Installing Tensorflow on macOS on an Arm MBP

Question

When attempting to install tensorflow on an ARM M1 MacBookPro, I am seeing the following issues:

% pip install tensorflow
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow

After searching around, I found a package called tensorflow-macos which produces the following issues:

clang: error: the clang compiler does not support 'faltivec', please use -maltivec and include altivec.h explicitly
    error: Command "gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -arch arm64 -arch x86_64 -g -DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Ibuild/src.macosx-10.9-universal2-3.9/numpy/core/src/umath -Ibuild/src.macosx-10.9-universal2-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.9-universal2-3.9/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-10.9-universal2-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/Users/thing/dev/SOTAMoon/venv/include -I/Library/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.9-universal2-3.9/numpy/core/src/common -Ibuild/src.macosx-10.9-universal2-3.9/numpy/core/src/npymath -c numpy/core/src/multiarray/descriptor.c -o build/temp.macosx-10.9-universal2-3.9/numpy/core/src/multiarray/descriptor.o -MMD -MF build/temp.macosx-10.9-universal2-3.9/numpy/core/src/multiarray/descriptor.o.d -faltivec -I/System/Library/Frameworks/vecLib.framework/Headers -std=c99" failed with exit status 1
    ----------------------------------------
    ERROR: Failed building wheel for numpy
  Failed to build numpy
  ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly
  ----------------------------------------
WARNING: Discarding https://files.pythonhosted.org/packages/a7/81/20d5d994c91ed8347efda90d32c396ea28254fd8eb9e071e28ee5700ffd5/h5py-3.1.0.tar.gz#sha256=1e2516f190652beedcb8c7acfa1c6fa92d99b42331cbef5e5c7ec2d65b0fc3c2 (from https://pypi.org/simple/h5py/) (requires-python:>=3.6). Command errored out with exit status 1: /Users/thing/dev/SOTAMoon/venv/bin/python3 /private/var/folders/8k/z291bhgd5gs06tp9b4j6_bb40000gn/T/pip-standalone-pip-8qa87uy0/__env_pip__.zip/pip install --ignore-installed --no-user --prefix /private/var/folders/8k/z291bhgd5gs06tp9b4j6_bb40000gn/T/pip-build-env-bxy_nyoo/normal --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- 'numpy==1.12; python_version == "3.6"' 'numpy==1.19.3; python_version >= "3.9"' 'numpy==1.14.5; python_version == "3.7"' pkgconfig 'Cython>=0.29; python_version < "3.8"' 'Cython>=0.29.14; python_version >= "3.8"' 'numpy==1.17.5; python_version == "3.8"' Check the logs for full command output.
ERROR: Could not find a version that satisfies the requirement h5py~=3.1.0 (from tensorflow-macos) (from versions: 2.2.1, 2.3.0b1, 2.3.0, 2.3.1, 2.4.0b1, 2.4.0, 2.5.0, 2.6.0, 2.7.0rc2, 2.7.0, 2.7.1, 2.8.0rc1, 2.8.0, 2.9.0rc1, 2.9.0, 2.10.0, 3.0.0rc1, 3.0.0, 3.1.0, 3.2.0, 3.2.1, 3.3.0)
ERROR: No matching distribution found for h5py~=3.1.0

Unfortunately I couldn't work out a solution to the 'faltivec' issue. I also found a tensorflow package made by Apple that seems specifically geared for M1's, however going by this tutorial produces the following issues:

% pip install --upgrade --force --no-dependencies https://github.com/apple/tensorflow_macos/releases/download/v0.1alpha3/tensorflow_addons_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl https://github.com/apple/tensorflow_macos/releases/download/v0.1alpha3/tensorflow_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl
ERROR: tensorflow_addons_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl is not a supported wheel on this platform.

After this, I am a bit stuck. I have upgraded pip to 21.1.3, and my python version is Python 3.9.6.

Answer 1

Tensorflow-macos and Tensorflow-metal Install

Currently, to harness the M1 GPU you need to install Tensorflow-macos and TensorFlow-metal as opposed to Tensorflow, the install steps are detailed here, they can be summarized as follows using mini-forge:

conda create -n tf python=3.8 -y
conda activate tf
conda install -c apple tensorflow-deps -y
# Navigate the issue with conda environments
# built against pre-macOS 11 SDK use (Just in case)
# this solves the Intel / M1 install issues:
SYSTEM_VERSION_COMPAT=0 pip install tensorflow-macos 
SYSTEM_VERSION_COMPAT=0 pip install tensorflow-metal

to test all is well we can run:

#!/usr/bin/env bash

PYCMD=$(cat <<EOF
import tensorflow as tf

tf.config.list_physical_devices()
with tf.device('/GPU'):
  a = tf.random.normal(shape=(2,), dtype=tf.float32)
  b = tf.nn.relu(a)

print(a)
print(b)

EOF
)

python3 -c "$PYCMD" 2>/dev/null

the expected output for my test machine:

>./val.sh
Metal device set to: Apple M1

systemMemory: 16.00 GB
maxCacheSize: 5.33 GB

tf.Tensor([-0.4044231   0.08157467], shape=(2,), dtype=float32)
tf.Tensor([0.         0.08157467], shape=(2,), dtype=float32)

ok, everything is (assumed) to be working...

Side-Note: Package Install Issues:

One thing to note is that that packages that rely on tensorflow will try to install tensorflow as they will likely not have an explicit dependency on tensorflow-macos or tensorflow-metal.

When you install tensorflow dependent packages or code you'll likely have to review the requirements.txt file before running:

python3 pip install -r requirements.txt

The requirements.txt will try to install the tensorflow package, not the tensorflow-macosx or tensorflow-metal packages which will result in a host of issues if you run the requirements.txt file unchanged. That and explicit versions of packages == which can also cause problems.

Hopefully, this points you in the right direction in thinking about the hurdles involved in getting python to work with the Apple-M1 laptops using the TensorFlow metal plugin to harness the M1 GPU features.

Stay safe and well.