12666615624328115}}, \"EndTime\": 1569266804.709855, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709836}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12620367560750884, \"sum\": 0.12620367560750884, \"min\": 0.12620367560750884}}, \"EndTime\": 1569266804.709918, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709901}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1266591762389851, \"sum\": 0.1266591762389851, \"min\": 0.1266591762389851}}, \"EndTime\": 1569266804.709972, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709961}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1344091763533173, \"sum\": 0.1344091763533173, \"min\": 0.1344091763533173}}, \"EndTime\": 1569266804.710005, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.709997}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1341920775388066, \"sum\": 0.1341920775388066, \"min\": 0.1341920775388066}}, \"EndTime\": 1569266804.710055, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.710038}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1295385140453424, \"sum\": 0.1295385140453424, \"min\": 0.1295385140453424}}, \"EndTime\": 1569266804.710117, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.7101}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13600568645361213, \"sum\": 0.13600568645361213, \"min\": 0.13600568645361213}}, \"EndTime\": 1569266804.71018, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.710162}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12381270274780344, \"sum\": 0.12381270274780344, \"min\": 0.12381270274780344}}, \"EndTime\": 1569266804.710234, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.710218}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12387291769967036, \"sum\": 0.12387291769967036, \"min\": 0.12387291769967036}}, \"EndTime\": 1569266804.710298, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.710282}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12382178178202913, \"sum\": 0.12382178178202913, \"min\": 0.12382178178202913}}, \"EndTime\": 1569266804.710358, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.710341}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12386115919429283, \"sum\": 0.12386115919429283, \"min\": 0.12386115919429283}}, \"EndTime\": 1569266804.710447, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.710429}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12396476534881473, \"sum\": 0.12396476534881473, \"min\": 0.12396476534881473}}, \"EndTime\": 1569266804.710507, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.71049}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1241441430477482, \"sum\": 0.1241441430477482, \"min\": 0.1241441430477482}}, \"EndTime\": 1569266804.710569, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.710551}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12393536752698639, \"sum\": 0.12393536752698639, \"min\": 0.12393536752698639}}, \"EndTime\": 1569266804.710643, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.710624}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1241945703945516, \"sum\": 0.1241945703945516, \"min\": 0.1241945703945516}}, \"EndTime\": 1569266804.710712, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266804.710693}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:44 INFO 140156479870784] #quality_metric: host=algo-1, epoch=0, validation mse_objective <loss>=0.126979574054\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:44 INFO 140156479870784] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=mse_objective, value=0.123812702748\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:44 INFO 140156479870784] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:44 INFO 140156479870784] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 414, \"sum\": 414.0, \"min\": 414}, \"Total Records Seen\": {\"count\": 1, \"max\": 413446, \"sum\": 413446.0, \"min\": 413446}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1569266804.712864, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569266769.120214}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:26:44 INFO 140156479870784] #throughput_metric: host=algo-1, train throughput=11278.8604521 records/second\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0020028411028095, \"sum\": 1.0020028411028095, \"min\": 1.0020028411028095}}, \"EndTime\": 1569266835.637043, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.636962}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0100617972585626, \"sum\": 1.0100617972585626, \"min\": 1.0100617972585626}}, \"EndTime\": 1569266835.637145, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637125}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0020883219806929, \"sum\": 1.0020883219806929, \"min\": 1.0020883219806929}}, \"EndTime\": 1569266835.637204, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637189}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0101034029915446, \"sum\": 1.0101034029915446, \"min\": 1.0101034029915446}}, \"EndTime\": 1569266835.637275, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637257}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0849934800450047, \"sum\": 1.0849934800450047, \"min\": 1.0849934800450047}}, \"EndTime\": 1569266835.637342, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637324}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.230157785163556, \"sum\": 1.230157785163556, \"min\": 1.230157785163556}}, \"EndTime\": 1569266835.637398, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637382}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0986729269051492, \"sum\": 1.0986729269051492, \"min\": 1.0986729269051492}}, \"EndTime\": 1569266835.637463, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637446}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.244730303452794, \"sum\": 1.244730303452794, \"min\": 1.244730303452794}}, \"EndTime\": 1569266835.637525, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637507}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0018006431979134, \"sum\": 1.0018006431979134, \"min\": 1.0018006431979134}}, \"EndTime\": 1569266835.637587, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637571}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0098409690191026, \"sum\": 1.0098409690191026, \"min\": 1.0098409690191026}}, \"EndTime\": 1569266835.63765, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637633}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.001996987359482, \"sum\": 1.001996987359482, \"min\": 1.001996987359482}}, \"EndTime\": 1569266835.63771, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637694}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0097790043324306, \"sum\": 1.0097790043324306, \"min\": 1.0097790043324306}}, \"EndTime\": 1569266835.637776, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637759}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0792609849582586, \"sum\": 1.0792609849582586, \"min\": 1.0792609849582586}}, \"EndTime\": 1569266835.637837, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.63782}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1668469885162582, \"sum\": 1.1668469885162582, \"min\": 1.1668469885162582}}, \"EndTime\": 1569266835.637894, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637877}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0976719660200085, \"sum\": 1.0976719660200085, \"min\": 1.0976719660200085}}, \"EndTime\": 1569266835.637953, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637936}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.182137374307152, \"sum\": 1.182137374307152, \"min\": 1.182137374307152}}, \"EndTime\": 1569266835.638013, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.637996}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9990152071907633, \"sum\": 0.9990152071907633, \"min\": 0.9990152071907633}}, \"EndTime\": 1569266835.638073, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638055}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0052146260934578, \"sum\": 1.0052146260934578, \"min\": 1.0052146260934578}}, \"EndTime\": 1569266835.638136, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638118}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9991388644173258, \"sum\": 0.9991388644173258, \"min\": 0.9991388644173258}}, \"EndTime\": 1569266835.638196, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638179}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.005132280078612, \"sum\": 1.005132280078612, \"min\": 1.005132280078612}}, \"EndTime\": 1569266835.63826, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638243}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0682701018754384, \"sum\": 1.0682701018754384, \"min\": 1.0682701018754384}}, \"EndTime\": 1569266835.638322, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638305}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1706655873134546, \"sum\": 1.1706655873134546, \"min\": 1.1706655873134546}}, \"EndTime\": 1569266835.638406, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638367}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0594000481584125, \"sum\": 1.0594000481584125, \"min\": 1.0594000481584125}}, \"EndTime\": 1569266835.638475, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638458}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.13311567805949, \"sum\": 1.13311567805949, \"min\": 1.13311567805949}}, \"EndTime\": 1569266835.638537, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.63852}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9924225772848153, \"sum\": 0.9924225772848153, \"min\": 0.9924225772848153}}, \"EndTime\": 1569266835.638607, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638588}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9926455801110018, \"sum\": 0.9926455801110018, \"min\": 0.9926455801110018}}, \"EndTime\": 1569266835.638667, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638652}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.992418225980459, \"sum\": 0.992418225980459, \"min\": 0.992418225980459}}, \"EndTime\": 1569266835.638727, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.63871}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9926521113293426, \"sum\": 0.9926521113293426, \"min\": 0.9926521113293426}}, \"EndTime\": 1569266835.63879, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638772}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9957643041444241, \"sum\": 0.9957643041444241, \"min\": 0.9957643041444241}}, \"EndTime\": 1569266835.638855, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638838}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0014615508957099, \"sum\": 1.0014615508957099, \"min\": 1.0014615508957099}}, \"EndTime\": 1569266835.638927, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.63891}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9956665943174291, \"sum\": 0.9956665943174291, \"min\": 0.9956665943174291}}, \"EndTime\": 1569266835.638989, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.638972}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0012745210642826, \"sum\": 1.0012745210642826, \"min\": 1.0012745210642826}}, \"EndTime\": 1569266835.639048, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266835.639032}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:15 INFO 140156479870784] #quality_metric: host=algo-1, epoch=1, train mse_objective <loss>=1.0020028411\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12619033325132065, \"sum\": 0.12619033325132065, \"min\": 0.12619033325132065}}, \"EndTime\": 1569266839.113183, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113112}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12787145515533574, \"sum\": 0.12787145515533574, \"min\": 0.12787145515533574}}, \"EndTime\": 1569266839.113295, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113272}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12618064359642606, \"sum\": 0.12618064359642606, \"min\": 0.12618064359642606}}, \"EndTime\": 1569266839.113359, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113341}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12786519554279427, \"sum\": 0.12786519554279427, \"min\": 0.12786519554279427}}, \"EndTime\": 1569266839.113425, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113408}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1419647785265143, \"sum\": 0.1419647785265143, \"min\": 0.1419647785265143}}, \"EndTime\": 1569266839.113484, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113467}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14794900156441007, \"sum\": 0.14794900156441007, \"min\": 0.14794900156441007}}, \"EndTime\": 1569266839.11354, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113529}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13785453010048693, \"sum\": 0.13785453010048693, \"min\": 0.13785453010048693}}, \"EndTime\": 1569266839.113607, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.11359}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.15137582775395791, \"sum\": 0.15137582775395791, \"min\": 0.15137582775395791}}, \"EndTime\": 1569266839.113668, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113653}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1261379743819196, \"sum\": 0.1261379743819196, \"min\": 0.1261379743819196}}, \"EndTime\": 1569266839.113721, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113707}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12782308514217353, \"sum\": 0.12782308514217353, \"min\": 0.12782308514217353}}, \"EndTime\": 1569266839.113781, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113764}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12616775541430403, \"sum\": 0.12616775541430403, \"min\": 0.12616775541430403}}, \"EndTime\": 1569266839.113843, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113826}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12784127217377148, \"sum\": 0.12784127217377148, \"min\": 0.12784127217377148}}, \"EndTime\": 1569266839.113909, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113891}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13909859994715856, \"sum\": 0.13909859994715856, \"min\": 0.13909859994715856}}, \"EndTime\": 1569266839.113974, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.113957}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14945900220474345, \"sum\": 0.14945900220474345, \"min\": 0.14945900220474345}}, \"EndTime\": 1569266839.114047, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114029}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13787725214508634, \"sum\": 0.13787725214508634, \"min\": 0.13787725214508634}}, \"EndTime\": 1569266839.114113, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114095}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14247790582320516, \"sum\": 0.14247790582320516, \"min\": 0.14247790582320516}}, \"EndTime\": 1569266839.114178, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114159}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12555147321043525, \"sum\": 0.12555147321043525, \"min\": 0.12555147321043525}}, \"EndTime\": 1569266839.114242, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114223}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12668596962017517, \"sum\": 0.12668596962017517, \"min\": 0.12668596962017517}}, \"EndTime\": 1569266839.114307, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.11429}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.125568827076452, \"sum\": 0.125568827076452, \"min\": 0.125568827076452}}, \"EndTime\": 1569266839.114393, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114353}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12668118154073027, \"sum\": 0.12668118154073027, \"min\": 0.12668118154073027}}, \"EndTime\": 1569266839.114464, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114445}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13132867782512803, \"sum\": 0.13132867782512803, \"min\": 0.13132867782512803}}, \"EndTime\": 1569266839.11453, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114512}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14580630760081123, \"sum\": 0.14580630760081123, \"min\": 0.14580630760081123}}, \"EndTime\": 1569266839.114594, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114575}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13000919600276734, \"sum\": 0.13000919600276734, \"min\": 0.13000919600276734}}, \"EndTime\": 1569266839.11466, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114641}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14000901320502251, \"sum\": 0.14000901320502251, \"min\": 0.14000901320502251}}, \"EndTime\": 1569266839.114723, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114706}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12382961380455826, \"sum\": 0.12382961380455826, \"min\": 0.12382961380455826}}, \"EndTime\": 1569266839.114788, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114771}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12386838603149421, \"sum\": 0.12386838603149421, \"min\": 0.12386838603149421}}, \"EndTime\": 1569266839.11485, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114832}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12383138222891191, \"sum\": 0.12383138222891191, \"min\": 0.12383138222891191}}, \"EndTime\": 1569266839.114922, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114904}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12384396302918835, \"sum\": 0.12384396302918835, \"min\": 0.12384396302918835}}, \"EndTime\": 1569266839.114997, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.114979}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12415498431079881, \"sum\": 0.12415498431079881, \"min\": 0.12415498431079881}}, \"EndTime\": 1569266839.115061, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.115044}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12451797053260945, \"sum\": 0.12451797053260945, \"min\": 0.12451797053260945}}, \"EndTime\": 1569266839.115129, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.115116}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12415834506205456, \"sum\": 0.12415834506205456, \"min\": 0.12415834506205456}}, \"EndTime\": 1569266839.1152, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.115181}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12449620955515059, \"sum\": 0.12449620955515059, \"min\": 0.12449620955515059}}, \"EndTime\": 1569266839.115272, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266839.115254}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:19 INFO 140156479870784] #quality_metric: host=algo-1, epoch=1, validation mse_objective <loss>=0.126190333251\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:19 INFO 140156479870784] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=mse_objective, value=0.123829613805\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:19 INFO 140156479870784] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 816, \"sum\": 816.0, \"min\": 816}, \"Total Records Seen\": {\"count\": 1, \"max\": 814892, \"sum\": 814892.0, \"min\": 814892}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1569266839.116375, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569266804.713138}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:19 INFO 140156479870784] #throughput_metric: host=algo-1, train throughput=11668.7952807 records/second\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.998317350087915, \"sum\": 0.998317350087915, \"min\": 0.998317350087915}}, \"EndTime\": 1569266870.359386, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.359298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0094238197535945, \"sum\": 1.0094238197535945, \"min\": 1.0094238197535945}}, \"EndTime\": 1569266870.35949, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.35947}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9983428046400113, \"sum\": 0.9983428046400113, \"min\": 0.9983428046400113}}, \"EndTime\": 1569266870.359549, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.359533}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0094483271192136, \"sum\": 1.0094483271192136, \"min\": 1.0094483271192136}}, \"EndTime\": 1569266870.359618, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.3596}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0698189849472997, \"sum\": 1.0698189849472997, \"min\": 1.0698189849472997}}, \"EndTime\": 1569266870.359685, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.359667}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2385146575699424, \"sum\": 1.2385146575699424, \"min\": 1.2385146575699424}}, \"EndTime\": 1569266870.359922, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.359896}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0762478012123013, \"sum\": 1.0762478012123013, \"min\": 1.0762478012123013}}, \"EndTime\": 1569266870.359997, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.359978}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2398266723328397, \"sum\": 1.2398266723328397, \"min\": 1.2398266723328397}}, \"EndTime\": 1569266870.360067, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360049}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9982381244764066, \"sum\": 0.9982381244764066, \"min\": 0.9982381244764066}}, \"EndTime\": 1569266870.360128, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360116}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.009260435489645, \"sum\": 1.009260435489645, \"min\": 1.009260435489645}}, \"EndTime\": 1569266870.360184, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360168}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9982991578061682, \"sum\": 0.9982991578061682, \"min\": 0.9982991578061682}}, \"EndTime\": 1569266870.360244, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360228}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0092399646635364, \"sum\": 1.0092399646635364, \"min\": 1.0092399646635364}}, \"EndTime\": 1569266870.360298, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360281}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0666491586585294, \"sum\": 1.0666491586585294, \"min\": 1.0666491586585294}}, \"EndTime\": 1569266870.360361, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360344}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2055817090269931, \"sum\": 1.2055817090269931, \"min\": 1.2055817090269931}}, \"EndTime\": 1569266870.360433, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360414}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0750244137580853, \"sum\": 1.0750244137580853, \"min\": 1.0750244137580853}}, \"EndTime\": 1569266870.360506, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360487}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2173518273408277, \"sum\": 1.2173518273408277, \"min\": 1.2173518273408277}}, \"EndTime\": 1569266870.360578, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.36056}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9966132022543738, \"sum\": 0.9966132022543738, \"min\": 0.9966132022543738}}, \"EndTime\": 1569266870.360646, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360629}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0047013571113719, \"sum\": 1.0047013571113719, \"min\": 1.0047013571113719}}, \"EndTime\": 1569266870.360715, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360697}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9966377925730109, \"sum\": 0.9966377925730109, \"min\": 0.9966377925730109}}, \"EndTime\": 1569266870.360775, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360758}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0046710279659736, \"sum\": 1.0046710279659736, \"min\": 1.0046710279659736}}, \"EndTime\": 1569266870.36084, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360822}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.05249127638667, \"sum\": 1.05249127638667, \"min\": 1.05249127638667}}, \"EndTime\": 1569266870.360903, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360885}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.174325071415699, \"sum\": 1.174325071415699, \"min\": 1.174325071415699}}, \"EndTime\": 1569266870.360963, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.360947}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0468542842722295, \"sum\": 1.0468542842722295, \"min\": 1.0468542842722295}}, \"EndTime\": 1569266870.361022, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361005}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.160451501860583, \"sum\": 1.160451501860583, \"min\": 1.160451501860583}}, \"EndTime\": 1569266870.361081, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361065}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9922671956278737, \"sum\": 0.9922671956278737, \"min\": 0.9922671956278737}}, \"EndTime\": 1569266870.361141, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361125}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9926730743940929, \"sum\": 0.9926730743940929, \"min\": 0.9926730743940929}}, \"EndTime\": 1569266870.361202, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361185}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9922672008029243, \"sum\": 0.9922672008029243, \"min\": 0.9922672008029243}}, \"EndTime\": 1569266870.361263, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361247}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9926937735312598, \"sum\": 0.9926937735312598, \"min\": 0.9926937735312598}}, \"EndTime\": 1569266870.361322, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361305}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9955703503996357, \"sum\": 0.9955703503996357, \"min\": 0.9955703503996357}}, \"EndTime\": 1569266870.36138, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361365}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0083237504079157, \"sum\": 1.0083237504079157, \"min\": 1.0083237504079157}}, \"EndTime\": 1569266870.361439, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361422}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9955024933089639, \"sum\": 0.9955024933089639, \"min\": 0.9955024933089639}}, \"EndTime\": 1569266870.361501, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361484}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0081434065897268, \"sum\": 1.0081434065897268, \"min\": 1.0081434065897268}}, \"EndTime\": 1569266870.361563, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266870.361545}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:50 INFO 140156479870784] #quality_metric: host=algo-1, epoch=2, train mse_objective <loss>=0.998317350088\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12460486924661826, \"sum\": 0.12460486924661826, \"min\": 0.12460486924661826}}, \"EndTime\": 1569266873.722485, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.722414}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12781161661034612, \"sum\": 0.12781161661034612, \"min\": 0.12781161661034612}}, \"EndTime\": 1569266873.722587, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.72257}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12460504427980719, \"sum\": 0.12460504427980719, \"min\": 0.12460504427980719}}, \"EndTime\": 1569266873.722652, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.722637}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12781145362613025, \"sum\": 0.12781145362613025, \"min\": 0.12781145362613025}}, \"EndTime\": 1569266873.722723, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.722705}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13582955772035166, \"sum\": 0.13582955772035166, \"min\": 0.13582955772035166}}, \"EndTime\": 1569266873.722796, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.722776}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14479624184797954, \"sum\": 0.14479624184797954, \"min\": 0.14479624184797954}}, \"EndTime\": 1569266873.722864, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.722845}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14086733441932392, \"sum\": 0.14086733441932392, \"min\": 0.14086733441932392}}, \"EndTime\": 1569266873.722922, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.722907}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14520923681930592, \"sum\": 0.14520923681930592, \"min\": 0.14520923681930592}}, \"EndTime\": 1569266873.722989, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.722971}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12458134049096518, \"sum\": 0.12458134049096518, \"min\": 0.12458134049096518}}, \"EndTime\": 1569266873.723052, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723035}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1277719820646355, \"sum\": 0.1277719820646355, \"min\": 0.1277719820646355}}, \"EndTime\": 1569266873.723118, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723099}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1245910363413885, \"sum\": 0.1245910363413885, \"min\": 0.1245910363413885}}, \"EndTime\": 1569266873.723183, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723166}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12777845300932256, \"sum\": 0.12777845300932256, \"min\": 0.12777845300932256}}, \"EndTime\": 1569266873.723247, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.72323}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13483769963821055, \"sum\": 0.13483769963821055, \"min\": 0.13483769963821055}}, \"EndTime\": 1569266873.723324, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723305}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14350823578217167, \"sum\": 0.14350823578217167, \"min\": 0.14350823578217167}}, \"EndTime\": 1569266873.72339, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723373}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1407137908968995, \"sum\": 0.1407137908968995, \"min\": 0.1407137908968995}}, \"EndTime\": 1569266873.723452, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723434}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14120379902700222, \"sum\": 0.14120379902700222, \"min\": 0.14120379902700222}}, \"EndTime\": 1569266873.723494, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723485}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12437377306029614, \"sum\": 0.12437377306029614, \"min\": 0.12437377306029614}}, \"EndTime\": 1569266873.723551, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723534}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12669440044245642, \"sum\": 0.12669440044245642, \"min\": 0.12669440044245642}}, \"EndTime\": 1569266873.723611, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723594}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12438353588324474, \"sum\": 0.12438353588324474, \"min\": 0.12438353588324474}}, \"EndTime\": 1569266873.723683, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723664}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12669054781183084, \"sum\": 0.12669054781183084, \"min\": 0.12669054781183084}}, \"EndTime\": 1569266873.72374, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723727}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13467667853071344, \"sum\": 0.13467667853071344, \"min\": 0.13467667853071344}}, \"EndTime\": 1569266873.723796, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723784}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14020648766327953, \"sum\": 0.14020648766327953, \"min\": 0.14020648766327953}}, \"EndTime\": 1569266873.723856, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723838}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1338083684136789, \"sum\": 0.1338083684136789, \"min\": 0.1338083684136789}}, \"EndTime\": 1569266873.723909, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723898}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13602028308128822, \"sum\": 0.13602028308128822, \"min\": 0.13602028308128822}}, \"EndTime\": 1569266873.723964, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723947}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12381312947909816, \"sum\": 0.12381312947909816, \"min\": 0.12381312947909816}}, \"EndTime\": 1569266873.724009, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.723993}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12385881336946727, \"sum\": 0.12385881336946727, \"min\": 0.12385881336946727}}, \"EndTime\": 1569266873.724079, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.72406}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12381943086394682, \"sum\": 0.12381943086394682, \"min\": 0.12381943086394682}}, \"EndTime\": 1569266873.724148, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.724129}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12384650684486757, \"sum\": 0.12384650684486757, \"min\": 0.12384650684486757}}, \"EndTime\": 1569266873.724211, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.724194}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12466912185876629, \"sum\": 0.12466912185876629, \"min\": 0.12466912185876629}}, \"EndTime\": 1569266873.724283, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.724264}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12490501072366811, \"sum\": 0.12490501072366811, \"min\": 0.12490501072366811}}, \"EndTime\": 1569266873.724359, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.724339}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12462684691554513, \"sum\": 0.12462684691554513, \"min\": 0.12462684691554513}}, \"EndTime\": 1569266873.724423, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.724405}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12459455707411923, \"sum\": 0.12459455707411923, \"min\": 0.12459455707411923}}, \"EndTime\": 1569266873.724497, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266873.724478}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:53 INFO 140156479870784] #quality_metric: host=algo-1, epoch=2, validation mse_objective <loss>=0.124604869247\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:53 INFO 140156479870784] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=mse_objective, value=0.123813129479\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:53 INFO 140156479870784] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1218, \"sum\": 1218.0, \"min\": 1218}, \"Total Records Seen\": {\"count\": 1, \"max\": 1216338, \"sum\": 1216338.0, \"min\": 1216338}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1569266873.725569, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569266839.116671}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:27:53 INFO 140156479870784] #throughput_metric: host=algo-1, train throughput=11599.4468621 records/second\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.995359725076956, \"sum\": 0.995359725076956, \"min\": 0.995359725076956}}, \"EndTime\": 1569266904.515842, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.515746}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0087791722171622, \"sum\": 1.0087791722171622, \"min\": 1.0087791722171622}}, \"EndTime\": 1569266904.515939, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.515925}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9953712585905841, \"sum\": 0.9953712585905841, \"min\": 0.9953712585905841}}, \"EndTime\": 1569266904.515985, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.515971}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0087914005574443, \"sum\": 1.0087914005574443, \"min\": 1.0087914005574443}}, \"EndTime\": 1569266904.516049, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516033}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.04962102830083, \"sum\": 1.04962102830083, \"min\": 1.04962102830083}}, \"EndTime\": 1569266904.516106, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.51609}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2280593480897366, \"sum\": 1.2280593480897366, \"min\": 1.2280593480897366}}, \"EndTime\": 1569266904.516172, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516154}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0580274242677, \"sum\": 1.0580274242677, \"min\": 1.0580274242677}}, \"EndTime\": 1569266904.516237, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516224}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2282158315758456, \"sum\": 1.2282158315758456, \"min\": 1.2282158315758456}}, \"EndTime\": 1569266904.516303, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516286}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9953174467395963, \"sum\": 0.9953174467395963, \"min\": 0.9953174467395963}}, \"EndTime\": 1569266904.516369, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.51635}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0086311723133572, \"sum\": 1.0086311723133572, \"min\": 1.0086311723133572}}, \"EndTime\": 1569266904.51643, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516413}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9953512452998363, \"sum\": 0.9953512452998363, \"min\": 0.9953512452998363}}, \"EndTime\": 1569266904.516492, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516476}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0086223366754012, \"sum\": 1.0086223366754012, \"min\": 1.0086223366754012}}, \"EndTime\": 1569266904.516555, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516537}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.045641782689273, \"sum\": 1.045641782689273, \"min\": 1.045641782689273}}, \"EndTime\": 1569266904.516619, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516601}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2170619395724556, \"sum\": 1.2170619395724556, \"min\": 1.2170619395724556}}, \"EndTime\": 1569266904.516673, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516659}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0571807639105362, \"sum\": 1.0571807639105362, \"min\": 1.0571807639105362}}, \"EndTime\": 1569266904.51673, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516713}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2190402466733556, \"sum\": 1.2190402466733556, \"min\": 1.2190402466733556}}, \"EndTime\": 1569266904.516786, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.51677}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9945647098988369, \"sum\": 0.9945647098988369, \"min\": 0.9945647098988369}}, \"EndTime\": 1569266904.516844, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516828}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0041888706273867, \"sum\": 1.0041888706273867, \"min\": 1.0041888706273867}}, \"EndTime\": 1569266904.516903, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.51689}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.994579351030383, \"sum\": 0.994579351030383, \"min\": 0.994579351030383}}, \"EndTime\": 1569266904.516972, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.516953}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0041751413702074, \"sum\": 1.0041751413702074, \"min\": 1.0041751413702074}}, \"EndTime\": 1569266904.517045, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517026}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0390992244425556, \"sum\": 1.0390992244425556, \"min\": 1.0390992244425556}}, \"EndTime\": 1569266904.517119, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517099}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.168982131158919, \"sum\": 1.168982131158919, \"min\": 1.168982131158919}}, \"EndTime\": 1569266904.51719, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517172}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0336136812509742, \"sum\": 1.0336136812509742, \"min\": 1.0336136812509742}}, \"EndTime\": 1569266904.517266, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517247}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1654963859881546, \"sum\": 1.1654963859881546, \"min\": 1.1654963859881546}}, \"EndTime\": 1569266904.517334, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517315}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9921750794218068, \"sum\": 0.9921750794218068, \"min\": 0.9921750794218068}}, \"EndTime\": 1569266904.517402, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517384}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9927011924229953, \"sum\": 0.9927011924229953, \"min\": 0.9927011924229953}}, \"EndTime\": 1569266904.517466, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517448}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9921668859467542, \"sum\": 0.9921668859467542, \"min\": 0.9921668859467542}}, \"EndTime\": 1569266904.51753, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517511}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9927375527855167, \"sum\": 0.9927375527855167, \"min\": 0.9927375527855167}}, \"EndTime\": 1569266904.517597, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517579}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9943841840406309, \"sum\": 0.9943841840406309, \"min\": 0.9943841840406309}}, \"EndTime\": 1569266904.517666, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517647}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0151539391876754, \"sum\": 1.0151539391876754, \"min\": 1.0151539391876754}}, \"EndTime\": 1569266904.517735, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517717}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9943509198804746, \"sum\": 0.9943509198804746, \"min\": 0.9943509198804746}}, \"EndTime\": 1569266904.517793, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517778}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0142611388399119, \"sum\": 1.0142611388399119, \"min\": 1.0142611388399119}}, \"EndTime\": 1569266904.517861, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266904.517843}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:28:24 INFO 140156479870784] #quality_metric: host=algo-1, epoch=3, train mse_objective <loss>=0.995359725077\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12414783953584606, \"sum\": 0.12414783953584606, \"min\": 0.12414783953584606}}, \"EndTime\": 1569266907.917683, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.917613}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12771320956869045, \"sum\": 0.12771320956869045, \"min\": 0.12771320956869045}}, \"EndTime\": 1569266907.917789, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.917768}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12414602177531886, \"sum\": 0.12414602177531886, \"min\": 0.12414602177531886}}, \"EndTime\": 1569266907.917853, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.917836}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12771401016023412, \"sum\": 0.12771401016023412, \"min\": 0.12771401016023412}}, \"EndTime\": 1569266907.917916, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.917898}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13106965607225232, \"sum\": 0.13106965607225232, \"min\": 0.13106965607225232}}, \"EndTime\": 1569266907.917985, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.917969}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1440046858558055, \"sum\": 0.1440046858558055, \"min\": 0.1440046858558055}}, \"EndTime\": 1569266907.91804, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918025}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13196080397244445, \"sum\": 0.13196080397244445, \"min\": 0.13196080397244445}}, \"EndTime\": 1569266907.918096, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918082}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14413214095698668, \"sum\": 0.14413214095698668, \"min\": 0.14413214095698668}}, \"EndTime\": 1569266907.918151, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.91814}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12412812509736483, \"sum\": 0.12412812509736483, \"min\": 0.12412812509736483}}, \"EndTime\": 1569266907.918213, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918196}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1276780193456037, \"sum\": 0.1276780193456037, \"min\": 0.1276780193456037}}, \"EndTime\": 1569266907.918274, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918256}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12412944368072149, \"sum\": 0.12412944368072149, \"min\": 0.12412944368072149}}, \"EndTime\": 1569266907.918334, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918319}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12768026269714425, \"sum\": 0.12768026269714425, \"min\": 0.12768026269714425}}, \"EndTime\": 1569266907.91841, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918397}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1306374657945391, \"sum\": 0.1306374657945391, \"min\": 0.1306374657945391}}, \"EndTime\": 1569266907.918469, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918457}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14318710366374116, \"sum\": 0.14318710366374116, \"min\": 0.14318710366374116}}, \"EndTime\": 1569266907.918534, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918517}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13194986768594708, \"sum\": 0.13194986768594708, \"min\": 0.13194986768594708}}, \"EndTime\": 1569266907.918595, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918579}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1418421340063302, \"sum\": 0.1418421340063302, \"min\": 0.1418421340063302}}, \"EndTime\": 1569266907.918656, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918639}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12400106787835993, \"sum\": 0.12400106787835993, \"min\": 0.12400106787835993}}, \"EndTime\": 1569266907.918723, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918706}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1266808996631778, \"sum\": 0.1266808996631778, \"min\": 0.1266808996631778}}, \"EndTime\": 1569266907.918787, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918769}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12400204350309253, \"sum\": 0.12400204350309253, \"min\": 0.12400204350309253}}, \"EndTime\": 1569266907.91885, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918832}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12667763370731253, \"sum\": 0.12667763370731253, \"min\": 0.12667763370731253}}, \"EndTime\": 1569266907.918912, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918895}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13041419551123615, \"sum\": 0.13041419551123615, \"min\": 0.13041419551123615}}, \"EndTime\": 1569266907.918982, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.918964}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1364804489458974, \"sum\": 0.1364804489458974, \"min\": 0.1364804489458974}}, \"EndTime\": 1569266907.919038, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919021}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13034555178959442, \"sum\": 0.13034555178959442, \"min\": 0.13034555178959442}}, \"EndTime\": 1569266907.919096, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919079}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13605229207141487, \"sum\": 0.13605229207141487, \"min\": 0.13605229207141487}}, \"EndTime\": 1569266907.919158, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919141}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12380883339305124, \"sum\": 0.12380883339305124, \"min\": 0.12380883339305124}}, \"EndTime\": 1569266907.919223, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919206}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12384390628118594, \"sum\": 0.12384390628118594, \"min\": 0.12384390628118594}}, \"EndTime\": 1569266907.919281, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919264}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12381863563172556, \"sum\": 0.12381863563172556, \"min\": 0.12381863563172556}}, \"EndTime\": 1569266907.919343, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919325}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12383420727598418, \"sum\": 0.12383420727598418, \"min\": 0.12383420727598418}}, \"EndTime\": 1569266907.919405, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919388}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12456985318250473, \"sum\": 0.12456985318250473, \"min\": 0.12456985318250473}}, \"EndTime\": 1569266907.919466, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919448}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12491889881851574, \"sum\": 0.12491889881851574, \"min\": 0.12491889881851574}}, \"EndTime\": 1569266907.919526, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919509}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12454038356096, \"sum\": 0.12454038356096, \"min\": 0.12454038356096}}, \"EndTime\": 1569266907.919595, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919578}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12510875114203282, \"sum\": 0.12510875114203282, \"min\": 0.12510875114203282}}, \"EndTime\": 1569266907.919655, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266907.919638}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:28:27 INFO 140156479870784] #quality_metric: host=algo-1, epoch=3, validation mse_objective <loss>=0.124147839536\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:28:27 INFO 140156479870784] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=mse_objective, value=0.123808833393\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:28:27 INFO 140156479870784] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1620, \"sum\": 1620.0, \"min\": 1620}, \"Total Records Seen\": {\"count\": 1, \"max\": 1617784, \"sum\": 1617784.0, \"min\": 1617784}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1569266907.920658, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569266873.725884}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:28:27 INFO 140156479870784] #throughput_metric: host=algo-1, train throughput=11739.9371598 records/second\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9932619992681869, \"sum\": 0.9932619992681869, \"min\": 0.9932619992681869}}, \"EndTime\": 1569266938.846761, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.846667}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0081445744768938, \"sum\": 1.0081445744768938, \"min\": 1.0081445744768938}}, \"EndTime\": 1569266938.846859, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.846844}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9932668443332586, \"sum\": 0.9932668443332586, \"min\": 0.9932668443332586}}, \"EndTime\": 1569266938.846919, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.846904}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0081509056947475, \"sum\": 1.0081509056947475, \"min\": 1.0081509056947475}}, \"EndTime\": 1569266938.846988, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.846971}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.033763811296953, \"sum\": 1.033763811296953, \"min\": 1.033763811296953}}, \"EndTime\": 1569266938.847058, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.84704}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2185132164705423, \"sum\": 1.2185132164705423, \"min\": 1.2185132164705423}}, \"EndTime\": 1569266938.847121, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847104}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0363141109628273, \"sum\": 1.0363141109628273, \"min\": 1.0363141109628273}}, \"EndTime\": 1569266938.847177, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847162}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2185264837783472, \"sum\": 1.2185264837783472, \"min\": 1.2185264837783472}}, \"EndTime\": 1569266938.84724, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847224}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.993245208359716, \"sum\": 0.993245208359716, \"min\": 0.993245208359716}}, \"EndTime\": 1569266938.847307, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847294}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0080061957532926, \"sum\": 1.0080061957532926, \"min\": 1.0080061957532926}}, \"EndTime\": 1569266938.84736, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847349}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9932579948444319, \"sum\": 0.9932579948444319, \"min\": 0.9932579948444319}}, \"EndTime\": 1569266938.847423, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847406}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.008001839882715, \"sum\": 1.008001839882715, \"min\": 1.008001839882715}}, \"EndTime\": 1569266938.847485, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847473}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0320060841852887, \"sum\": 1.0320060841852887, \"min\": 1.0320060841852887}}, \"EndTime\": 1569266938.847542, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847526}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2147945444007169, \"sum\": 1.2147945444007169, \"min\": 1.2147945444007169}}, \"EndTime\": 1569266938.847602, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847584}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0358495370764982, \"sum\": 1.0358495370764982, \"min\": 1.0358495370764982}}, \"EndTime\": 1569266938.847663, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847646}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2131424737107426, \"sum\": 1.2131424737107426, \"min\": 1.2131424737107426}}, \"EndTime\": 1569266938.847729, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847711}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9930044713959729, \"sum\": 0.9930044713959729, \"min\": 0.9930044713959729}}, \"EndTime\": 1569266938.847796, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847777}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0037024632023457, \"sum\": 1.0037024632023457, \"min\": 1.0037024632023457}}, \"EndTime\": 1569266938.847859, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847843}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.993010954060162, \"sum\": 0.993010954060162, \"min\": 0.993010954060162}}, \"EndTime\": 1569266938.847926, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847908}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0036953325913731, \"sum\": 1.0036953325913731, \"min\": 1.0036953325913731}}, \"EndTime\": 1569266938.84798, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.847969}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0236543834809948, \"sum\": 1.0236543834809948, \"min\": 1.0236543834809948}}, \"EndTime\": 1569266938.848021, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848007}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.160895190916751, \"sum\": 1.160895190916751, \"min\": 1.160895190916751}}, \"EndTime\": 1569266938.848089, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848071}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0219308142911763, \"sum\": 1.0219308142911763, \"min\": 1.0219308142911763}}, \"EndTime\": 1569266938.84816, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848141}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.160438614766794, \"sum\": 1.160438614766794, \"min\": 1.160438614766794}}, \"EndTime\": 1569266938.848212, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848201}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9921138336105537, \"sum\": 0.9921138336105537, \"min\": 0.9921138336105537}}, \"EndTime\": 1569266938.848272, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848255}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9927420377279458, \"sum\": 0.9927420377279458, \"min\": 0.9927420377279458}}, \"EndTime\": 1569266938.848337, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848322}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9921135975369194, \"sum\": 0.9921135975369194, \"min\": 0.9921135975369194}}, \"EndTime\": 1569266938.848401, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848384}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9927791668899042, \"sum\": 0.9927791668899042, \"min\": 0.9927791668899042}}, \"EndTime\": 1569266938.84846, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848442}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.994110820208999, \"sum\": 0.994110820208999, \"min\": 0.994110820208999}}, \"EndTime\": 1569266938.848527, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848508}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0194526530049388, \"sum\": 1.0194526530049388, \"min\": 1.0194526530049388}}, \"EndTime\": 1569266938.848587, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.84857}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9941049784900542, \"sum\": 0.9941049784900542, \"min\": 0.9941049784900542}}, \"EndTime\": 1569266938.848644, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848627}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0191613898907517, \"sum\": 1.0191613898907517, \"min\": 1.0191613898907517}}, \"EndTime\": 1569266938.84871, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266938.848692}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:28:58 INFO 140156479870784] #quality_metric: host=algo-1, epoch=4, train mse_objective <loss>=0.993261999268\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12391744317820466, \"sum\": 0.12391744317820466, \"min\": 0.12391744317820466}}, \"EndTime\": 1569266942.238072, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238002}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1275871535927394, \"sum\": 0.1275871535927394, \"min\": 0.1275871535927394}}, \"EndTime\": 1569266942.238174, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238159}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12391601288528398, \"sum\": 0.12391601288528398, \"min\": 0.12391601288528398}}, \"EndTime\": 1569266942.238237, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.23822}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12758780936855021, \"sum\": 0.12758780936855021, \"min\": 0.12758780936855021}}, \"EndTime\": 1569266942.238287, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238271}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1290470250698453, \"sum\": 0.1290470250698453, \"min\": 0.1290470250698453}}, \"EndTime\": 1569266942.238343, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238332}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14277912919921049, \"sum\": 0.14277912919921049, \"min\": 0.14277912919921049}}, \"EndTime\": 1569266942.238414, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238395}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13004238168723994, \"sum\": 0.13004238168723994, \"min\": 0.13004238168723994}}, \"EndTime\": 1569266942.238476, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238459}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14283328340360532, \"sum\": 0.14283328340360532, \"min\": 0.14283328340360532}}, \"EndTime\": 1569266942.238531, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238516}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12389950735059056, \"sum\": 0.12389950735059056, \"min\": 0.12389950735059056}}, \"EndTime\": 1569266942.238583, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238569}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12755508450978503, \"sum\": 0.12755508450978503, \"min\": 0.12755508450978503}}, \"EndTime\": 1569266942.238641, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238624}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12389898399945518, \"sum\": 0.12389898399945518, \"min\": 0.12389898399945518}}, \"EndTime\": 1569266942.238706, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238689}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12755589304528886, \"sum\": 0.12755589304528886, \"min\": 0.12755589304528886}}, \"EndTime\": 1569266942.238744, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238735}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12888933164544447, \"sum\": 0.12888933164544447, \"min\": 0.12888933164544447}}, \"EndTime\": 1569266942.238791, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238776}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14281374202182534, \"sum\": 0.14281374202182534, \"min\": 0.14281374202182534}}, \"EndTime\": 1569266942.238845, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.23883}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12997692740858416, \"sum\": 0.12997692740858416, \"min\": 0.12997692740858416}}, \"EndTime\": 1569266942.238907, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.23889}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.14166680966555123, \"sum\": 0.14166680966555123, \"min\": 0.14166680966555123}}, \"EndTime\": 1569266942.238975, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.238958}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12381421654732913, \"sum\": 0.12381421654732913, \"min\": 0.12381421654732913}}, \"EndTime\": 1569266942.239032, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239016}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12664246678397492, \"sum\": 0.12664246678397492, \"min\": 0.12664246678397492}}, \"EndTime\": 1569266942.239091, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239074}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1238147872581504, \"sum\": 0.1238147872581504, \"min\": 0.1238147872581504}}, \"EndTime\": 1569266942.23916, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239142}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12663991346595102, \"sum\": 0.12663991346595102, \"min\": 0.12663991346595102}}, \"EndTime\": 1569266942.239227, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239211}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12844516362425734, \"sum\": 0.12844516362425734, \"min\": 0.12844516362425734}}, \"EndTime\": 1569266942.239296, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239278}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13577442382422067, \"sum\": 0.13577442382422067, \"min\": 0.13577442382422067}}, \"EndTime\": 1569266942.239367, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239348}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12723267964730858, \"sum\": 0.12723267964730858, \"min\": 0.12723267964730858}}, \"EndTime\": 1569266942.23943, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239412}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.13578598756954172, \"sum\": 0.13578598756954172, \"min\": 0.13578598756954172}}, \"EndTime\": 1569266942.239495, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239478}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.1238100220603515, \"sum\": 0.1238100220603515, \"min\": 0.1238100220603515}}, \"EndTime\": 1569266942.239566, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239548}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12383832699851391, \"sum\": 0.12383832699851391, \"min\": 0.12383832699851391}}, \"EndTime\": 1569266942.239629, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239612}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12381176737500288, \"sum\": 0.12381176737500288, \"min\": 0.12381176737500288}}, \"EndTime\": 1569266942.239695, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239677}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12383040664218868, \"sum\": 0.12383040664218868, \"min\": 0.12383040664218868}}, \"EndTime\": 1569266942.239757, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.23974}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12406128127184232, \"sum\": 0.12406128127184232, \"min\": 0.12406128127184232}}, \"EndTime\": 1569266942.239822, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239803}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12498644661574125, \"sum\": 0.12498644661574125, \"min\": 0.12498644661574125}}, \"EndTime\": 1569266942.239891, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.239873}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12403980091762644, \"sum\": 0.12403980091762644, \"min\": 0.12403980091762644}}, \"EndTime\": 1569266942.239958, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.23994}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12496744496713888, \"sum\": 0.12496744496713888, \"min\": 0.12496744496713888}}, \"EndTime\": 1569266942.240023, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266942.240005}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:02 INFO 140156479870784] #quality_metric: host=algo-1, epoch=4, validation mse_objective <loss>=0.123917443178\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:02 INFO 140156479870784] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=mse_objective, value=0.12381002206\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:02 INFO 140156479870784] Early stop condition met. Stopping training.\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:02 INFO 140156479870784] #progress_metric: host=algo-1, completed 100 % epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 402, \"sum\": 402.0, \"min\": 402}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 2022, \"sum\": 2022.0, \"min\": 2022}, \"Total Records Seen\": {\"count\": 1, \"max\": 2019230, \"sum\": 2019230.0, \"min\": 2019230}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1569266942.241223, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569266907.920931}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:02 INFO 140156479870784] #throughput_metric: host=algo-1, train throughput=11697.0033993 records/second\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:02 WARNING 140156479870784] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:02 WARNING 140156479870784] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m[09/23/2019 19:29:05 INFO 140156479870784] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=mse_objective, value=0.12381002206\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:08 INFO 140156479870784] #validation_score (algo-1) : ('mse_objective', 0.12381270274780344)\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:08 INFO 140156479870784] #validation_score (algo-1) : ('mse', 0.12381270274780344)\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:08 INFO 140156479870784] #validation_score (algo-1) : ('absolute_loss', 0.2982221604799902)\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:08 INFO 140156479870784] #quality_metric: host=algo-1, validation mse_objective <loss>=0.123812702748\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:08 INFO 140156479870784] #quality_metric: host=algo-1, validation mse <loss>=0.123812702748\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:08 INFO 140156479870784] #quality_metric: host=algo-1, validation absolute_loss <loss>=0.29822216048\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:08 INFO 140156479870784] Best model found for hyperparameters: {\"lr_scheduler_step\": 10, \"wd\": 100, \"optimizer\": \"adam\", \"lr_scheduler_factor\": 0.99, \"l1\": 0.0, \"learning_rate\": 0.005, \"lr_scheduler_minimum_lr\": 1e-05}\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:08 INFO 140156479870784] Saved checkpoint to \"/tmp/tmpYwtiUg/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:29:08 INFO 140156479870784] Test data is not provided.\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 180551.6791343689, \"sum\": 180551.6791343689, \"min\": 180551.6791343689}, \"finalize.time\": {\"count\": 1, \"max\": 6335.036993026733, \"sum\": 6335.036993026733, \"min\": 6335.036993026733}, \"initialize.time\": {\"count\": 1, \"max\": 996.2260723114014, \"sum\": 996.2260723114014, \"min\": 996.2260723114014}, \"check_early_stopping.time\": {\"count\": 6, \"max\": 1.0612010955810547, \"sum\": 2.397298812866211, \"min\": 0.23698806762695312}, \"setuptime\": {\"count\": 1, \"max\": 28.40113639831543, \"sum\": 28.40113639831543, \"min\": 28.40113639831543}, \"update.time\": {\"count\": 5, \"max\": 35592.435121536255, \"sum\": 173118.80016326904, \"min\": 34194.59009170532}, \"epochs\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1569266948.588946, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1569266768.1234}\n",
      "\u001b[0m\n",
      "\n",
      "2019-09-23 19:29:19 Uploading - Uploading generated training model\n",
      "2019-09-23 19:29:19 Completed - Training job completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:29:35,699 - models.aws_base - INFO - Done with fitting model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training seconds: 262\n",
      "Billable seconds: 262\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:29:37,121 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 21:29:37,123 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 21:29:37,123 - models.aws_base - INFO - Creating the transformer job\n",
      "2019-09-23 21:29:37,331 - sagemaker - INFO - Creating model with name: linear-learner-2019-09-23-19-23-11-675\n",
      "2019-09-23 21:29:37,724 - sagemaker - INFO - Creating transform job with name: linear-learner-2019-09-23-19-29-37-723\n",
      "2019-09-23 21:29:37,978 - models.aws_base - INFO - Waiting for the transformer job\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..................\u001b[31mDocker entrypoint called with argument(s): serve\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded entry point class algorithm.serve.server_config:config_api\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loading entry points\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded request iterator text/csv\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded request iterator application/x-recordio-protobuf\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded request iterator application/json\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded request iterator application/jsonlines\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded response encoder text/csv\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded response encoder application/x-recordio-protobuf\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded response encoder application/json\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded response encoder application/jsonlines\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loaded entry point class algorithm:model\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] loading model...\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] ...model loaded.\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:32:33 INFO 140350347982656] Number of server workers: 4\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:32:33 +0000] [1] [INFO] Starting gunicorn 19.7.1\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:32:33 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:32:33 +0000] [1] [INFO] Using worker: sync\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:32:33 +0000] [76] [INFO] Booting worker with pid: 76\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:32:33 +0000] [85] [INFO] Booting worker with pid: 85\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:32:34 +0000] [94] [INFO] Booting worker with pid: 94\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:32:34 +0000] [103] [INFO] Booting worker with pid: 103\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"execution_parameters.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267169.927908, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267153.803151}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 65.5369758605957, \"sum\": 65.5369758605957, \"min\": 65.5369758605957}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267171.546313, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267153.803151}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 45.73798179626465, \"sum\": 45.73798179626465, \"min\": 45.73798179626465}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267171.629194, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267153.803151}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 30.210018157958984, \"sum\": 30.210018157958984, \"min\": 30.210018157958984}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267171.685855, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267153.803151}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 40.54903984069824, \"sum\": 40.54903984069824, \"min\": 40.54903984069824}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267171.81283, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267169.928023}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 21.809101104736328, \"sum\": 21.809101104736328, \"min\": 21.809101104736328}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267172.289372, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267171.546442}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.90196990966797, \"sum\": 16.90196990966797, \"min\": 16.90196990966797}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267172.410679, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267171.685973}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 12.482166290283203, \"sum\": 12.482166290283203, \"min\": 12.482166290283203}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267172.459602, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267171.629881}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.766071319580078, \"sum\": 16.766071319580078, \"min\": 16.766071319580078}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267172.579771, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267171.813524}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 8.835077285766602, \"sum\": 8.835077285766602, \"min\": 8.835077285766602}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267172.891854, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267172.459718}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 13.984918594360352, \"sum\": 13.984918594360352, \"min\": 13.984918594360352}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267172.96723, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267172.289457}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 11.510133743286133, \"sum\": 11.510133743286133, \"min\": 11.510133743286133}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267173.028214, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267172.410864}\n",
      "\u001b[0m\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:33:24,625 - models.aws_base - INFO - Loading the results of the transformer job\n",
      "2019-09-23 21:33:24,645 - executors.sagemaker - INFO - Downloading data from s3: from s3://sagemaker-eu-west-1-729071960169/data/linear_learner/output_data/test.csv.out to data/temp/linear_learner/test.csv.out\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.csv.out\n",
      "data/temp/linear_learner\n"
     ]
    }
   ],
   "source": [
    "# Initialise and try the \"dumb\" linear learner as a benchmark\n",
    "dumb_ll = LinearAwsLinearLearner(data=data_loader, aws_executor=sagemaker)\n",
    "dumb_ll.train()\n",
    "dumb_predictions = dumb_ll.predict() # By default, the test values are predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:33:32,854 - models.meta_model - INFO - Predicting the data of the meta model\n",
      "2019-09-23 21:33:32,854 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 21:33:32,855 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 21:33:32,856 - models.aws_base - INFO - Creating the transformer job\n",
      "2019-09-23 21:33:32,857 - models.aws_base - INFO - Loading best model from tuning job\n",
      "2019-09-23 21:33:33,071 - models.aws_base - INFO - Loading already trained model xgboost-190923-1844-002-c4b5f3de\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-09-23 16:50:07 Starting - Preparing the instances for training\n",
      "2019-09-23 16:50:07 Downloading - Downloading input data\n",
      "2019-09-23 16:50:07 Training - Training image download completed. Training in progress.\n",
      "2019-09-23 16:50:07 Uploading - Uploading generated training model\n",
      "2019-09-23 16:50:07 Completed - Training job completed\u001b[31mArguments: train\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] Running standalone xgboost training.\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] Setting up HPO optimized metric to be : rmse\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] File size need to be processed in the node: 586.01mb. Available memory size in the node: 8587.27mb\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:23:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[16:47:23] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[31m[16:47:27] 401446x310 matrix with 124448260 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[31m[2019-09-23:16:47:27:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[16:47:27] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[31m[16:47:27] 50181x310 matrix with 15556110 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[31m[16:47:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 1002 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[0]#011train-rmse:0.35308#011validation-rmse:0.351838\u001b[0m\n",
      "\u001b[31mMultiple eval metrics have been passed: 'validation-rmse' will be used for early stopping.\n",
      "\u001b[0m\n",
      "\u001b[31mWill train until validation-rmse hasn't improved in 10 rounds.\u001b[0m\n",
      "\u001b[31m[16:47:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 1014 pruned nodes, max_depth=2\u001b[0m\n",
      "\u001b[31m[1]#011train-rmse:0.353045#011validation-rmse:0.351824\u001b[0m\n",
      "\u001b[31m[16:47:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 1016 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[2]#011train-rmse:0.353005#011validation-rmse:0.351786\u001b[0m\n",
      "\u001b[31m[16:47:50] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 984 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[3]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:47:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[4]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:47:59] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[5]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:48:03] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1014 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[6]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:48:08] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[7]#011train-rmse:0.352965#011validation-rmse:0.35175\u001b[0m\n",
      "\u001b[31m[16:48:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 1016 pruned nodes, max_depth=2\u001b[0m\n",
      "\u001b[31m[8]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1014 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[9]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:21] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[10]#011train-rmse:0.352942#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:26] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[11]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:30] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[12]#011train-rmse:0.352943#011validation-rmse:0.351725\u001b[0m\n",
      "\u001b[31m[16:48:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 1016 pruned nodes, max_depth=2\u001b[0m\n",
      "\u001b[31m[13]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[14]#011train-rmse:0.352919#011validation-rmse:0.351707\u001b[0m\n",
      "\u001b[31m[16:48:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[15]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[16]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:53] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[17]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:48:57] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1016 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[18]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:02] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[19]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:06] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1016 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[20]#011train-rmse:0.352919#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:11] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[21]#011train-rmse:0.352919#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:15] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 1018 pruned nodes, max_depth=1\u001b[0m\n",
      "\u001b[31m[22]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:19] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[23]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:24] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1008 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[24]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:28] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[25]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:33] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1020 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[26]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[27]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1018 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[28]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31m[16:49:46] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1012 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[29]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:51] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1016 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[30]#011train-rmse:0.352908#011validation-rmse:0.351706\u001b[0m\n",
      "\u001b[31m[16:49:55] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 0 extra nodes, 1022 pruned nodes, max_depth=0\u001b[0m\n",
      "\u001b[31m[31]#011train-rmse:0.352908#011validation-rmse:0.351705\u001b[0m\n",
      "\u001b[31mStopping. Best iteration:\u001b[0m\n",
      "\u001b[31m[21]#011train-rmse:0.352919#011validation-rmse:0.351705\n",
      "\u001b[0m\n",
      "Training seconds: 208\n",
      "Billable seconds: 208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:33:33,804 - sagemaker - INFO - Creating model with name: xgboost-190923-1844-002-c4b5f3de\n",
      "2019-09-23 21:33:34,072 - sagemaker - WARNING - Using already existing model: xgboost-190923-1844-002-c4b5f3de\n",
      "2019-09-23 21:33:34,073 - sagemaker - INFO - Creating transform job with name: xgboost-190923-1844-002-c4b5f3de-2019-09-23-19-33-34-073\n",
      "2019-09-23 21:33:34,551 - models.aws_base - INFO - Waiting for the transformer job\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".................\u001b[31mArguments: serve\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:36:22 +0000] [1] [INFO] Starting gunicorn 19.7.1\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:36:22 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:36:22 +0000] [1] [INFO] Using worker: gevent\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:36:22 +0000] [38] [INFO] Booting worker with pid: 38\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:36:22 +0000] [39] [INFO] Booting worker with pid: 39\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:36:22 +0000] [40] [INFO] Booting worker with pid: 40\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:36:22 +0000] [41] [INFO] Booting worker with pid: 41\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:22:INFO] Model loaded successfully for worker : 39\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:22:INFO] Model loaded successfully for worker : 38\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:22:INFO] Model loaded successfully for worker : 40\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:22:INFO] Model loaded successfully for worker : 41\u001b[0m\n",
      "\n",
      "\u001b[31m[2019-09-23:19:36:39:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:39:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:39:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:39:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:39:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:39:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:39:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:39:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:40:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:40:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:40:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:40:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:40:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:40:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:40:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:40:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:41:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:41:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:41:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:41:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:41:INFO] Sniff delimiter as ','\u001b[0m\n",
      "\u001b[31m[2019-09-23:19:36:41:INFO] Determined delimiter of CSV input is ','\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:37:00,629 - models.aws_base - INFO - Loading the results of the transformer job\n",
      "2019-09-23 21:37:00,638 - executors.sagemaker - INFO - Downloading data from s3: from s3://sagemaker-eu-west-1-729071960169/data/linear_xgboost/output_data/test.csv.out to data/temp/linear_xgboost/test.csv.out\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.csv.out\n",
      "data/temp/linear_xgboost\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:37:01,324 - models.aws_base - INFO - Preparing data for usage\n",
      "2019-09-23 21:37:01,325 - models.aws_base - INFO - Found s3 data in cache.\n",
      "2019-09-23 21:37:01,326 - models.aws_base - INFO - Creating the transformer job\n",
      "2019-09-23 21:37:01,326 - models.aws_base - INFO - Loading best model from tuning job\n",
      "2019-09-23 21:37:01,539 - models.aws_base - INFO - Loading already trained model linear-learner-190923-1854-009-f7872322\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-09-23 17:19:45 Starting - Preparing the instances for training\n",
      "2019-09-23 17:19:45 Downloading - Downloading input data\n",
      "2019-09-23 17:19:45 Training - Training image download completed. Training in progress.\n",
      "2019-09-23 17:19:45 Uploading - Uploading generated training model\n",
      "2019-09-23 17:19:45 Completed - Training job completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:37:02,254 - sagemaker - INFO - Creating model with name: linear-learner-190923-1854-009-f7872322\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-input.json: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'1000', u'huber_delta': u'1.0', u'num_classes': u'1', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'loss': u'auto', u'mini_batch_size': u'255', u'optimizer': u'auto', u'predictor_type': u'regressor', u'_tuning_objective_metric': u'validation:objective_loss', u'learning_rate': u'0.03719555404301278', u'epochs': u'10', u'feature_dim': u'310', u'use_bias': u'True'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Final configuration: {u'loss_insensitivity': u'0.01', u'epochs': u'10', u'feature_dim': u'310', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'True', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'validation:objective_loss', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'0.03719555404301278', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'255', u'huber_delta': u'1.0', u'num_classes': u'1', u'predictor_type': u'regressor', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 WARNING 140514251040576] Loggers have already been setup.\u001b[0m\n",
      "\u001b[31mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Using default worker.\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:38 INFO 140514251040576] Create Store: local\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7fcbb4aa08d0>\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] Scaling model computed with parameters:\n",
      " {'stdev_weight': \u001b[0m\n",
      "\u001b[31m[0.3537611  0.35095778 0.35166723 0.35195994 0.35307026 0.35241228\n",
      " 0.3514709  0.3531433  0.35244408 0.35103846 0.35305503 0.3533745\n",
      " 0.35036698 0.33419597 0.33837804 0.33732823 0.31661114 0.35302675\n",
      " 0.32048059 0.29292822 0.35306332 0.34554133 0.34256738 0.34457472\n",
      " 0.34219706 0.33734596 0.29251617 0.3357016  0.3380498  0.354191\n",
      " 0.35095403 0.34213048 0.34198478 0.28873786 0.33441877 0.33442068\n",
      " 0.26527917 0.33953795 0.28715953 0.34540525 0.34145442 0.3418308\n",
      " 0.35205427 0.3048389  0.33812737 0.35354832 0.335374   0.34203467\n",
      " 0.34926325 0.34260625 0.33443332 0.351621   0.3030377  0.33788905\n",
      " 0.3526646  0.34807053 0.3374744  0.3536615  0.265267   0.3035835\n",
      " 0.29048425 0.34059906 0.35073793 0.3399737  0.34549683 0.35311547\n",
      " 0.35487202 0.2895118  0.35271186 0.34348562 0.30198482 0.28891417\n",
      " 0.35036567 0.33964518 0.3530212  0.34225366 0.29127002 0.35360232\n",
      " 0.34012598 0.33247417 0.3520331  0.3521761  0.34117463 0.32154047\n",
      " 0.29039764 0.34708267 0.33905157 0.35256395 0.28637356 0.34654653\n",
      " 0.31584507 0.34440574 0.34920245 0.3317727  0.3532545  0.34593198\n",
      " 0.35230166 0.34620667 0.35197338 0.33754084 0.35360718 0.35329992\n",
      " 0.33683082 0.28342342 0.29841852 0.35228258 0.35181993 0.3538608\n",
      " 0.3513658  0.3396064  0.33868167 0.35104877 0.35331288 0.35112768\n",
      " 0.28377047 0.3249551  0.3538427  0.17767234 0.35226056 0.35156608\n",
      " 0.35085157 0.3310198  0.33817074 0.30524072 0.3534917  0.35303426\n",
      " 0.3313532  0.3399587  0.34005126 0.33656815 0.30603644 0.35211098\n",
      " 0.29991305 0.35371295 0.3262256  0.17681037 0.35163218 0.35192516\n",
      " 0.35059574 0.3550061  0.35402036 0.35112074 0.3530585  0.351962\n",
      " 0.3515507  0.35268438 0.3520931  0.3539431  0.35291067 0.35377726\n",
      " 0.341418   0.34554633 0.34607992 0.35304102 0.33980057 0.33981326\n",
      " 0.35219172 0.34040475 0.35396844 0.33941206 0.34656715 0.35223365\n",
      " 0.3515711  0.3406446  0.34477025 0.35316995 0.34048668 0.33700305\n",
      " 0.33933833 0.34320885 0.3333512  0.3528724  0.3460018  0.35629132\n",
      " 0.35376993 0.34339944 0.35239097 0.34780326 0.3536386  0.336963\n",
      " 0.33734035 0.35306883 0.35205635 0.35382834 0.35373518 0.35556582\n",
      " 0.3455203  0.35095152 0.33670777 0.35207164 0.33653432 0.3361133\n",
      " 0.35298866 0.33381426 0.35439444 0.35152832 0.35448724 0.35434037\n",
      " 0.35301417 0.35451335 0.34149125 0.35133028 0.3526697  0.35626987\n",
      " 0.33485204 0.3541649  0.35379592 0.3549642  0.35189438 0.3457919\n",
      " 0.33941442 0.34137413 0.35275692 0.35517013 0.35310125 0.3317456\n",
      " 0.35177937 0.3559507  0.3517823  0.3330517  0.3521959  0.3396734\n",
      " 0.344751   0.33971256 0.35308772 0.35053355 0.3532979  0.33916107\n",
      " 0.35174635 0.34174645 0.35213804 0.3563388  0.3510854  0.3408226\n",
      " 0.35277402 0.34890404 0.3445582  0.33885193 0.35224056 0.339613\n",
      " 0.3515716  0.35395107 0.3332635  0.3484597  0.35597005 0.33757678\n",
      " 0.33877686 0.3552606  0.3543929  0.3523425  0.35404626 0.35188335\n",
      " 0.33853662 0.3375497  0.3534921  0.35468644 0.33685172 0.3377131\n",
      " 0.3499684  0.35410747 0.35129902 0.35362667 0.34990337 0.35143253\n",
      " 0.35384825 0.35299143 0.3515194  0.34804285 0.3507869  0.3330553\n",
      " 0.35250968 0.35244545 0.35260624 0.34955972 0.33284464 0.3540037\n",
      " 0.34492967 0.35433874 0.35231876 0.34759888 0.35430488 0.3518586\n",
      " 0.34808582 0.35560977 0.34717858 0.35280612 0.30060324 0.33975565\n",
      " 0.35188562 0.35235375 0.352591   0.34797797 0.35434967 0.34656346\n",
      " 0.35149682 0.30187145 0.33985385 0.34720308 0.3562008  0.35141775\n",
      " 0.3510822  0.35224736 0.35173705 0.34994438 0.34995598 0.3515191\n",
      " 0.35295492 0.35066628 0.34610516 0.35386616]\u001b[0m\n",
      "\u001b[31m<NDArray 310 @cpu(0)>, 'stdev_label': \u001b[0m\n",
      "\u001b[31m[0.3544432]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_label': \u001b[0m\n",
      "\u001b[31m[0.5010538]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_weight': \u001b[0m\n",
      "\u001b[31m[0.50039214 0.5029657  0.50071067 0.49830884 0.49676472 0.49830878\n",
      " 0.4980637  0.49865195 0.4977696  0.49730393 0.49830884 0.49598038\n",
      " 0.49941176 0.5020588  0.49411765 0.503848   0.5051961  0.4952206\n",
      " 0.49926472 0.49862745 0.5039217  0.5008333  0.5007353  0.502451\n",
      " 0.49830884 0.49539214 0.4979657  0.49742648 0.5064951  0.50147057\n",
      " 0.49769607 0.502353   0.49634805 0.49926472 0.49848038 0.5010294\n",
      " 0.5008578  0.498603   0.5016176  0.4970098  0.49857843 0.50453436\n",
      " 0.5019607  0.4954657  0.4954902  0.49688727 0.49757352 0.4960049\n",
      " 0.5006862  0.5021324  0.49779412 0.5038236  0.5005148  0.49468136\n",
      " 0.50296557 0.4977696  0.4954902  0.5017892  0.5010294  0.4944608\n",
      " 0.50007343 0.49605393 0.5019364  0.5000736  0.49909315 0.49536765\n",
      " 0.49816176 0.49943617 0.5000245  0.5012991  0.50002444 0.49830884\n",
      " 0.50367653 0.50164217 0.50289214 0.5009559  0.49843138 0.50147057\n",
      " 0.4992157  0.4996078  0.50355387 0.5019608  0.4982598  0.49953437\n",
      " 0.49884805 0.49767157 0.49436274 0.50034314 0.50247544 0.5039706\n",
      " 0.5038971  0.5034803  0.50022054 0.5006373  0.50171566 0.50044125\n",
      " 0.5010784  0.50007355 0.50237745 0.50014716 0.4922549  0.503701\n",
      " 0.5009314  0.5014216  0.5038726  0.5029166  0.5058332  0.4982353\n",
      " 0.5040441  0.50409317 0.50078434 0.5001961  0.50213236 0.50492656\n",
      " 0.5011274  0.502157   0.49414214 0.5010784  0.5054901  0.5059068\n",
      " 0.50585777 0.4969608  0.49818623 0.5028676  0.5050246  0.5041912\n",
      " 0.49830884 0.50534314 0.49708334 0.4997303  0.5042646  0.49916667\n",
      " 0.5039216  0.49811274 0.5021323  0.5005882  0.49919116 0.49830884\n",
      " 0.5023284  0.49970594 0.4993137  0.4967157  0.49933815 0.5027451\n",
      " 0.50166655 0.4990196  0.49732843 0.4998775  0.4992157  0.5009805\n",
      " 0.50382346 0.49816176 0.4954902  0.50071067 0.5036764  0.50220585\n",
      " 0.50225496 0.5009069  0.505      0.502794   0.5012255  0.4997549\n",
      " 0.49916667 0.5017158  0.50605404 0.49882352 0.49955878 0.5050736\n",
      " 0.5016421  0.50794125 0.50210786 0.5039216  0.49438727 0.5073529\n",
      " 0.50553906 0.5065197  0.50237745 0.5026716  0.49732843 0.5031373\n",
      " 0.49926472 0.50737745 0.50291675 0.50276965 0.49968138 0.502598\n",
      " 0.5052451  0.5009559  0.504044   0.50316167 0.5021078  0.50240195\n",
      " 0.5050244  0.4986275  0.50416666 0.49963236 0.50357836 0.50928915\n",
      " 0.50102943 0.49933824 0.5011765  0.5059314  0.4977206  0.5045098\n",
      " 0.49943638 0.4985049  0.5082107  0.5023039  0.5000246  0.50485295\n",
      " 0.49977937 0.5062009  0.4977696  0.50002444 0.49561274 0.5000733\n",
      " 0.5011274  0.5015686  0.5054657  0.501348   0.4985294  0.4986274\n",
      " 0.49904412 0.50242645 0.50536764 0.49992648 0.50676477 0.50061274\n",
      " 0.49990192 0.5048039  0.49992648 0.507549   0.5022304  0.50321066\n",
      " 0.5059803  0.50017154 0.50416666 0.501299   0.50115186 0.5001226\n",
      " 0.49941176 0.49931368 0.5012746  0.5018872  0.505049   0.4995098\n",
      " 0.5063481  0.50176466 0.49992648 0.50174016 0.4992892  0.5012745\n",
      " 0.49911764 0.4975     0.49933824 0.5065687  0.50485295 0.50031865\n",
      " 0.50313723 0.5001961  0.501397   0.49605393 0.5004657  0.5002941\n",
      " 0.50242645 0.50220597 0.4994608  0.50698537 0.49990195 0.5042158\n",
      " 0.5037255  0.4968137  0.49735293 0.5012746  0.5041422  0.50247556\n",
      " 0.49953422 0.5027942  0.49882352 0.5067157  0.49894607 0.49919116\n",
      " 0.50068617 0.49985296 0.49968138 0.49906862 0.49769607 0.502696\n",
      " 0.49980402 0.49632353 0.49764705 0.50142163 0.50014704 0.505343\n",
      " 0.5031618  0.4989706  0.5028186  0.5012255  0.49855393 0.50208324\n",
      " 0.50102925 0.5029658  0.5035538  0.50147057 0.49958333 0.5025736\n",
      " 0.5000736  0.50223047 0.5072059  0.50232846]\u001b[0m\n",
      "\u001b[31m<NDArray 310 @cpu(0)>}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] nvidia-smi took: 0.0251808166504 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:14:39 INFO 140514251040576] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 40, \"sum\": 40.0, \"min\": 40}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 41, \"sum\": 41.0, \"min\": 41}, \"Total Records Seen\": {\"count\": 1, \"max\": 10455, \"sum\": 10455.0, \"min\": 10455}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 10200, \"sum\": 10200.0, \"min\": 10200}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1569258879.422365, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1569258879.422326}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.139855016040277, \"sum\": 1.139855016040277, \"min\": 1.139855016040277}}, \"EndTime\": 1569258909.898884, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569258909.898845}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:09 INFO 140514251040576] #quality_metric: host=algo-1, epoch=0, train mse_objective <loss>=1.13985501604\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12951701730908427, \"sum\": 0.12951701730908427, \"min\": 0.12951701730908427}}, \"EndTime\": 1569258914.345346, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569258914.345303}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #quality_metric: host=algo-1, epoch=0, validation mse_objective <loss>=0.129517017309\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=mse_objective, value=0.129517017309\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1616, \"sum\": 1616.0, \"min\": 1616}, \"Total Records Seen\": {\"count\": 1, \"max\": 411901, \"sum\": 411901.0, \"min\": 411901}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1569258914.347123, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1569258879.422564}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:14 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11494.6234769 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0223224015182084, \"sum\": 1.0223224015182084, \"min\": 1.0223224015182084}}, \"EndTime\": 1569258944.16863, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569258944.168593}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:44 INFO 140514251040576] #quality_metric: host=algo-1, epoch=1, train mse_objective <loss>=1.02232240152\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12585277085676658, \"sum\": 0.12585277085676658, \"min\": 0.12585277085676658}}, \"EndTime\": 1569258947.800369, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569258947.800326}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #quality_metric: host=algo-1, epoch=1, validation mse_objective <loss>=0.125852770857\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=mse_objective, value=0.125852770857\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] Epoch 1: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 3191, \"sum\": 3191.0, \"min\": 3191}, \"Total Records Seen\": {\"count\": 1, \"max\": 813347, \"sum\": 813347.0, \"min\": 813347}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1569258947.802053, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1569258914.347374}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:15:47 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11999.649369 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9981346370066798, \"sum\": 0.9981346370066798, \"min\": 0.9981346370066798}}, \"EndTime\": 1569258978.132421, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569258978.132387}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:18 INFO 140514251040576] #quality_metric: host=algo-1, epoch=2, train mse_objective <loss>=0.998134637007\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12425399832422306, \"sum\": 0.12425399832422306, \"min\": 0.12425399832422306}}, \"EndTime\": 1569258981.708207, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569258981.708159}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #quality_metric: host=algo-1, epoch=2, validation mse_objective <loss>=0.124253998324\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=mse_objective, value=0.124253998324\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] Epoch 2: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 4766, \"sum\": 4766.0, \"min\": 4766}, \"Total Records Seen\": {\"count\": 1, \"max\": 1214793, \"sum\": 1214793.0, \"min\": 1214793}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1569258981.710058, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1569258947.802305}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:21 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11839.3085984 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9918992249138863, \"sum\": 0.9918992249138863, \"min\": 0.9918992249138863}}, \"EndTime\": 1569259011.548878, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569259011.548841}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:51 INFO 140514251040576] #quality_metric: host=algo-1, epoch=3, train mse_objective <loss>=0.991899224914\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12368582337468151, \"sum\": 0.12368582337468151, \"min\": 0.12368582337468151}}, \"EndTime\": 1569259015.203788, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569259015.203744}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #quality_metric: host=algo-1, epoch=3, validation mse_objective <loss>=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=mse_objective, value=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] Epoch 3: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 6341, \"sum\": 6341.0, \"min\": 6341}, \"Total Records Seen\": {\"count\": 1, \"max\": 1616239, \"sum\": 1616239.0, \"min\": 1616239}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1569259015.205427, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1569258981.710352}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:16:55 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=11985.1745353 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9902387193278305, \"sum\": 0.9902387193278305, \"min\": 0.9902387193278305}}, \"EndTime\": 1569259044.998423, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569259044.998389}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:24 INFO 140514251040576] #quality_metric: host=algo-1, epoch=4, train mse_objective <loss>=0.990238719328\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364221331402393, \"sum\": 0.12364221331402393, \"min\": 0.12364221331402393}}, \"EndTime\": 1569259048.599141, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569259048.599098}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #quality_metric: host=algo-1, epoch=4, validation mse_objective <loss>=0.123642213314\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=mse_objective, value=0.123642213314\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #progress_metric: host=algo-1, completed 50 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 7916, \"sum\": 7916.0, \"min\": 7916}, \"Total Records Seen\": {\"count\": 1, \"max\": 2017685, \"sum\": 2017685.0, \"min\": 2017685}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1569259048.600234, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1569259015.205676}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:28 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12021.2539263 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9898242556490723, \"sum\": 0.9898242556490723, \"min\": 0.9898242556490723}}, \"EndTime\": 1569259078.239435, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1569259078.2394}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:17:58 INFO 140514251040576] #quality_metric: host=algo-1, epoch=5, train mse_objective <loss>=0.989824255649\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364073699174565, \"sum\": 0.12364073699174565, \"min\": 0.12364073699174565}}, \"EndTime\": 1569259081.825879, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1569259081.825844}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #quality_metric: host=algo-1, epoch=5, validation mse_objective <loss>=0.123640736992\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=mse_objective, value=0.123640736992\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 9491, \"sum\": 9491.0, \"min\": 9491}, \"Total Records Seen\": {\"count\": 1, \"max\": 2419131, \"sum\": 2419131.0, \"min\": 2419131}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1569259081.826992, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1569259048.600468}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:01 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12082.0442619 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9898115187021317, \"sum\": 0.9898115187021317, \"min\": 0.9898115187021317}}, \"EndTime\": 1569259111.664931, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1569259111.664896}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:31 INFO 140514251040576] #quality_metric: host=algo-1, epoch=6, train mse_objective <loss>=0.989811518702\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364087580199667, \"sum\": 0.12364087580199667, \"min\": 0.12364087580199667}}, \"EndTime\": 1569259115.256418, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1569259115.256376}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #quality_metric: host=algo-1, epoch=6, validation mse_objective <loss>=0.123640875802\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=mse_objective, value=0.123640875802\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #progress_metric: host=algo-1, completed 70 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 11066, \"sum\": 11066.0, \"min\": 11066}, \"Total Records Seen\": {\"count\": 1, \"max\": 2820577, \"sum\": 2820577.0, \"min\": 2820577}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}}, \"EndTime\": 1569259115.257311, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1569259081.827278}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:18:35 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12008.4952158 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9898111228715721, \"sum\": 0.9898111228715721, \"min\": 0.9898111228715721}}, \"EndTime\": 1569259145.095741, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1569259145.095707}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:05 INFO 140514251040576] #quality_metric: host=algo-1, epoch=7, train mse_objective <loss>=0.989811122872\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 0.12364088864916668, \"sum\": 0.12364088864916668, \"min\": 0.12364088864916668}}, \"EndTime\": 1569259148.633992, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1569259148.633956}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #quality_metric: host=algo-1, epoch=7, validation mse_objective <loss>=0.123640888649\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=0.123640888649\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] Early stop condition met. Stopping training.\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #progress_metric: host=algo-1, completed 100 % epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1575, \"sum\": 1575.0, \"min\": 1575}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Total Batches Seen\": {\"count\": 1, \"max\": 12641, \"sum\": 12641.0, \"min\": 12641}, \"Total Records Seen\": {\"count\": 1, \"max\": 3222023, \"sum\": 3222023.0, \"min\": 3222023}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 401446, \"sum\": 401446.0, \"min\": 401446}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1569259148.63497, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1569259115.257547}\n",
      "\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 INFO 140514251040576] #throughput_metric: host=algo-1, train throughput=12027.4209355 records/second\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 WARNING 140514251040576] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:08 WARNING 140514251040576] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:12 INFO 140514251040576] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=0.123640888649\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #validation_score (algo-1) : ('mse_objective', 0.12368582337468151)\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #validation_score (algo-1) : ('mse', 0.12368582337468151)\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #validation_score (algo-1) : ('absolute_loss', 0.30015450932553356)\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #quality_metric: host=algo-1, validation mse_objective <loss>=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #quality_metric: host=algo-1, validation mse <loss>=0.123685823375\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] #quality_metric: host=algo-1, validation absolute_loss <loss>=0.300154509326\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] Best model found for hyperparameters: {\"lr_scheduler_step\": 10, \"wd\": 0.0001, \"optimizer\": \"adam\", \"lr_scheduler_factor\": 0.99, \"l1\": 0.0, \"learning_rate\": 0.03719555404301278, \"lr_scheduler_minimum_lr\": 1e-05}\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] Saved checkpoint to \"/tmp/tmpwM7FyB/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[31m[09/23/2019 17:19:15 INFO 140514251040576] Test data is not provided.\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 277520.5268859863, \"sum\": 277520.5268859863, \"min\": 277520.5268859863}, \"finalize.time\": {\"count\": 1, \"max\": 7273.230791091919, \"sum\": 7273.230791091919, \"min\": 7273.230791091919}, \"initialize.time\": {\"count\": 1, \"max\": 934.1781139373779, \"sum\": 934.1781139373779, \"min\": 934.1781139373779}, \"check_early_stopping.time\": {\"count\": 9, \"max\": 1.1758804321289062, \"sum\": 5.347013473510742, \"min\": 0.17595291137695312}, \"setuptime\": {\"count\": 1, \"max\": 29.300928115844727, \"sum\": 29.300928115844727, \"min\": 29.300928115844727}, \"update.time\": {\"count\": 8, \"max\": 34924.36408996582, \"sum\": 269209.0108394623, \"min\": 33226.33981704712}, \"epochs\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1569259155.915844, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1569258878.487937}\n",
      "\u001b[0m\n",
      "Training seconds: 368\n",
      "Billable seconds: 368\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:37:02,546 - sagemaker - WARNING - Using already existing model: linear-learner-190923-1854-009-f7872322\n",
      "2019-09-23 21:37:02,547 - sagemaker - INFO - Creating transform job with name: linear-learner-190923-1854-009-f7872322-2019-09-23-19-37-02-547\n",
      "2019-09-23 21:37:03,009 - models.aws_base - INFO - Waiting for the transformer job\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...................\u001b[31mDocker entrypoint called with argument(s): serve\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loaded entry point class algorithm.serve.server_config:config_api\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loading entry points\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loaded request iterator text/csv\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loaded request iterator application/x-recordio-protobuf\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loaded request iterator application/json\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loaded request iterator application/jsonlines\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loaded response encoder text/csv\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loaded response encoder application/x-recordio-protobuf\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loaded response encoder application/json\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:06 INFO 139751404853056] loaded response encoder application/jsonlines\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:07 INFO 139751404853056] loaded entry point class algorithm:model\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:07 INFO 139751404853056] loading model...\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:07 INFO 139751404853056] ...model loaded.\u001b[0m\n",
      "\u001b[31m[09/23/2019 19:40:07 INFO 139751404853056] Number of server workers: 4\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:40:07 +0000] [1] [INFO] Starting gunicorn 19.7.1\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:40:07 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:40:07 +0000] [1] [INFO] Using worker: sync\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:40:07 +0000] [71] [INFO] Booting worker with pid: 71\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:40:07 +0000] [80] [INFO] Booting worker with pid: 80\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:40:07 +0000] [89] [INFO] Booting worker with pid: 89\u001b[0m\n",
      "\u001b[31m[2019-09-23 19:40:07 +0000] [98] [INFO] Booting worker with pid: 98\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"execution_parameters.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267620.63962, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267607.025042}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 71.08092308044434, \"sum\": 71.08092308044434, \"min\": 71.08092308044434}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267622.456875, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267607.025042}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 49.43203926086426, \"sum\": 49.43203926086426, \"min\": 49.43203926086426}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267622.518017, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267620.63973}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 50.94599723815918, \"sum\": 50.94599723815918, \"min\": 50.94599723815918}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267622.527947, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267607.025042}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 23.077964782714844, \"sum\": 23.077964782714844, \"min\": 23.077964782714844}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267622.614662, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267607.025042}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.014005661010742, \"sum\": 14.014005661010742, \"min\": 14.014005661010742}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267623.201628, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267622.457008}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 14.240026473999023, \"sum\": 14.240026473999023, \"min\": 14.240026473999023}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267623.221587, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267622.518188}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.41106605529785, \"sum\": 16.41106605529785, \"min\": 16.41106605529785}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267623.34106, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267622.614794}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 16.65806770324707, \"sum\": 16.65806770324707, \"min\": 16.65806770324707}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267623.474673, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267622.528061}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 10.681867599487305, \"sum\": 10.681867599487305, \"min\": 10.681867599487305}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267623.721939, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267623.221643}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 6.183147430419922, \"sum\": 6.183147430419922, \"min\": 6.183147430419922}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267623.87035, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267623.341418}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"json.encoder.time\": {\"count\": 1, \"max\": 8.47315788269043, \"sum\": 8.47315788269043, \"min\": 8.47315788269043}, \"invocations.count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1569267623.920076, \"Dimensions\": {\"Host\": \"UNKNOWN\", \"Operation\": \"scoring\", \"Algorithm\": \"LinearLearnerModel\"}, \"StartTime\": 1569267623.201783}\n",
      "\u001b[0m\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:40:51,582 - models.aws_base - INFO - Loading the results of the transformer job\n",
      "2019-09-23 21:40:51,596 - executors.sagemaker - INFO - Downloading data from s3: from s3://sagemaker-eu-west-1-729071960169/data/linear_learner/output_data/test.csv.out to data/temp/linear_learner/test.csv.out\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.csv.out\n",
      "data/temp/linear_learner\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:40:58,764 - models.aws_pytorch - INFO - Starting the PyTorch predictions\n",
      "2019-09-23 21:40:58,765 - models.aws_pytorch - INFO - Deploying the predictor\n",
      "2019-09-23 21:40:58,988 - sagemaker - INFO - Creating model with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 21:40:59,835 - sagemaker - WARNING - Using already existing model: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 21:41:00,348 - sagemaker - INFO - Creating endpoint with name sagemaker-pytorch-2019-09-23-17-53-49-735\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------!"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-09-23 21:48:12,202 - models.aws_pytorch - WARNING - Don't forget to delete the predicion endpoint\n",
      "2019-09-23 21:48:12,202 - models.aws_pytorch - INFO - Splitting the data in batches\n",
      "2019-09-23 21:48:12,210 - models.aws_pytorch - INFO - Done splitting the data in batches\n",
      "2019-09-23 21:48:12,210 - models.aws_pytorch - INFO - Sending the data to predict to the model\n",
      "2019-09-23 21:49:26,966 - models.aws_pytorch - INFO - Got the predictions\n",
      "2019-09-23 21:49:26,967 - models.aws_pytorch - INFO - Deleting the pytorch endpoint\n",
      "2019-09-23 21:49:26,967 - sagemaker - INFO - Deleting endpoint configuration with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 21:49:27,224 - sagemaker - INFO - Deleting endpoint with name: sagemaker-pytorch-2019-09-23-17-53-49-735\n",
      "2019-09-23 21:49:27,302 - models.aws_pytorch - INFO - Deleting the endpoint\n",
      "2019-09-23 21:49:27,309 - models.meta_model - INFO - Done with the predictions of the meta model\n"
     ]
    }
   ],
   "source": [
    "# We already trained the model above, so we just predict the normal values here\n",
    "smart_predictions = meta_model.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dumb score is 0.9468%\n"
     ]
    }
   ],
   "source": [
    "dumb_score = data_loader.score(dumb_predictions)\n",
    "print(f\"The dumb score is {dumb_score*100:0.4f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The smart score is 5.8459%\n"
     ]
    }
   ],
   "source": [
    "smart_score = data_loader.score(smart_predictions)\n",
    "print(f\"The smart score is {smart_score*100:0.4f}%\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}