Deploying with custom Python environments
When you deploy, Modelbit will detect the
pip packages needed to replicate your Notebook's environment.
To further customize the Python environment, use the
To deploy to an environment with a specific version of
To deploy to an environment with a specific versions of multiple
mb.deploy(my_deploy_function, python_packages=["scikit-learn==1.0.2", "xgboost==1.5.2"])
To deploy to an environment with a specific version of Python:
To deploy to an environment with specific system libraries (installed using
With customized Python environment it's possible to deploy many kinds of models. Here are some examples:
- Example sklearn deployment
- Example XGBoost deployment
- Example Prince PCA deployment
- Example BTYD deployment
- Example Prophet deployment
- Example fasttext deployment
Including additional files
Modelbit has different workflows depending on how you want to add additional Python files with your deployments:
- Extra Files for one deployment: If you have one or two
.pyfiles that only one deployment depends on.
- Common files for all deployments: If you have Python files that should be available in all deployments.
- Private packages & wheels: If you have a
pip installto make your libraries available for import.