Pyfunc model example

This example demonstrates the use of a pyfunc model with custom inference logic. More specifically:

• train a simple classification model
• create a pyfunc model that encapsulates the classification model with an attached module for custom inference logic

Structure of this example

This examples contains a train.py file that trains a scikit-learn model with iris dataset and uses MLflow Tracking APIs to log the model. The nested mlflow run delivers the packaging of pyfunc model and custom_code module is attached to act as a custom inference logic layer in inference time.

├── train.py
├── infer_model_code_path.py
└── custom_code.py

Running this example

1. Train and log the model

$ python train.py

or train and log the model using inferred code paths

$ python infer_model_code_paths.py

2. Serve the pyfunc model

# Replace <pyfunc_run_id> with the run ID obtained in the previous step
$ mlflow models serve -m "runs:/<pyfunc_run_id>/model" -p 5001

3. Send a request

$ curl http://127.0.0.1:5001/invocations -H 'Content-Type: application/json' -d '{
  "dataframe_records": [[1, 1, 1, 1]]
}'

The response should look like this:

[0]