This pre-release delivers hardware-accelerated TensorFlow and TensorFlow Addons for macOS 11.0+. Native hardware acceleration is supported on M1 Macs and Intel-based Macs through Apple’s ML Compute framework.
An archive containing Python packages and an installation script can be downloaded from the releases.
To quickly try this out, copy and paste the following into Terminal:
% /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/apple/tensorflow_macos/master/scripts/download_and_install.sh)"
This will verify your system, ask you for confirmation, then create a virtual environment with TensorFlow for macOS installed.
Alternatively, download the archive file from the releases. The archive contains an installation script, accelerated versions of TensorFlow, TensorFlow Addons, and needed dependencies.
% curl -fLO https://github.com/apple/tensorflow_macos/releases/download/v0.1alpha2/tensorflow_macos-${VERSION}.tar.gz
% tar xvzf tensorflow_macos-${VERSION}.tar
% cd tensorflow_macos
% ./install_venv.sh --prompt
This pre-release version supports installation and testing using the Python from Xcode Command Line Tools. See #153 for more information on installation in a Conda environment.
For M1 Macs, the following packages are currently unavailable:
When installing pip packages in a virtual environment, you may need to specify --target
as follows:
% pip install --upgrade -t "${VIRTUAL_ENV}/lib/python3.8/site-packages/" PACKAGE_NAME
Please submit feature requests or report issues via GitHub Issues.
It is not necessary to make any changes to your existing TensorFlow scripts to use ML Compute as a backend for TensorFlow and TensorFlow Addons.
There is an optional mlcompute.set_mlc_device(device_name='any')
API for ML Compute device selection. The default value for device_name
is 'any'
, which means ML Compute will select the best available device on your system, including multiple GPUs on multi-GPU configurations. Other available options are 'cpu'
and 'gpu'
. Please note that in eager mode, ML Compute will use the CPU. For example, to choose the CPU device, you may do the following:
# Import mlcompute module to use the optional set_mlc_device API for device selection with ML Compute.
from tensorflow.python.compiler.mlcompute import mlcompute
# Select CPU device.
mlcompute.set_mlc_device(device_name='cpu') # Available options are 'cpu', 'gpu', and 'any'.
The following TensorFlow features are currently not supported in this fork:
Logging provides more information about what happens when a TensorFlow model is optimized by ML Compute. Turn logging on by setting the environment variable TF_MLC_LOGGING=1
when executing the model script. The following is the list of information that is logged in graph mode:
Unlike graph mode, logging in eager mode is controlled by TF_CPP_MIN_VLOG_LEVEL
. The following is the list of information that is logged in eager mode:
MLCTraining
or MLCInference
graph. This key is used to retrieve the graph and run a backward pass or an optimizer update.MLCSubgraphOp
, can execute concurrently. As a result, there may be overlapping logging information. To avoid this during the debugging process, set TensorFlow to execute operators sequentially by setting the number of threads to 1 (see tf.config.threading.set_inter_op_parallelism_threads
).TF_DISABLE_MLC_EAGER=“;Op1;Op2;...”
. The gradient op may also need to be disabled by modifying the file $PYTHONHOME/site-packages/tensorflow/python/ops/_grad.py
(this avoids TensorFlow recompilation).TF_MLC_ALLOCATOR_INIT_VALUE=<init-value>
.TF_DISABLE_MLC=1
.