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 search for experiments on a tracking server hosted at http://localhost:5000, make a POST request to http://localhost:5000/api/2.0/mlflow/experiments/search.

Important

The MLflow REST API requires content type application/json for all POST requests.

Table of Contents

Create Experiment
Search Experiments
Get Experiment
Get Experiment By Name
Delete Experiment
Restore Experiment
Update Experiment
Create Run
Delete Run
Restore Run
Get Run
Log Metric
Log Batch
Log Model
Log Inputs
Set Experiment Tag
Set Tag
Delete Tag
Log Param
Get Metric History
Search Runs
List Artifacts
Update Run
Create RegisteredModel
Get RegisteredModel
Rename RegisteredModel
Update RegisteredModel
Delete RegisteredModel
Get Latest ModelVersions
Create ModelVersion
Get ModelVersion
Update ModelVersion
Delete ModelVersion
Search ModelVersions
Get Download URI For ModelVersion Artifacts
Transition ModelVersion Stage
Search RegisteredModels
Set Registered Model Tag
Set Model Version Tag
Delete Registered Model Tag
Delete Model Version Tag
Delete Registered Model Alias
Get Model Version by Alias
Set Registered Model Alias
Data Structures

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.
tags	An array of ExperimentTag	A collection of tags to set on the experiment. Maximum tag size and number of tags per request depends on the storage backend. All storage backends are guaranteed to support tag keys up to 250 bytes in size and tag values up to 5000 bytes in size. All storage backends are also guaranteed to support up to 20 tags per request.

Response Structure

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

Search Experiments

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

Request Structure

Field Name	Type	Description
max_results	`INT64`	Maximum number of experiments desired. Servers may select a desired default max_results value. All servers are guaranteed to support a max_results threshold of at least 1,000 but may support more. Callers of this endpoint are encouraged to pass max_results explicitly and leverage page_token to iterate through experiments.
page_token	`STRING`	Token indicating the page of experiments to fetch
filter	`STRING`	A filter expression over experiment attributes and tags that allows returning a subset of experiments. The syntax is a subset of SQL that supports ANDing together binary operations between an attribute or tag, and a constant. Example: `name LIKE 'test-%' AND tags.key = 'value'` You can select columns with special characters (hyphen, space, period, etc.) by using double quotes or backticks. Example: `tags."extra-key" = 'value'` or tags.`extra-key` = 'value' Supported operators are `=`, `!=`, `LIKE`, and `ILIKE`.
order_by	An array of `STRING`	List of columns for ordering search results, which can include experiment name and id with an optional “DESC” or “ASC” annotation, where “ASC” is the default. Tiebreaks are done by experiment id DESC.
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	Experiments that match the search criteria
next_page_token	`STRING`	Token that can be used to retrieve the next page of experiments. An empty token means that no more experiments are available for retrieval.

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.

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.
run_name	`STRING`	Name of the run.
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.

Log Model

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

Note

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

Request Structure

Field Name	Type	Description
run_id	`STRING`	ID of the run to log under
model_json	`STRING`	MLmodel file in json format.

Log Inputs

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

Note

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

Request Structure

Note

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

Field Name	Type	Description
run_id	`STRING`	ID of the run to log under This field is required.
datasets	An array of DatasetInput	Dataset inputs

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.
page_token	`STRING`	Token indicating the page of metric history to fetch
max_results	`INT32`	Maximum number of logged instances of a metric for a run to return per call. Backend servers may restrict the value of max_results depending on performance requirements. Requests that do not specify this value will behave as non-paginated queries where all metric history values for a given metric within a run are returned in a single response.

Response Structure

Field Name	Type	Description
metrics	An array of Metric	All logged values for this metric.
next_page_token	`STRING`	Token that can be used to issue a query for the next page of metric history values. A missing token indicates that no additional metrics are available to fetch.

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. If unspecified, defaults to 1000. All servers are guaranteed to support a max_results threshold of at least 50,000 but may support more. Callers of this endpoint are encouraged to pass max_results explicitly and leverage page_token to iterate through experiments.
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).
page_token	`STRING`	Token indicating the page of artifact results to fetch

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.
next_page_token	`STRING`	Token that can be used to retrieve the next page of artifact results

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.
run_name	`STRING`	Updated name of the run.

Response Structure

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

Create RegisteredModel

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

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.
tags	An array of RegisteredModelTag	Additional metadata for registered model.
description	`STRING`	Optional description for registered model.

Response Structure

Field Name	Type	Description
registered_model	RegisteredModel

Get RegisteredModel

Endpoint	HTTP Method
`2.0/mlflow/registered-models/get`	`GET`

Request Structure

Field Name	Type	Description
name	`STRING`	Registered model unique name identifier. This field is required.

Response Structure

Field Name	Type	Description
registered_model	RegisteredModel

Rename RegisteredModel

Endpoint	HTTP Method
`2.0/mlflow/registered-models/rename`	`POST`

Request Structure

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

Response Structure

Field Name	Type	Description
registered_model	RegisteredModel

Update RegisteredModel

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

Request Structure

Field Name	Type	Description
name	`STRING`	Registered model unique name identifier. This field is required.
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/mlflow/registered-models/delete`	`DELETE`

Request Structure

Field Name	Type	Description
name	`STRING`	Registered model unique name identifier. This field is required.

Get Latest ModelVersions

Warning

Model Stages are deprecated and will be removed in a future major release. To learn more about this deprecation, see our migration guide.

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

Request Structure

Field Name	Type	Description
name	`STRING`	Registered model unique name identifier. This field is required.
stages	An array of `STRING`	List of stages.

Response Structure

Field Name	Type	Description
model_versions	An array of ModelVersion	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/mlflow/model-versions/create`	`POST`

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
tags	An array of ModelVersionTag	Additional metadata for model version.
run_link	`STRING`	MLflow run link - this is the exact link of the run that generated this model version, potentially hosted at another instance of MLflow.
description	`STRING`	Optional description for model version.

Response Structure

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

Get ModelVersion

Endpoint	HTTP Method
`2.0/mlflow/model-versions/get`	`GET`

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model This field is required.
version	`STRING`	Model version number This field is required.

Response Structure

Field Name	Type	Description
model_version	ModelVersion

Update ModelVersion

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

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model This field is required.
version	`STRING`	Model version number This field is required.
description	`STRING`	If provided, updates the description for this `registered_model`.

Response Structure

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

Delete ModelVersion

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

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model This field is required.
version	`STRING`	Model version number This field is required.

Search ModelVersions

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

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 200K. Backends may choose a lower default value and maximum threshold.
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	An array of ModelVersion	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/mlflow/model-versions/get-download-uri`	`GET`

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model This field is required.
version	`STRING`	Model version number This field is required.

Response Structure

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

Transition ModelVersion Stage

Warning

Model Stages are deprecated and will be removed in a future major release. To learn more about this deprecation, see our migration guide.

Endpoint	HTTP Method
`2.0/mlflow/model-versions/transition-stage`	`POST`

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model This field is required.
version	`STRING`	Model version number This field is required.
stage	`STRING`	Transition model_version to new stage. This field is required.
archive_existing_versions	`BOOL`	When transitioning a model version to a particular stage, this flag dictates whether all existing model versions in that stage should be atomically moved to the “archived” stage. This ensures that at-most-one model version exists in the target stage. This field is required when transitioning a model versions’s stage This field is required.

Response Structure

Field Name	Type	Description
model_version	ModelVersion	Updated model version

Search RegisteredModels

Endpoint	HTTP Method
`2.0/mlflow/registered-models/search`	`GET`

Request Structure

Field Name	Type	Description
filter	`STRING`	String filter condition, like “name LIKE ‘my-model-name’”. Interpreted in the backend automatically as “name LIKE ‘%my-model-name%’”. Single boolean condition, with string values wrapped in single quotes.
max_results	`INT64`	Maximum number of models desired. Default is 100. Max threshold is 1000.
order_by	An array of `STRING`	List of columns for ordering search results, which can include model name and last updated timestamp with an optional “DESC” or “ASC” annotation, where “ASC” is the default. Tiebreaks are done by model name ASC.
page_token	`STRING`	Pagination token to go to the next page based on a previous search query.

Response Structure

Field Name	Type	Description
registered_models	An array of RegisteredModel	Registered Models that match the search criteria.
next_page_token	`STRING`	Pagination token to request the next page of models.

Set Registered Model Tag

Endpoint	HTTP Method
`2.0/mlflow/registered-models/set-tag`	`POST`

Request Structure

Field Name	Type	Description
name	`STRING`	Unique name of the model. This field is required.
key	`STRING`	Name of the tag. Maximum size depends on storage backend. If a tag with this name already exists, its preexisting value will be replaced by the specified value. 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. This field is required.

Set Model Version Tag

Endpoint	HTTP Method
`2.0/mlflow/model-versions/set-tag`	`POST`

Request Structure

Field Name	Type	Description
name	`STRING`	Unique name of the model. This field is required.
version	`STRING`	Model version number. This field is required.
key	`STRING`	Name of the tag. Maximum size depends on storage backend. If a tag with this name already exists, its preexisting value will be replaced by the specified value. 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. This field is required.

Delete Registered Model Tag

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

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model that the tag was logged under. This field is required.
key	`STRING`	Name of the tag. The name must be an exact match; wild-card deletion is not supported. Maximum size is 250 bytes. This field is required.

Delete Model Version Tag

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

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model that the tag was logged under. This field is required.
version	`STRING`	Model version number that the tag was logged under. This field is required.
key	`STRING`	Name of the tag. The name must be an exact match; wild-card deletion is not supported. Maximum size is 250 bytes. This field is required.

Delete Registered Model Alias

Endpoint	HTTP Method
`2.0/mlflow/registered-models/alias`	`DELETE`

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model. This field is required.
alias	`STRING`	Name of the alias. The name must be an exact match; wild-card deletion is not supported. Maximum size is 256 bytes. This field is required.

Get Model Version by Alias

Endpoint	HTTP Method
`2.0/mlflow/registered-models/alias`	`GET`

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model. This field is required.
alias	`STRING`	Name of the alias. Maximum size is 256 bytes. This field is required.

Response Structure

Field Name	Type	Description
model_version	ModelVersion

Set Registered Model Alias

Endpoint	HTTP Method
`2.0/mlflow/registered-models/alias`	`POST`

Request Structure

Field Name	Type	Description
name	`STRING`	Name of the registered model. This field is required.
alias	`STRING`	Name of the alias. Maximum size depends on storage backend. If an alias with this name already exists, its preexisting value will be replaced by the specified version. All storage backends are guaranteed to support alias name values up to 256 bytes in size. This field is required.
version	`STRING`	Model version number. This field is required.

Data Structures

Dataset

Note

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

Dataset. Represents a reference to data used for training, testing, or evaluation during the model development process.

Field Name	Type	Description
name	`STRING`	The name of the dataset. E.g. ?my.uc.table@2? ?nyc-taxi-dataset?, ?fantastic-elk-3? This field is required.
digest	`STRING`	Dataset digest, e.g. an md5 hash of the dataset that uniquely identifies it within datasets of the same name. This field is required.
source_type	`STRING`	Source information for the dataset. Note that the source may not exactly reproduce the dataset if it was transformed / modified before use with MLflow. This field is required.
source	`STRING`	The type of the dataset source, e.g. ?databricks-uc-table?, ?DBFS?, ?S3?, … This field is required.
schema	`STRING`	The schema of the dataset. E.g., MLflow ColSpec JSON for a dataframe, MLflow TensorSpec JSON for an ndarray, or another schema format.
profile	`STRING`	The profile of the dataset. Summary statistics for the dataset, such as the number of rows in a table, the mean / std / mode of each column in a table, or the number of elements in an array.

DatasetInput

Note

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

DatasetInput. Represents a dataset and input tags.

Field Name	Type	Description
tags	An array of InputTag	A list of tags for the dataset input, e.g. a ?context? tag with value ?training?
dataset	Dataset	The dataset being used as a Run input. This field is required.

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

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.

InputTag

Note

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

Tag for an input.

Field Name	Type	Description
key	`STRING`	The tag key. This field is required.
value	`STRING`	The tag value. This field is required.

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

Field Name	Type	Description
name	`STRING`	Unique name of the model
version	`STRING`	Model’s version number.
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.
tags	An array of ModelVersionTag	Tags: Additional metadata key-value pairs for this `model_version`.
run_link	`STRING`	Run Link: Direct link to the run that generated this version. This field is set at model version creation time only for model versions whose source run is from a tracking server that is different from the registry server.
aliases	An array of `STRING`	Aliases pointing to this `model_version`.

ModelVersionTag

Tag for a model version.

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

Param

Param associated with a run.

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

RegisteredModel

Field Name	Type	Description
name	`STRING`	Unique name for the 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` NOTE: this field is not currently returned.
description	`STRING`	Description of this `registered_model`.
latest_versions	An array of ModelVersion	Collection of latest model versions for each stage. Only contains models with current `READY` status.
tags	An array of RegisteredModelTag	Tags: Additional metadata key-value pairs for this `registered_model`.
aliases	An array of RegisteredModelAlias	Aliases pointing to model versions associated with this `registered_model`.

RegisteredModelAlias

Alias for a registered model

Field Name	Type	Description
alias	`STRING`	The name of the alias.
version	`STRING`	The model version number that the alias points to.

RegisteredModelTag

Tag for a registered model

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

Run

A single run.

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

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.
run_name	`STRING`	The name of the run.
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”)

RunInputs

Note

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

Run inputs.

Field Name	Type	Description
dataset_inputs	An array of DatasetInput	Dataset inputs to the Run.

RunTag

Tag for a run.

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

ModelVersionStatus

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.

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.