The current version of OpenVINO™ Training Extensions was tested in the following environment:
Python 3.8 ~ 3.10
(Optional) To use the NVidia GPU for the training: CUDA Toolkit 11.7
If using CUDA, make sure you are using a proper driver version. To do so, use
ls -la /usr/local | grep cuda.
If necessary, install CUDA 11.7 (requires ‘sudo’ permission) and select it with
Install OpenVINO™ Training Extensions for users#
1. Clone the training_extensions repository with the following command:
git clone https://github.com/openvinotoolkit/training_extensions.git cd training_extensions git checkout develop
2. Set up a virtual environment.
# Create virtual env. python -m venv .otx # Activate virtual env. source .otx/bin/activate
3. Install PyTorch according to your system environment. Refer to the official installation guide
Currently, only torch==1.13.1 ~ 2.0.1 was fully validated. (older versions are not supported due to security issues).
# Install command for torch==2.0.1 for CUDA 11.7: pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url https://download.pytorch.org/whl/cu117 # Or, install command for torch==1.13.1 for CUDA 11.7: pip install torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu117
Install OpenVINO™ Training Extensions package from either:
A local source in development mode
pip install -e .[full]
pip install otx[full]
5. Once the package is installed in the virtual environment, you can use full OpenVINO™ Training Extensions command line functionality.
Install OpenVINO™ Training Extensions for developers#
tox and create a development environment:
pip install tox # -- need to replace '310' below if another python version needed tox devenv venv/otx -e tests-all-py310 source venv/otx/bin/activate
Then you may change code, and all fixes will be directly applied to the editable package.
Install OpenVINO™ Training Extensions by using Docker#
$ docker build \ -t trainer \ # image tag, required --build-arg UBUNTU_VER=20.04 \ # default Ubunutu version, optional --build-arg PYTHON_VER=3.9 \ # default Python version, optional --build-arg SOURCE=https://download.pytorch.org/whl/cpu \ # default (CPU) deps, optional . # training_extensions/ $ docker run \ -it \ # enter interactive terminal --rm \ # remove container after use -v "$(pwd)/shared:/mnt/shared:rw" \ # shared volume to host machine --shm-size=4g \ # increase mounted shared memory trainer trainer$ otx # ... installed on Ubuntu 20.04 with /mnt/shared as shared directory
To run some tests, need to have development environment on your host. The development requirements file (requirements/dev.txt) would be used to setup them.
$ pip install -r requirements/dev.txt $ pytest tests/
Another option to run the tests is using the testing automation tool tox. Following commands will install
tox to your host and run all test codes inside of
$ pip install tox $ tox -e tests-all-py310 -- tests/
When running the
tox command above first time, it will create virtual env by installing all dependencies of this project into
the newly created environment for your testing before running the actual testing. So, it is expected to wait more than 10 minutes
before to see the actual testing results.
1. If you have problems when you try to use
pip install command,
please update pip version by following command:
python -m pip install --upgrade pip
2. If you’re facing a problem with
mmcv installation, please check that your CUDA version is compatible with torch version.
Consider updating CUDA and CUDA drivers if needed.
Check the command example to install CUDA 11.7 with drivers on Ubuntu 20.04.
3. If you use Anaconda environment, you should consider that OpenVINO has limited Conda support for Python 3.6 and 3.7 versions only.
So to use these python versions, please use other tools to create the environment (like
virtualenv) and use
pip as a package manager.
4. If you have access to the Internet through the proxy server only, please use pip with proxy call as demonstrated by command below:
python -m pip install --proxy http://<usr_name>:<password>@<proxyserver_name>:<port#> <pkg_name>