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PlaidML Python Wheel

The PlaidML Python Wheel contains the PlaidML Python API, as well as the PlaidML C++ headers, runtime libraries, and configuration files needed to use PlaidML from other languages.

You can get the latest release of the PlaidML Python Wheel by running:

pip install plaidml
plaidml-setup

You can also build and install the wheel from source, by following these instructions.

Install Anaconda

Install Anaconda. You'll want to use a Python 3 version.

After installing Anaconda, you'll need to restart your shell, to pick up its environment variable modifications (i.e. the path to the conda tool and shell integrations).

For Microsoft Windows* OS, you'll also need the Visual C++ compiler and the Windows* SDK, following the Bazel-on-Windows instructions. Note that Conda will take care of the getting you a working Python and an msys2 shell (you shouldn't need to set BAZEL_* environment variables unless you've installed tools in unusual locations).

Create and activate a Conda environment

Navigate to the root of the PlaidML source directory to execute the following commands for setting up a Conda environment.

macOS* and Linux*:

conda env create -n plaidml
conda activate plaidml

Microsoft Windows* OS:

conda env create -n plaidml -f environment-windows.yml
conda activate plaidml

Build the PlaidML Python wheel

macOS*:

bazel build --config macos_x86_64 //plaidml:wheel

Linux*:

bazel build --config linux_x86_64 //plaidml:wheel

Microsoft Windows* OS:

bazel build --config windows_x86_64 //plaidml:wheel

Install the PlaidML Python wheel

pip install -U bazel-bin/plaidml/*.whl
plaidml-setup

PlaidML with nGraph

Follow these instructions if you are wanting to work with the PlaidML backend.

Building the nGraph PlaidML backend requires that you've installed the PlaidML Python wheel. You can do this by running:

pip install plaidml
plaidml-setup

or by following the instructions for building the PlaidML Python Wheel, above.

When the PlaidML wheel is installed, the default nGraph build contains the PlaidML backend. From the nGraph source directory, you can run:

mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=~/ngraph_plaidml_dist -DNGRAPH_PLAIDML_ENABLE=ON -DNGRAPH_CPU_ENABLE=OFF
make
make install

Running the build with the above options will install nGraph into ~/ngraph_plaidml_dist. When you do not explicitly use -DNGRAPH_CPU_ENABLE=FALSE, the default build enables CPU for operations.

After running make and make install, be sure to set the environment variables to the correct location where the libraries were built. Continuing the above example, this would be as follows for each respective OS:

Linux*

Most Linux distributions support LD_LIBRARY_PATH; consult the distribution's documentation for specifics.

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/ngraph_plaidml_dist/lib
export NGRAPH_CPP_BUILD_PATH=~/ngraph_plaidml_dist

macOS*

MacOS usually requires use of DYLD_LIBRARY_PATH.

export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:~/ngraph_plaidml_dist/lib
export NGRAPH_CPP_BUILD_PATH=~/ngraph_plaidml_dist

Microsoft Windows* OS

Windows requires that dynamic libraries are on your PATH.

set PATH=%PATH%:~/ngraph_plaidml_dist

Test

  • To run the tests on experimental device #0, try something like ~/path/to/ngraph/build/test/unit-test --gtest_filter=PlaidML.*.

  • To run nBench, try something like ~/ngraph_plaidml_dist/bin/nbench -b "PlaidML:0" -f ~/test/model_inference_batch1_float32.json.

    This runs the nGraph model specified in the file given by the -f option on non-experimental device #0. If you want to use an experimental device, set the environment variable PLAIDML_EXPERIMENTAL=1.

PlaidML with Keras*

The PlaidML-Keras Python Wheel contains the code needed for integration with Keras*.

You can get the latest release of the PlaidML-Keras Python Wheel by running:

pip install plaidml-keras

You can also build and install the wheel from source.

Set up a build environment

Follow the setup instructions for building the PlaidML Python Wheel, above.

Build the PlaidML-Keras wheel

macOS*: bazel build --config macos_x86_64 //plaidml/keras:wheel

Linux*: bazel build --config linux_x86_64 //plaidml/keras:wheel

Microsoft Windows* OS: bazel build --config windows_x86_64 //plaidml/keras:wheel

Install the PlaidML-Keras Python wheel

pip install -U bazel-bin/plaidml/keras/*.whl

Testing PlaidML

Unit tests are executed through bazel:

bazel test //...

Unit tests for frontends are marked manual and must be executed individually (requires running plaidml-setup prior to execution)

bazel test //plaidml/keras:backend_test