mlflow.deployments

Exposes 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.

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, endpoint=None)[source]

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
endpoint – (optional) Endpoint to create the deployment under. May not be supported by all targets

Returns

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

create_endpoint(name, config=None)[source]

Create an endpoint with the specified target. By default, this method should block until creation completes (i.e. until it’s possible to create a deployment within the endpoint). In the case of conflicts (e.g. if it’s not possible to create the specified endpoint due to conflict with an existing endpoint), raises a mlflow.exceptions.MlflowException. See target-specific plugin documentation for additional detail on support for asynchronous creation and other configuration.

Parameters

name – Unique name to use for endpoint. If another endpoint exists with the same name, raises a mlflow.exceptions.MlflowException.
config – (optional) Dict containing target-specific configuration for the endpoint.

Returns

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

abstract delete_deployment(name, config=None, endpoint=None)[source]

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
config – (optional) dict containing updated target-specific configuration for the deployment
endpoint – (optional) Endpoint containing the deployment to delete. May not be supported by all targets

Returns

None

delete_endpoint(endpoint)[source]

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

Parameters: endpoint – Name of endpoint to delete
Returns: None

explain(deployment_name=None, df=None, endpoint=None)[source]

Note

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

Generate explanations of model predictions on the specified input pandas Dataframe df for the deployed model. Explanation output formats vary by deployment target, and can include details like feature importance for understanding/debugging predictions.

Parameters

deployment_name – Name of deployment to predict against
df – Pandas DataFrame to use for explaining feature importance in model prediction
endpoint – Endpoint to predict against. May not be supported by all targets

Returns

A JSON-able object (pandas dataframe, numpy array, dictionary), or an exception if the implementation is not available in deployment target’s class

abstract get_deployment(name, endpoint=None)[source]

Returns a dictionary describing the specified deployment, throwing a mlflow.exceptions.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
endpoint – (optional) Endpoint containing the deployment to get. May not be supported by all targets

Returns

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

get_endpoint(endpoint)[source]

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

Parameters: endpoint – Name of endpoint to fetch
Returns: A dict corresponding to the retrieved endpoint. The dict is guaranteed to contain a ‘name’ key corresponding to the endpoint name. The other fields of the returned dictionary and their types may vary across targets.

abstract list_deployments(endpoint=None)[source]

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).

Parameters: endpoint – (optional) List deployments in the specified endpoint. May not be supported by all targets
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.

list_endpoints()[source]

List endpoints in the specified target. This method is expected to return an unpaginated list of all endpoints (an alternative would be to return a dict with an ‘endpoints’ field containing the actual endpoints, 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 endpoints. Each dict is guaranteed to contain a ‘name’ key containing the endpoint name. The other fields of the returned dictionary and their types may vary across targets.

abstract predict(deployment_name=None, df=None, endpoint=None)[source]

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
endpoint – Endpoint to predict against. May not be supported by all targets

Returns

A pandas DataFrame, pandas Series, or numpy array

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

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
endpoint – (optional) Endpoint containing the deployment to update. May not be supported by all targets

Returns

None

update_endpoint(endpoint, config=None)[source]

Update the endpoint with the specified name. You can update any target-specific attributes of the endpoint (via config). By default, this method should block until the update completes (i.e. until it’s possible to create a deployment within the endpoint). See target-specific plugin documentation for additional detail on support for asynchronous update and other configuration.

Parameters

endpoint – Unique name of endpoint to update
config – (optional) dict containing target-specific configuration for the endpoint

Returns

None

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]

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