(1) # Download the archive for this repo from https://github.com/apple/tensorflow_macos/releases
cd $HOME/Downloads/
curl -fsSLO https://github.com/apple/tensorflow_macos/releases/download/v0.1alpha0/tensorflow_macos-0.1alpha0.tar.gz
tar xzvf tensorflow_macos-0.1alpha0.tar.gz
/bin/bash ./tensorflow_macos/install_venv.sh --help
(2) # Download Miniconda from https://conda-forge.org/blog/posts/2020-10-29-macos-arm64/
(3) # Install Miniconda and after installtion, exit shell and login again
/bin/bash -c "$(curl -fsSL https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh)"
(4) # Once it's installed, create a Python 3.8 env by running
conda create --name python38 python=3.8
(5) # Put a path to where the arm64 libraries are. For example...
libs="$HOME/Downloads/tensorflow_macos/arm64/"
(6) # Replace this with the path of your Conda environment
env="$HOME/miniforge3/envs/python38"
(7) # upgrade
conda upgrade -c conda-forge pip setuptools cached-property six
(8) # activate env
conda activate python38
# conda deactivate
(9) pip install --upgrade -t "$env/lib/python3.8/site-packages/" --no-dependencies --force "$libs/grpcio-1.33.2-cp38-cp38-macosx_11_0_arm64.whl"
(10) pip install --upgrade -t "$env/lib/python3.8/site-packages/" --no-dependencies --force "$libs/h5py-2.10.0-cp38-cp38-macosx_11_0_arm64.whl"
(11) pip install --upgrade -t "$env/lib/python3.8/site-packages/" --no-dependencies --force "$libs/numpy-1.18.5-cp38-cp38-macosx_11_0_arm64.whl"
(12) pip install --upgrade -t "$env/lib/python3.8/site-packages/" --no-dependencies --force "$libs/tensorflow_addons-0.11.2+mlcompute-cp38-cp38-macosx_11_0_arm64.whl"
(13) # install these
conda install -c conda-forge -y absl-py conda install -c conda-forge -y astunparse conda install -c conda-forge -y gast conda install -c conda-forge -y opt_einsum conda install -c conda-forge -y termcolor conda install -c conda-forge -y typing_extensions conda install -c conda-forge -y wheel conda install -c conda-forge -y typeguard pip install tensorboard pip install wrapt flatbuffers tensorflow_estimator google_pasta keras_preprocessing protobuf
(14) pip install --upgrade -t "$env/lib/python3.8/site-packages/" --no-dependencies --force "$libs/tensorflow_macos-0.1a0-cp38-cp38-macosx_11_0_arm64.whl"
(15) # Run this to test
time python tftest.py
- tftest.py Select all
from datetime import datetime
import numpy as np
import tensorflow as tf
from tensorflow.python.compiler.mlcompute import mlcompute
mlcompute.set_mlc_device(device_name="cpu")
# tensorflow:Eager mode on GPU is extremely slow. So use CPU instead
print("Hello, Tensorflow! ", end='')
print(tf.__version__)
print("start" , datetime.now())
X_raw = np.array([2013, 2014, 2015, 2016, 2017, 2018], dtype=np.float32)
y_raw = np.array([12000, 14000, 15000, 16500, 17500, 19000], dtype=np.float32)
X = (X_raw - X_raw.min()) / (X_raw.max() - X_raw.min())
y = (y_raw - y_raw.min()) / (y_raw.max() - y_raw.min())
X = tf.constant(X)
y = tf.constant(y)
a = tf.Variable(initial_value=0.)
b = tf.Variable(initial_value=0.)
variables = [a, b]
num_epoch = 10000
optimizer = tf.keras.optimizers.SGD(learning_rate=1e-3)
for e in range(num_epoch):
with tf.GradientTape() as tape:
y_pred = a * X + b
loss = 0.5 * tf.reduce_sum(tf.square(y_pred - y))
grads = tape.gradient(loss, variables)
optimizer.apply_gradients(grads_and_vars=zip(grads, variables))
print(a, b)
print("end" , datetime.now())
(16) Test run this cnn.py https://github.com/apple/tensorflow_macos/issues/25
