🌐 Cloud Scope

Lambda Labs Availability Monitor

📜 Overview

This application monitors the availability of cloud instances using a configurable interval. It sends requests to the Lambda Labs API to fetch instance availability information. The application logs the availability information to the console and to log files.

Lambda Labs is a cloud service that provides access to GPU-enabled cloud instances for machine learning and deep learning applications. I am not affiliated with Lambda Labs in any way, and this application is not endorsed by Lambda Labs. As far as I know, Lambda Labs currently provides the lowest cost cloud instances with GPU support.

Lamda Labs API Documentation: https://cloud.lambdalabs.com/api/v1/docs#operation/instanceTypes

Pydantic is used in this application. If you are not familiar with Pydantic, the code will look weird. You can find more information here: https://pydantic-docs.helpmanual.io/

📋 Requirements

• Python 3.12 (Although it may work with older versions)
• Pip
• Pipenv

🛠️ Setup

1. Clone the Repository
   • Clone the repository to your local machine:

     git clone https://github.com/MetaRossi/LambdaLabsAvailability.git

2. Change Directory
   • Change to the project directory:

     cd CloudScope

3. Install Dependencies Using Pipenv
   • If missing on your system, install Pipenv:

     pip install pipenv

   • Install the dependencies using Pipenv:

     pipenv install

   • Activate the virtual environment:

     pipenv shell

4. Get a Lambda Labs API Key
   • If you do not already have a Lambda Labs account, create one here: https://lambdalabs.com/
   • Once you have an account, you can get an API key here: https://cloud.lambdalabs.com/api-keys
   • You will need the API key to configure the application.

⚙️ Configuration

Create a TOML configuration file in the location of your choosing (e.g., config.toml). Use the following template to configure the application.

I like well annotated configuration files, so I have included comments for each setting.

# REQUIRED: Namespace for this configuration.
[default]

# REQUIRED: The interval between API requests in milliseconds.
min_poll_delay_ms = 1500
# Lambda Labs will rate limit requests to the API if polled at less than one request per second.
# If this occurs, you will see HTML in the console output.
# A minimum delay of 1100 ms is required by the application to add a little buffer.
 
# REQUIRED: Directory to store log files.
log_dir = "__logs"
# If the logging directory does not exist, it will be created on start.

# REQUIRED: Lambda Labs API Key
api_key = "your_api_key_here"
 
# OPTIONAL: Enable OS voice notifications for notify, launch, and new region alerts.
#           If absent, disabled by default.
enable_voice_notifications = false
# The OS will say "Instance Available", "Launch In Progress", or "New Region Detected" when an alert occurs.
# A new region will only alert once per application run.
# These alerts only exist because Lambda Labs sometimes adds new regions without notice.
# There is no way to get a list of all regions from the API.
# Currently, the region list is hardcoded in the application.

🚀 Usage

1. Run the Application

   • Execute the main script with required the configuration file and namespace arguments:

     python cloud_scope.py <path_to_config_toml> <toml_namespace_default>

   • The application will start monitoring based on the settings in the specified namespace and log information accordingly.

2. Monitor Console and Logs

   • The application logs the status of the instances to both the console and log files.
   • Log files are stored in the directory specified in the configuration file.
   • Log files are named based on the date and time at the start of the application.

3. Command Line Arguments

   • config_file: Required. Specifies the path to the TOML configuration file.
   • namespace: Required. Specifies the namespace within the configuration file.
   • --help: Optional. Displays the help message.

⚖️ License

MIT license. See the LICENSE file.

🤖 Acknowledgements

This project received assistance from OpenAI's ChatGPT and GitHub Copilot for various tasks including: code snippets, unit tests, generating the header image, and documentation.