Your first step is to install ZenML, which comes bundled as a good old
We highly encourage you to install ZenML in a virtual environment. We install dependencies like
Tensorflow that might cause your base installations to be overridden.
We like to use virtualenvwrapper to manage our Python virtual environments.
When you're set with your environment, run:
pip install zenml
Alternatively, if you’re feeling brave, feel free to install the bleeding edge: NOTE: Do so at your own risk, no guarantees given!
pip install git+https://github.com/maiot-io/[email protected] --upgrade
The ZenML base package does not come up with all integrations pre-installed. Read more here. In order to install an integration, use the pattern:
pip install zenml[INTEGRATION]
pip install zenml[pytorch]
Use the keyword
all in the square brackets if you would like to install all integrations.
Once the installation is completed, you can check whether the installation was successful through:
If you would like to learn more about the current release, please visit the PyPi homepage.
For Bash, add this to
eval "$(_ZENML_COMPLETE=source_bash zenml)"
For Zsh, add this to
eval "$(_ZENML_COMPLETE=source_zsh zenml)"
For Fish, add this to
eval (env _ZENML_COMPLETE=source_fish zenml)
In order to get the Tensorflow Model Analysis evaluation visualizations to work, you must also run:
jupyter nbextension install --py --symlink tensorflow_model_analysisjupyter nbextension enable --py tensorflow_model_analysis