setup.mac

# Macbook Pro M2
#


# Make sure conda has the channels
#  channels:
#  - apple
#  - conda-forge
#
conda config --add channels apple
conda config --add channels conda-forge
#
# check the channels
# (I think apple needs to be first..not sure yet)
#
conda config --get

#
# install a python ARM version
#
CONDA_SUBDIR=osx-arm64 conda create -n ww0.7 python=3.10

#
# check the platform: 'macOS-13.2.1-arm64-arm-64bit'
#
pip3 install ipython
ipython
import platform
platform.platform()

#
# Veclib Perform floating-point arithmetic, transcendental, and trigonometric functions on 128-bit vectors.
# I guess I need this ? not sure
#
brew install veclibfort


#
# install numpy from the apple channel
#  this uses libblas, etc
#
# https://developer.apple.com/forums/thread/695963
#
conda install cython pybind11
pip3 install --no-binary :all: --no-use-pep517 numpy


#
# If NumPy is using the Accelerate framework, you should see a line in the output that says accelerate_info: {'libraries': ['System', 'Accelerate'], 'library_dirs': ['/usr/lib']}.
#
import numpy as np
np.__config__.show()
#
# you should see
#
# lapack_opt_info:
#    extra_compile_args = ['-I/System/Library/Frameworks/vecLib.framework/Headers']
#    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
#    define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]
# Supported SIMD extensions in this NumPy install:
#    baseline = NEON,NEON_FP16,NEON_VFPV4,ASIMD
#    found = ASIMDHP,ASIMDDP
#    not found = ASIMDFHM
#

#
# test the performance of the SVD
#  average speed should be
# 
# mean of 10 runs: 0.08435s
#
python checks/check_svd_perf.py

# install the tensorflow dependencies
# note: numpy must match
#
# this will not work
#conda install -c apple tensorflow-deps
#pip3 install tensorflow-macos
#pip3 install tensorflow-metal

# instead,use
SYSTEM_VERSION_COMPAT=0 pip3 install tensorflow-macos tensorflow-metal

#
# test that tensorflow can be imported and the GPU and CPU are present
#
# [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),
# PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
#
ipython
import tensorflow as tf
tf.__version__ (currently '2.11.0')
tf.config.list_physical_devices()

#
# install pytorch
# https://towardsdatascience.com/installing-pytorch-on-apple-m1-chip-with-gpu-acceleration-3351dc44d67c
#
pip3 install torch torchvision torchaudio

# check that the MPS beckend is available and built
ipython

import torch
torch.backends.mps.is_available()
torch.backends.mps.is_built()


#
# install scipy (can not be accelerated)
#
# scipy needs OPENBLAS, it can not use the Accelerate library
# Note: we can not both install pythran and tensorflow, so we can not build scipy from scratch

brew install openblas gfortran
OPENBLAS="$(brew --prefix openblas)" pip3 install scipy

#
# test the performance of the scipy SVD
#
python checks/check_scipy_svd.py
#
# currently I find that np.linalg.svd is faster:
#
#  python checks/checkscipy_svd.py 
#  np.linalg.svd took 0.05757 seconds
#  scipy.linalg.svd took 0.18015 seconds
#  numpy.linalg.svd is faster
#

# confirm the linalg is found so the code can switch on this
python checks/check_mac_M12.py

#
# install remaining packages
#
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
export CFLAGS="-falign-functions=8 ${CFLAGS}"
pip3 install scikit-learn pandas powerlaw

# for testing and notebooks
pip3 install matplotlib matplotlib-inline tqdm transformers
#