guide on installing TensorFlow 2.14 on your Mac M1/M2 chip running macOS 13.5, Ventura. In this tutorial, we'll walk you through the process step by step. We've streamlined it to get you up and running swiftly, but keep in mind that you can tailor your environment with specific packages for your unique projects.

install-tensorflow-mac.md

TensorFlow Installation on Mac M1/M2(Apple Silicon) Chip

I am always trying to minimize disk consumption, so when looking to install tensorflow, it really matters how you've bundled virtual envs to service more than project. I've got a good bundle going with a localGPT virtual env. Using miniconda and running Python 3.10, this localGPT venv is wired for localGPT, TensorFlow dependencies, and Melodia generative AI. conda commands you may like to know:

• conda env list
• conda create -n env-name python=3.10
• conda activate env-name

Step 1: Installing Xcode Command Line Tools xcode-select --install

Step 2: Installing Anaconda for M Chip Download Anaconda for M Chip: https://www.anaconda.com/download, miniconda also works, and saves about a gig of disk.

Step 3: Installing TensorFlow Dependencies conda install -c apple tensorflow-deps

Step 4: Creating a Conda Environment conda create -n tensorflow python=3.10

Step 5: Installing TensorFlow and Plugins pip install tensorflow-macos tensorflow-metal

Step 6: Installing Additional Packages (Customization) conda install numpy pandas matplotlib scikit-learn scipy plotly jupyter

Step 7: Troubleshooting Tips

Step 8: Verifying TensorFlow Installation:

import sys
import tensorflow.keras
import pandas as pd
import sklearn as sk
import scipy as sp
import tensorflow as tf
# import keras
import platform

print (f"Python Platform: {platform.platform()}")
print(f"Tensor Flow Version: {tf.__version__}")
print (f"Keras Version: {tensorflow.keras.__version__}")
print ()
print (f"Python {sys.version}")
print (f"Pandas {pd.__version__}")
print(f"Scikit-Learn {sk.__version__}")
print(f"SciPy {sp.__version__}")
gpu = len(tf.config.list_physical_devices ('GPU'))>0
print("GPU is", "available" if gpu else "NOT AVAILABLE" )