Generic exception thrown to surface failure information about external-facing operations. The error message associated with this exception ma

Tag object associated with a run.

Client of an MLflow Tracking Server that creates and manages experiments and runs, and of an MLflow Registry Server that creates and manages

Metric object.

Parameter object.

Exception thrown when executing a project fails

Metadata about a file or directory.

An MLflow Model that can support multiple model flavors. Provides APIs for implementing new Model flavors.

.. note:: Experimental: This entity may change or be removed in a future release without warning. MLflow entity for Model Version.

Protobuf type {@code mlflow.SetTag.Response}

Provides a hostname and optional authentication for talking to an MLflow tracking server. :param host: Hostname (e.g., http://localhost:5000)

.. note:: Experimental: This entity may change or be removed in a future release without warning. MLflow entity for Registered Model

Run object.

Run data (metrics and parameters).

Metadata about a run.

Stores artifacts on Amazon S3.

SQLAlchemy compliant backend store for tracking meta data for MLflow entities. MLflow supports the database dialects ``mysql``, ``mssql``, ``

Tag object associated with an experiment.

Stores artifacts on HDFS. This repository is used with URIs of the form ``hdfs:/<path>``. The repository can only be used together with

Stores artifacts on Azure Blob Storage. This repository is used with URIs of the form ``wasbs://<container-name>@<ystorage-account-name>

Stores artifacts as files in a remote directory, via ftp.

Client for a remote tracking server accessed via REST API calls :param get_host_creds: Method to be invoked prior to every REST request to g

Stores artifacts on Google Cloud Storage. Assumes the google credentials are available in the environment, see https://google-cloud.read

Helper class for running an MLflow project as a Databricks Job. :param databricks_profile: Optional Databricks CLI profile to use to fetch ho

Protobuf type {@code mlflow.CreateExperiment}

Experiment object.

Protobuf type {@code mlflow.GetExperimentByName}

Protobuf type {@code mlflow.GetLatestVersions}

Exception thrown on non 200-level responses from the REST API

Scheme-based registry for artifact repository implementations This class allows the registration of a function or class to provide an implementat

An entry point in an MLproject specification.

Stores artifacts as files in a remote directory, via sftp.

Scheme-based registry for tracking store implementations This class allows the registration of a function or class to provide an implementati

Protobuf type {@code mlflow.CreateModelVersion}

Protobuf type {@code mlflow.CreateRegisteredModel}

Protobuf type {@code mlflow.DeleteModelVersion}

Protobuf type {@code mlflow.DeleteRegisteredModel}

Protobuf type {@code mlflow.GetModelVersion}

Protobuf type {@code mlflow.GetModelVersionDownloadUri}

Protobuf type {@code mlflow.GetRegisteredModel}

Protobuf type {@code mlflow.ListRegisteredModels}

A custom Python model class defined in the test module scope.

Protobuf type {@code mlflow.RenameRegisteredModel}

Registry for run context provider implementations This class allows the registration of a run context provider which can be used to infer meta

Protobuf type {@code mlflow.SearchModelVersions}

Protobuf type {@code mlflow.SearchRuns}

Protobuf type {@code mlflow.TransitionModelVersionStage}

Protobuf type {@code mlflow.UpdateModelVersion}

Protobuf type {@code mlflow.UpdateRegisteredModel}

Protobuf type {@code mlflow.CreateRun}

Protobuf type {@code mlflow.DeleteExperiment}

Protobuf type {@code mlflow.DeleteRun}

Protobuf type {@code mlflow.DeleteTag}

Protobuf type {@code mlflow.ListExperiments}

Protobuf type {@code mlflow.LogBatch}

Protobuf type {@code mlflow.LogMetric}

Protobuf type {@code mlflow.LogModel}

Protobuf type {@code mlflow.LogParam}

Exception thrown when expected configuration file/directory not found

Handles artifacts associated with a model version in the model registry via URIs of the form: - `models:/<model_name>/<model_version>`

A project specification loaded from an MLproject file in the passed-in directory.

Subscriber, intended to be instantiated once per Python process, that logs Spark table information propagated from Java to the current MLflow

Protobuf type {@code mlflow.RestoreExperiment}

Protobuf type {@code mlflow.RestoreRun}

Protobuf type {@code mlflow.SetExperimentTag}

Protobuf type {@code mlflow.SetTag}

DB model for :py:class:`mlflow.entities.RunTag`. These are recorded in ``tags`` table.

Callback for auto-logging metrics and parameters. Records available logs after each epoch. Records model structural informati

Databricks-specific RestStore implementation that provides different fallback behavior when hitting the GetExperimentByName REST API fails -

Stores artifacts on DBFS using the DBFS REST API. This repository is used with URIs of the form ``dbfs:/<path>``. The repository can only be

Protobuf type {@code mlflow.GetExperiment}

Protobuf type {@code mlflow.GetMetricHistory}

Protobuf type {@code mlflow.GetRun}

Stores artifacts as files in a local directory.

Instance of ``SubmittedRun`` corresponding to a subprocess launched to run an entry point command locally.

Client of an MLflow Model Registry Server that creates and manages registered models and model versions.

Special json encoder for numpy types. Note that some numpy types doesn't have native python equivalence, hence json.dumps will raise TypeErro

Handles artifacts associated with a Run via URIs of the form `runs:/<run_id>/run-relative/path/to/artifact`. It is a light wrapper that