Mlflow-WatsonML

A plugin that integrates WatsonML with MLflow pipeline. mlflow_watsonml enables mlflow users to deploy mlflow pipeline models into WatsonML. Command line APIs of the plugin (also accessible through mlflow's python package) makes the deployment process seamless.

Installation

Plugin package which is available in pypi and can be installed using

pip install mlflow-watsonml

What does it do

Installing this package uses python's entrypoint mechanism to register the plugin into MLflow's plugin registry. This registry will be invoked each time you launch MLflow script or command line argument.

Authentication

In order to connect to WatsonML, refer to .env.template

Create deployment

The create command line argument and create_deployment python APIs does the deployment of a model built with MLflow to WatsonML.

CLI

mlflow deployments create -t watsonml -m <model-uri> --name <deployment-name> -C "software_spec_type=runtime-22.2-py3.10"

Python API

from mlflow.deployments import get_deploy_client

target_uri = 'watsonml'
plugin = get_deploy_client(target_uri)

plugin.create_deployment(
    name=<deployment-name>, 
    model_uri=<model-uri>, 
    config={"software_spec_type": "runtime-22.2-py3.10"}
)

Update deployment

Update API can used to modify the configuration parameters such as number of workers, version etc., of an already deployed model. WatsonML will make sure the user experience is seamless while changing the model in a live environment.

CLI

mlflow deployments update -t watsonml --name <deployment name> -C "software_spec_type=runtime-22.1-py3.10"

Python API

plugin.update_deployment(name=<deployment name>, config={"software_spec_type": "runtime-22.1-py3.10"})

Delete deployment

Delete an existing deployment. Exception will be raised if the model is not already deployed.

CLI

mlflow deployments delete -t watsonml --name <deployment name / version number>

Python API

plugin.delete_deployment(name=<deployment name>)

List all deployments

Lists the names of all the models deployed on the configured WatsonML.

CLI

mlflow deployments list -t watsonml

Python API

plugin.list_deployments()

Get deployment details

Get API fetches the details of the deployed model. By default, Get API fetches all the versions of the deployed model.

CLI

mlflow deployments get -t watsonml --name <deployment name>

Python API

plugin.get_deployment(name=<deployment name>)

Run Prediction on deployed model

Predict API enables to run prediction on the deployed model.

For the prediction inputs, DataFrame and JSON formats are supported. The python API supports all of these three formats. When invoked via command line, one needs to pass the json file path that contains the inputs.

CLI

mlflow deployments predict -t watsonml --name <deployment name> --input-path <input file path> --output-path <output file path>

output-path is an optional parameter. Without output path parameter result will be printed in console.

Python API

plugin.predict(name=<deployment name>, df=<prediction input>)

Plugin help

Run the following command to get the plugin help string.

CLI

mlflow deployments help -t watsonml