mlflow.deployments

Exposes experimental functionality for deploying MLflow models to custom serving tools.

Note: model deployment to AWS Sagemaker and AzureML can currently be performed via the mlflow.sagemaker and mlflow.azureml modules, respectively.

MLflow does not currently provide built-in support for any other deployment targets, but support for custom targets can be installed via third-party plugins. See a list of known plugins here.

This page largely focuses on the user-facing deployment APIs. For instructions on implementing your own plugin for deployment to a custom serving tool, see plugin docs.

mlflow.deployments.get_deploy_client(target_uri)[source]

Returns a subclass of mlflow.deployments.BaseDeploymentClient exposing standard APIs for deploying models to the specified target. See available deployment APIs by calling help() on the returned object or viewing docs for mlflow.deployments.BaseDeploymentClient. You can also run mlflow deployments help -t <target-uri> via the CLI for more details on target-specific configuration options.

Parameters

target_uri – URI of target to deploy to.

Example
from mlflow.deployments import get_deploy_client
import pandas as pd
client = get_deploy_client('redisai')
# Deploy the model stored at artifact path 'myModel' under run with ID 'someRunId'. The
# model artifacts are fetched from the current tracking server and then used for deployment.
client.create_deployment("spamDetector", "runs:/someRunId/myModel")
# Load a CSV of emails and score it against our deployment
emails_df = pd.read_csv("...")
prediction_df = client.predict_deployment("spamDetector", emails_df)
# List all deployments, get details of our particular deployment
print(client.list_deployments())
print(client.get_deployment("spamDetector"))
# Update our deployment to serve a different model
client.update_deployment("spamDetector", "runs:/anotherRunId/myModel")
# Delete our deployment
client.delete_deployment("spamDetector")
mlflow.deployments.run_local(target, name, model_uri, flavor=None, config=None)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Deploys the specified model locally, for testing. Note that models deployed locally cannot be managed by other deployment APIs (e.g. update_deployment, delete_deployment, etc).

Parameters
  • target – Target to deploy to.

  • name – Name to use for deployment

  • model_uri – URI of model to deploy

  • flavor – (optional) Model flavor to deploy. If unspecified, a default flavor will be chosen.

  • config – (optional) Dict containing updated target-specific configuration for the deployment

Returns

None

class mlflow.deployments.BaseDeploymentClient(target_uri)[source]

Base class exposing Python model deployment APIs.

Plugin implementors should define target-specific deployment logic via a subclass of BaseDeploymentClient within the plugin module, and customize method docstrings with target-specific information.

Note

Subclasses should raise mlflow.exceptions.MlflowException in error cases (e.g. on failure to deploy a model).

abstract create_deployment(name, model_uri, flavor=None, config=None)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Deploy a model to the specified target. By default, this method should block until deployment completes (i.e. until it’s possible to perform inference with the deployment). In the case of conflicts (e.g. if it’s not possible to create the specified deployment without due to conflict with an existing deployment), raises a mlflow.exceptions.MlflowException. See target-specific plugin documentation for additional detail on support for asynchronous deployment and other configuration.

Parameters
  • name – Unique name to use for deployment. If another deployment exists with the same name, raises a mlflow.exceptions.MlflowException

  • model_uri – URI of model to deploy

  • flavor – (optional) Model flavor to deploy. If unspecified, a default flavor will be chosen.

  • config – (optional) Dict containing updated target-specific configuration for the deployment

Returns

Dict corresponding to created deployment, which must contain the ‘name’ key.

abstract delete_deployment(name)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Delete the deployment with name name from the specified target. Deletion should be idempotent (i.e. deletion should not fail if retried on a non-existent deployment).

Parameters

name – Name of deployment to delete

Returns

None

abstract get_deployment(name)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Returns a dictionary describing the specified deployment, throwing a py:class:mlflow.exception.MlflowException if no deployment exists with the provided ID. The dict is guaranteed to contain an ‘name’ key containing the deployment name. The other fields of the returned dictionary and their types may vary across deployment targets.

Parameters

name – ID of deployment to fetch

abstract list_deployments()[source]

Note

Experimental: This method may change or be removed in a future release without warning.

List deployments. This method is expected to return an unpaginated list of all deployments (an alternative would be to return a dict with a ‘deployments’ field containing the actual deployments, with plugins able to specify other fields, e.g. a next_page_token field, in the returned dictionary for pagination, and to accept a pagination_args argument to this method for passing pagination-related args).

Returns

A list of dicts corresponding to deployments. Each dict is guaranteed to contain a ‘name’ key containing the deployment name. The other fields of the returned dictionary and their types may vary across deployment targets.

abstract predict(deployment_name, df)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Compute predictions on the pandas DataFrame df using the specified deployment. Note that the input/output types of this method matches that of mlflow pyfunc predict (we accept a pandas DataFrame as input and return either a pandas DataFrame, pandas Series, or numpy array as output).

Parameters
  • deployment_name – Name of deployment to predict against

  • df – Pandas DataFrame to use for inference

Returns

A pandas DataFrame, pandas Series, or numpy array

abstract update_deployment(name, model_uri=None, flavor=None, config=None)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Update the deployment with the specified name. You can update the URI of the model, the flavor of the deployed model (in which case the model URI must also be specified), and/or any target-specific attributes of the deployment (via config). By default, this method should block until deployment completes (i.e. until it’s possible to perform inference with the updated deployment). See target-specific plugin documentation for additional detail on support for asynchronous deployment and other configuration.

Parameters
  • name – Unique name of deployment to update

  • model_uri – URI of a new model to deploy.

  • flavor – (optional) new model flavor to use for deployment. If provided, model_uri must also be specified. If flavor is unspecified but model_uri is specified, a default flavor will be chosen and the deployment will be updated using that flavor.

  • config – (optional) dict containing updated target-specific configuration for the deployment

Returns

None