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.


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.


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.


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).

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.

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.

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/preview/mlflow/registered-models/get

GET

Note

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

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/preview/mlflow/registered-models/rename

POST

Note

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

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/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

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/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

name

STRING

Registered model unique name identifier. 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.

Request Structure

Field Name

Type

Description

max_results

INT64

Maximum number of registered models desired. Max threshold is 1000.

page_token

STRING

Pagination token to go to the next page based on a previous query.

Response Structure

Field Name

Type

Description

registered_models

An array of RegisteredModel

next_page_token

STRING

Pagination token to request next page of models for the same query.


Get Latest ModelVersions

Endpoint

HTTP Method

2.0/preview/mlflow/registered-models/get-latest-versions

GET

Note

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

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/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

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/preview/mlflow/model-versions/get

GET

Note

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

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/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

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/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

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/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 200K.

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/preview/mlflow/model-versions/get-download-uri

GET

Note

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

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

Endpoint

HTTP Method

2.0/preview/mlflow/model-versions/transition-stage

POST

Note

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

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/preview/mlflow/registered-models/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 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

Note

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

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. All storage backends are guaranteed to support key values up to 5000 bytes in size. This field is required.


Set Model Version Tag

Endpoint

HTTP Method

2.0/mlflow/model-versions/set-tag

POST

Note

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

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. All storage backends are guaranteed to support key values up to 5000 bytes in size. This field is required.


Delete Registered Model Tag

Endpoint

HTTP Method

2.0/mlflow/registered-models/delete-tag

DELETE

Note

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

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

Note

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

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.

Data Structures

Experiment

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

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

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

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.

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.

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.

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.

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.