REST API

The MLflow REST API allows you to create, list, and get experiments and runs, and log parameters, metrics, and artifacts. The API is hosted under the /api route on the MLflow tracking server. For example, to list experiments on a tracking server hosted at http://localhost:5000, access http://localhost:5000/api/2.0/preview/mlflow/experiments/list.

MLflow also provides a health check endpoint at the /health route, which responds with a 200 response code and OK in the response body.

Table of Contents

Create Experiment

Endpoint	HTTP Method
`2.0/mlflow/experiments/create`	`POST`

Create an experiment with a name. Returns the ID of the newly created experiment. Validates that another experiment with the same name does not already exist and fails if another experiment with the same name already exists.

Throws RESOURCE_ALREADY_EXISTS if a experiment with the given name exists.

Request Structure

Field Name	Type	Description
name	`STRING`	Experiment name. This field is required.
artifact_location	`STRING`	Location where all artifacts for the experiment are stored. If not provided, the remote server will select an appropriate default.

Response Structure

Field Name	Type	Description
experiment_id	`STRING`	Unique identifier for the experiment.

List Experiments

Endpoint	HTTP Method
`2.0/mlflow/experiments/list`	`GET`

Get a list of all experiments.

Request Structure

Field Name	Type	Description
view_type	ViewType	Qualifier for type of experiments to be returned. If unspecified, return only active experiments.

Response Structure

Field Name	Type	Description
experiments	An array of Experiment	All experiments.

Get Experiment

Endpoint	HTTP Method
`2.0/mlflow/experiments/get`	`GET`

Get metadata for an experiment. This method works on deleted experiments.

Request Structure

Field Name	Type	Description
experiment_id	`STRING`	ID of the associated experiment. This field is required.

Response Structure

Field Name

Type

Description

experiment

Experiment

Experiment details.

runs

An array of RunInfo

A collection of active runs in the experiment. Note: this may not contain all of the experiment’s active runs.

This field is deprecated. Please use the “Search Runs” API to fetch runs within an experiment.

Get Experiment By Name

Endpoint	HTTP Method
`2.0/mlflow/experiments/get-by-name`	`GET`

Get metadata for an experiment.

This endpoint will return deleted experiments, but prefers the active experiment if an active and deleted experiment share the same name. If multiple deleted experiments share the same name, the API will return one of them.

Throws RESOURCE_DOES_NOT_EXIST if no experiment with the specified name exists.

Request Structure

Field Name	Type	Description
experiment_name	`STRING`	Name of the associated experiment. This field is required.

Response Structure

Field Name	Type	Description
experiment	Experiment	Experiment details.

Delete Experiment

Endpoint	HTTP Method
`2.0/mlflow/experiments/delete`	`POST`

Mark an experiment and associated metadata, runs, metrics, params, and tags for deletion. If the experiment uses FileStore, artifacts associated with experiment are also deleted.

Request Structure

Field Name	Type	Description
experiment_id	`STRING`	ID of the associated experiment. This field is required.

Restore Experiment

Endpoint	HTTP Method
`2.0/mlflow/experiments/restore`	`POST`

Restore an experiment marked for deletion. This also restores associated metadata, runs, metrics, params, and tags. If experiment uses FileStore, underlying artifacts associated with experiment are also restored.

Throws RESOURCE_DOES_NOT_EXIST if experiment was never created or was permanently deleted.

Request Structure

Field Name	Type	Description
experiment_id	`STRING`	ID of the associated experiment. This field is required.

Update Experiment

Endpoint	HTTP Method
`2.0/mlflow/experiments/update`	`POST`

Update experiment metadata.

Request Structure

Field Name	Type	Description
experiment_id	`STRING`	ID of the associated experiment. This field is required.
new_name	`STRING`	If provided, the experiment’s name is changed to the new name. The new name must be unique.

Create Run

Endpoint	HTTP Method
`2.0/mlflow/runs/create`	`POST`

Create a new run within an experiment. A run is usually a single execution of a machine learning or data ETL pipeline. MLflow uses runs to track Param, Metric, and RunTag associated with a single execution.

Request Structure

Field Name	Type	Description
experiment_id	`STRING`	ID of the associated experiment.
user_id	`STRING`	ID of the user executing the run. This field is deprecated as of MLflow 1.0, and will be removed in a future MLflow release. Use ‘mlflow.user’ tag instead.
start_time	`INT64`	Unix timestamp in milliseconds of when the run started.
tags	An array of RunTag	Additional metadata for run.

Response Structure

Field Name	Type	Description
run	Run	The newly created run.

Delete Run

Endpoint	HTTP Method
`2.0/mlflow/runs/delete`	`POST`

Mark a run for deletion.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run to delete. This field is required.

Restore Run

Endpoint	HTTP Method
`2.0/mlflow/runs/restore`	`POST`

Restore a deleted run.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run to restore. This field is required.

Get Run

Endpoint	HTTP Method
`2.0/mlflow/runs/get`	`GET`

Get metadata, metrics, params, and tags for a run. In the case where multiple metrics with the same key are logged for a run, return only the value with the latest timestamp. If there are multiple values with the latest timestamp, return the maximum of these values.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run to fetch. Must be provided.
run_uuid	`STRING`	[Deprecated, use run_id instead] ID of the run to fetch. This field will be removed in a future MLflow version.

Response Structure

Field Name	Type	Description
run	Run	Run metadata (name, start time, etc) and data (metrics, params, and tags).

Log Metric

Endpoint	HTTP Method
`2.0/mlflow/runs/log-metric`	`POST`

Log a metric for a run. A metric is a key-value pair (string key, float value) with an associated timestamp. Examples include the various metrics that represent ML model accuracy. A metric can be logged multiple times.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run under which to log the metric. Must be provided.
run_uuid	`STRING`	[Deprecated, use run_id instead] ID of the run under which to log the metric. This field will be removed in a future MLflow version.
key	`STRING`	Name of the metric. This field is required.
value	`DOUBLE`	Double value of the metric being logged. This field is required.
timestamp	`INT64`	Unix timestamp in milliseconds at the time metric was logged. This field is required.
step	`INT64`	Step at which to log the metric

Log Batch

Endpoint	HTTP Method
`2.0/mlflow/runs/log-batch`	`POST`

Log a batch of metrics, params, and tags for a run. If any data failed to be persisted, the server will respond with an error (non-200 status code). In case of error (due to internal server error or an invalid request), partial data may be written.

You can write metrics, params, and tags in interleaving fashion, but within a given entity type are guaranteed to follow the order specified in the request body. That is, for an API request like

{
   "run_id": "2a14ed5c6a87499199e0106c3501eab8",
   "metrics": [
     {"key": "mae", "value": 2.5, "timestamp": 1552550804},
     {"key": "rmse", "value": 2.7, "timestamp": 1552550804},
   ],
   "params": [
     {"key": "model_class", "value": "LogisticRegression"},
   ]
}

the server is guaranteed to write metric “rmse” after “mae”, though it may write param “model_class” before both metrics, after “mae”, or after both metrics.

The overwrite behavior for metrics, params, and tags is as follows:

Metrics: metric values are never overwritten. Logging a metric (key, value, timestamp) appends to the set of values for the metric with the provided key.
Tags: tag values can be overwritten by successive writes to the same tag key. That is, if multiple tag values with the same key are provided in the same API request, the last-provided tag value is written. Logging the same tag (key, value) is permitted - that is, logging a tag is idempotent.
Params: once written, param values cannot be changed (attempting to overwrite a param value will result in an error). However, logging the same param (key, value) is permitted - that is, logging a param is idempotent.

Request Limits

A single JSON-serialized API request may be up to 1 MB in size and contain:

No more than 1000 metrics, params, and tags in total
Up to 1000 metrics
Up to 100 params
Up to 100 tags

For example, a valid request might contain 900 metrics, 50 params, and 50 tags, but logging 900 metrics, 50 params, and 51 tags is invalid. The following limits also apply to metric, param, and tag keys and values:

Metric, param, and tag keys can be up to 250 characters in length
Param and tag values can be up to 250 characters in length

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run to log under
metrics	An array of Metric	Metrics to log. A single request can contain up to 1000 metrics, and up to 1000 metrics, params, and tags in total.
params	An array of Param	Params to log. A single request can contain up to 100 params, and up to 1000 metrics, params, and tags in total.
tags	An array of RunTag	Tags to log. A single request can contain up to 100 tags, and up to 1000 metrics, params, and tags in total.

Set Experiment Tag

Endpoint	HTTP Method
`2.0/mlflow/experiments/set-experiment-tag`	`POST`

Set a tag on an experiment. Experiment tags are metadata that can be updated.

Request Structure

Field Name	Type	Description
experiment_id	`STRING`	ID of the experiment under which to log the tag. Must be provided. This field is required.
key	`STRING`	Name of the tag. Maximum size depends on storage backend. All storage backends are guaranteed to support key values up to 250 bytes in size. This field is required.
value	`STRING`	String value of the tag being logged. Maximum size depends on storage backend. All storage backends are guaranteed to support key values up to 5000 bytes in size. This field is required.

Set Tag

Endpoint	HTTP Method
`2.0/mlflow/runs/set-tag`	`POST`

Set a tag on a run. Tags are run metadata that can be updated during a run and after a run completes.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run under which to log the tag. Must be provided.
run_uuid	`STRING`	[Deprecated, use run_id instead] ID of the run under which to log the tag. This field will be removed in a future MLflow version.
key	`STRING`	Name of the tag. Maximum size depends on storage backend. All storage backends are guaranteed to support key values up to 250 bytes in size. This field is required.
value	`STRING`	String value of the tag being logged. Maximum size depends on storage backend. All storage backends are guaranteed to support key values up to 5000 bytes in size. This field is required.

Delete Tag

Endpoint	HTTP Method
`2.0/mlflow/runs/delete-tag`	`POST`

Delete a tag on a run. Tags are run metadata that can be updated during a run and after a run completes.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run that the tag was logged under. Must be provided. This field is required.
key	`STRING`	Name of the tag. Maximum size is 255 bytes. Must be provided. This field is required.

Log Param

Endpoint	HTTP Method
`2.0/mlflow/runs/log-parameter`	`POST`

Log a param used for a run. A param is a key-value pair (string key, string value). Examples include hyperparameters used for ML model training and constant dates and values used in an ETL pipeline. A param can be logged only once for a run.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run under which to log the param. Must be provided.
run_uuid	`STRING`	[Deprecated, use run_id instead] ID of the run under which to log the param. This field will be removed in a future MLflow version.
key	`STRING`	Name of the param. Maximum size is 255 bytes. This field is required.
value	`STRING`	String value of the param being logged. Maximum size is 500 bytes. This field is required.

Get Metric History

Endpoint	HTTP Method
`2.0/mlflow/metrics/get-history`	`GET`

Get a list of all values for the specified metric for a given run.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run from which to fetch metric values. Must be provided.
run_uuid	`STRING`	[Deprecated, use run_id instead] ID of the run from which to fetch metric values. This field will be removed in a future MLflow version.
metric_key	`STRING`	Name of the metric. This field is required.

Response Structure

Field Name	Type	Description
metrics	An array of Metric	All logged values for this metric.

Search Runs

Endpoint	HTTP Method
`2.0/mlflow/runs/search`	`POST`

Search for runs that satisfy expressions. Search expressions can use Metric and Param keys.

Request Structure

Field Name	Type	Description
experiment_ids	An array of `STRING`	List of experiment IDs to search over.
filter	`STRING`	A filter expression over params, metrics, and tags, that allows returning a subset of runs. The syntax is a subset of SQL that supports ANDing together binary operations between a param, metric, or tag and a constant. Example: `metrics.rmse < 1 and params.model_class = 'LogisticRegression'` You can select columns with special characters (hyphen, space, period, etc.) by using double quotes: `metrics."model class" = 'LinearRegression' and tags."user-name" = 'Tomas'` Supported operators are `=`, `!=`, `>`, `>=`, `<`, and `<=`.
run_view_type	ViewType	Whether to display only active, only deleted, or all runs. Defaults to only active runs.
max_results	`INT32`	Maximum number of runs desired. Max threshold is 50000
order_by	An array of `STRING`	List of columns to be ordered by, including attributes, params, metrics, and tags with an optional “DESC” or “ASC” annotation, where “ASC” is the default. Example: [“params.input DESC”, “metrics.alpha ASC”, “metrics.rmse”] Tiebreaks are done by start_time DESC followed by run_id for runs with the same start time (and this is the default ordering criterion if order_by is not provided).
page_token	`STRING`

Response Structure

Field Name	Type	Description
runs	An array of Run	Runs that match the search criteria.
next_page_token	`STRING`

List Artifacts

Endpoint	HTTP Method
`2.0/mlflow/artifacts/list`	`GET`

List artifacts for a run. Takes an optional artifact_path prefix which if specified, the response contains only artifacts with the specified prefix.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run whose artifacts to list. Must be provided.
run_uuid	`STRING`	[Deprecated, use run_id instead] ID of the run whose artifacts to list. This field will be removed in a future MLflow version.
path	`STRING`	Filter artifacts matching this path (a relative path from the root artifact directory).

Response Structure

Field Name	Type	Description
root_uri	`STRING`	Root artifact directory for the run.
files	An array of FileInfo	File location and metadata for artifacts.

Update Run

Endpoint	HTTP Method
`2.0/mlflow/runs/update`	`POST`

Update run metadata.

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run to update. Must be provided.
run_uuid	`STRING`	[Deprecated, use run_id instead] ID of the run to update.. This field will be removed in a future MLflow version.
status	RunStatus	Updated status of the run.
end_time	`INT64`	Unix timestamp in milliseconds of when the run ended.

Response Structure

Field Name	Type	Description
run_info	RunInfo	Updated metadata of the run.

Create RegisteredModel

Endpoint	HTTP Method
`2.0/preview/mlflow/registered-models/create`	`POST`

Note

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

Throws RESOURCE_ALREADY_EXISTS if a registered model with the given name exists.

Request Structure

Field Name	Type	Description
name	`STRING`	Register models under this name This field is required.

Response Structure

Field Name	Type	Description
registered_model	RegisteredModel

Get RegisteredModel Details

Endpoint	HTTP Method
`2.0/preview/mlflow/registered-models/get-details`	`POST`

Note

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

Request Structure

Field Name	Type	Description
registered_model	RegisteredModel	Registered model. This field is required.

Response Structure

Field Name	Type	Description
registered_model_detailed	RegisteredModelDetailed

Update RegisteredModel

Endpoint	HTTP Method
`2.0/preview/mlflow/registered-models/update`	`PATCH`

Note

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

Request Structure

Field Name	Type	Description
registered_model	RegisteredModel	Registered model. This field is required.
name	`STRING`	If provided, updates the name for this `registered_model`.
description	`STRING`	If provided, updates the description for this `registered_model`.

Response Structure

Field Name	Type	Description
registered_model	RegisteredModel

Delete RegisteredModel

Endpoint	HTTP Method
`2.0/preview/mlflow/registered-models/delete`	`DELETE`

Note

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

Request Structure

Field Name	Type	Description
registered_model	RegisteredModel	Registered model. This field is required.

List RegisteredModels

Endpoint	HTTP Method
`2.0/preview/mlflow/registered-models/list`	`GET`

Note

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

Response Structure

Field Name	Type	Description
registered_models_detailed	An array of RegisteredModelDetailed

Get Latest ModelVersions

Endpoint	HTTP Method
`2.0/preview/mlflow/registered-models/get-latest-versions`	`POST`

Note

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

Request Structure

Field Name	Type	Description
registered_model	RegisteredModel	Registered model. This field is required.
stages	An array of `STRING`	List of stages.

Response Structure

Field Name	Type	Description
model_versions_detailed	An array of ModelVersionDetailed	Latest version models for each requests stage. Only return models with current `READY` status. If no `stages` provided, returns the latest version for each stage, including `"None"`.

Create ModelVersion

Endpoint	HTTP Method
`2.0/preview/mlflow/model-versions/create`	`POST`

Note

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

Request Structure

Field Name	Type	Description
name	`STRING`	Register model under this name This field is required.
source	`STRING`	URI indicating the location of the model artifacts. This field is required.
run_id	`STRING`	MLflow run ID for correlation, if `source` was generated by an experiment run in MLflow tracking server

Response Structure

Field Name	Type	Description
model_version	ModelVersion	Return new version number generated for this model in registry.

Get ModelVersion Details

Endpoint	HTTP Method
`2.0/preview/mlflow/model-versions/get-details`	`POST`

Note

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

Request Structure

Field Name	Type	Description
model_version	ModelVersion	Model version. This field is required.

Response Structure

Field Name	Type	Description
model_version_detailed	ModelVersionDetailed

Update ModelVersion

Endpoint	HTTP Method
`2.0/preview/mlflow/model-versions/update`	`PATCH`

Note

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

Request Structure

Field Name	Type	Description
model_version	ModelVersion	Model version. This field is required.
stage	`STRING`	If provided, transition `model_version` to new stage.
description	`STRING`	If provided, updates the description for this `registered_model`.

Delete ModelVersion

Endpoint	HTTP Method
`2.0/preview/mlflow/model-versions/delete`	`DELETE`

Note

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

Request Structure

Field Name	Type	Description
model_version	ModelVersion	Model version. This field is required.

Search ModelVersions

Endpoint	HTTP Method
`2.0/preview/mlflow/model-versions/search`	`GET`

Note

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

Request Structure

Field Name	Type	Description
filter	`STRING`	String filter condition, like “name=’my-model-name’”. Must be a single boolean condition, with string values wrapped in single quotes.
max_results	`INT64`	Maximum number of models desired. Max threshold is 1000.
order_by	An array of `STRING`	List of columns to be ordered by including model name, version, stage with an optional “DESC” or “ASC” annotation, where “ASC” is the default. Tiebreaks are done by latest stage transition timestamp, followed by name ASC, followed by version DESC.
page_token	`STRING`	Pagination token to go to next page based on previous search query.

Response Structure

Field Name	Type	Description
model_versions_detailed	An array of ModelVersionDetailed	Models that match the search criteria
next_page_token	`STRING`	Pagination token to request next page of models for the same search query.

Get Download URI For ModelVersion Artifacts

Endpoint	HTTP Method
`2.0/preview/mlflow/model-versions/get-download-uri`	`POST`

Note

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

Request Structure

Field Name	Type	Description
model_version	ModelVersion	Name and version of model This field is required.

Response Structure

Field Name	Type	Description
artifact_uri	`STRING`	URI corresponding to where artifacts for this model version are stored.

Data Structures

Experiment

Field Name	Type	Description
experiment_id	`STRING`	Unique identifier for the experiment.
name	`STRING`	Human readable name that identifies the experiment.
artifact_location	`STRING`	Location where artifacts for the experiment are stored.
lifecycle_stage	`STRING`	Current life cycle stage of the experiment: “active” or “deleted”. Deleted experiments are not returned by APIs.
last_update_time	`INT64`	Last update time
creation_time	`INT64`	Creation time
tags	An array of ExperimentTag	Tags: Additional metadata key-value pairs.

ExperimentTag

Tag for an experiment.

Field Name	Type	Description
key	`STRING`	The tag key.
value	`STRING`	The tag value.

FileInfo

Metadata of a single artifact file or directory.

Field Name	Type	Description
path	`STRING`	Path relative to the root artifact directory run.
is_dir	`BOOL`	Whether the path is a directory.
file_size	`INT64`	Size in bytes. Unset for directories.

Metric

Metric associated with a run, represented as a key-value pair.

Field Name	Type	Description
key	`STRING`	Key identifying this metric.
value	`DOUBLE`	Value associated with this metric.
timestamp	`INT64`	The timestamp at which this metric was recorded.
step	`INT64`	Step at which to log the metric.

ModelVersion

Note

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

Field Name	Type	Description
registered_model	RegisteredModel	Registered model.
version	`INT64`	Model’s version number.

ModelVersionDetailed

Note

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

Field Name	Type	Description
model_version	ModelVersion	Model Version
creation_timestamp	`INT64`	Timestamp recorded when this `model_version` was created.
last_updated_timestamp	`INT64`	Timestamp recorded when metadata for this `model_version` was last updated.
user_id	`STRING`	User that created this `model_version`.
current_stage	`STRING`	Current stage for this `model_version`.
description	`STRING`	Description of this `model_version`.
source	`STRING`	URI indicating the location of the source model artifacts, used when creating `model_version`
run_id	`STRING`	MLflow run ID used when creating `model_version`, if `source` was generated by an experiment run stored in MLflow tracking server.
status	ModelVersionStatus	Current status of `model_version`
status_message	`STRING`	Details on current `status`, if it is pending or failed.

Param

Param associated with a run.

Field Name	Type	Description
key	`STRING`	Key identifying this param.
value	`STRING`	Value associated with this param.

RegisteredModel

Note

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

Field Name	Type	Description
name	`STRING`	Unique name for the model.

RegisteredModelDetailed

Note

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

Field Name	Type	Description
registered_model	RegisteredModel	Registered model.
creation_timestamp	`INT64`	Timestamp recorded when this `registered_model` was created.
last_updated_timestamp	`INT64`	Timestamp recorded when metadata for this `registered_model` was last updated.
user_id	`STRING`	User that created this `registered_model`
description	`STRING`	Description of this `registered_model`.
latest_versions	An array of ModelVersionDetailed	Collection of latest model versions for each stage. Only contains models with current `READY` status.

Run

A single run.

Field Name	Type	Description
info	RunInfo	Run metadata.
data	RunData	Run data.

RunData

Run data (metrics, params, and tags).

Field Name	Type	Description
metrics	An array of Metric	Run metrics.
params	An array of Param	Run parameters.
tags	An array of RunTag	Additional metadata key-value pairs.

RunInfo

Metadata of a single run.

Field Name	Type	Description
run_id	`STRING`	Unique identifier for the run.
run_uuid	`STRING`	[Deprecated, use run_id instead] Unique identifier for the run. This field will be removed in a future MLflow version.
experiment_id	`STRING`	The experiment ID.
user_id	`STRING`	User who initiated the run. This field is deprecated as of MLflow 1.0, and will be removed in a future MLflow release. Use ‘mlflow.user’ tag instead.
status	RunStatus	Current status of the run.
start_time	`INT64`	Unix timestamp of when the run started in milliseconds.
end_time	`INT64`	Unix timestamp of when the run ended in milliseconds.
artifact_uri	`STRING`	URI of the directory where artifacts should be uploaded. This can be a local path (starting with “/”), or a distributed file system (DFS) path, like `s3://bucket/directory` or `dbfs:/my/directory`. If not set, the local `./mlruns` directory is chosen.
lifecycle_stage	`STRING`	Current life cycle stage of the experiment : OneOf(“active”, “deleted”)

RunTag

Tag for a run.

Field Name	Type	Description
key	`STRING`	The tag key.
value	`STRING`	The tag value.

ModelVersionStatus

Note

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

Name	Description
PENDING_REGISTRATION	Request to register a new model version is pending as server performs background tasks.
FAILED_REGISTRATION	Request to register a new model version has failed.
READY	Model version is ready for use.
PENDING_DELETION	Request to delete an existing model version is pending as server performs background tasks.
FAILED_DELETION	Request to delete an existing model version has failed.

RunStatus

Status of a run.

Name	Description
RUNNING	Run has been initiated.
SCHEDULED	Run is scheduled to run at a later time.
FINISHED	Run has completed.
FAILED	Run execution failed.
KILLED	Run killed by user.

ViewType

View type for ListExperiments query.

Name	Description
ACTIVE_ONLY	Default. Return only active experiments.
DELETED_ONLY	Return only deleted experiments.
ALL	Get all experiments.