External model specification

An external model can be provided to CHAP in two ways:

  • By specifying a path to a local code base

  • or by specifying a github URL to a git repo. The url needs to start with https://github.com/

In either case, the directory or repo should be a valid MLproject directory, with an MLproject file. Se the specification in the MLflow documentation for details. In addition, we require the following:

  • An entry point in the MLproject file called train with parameters train_data and model

  • An entry point in the MLproject file called predict with parameters historic_data, future_data, model and out_file

These should contain commands that can be run to train a model and predict the future using that model. The model parameter should be used to save a model in the train step that can be read and used in the predict step. CHAP will provide all the data (the other parameters) when running a model.

Here is an example of a valid directory with an MLproject file.

The following shows how you can run models that follow the specification above. If you have your own model that you want to make compatible with CHAP, follow this guide.

The MLproject file can specify a docker image or Python virtual environment that will be used when running the commands.

Defining an environment for the model

If needed, CHAP currently supports specifying a docker image or a python environment file that will be used when running your model.

For models that are to be run in a production environment, this is necessary for handling dependencies.

We implement the MLProject standard, as described in the MLflow documentation (except for conda support).

Specifying a Python environment requires that you have pyenv installed and available.