Initializing TensorFlow Environment on M3, M3 Pro and M3 Max Macbook Pros.

TensorFlow on M3, M3 Pro, and M3 Max MacBook Pros: Harnessing Computational Power with Apple Silicon

Description:

Unlock the full potential of your Apple Silicon-powered M3, M3 Pro, and M3 Max MacBook Pros by leveraging TensorFlow, the open-source machine learning framework. This repository is tailored to provide an optimized environment for setting up and running TensorFlow on Apple's cutting-edge M3 chips.

Key Features:

1. Efficient ML Workflows: Streamline your machine learning workflows on Apple Silicon for enhanced efficiency and performance.

2. Tailored Configurations: Discover configurations and settings specifically designed for M3, M3 Pro, and M3 Max MacBook Pros, ensuring optimal resource utilization.

3. Performance Boost: Leverage the native capabilities of Apple Silicon to achieve accelerated training and inference speeds, tapping into the computational prowess of your M3 MacBook Pro.

4. Compatibility and Updates: Stay up-to-date with the latest TensorFlow releases and compatibility updates tailored for Apple Silicon architecture.

Getting Started: Follow all the necessary steps mentioned below.

Steps

1. Install Homebrew from https://brew.sh.
2. Download Miniforge3 for macOS arm64 chips.
3. Install Miniforge3 into the home directory of your Macbook Pro.
4. Type the following Bash code in the terminal.

chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh

sh ~/Downloads/Miniforge3-MacOSX-arm64.sh

source ~/miniforge3/bin/activate

5. Restart terminal to prevent any errors.
6. Create a new directory to setup the custom TensorFlow environment.

mkdir tensorflow-test

cd tensorflow-test

7. Type ls in terminal to crosscheck the current directory.
8. Initialize and activate the Conda environment.

conda create --prefix ./env python=3.8

conda activate ./env

9. Install TensorFlow dependencies from Apple Conda.

conda install -c apple tensorflow-deps

10. Install base TensorFlow.

python -m pip install tensorflow-macos

11. Install Apple's tensorflow-metal to utilize the Apple Metal (Apple's GPU framework) for M3, M3 Pro, M3 Max GPU access.

python -m pip install tensorflow-metal

12. Install data science packages.

conda install jupyter pandas numpy matplotlib scikit-learn

13. Start Jupyter Notebook.

jupyter notebook

14. Type the following code to check TensorFlow version/GPU access.

import numpy as np
import pandas as pd
import sklearn
import tensorflow as tf
import matplotlib.pyplot as plt

# Check for TensorFlow GPU access
print(f"TensorFlow has access to the following devices:\n{tf.config.list_physical_devices()}")

# See TensorFlow version
print(f"TensorFlow version: {tf.__version__}")

That's It!!

You should now be able to run all your ML models on Apple's GPU.

Thank You.