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 python_version, python_packages and system_packages parameters.

To deploy to an environment with a specific version of sklearn:

mb.deploy(my_deploy_function, python_packages=["scikit-learn==1.0.2",])

To deploy to an environment with a specific versions of multiple pip packages:

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:

mb.deploy(my_deploy_function, python_version="3.9")

To deploy to an environment with specific system libraries (installed using apt-get):

mb.deploy(my_deploy_function, system_packages=["libgomp1"])

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 .py files 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 setup.py or pyproject.toml and use pip install to make your libraries available for import.