We suggest to install or use the package in the Python virtual environment.
If you want to optimize a model from PyTorch, install PyTorch by following PyTorch installation guide. For other backend follow: TensorFlow installation guide, ONNX installation guide, OpenVINO installation guide.
NNCF can be installed as a regular PyPI package via pip:
pip install nncfIf you want to install both NNCF and the supported PyTorch version in one line, you can do this by simply running:
pip install nncf[torch]Other viable options besides [torch] are [tf], [onnx] and [openvino].
Install the package and its dependencies by running the following command in the repository root directory:
pip install .Use the same pip install syntax as above to install NNCF along with the backend package version in one go:
pip install .[<BACKEND>]List of supported backends: torch, tf, onnx and openvino.
For development purposes install extra packages by
pip install .[dev,tests]NB: For launching example scripts in this repository, we recommend setting the PYTHONPATH variable to the root of the checked-out repository once the installation is completed.
NNCF is also available via conda:
conda install -c conda-forge nncfpip install git+https://github.com/openvinotoolkit/nncf@bd189e2#egg=nncfNote that in order for this to work for pip versions >= 21.3, your Git version must be at least 2.22.
Use one of the Dockerfiles in the docker directory to build an image with an environment already set up and ready for running NNCF sample scripts.
The following table lists the recommended corresponding versions of backend packages as well as the supported versions of Python:
| NNCF | OpenVINO | PyTorch | ONNX | TensorFlow | Python |
|---|---|---|---|---|---|
develop |
2023.2.0 |
2.1 |
1.13.1 |
2.12.0 |
3.8 |
2.7.0 |
2023.2.0 |
2.1 |
1.13.1 |
2.12.0 |
3.8 |
2.6.0 |
2023.1.0 |
2.0.1 |
1.13.1 |
2.12.0 |
3.8 |
2.5.0 |
2023.0.0 |
1.13.1 |
1.13.1 |
2.11.1 |
3.8 |
2.4.0 |
2022.1.0 |
1.12.1 |
1.12.0 |
2.8.2 |
3.8 |