TensorFlow Model Optimization Toolkit
The TensorFlow Model Optimization Toolkit is a suite of tools that users,
both novice and advanced, can use to optimize machine learning models for
deployment and execution.
For an overview of this project and individual tools, the optimization gains,
and our roadmap refer to
tensorflow.org/model_optimization.
The website also provides various tutorials and API docs.
The toolkit provides stable Python APIs.
Installation
Stable Builds
To install the latest version, run the following:
# Installing with the `--upgrade` flag ensures you'll get the latest version.
pip install --user --upgrade tensorflow-model-optimization
For release details, see our
release notes.
For the required version of TensorFlow and other compatibility information, see
the API Compatibility Matrix section of the Overview page for the technique you
intend to use. For instance, for pruning, the Overview page is
here.
Since TensorFlow is not included as a dependency of the TensorFlow Model
Optimization package (in setup.py), you must explicitly install the TensorFlow
package (tf-nightly or tf-nightly-gpu). This allows us to maintain one
package instead of separate packages for CPU and GPU-enabled TensorFlow.
Installing from Source
You can also install from source. This requires the
Bazel build system.
# To install dependencies on Ubuntu:
# sudo apt-get install bazel git python-pip
# For other platforms, see Bazel docs above.
git clone https://github.com/tensorflow/model-optimization.git
cd model_optimization
bazel build --copt=-O3 --copt=-march=native :pip_pkg
PKGDIR=$(mktemp -d)
./bazel-bin/pip_pkg $PKGDIR
pip install --user --upgrade $PKGDIR/*.whl
Contribution guidelines
If you want to contribute to TensorFlow Model Optimization, be sure to review
the contribution guidelines. This project adheres to
TensorFlow's
code of conduct.
By participating, you are expected to uphold this code.
We use
GitHub issues for
tracking requests and bugs.
Community
As part of TensorFlow, we're committed to fostering an open and welcoming
environment.
- TensorFlow Blog: Stay up to date on content
from the TensorFlow team and best articles from the community.
