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Showing content from https://mlflow.org/docs/latest/python_api/../rest-api.html below:

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.

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.

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.

Field Name

Type

Description

experiment_id

STRING

Unique identifier for the experiment.

Endpoint

HTTP Method

2.0/mlflow/experiments/search

POST

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.

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.

Endpoint

HTTP Method

2.0/mlflow/experiments/get

GET

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

Field Name

Type

Description

experiment_id

STRING

ID of the associated experiment. This field is required.

Field Name

Type

Description

experiment

Experiment

Experiment details.

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.

Field Name

Type

Description

experiment_name

STRING

Name of the associated experiment. This field is required.

Field Name

Type

Description

experiment

Experiment

Experiment details.

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.

Field Name

Type

Description

experiment_id

STRING

ID of the associated experiment. This field is required.

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.

Field Name

Type

Description

experiment_id

STRING

ID of the associated experiment. This field is required.

Endpoint

HTTP Method

2.0/mlflow/experiments/update

POST

Update experiment metadata.

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.

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.

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.

Field Name

Type

Description

run

Run

The newly created run.

Endpoint

HTTP Method

2.0/mlflow/runs/delete

POST

Mark a run for deletion.

Field Name

Type

Description

run_id

STRING

ID of the run to delete. This field is required.

Endpoint

HTTP Method

2.0/mlflow/runs/restore

POST

Restore a deleted run.

Field Name

Type

Description

run_id

STRING

ID of the run to restore. This field is required.

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.

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.

Field Name

Type

Description

run

Run

Run metadata (name, start time, etc) and data (metrics, params, and tags).

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.

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

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.

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

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.

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.

Field Name

Type

Description

run_id

STRING

ID of the run to log under

model_json

STRING

MLmodel file in json format.

Endpoint

HTTP Method

2.0/mlflow/runs/log-inputs

POST

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

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.

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.

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.

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.

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.

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.

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.

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 6000 bytes. This field is required.

Endpoint

HTTP Method

2.0/mlflow/metrics/get-history

GET

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

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.

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.

Endpoint

HTTP Method

2.0/mlflow/runs/search

POST

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

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

Field Name

Type

Description

runs

An array of Run

Runs that match the search criteria.

next_page_token

STRING

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.

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

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

Endpoint

HTTP Method

2.0/mlflow/runs/update

POST

Update run metadata.

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.

Field Name

Type

Description

run_info

RunInfo

Updated metadata of the run.

Endpoint

HTTP Method

2.0/mlflow/registered-models/create

POST

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

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.

deployment_job_id

STRING

Deployment job id for this model.

Endpoint

HTTP Method

2.0/mlflow/registered-models/get

GET

Field Name

Type

Description

name

STRING

Registered model unique name identifier. This field is required.

Endpoint

HTTP Method

2.0/mlflow/registered-models/rename

POST

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.

Endpoint

HTTP Method

2.0/mlflow/registered-models/update

PATCH

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.

deployment_job_id

STRING

Deployment job id for this model.

Endpoint

HTTP Method

2.0/mlflow/registered-models/delete

DELETE

Field Name

Type

Description

name

STRING

Registered model unique name identifier. This field is required.

Endpoint

HTTP Method

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

POST

Field Name

Type

Description

name

STRING

Registered model unique name identifier. This field is required.

stages

An array of STRING

List of stages.

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".

Endpoint

HTTP Method

2.0/mlflow/model-versions/create

POST

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.

model_id

STRING

Optional model_id for model version that is used to link the registered model to the source logged model

Field Name

Type

Description

model_version

ModelVersion

Return new version number generated for this model in registry.

Endpoint

HTTP Method

2.0/mlflow/model-versions/get

GET

Field Name

Type

Description

name

STRING

Name of the registered model This field is required.

version

STRING

Model version number This field is required.

Endpoint

HTTP Method

2.0/mlflow/model-versions/update

PATCH

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.

Field Name

Type

Description

model_version

ModelVersion

Return new version number generated for this model in registry.

Endpoint

HTTP Method

2.0/mlflow/model-versions/delete

DELETE

Field Name

Type

Description

name

STRING

Name of the registered model This field is required.

version

STRING

Model version number This field is required.

Endpoint

HTTP Method

2.0/mlflow/model-versions/search

GET

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.

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.

Endpoint

HTTP Method

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

GET

Field Name

Type

Description

name

STRING

Name of the registered model This field is required.

version

STRING

Model version number This field is required.

Field Name

Type

Description

artifact_uri

STRING

URI corresponding to where artifacts for this model version are stored.

Endpoint

HTTP Method

2.0/mlflow/model-versions/transition-stage

POST

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.

Field Name

Type

Description

model_version

ModelVersion

Updated model version

Endpoint

HTTP Method

2.0/mlflow/registered-models/search

GET

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.

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.

Endpoint

HTTP Method

2.0/mlflow/registered-models/set-tag

POST

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.

Endpoint

HTTP Method

2.0/mlflow/model-versions/set-tag

POST

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.

Endpoint

HTTP Method

2.0/mlflow/registered-models/delete-tag

DELETE

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.

Endpoint

HTTP Method

2.0/mlflow/model-versions/delete-tag

DELETE

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.

Endpoint

HTTP Method

2.0/mlflow/registered-models/alias

DELETE

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.

Endpoint

HTTP Method

2.0/mlflow/registered-models/alias

GET

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.

Endpoint

HTTP Method

2.0/mlflow/registered-models/alias

POST

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.

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

The type of the dataset source, e.g. ?databricks-uc-table?, ?DBFS?, ?S3?, … This field is required.

source

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.

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. 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.

Tag for an experiment.

Field Name

Type

Description

key

STRING

The tag key.

value

STRING

The tag value.

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.

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 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.

Represents a LoggedModel or Registered Model Version input to a Run.

Field Name

Type

Description

model_id

STRING

The unique identifier of the model. This field is required.

Metric associated with a model, represented as a key-value pair. Copied from MLflow metric

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.

Represents a LoggedModel output of a Run.

Field Name

Type

Description

model_id

STRING

The unique identifier of the model. This field is required.

step

INT64

Step at which the model was produced. This field is required.

Param for a model version.

Field Name

Type

Description

name

STRING

Name of the param.

value

STRING

Value of the param associated with the name, could be empty

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.

model_id

STRING

Optional model_id for model version that is used to link the registered model to the source logged model

model_params

An array of ModelParam

Optional parameters for the model.

model_metrics

An array of ModelMetric

Optional metrics for the model.

deployment_job_state

ModelVersionDeploymentJobState

Deployment job state for this model version.

Tag for a model version.

Field Name

Type

Description

key

STRING

The tag key.

value

STRING

The tag value.

Param associated with a run.

Field Name

Type

Description

key

STRING

Key identifying this param.

value

STRING

Value associated with this param.

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.

deployment_job_id

STRING

Deployment job id for this model.

deployment_job_state

State

Deployment job state for this model.

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.

Tag for a registered model

Field Name

Type

Description

key

STRING

The tag key.

value

STRING

The tag value.

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.

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”)

Run inputs.

Field Name

Type

Description

dataset_inputs

An array of DatasetInput

Dataset inputs to the Run.

model_inputs

An array of ModelInput

Model inputs to the Run.

Outputs of a Run.

Field Name

Type

Description

model_outputs

An array of ModelOutput

Model outputs of the Run.

Tag for a run.

Field Name

Type

Description

key

STRING

The tag key.

value

STRING

The tag value.

Name

Description

DEPLOYMENT_JOB_RUN_STATE_UNSPECIFIED

NO_VALID_DEPLOYMENT_JOB_FOUND

RUNNING

SUCCEEDED

FAILED

PENDING

APPROVAL

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.

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.

Name

Description

DEPLOYMENT_JOB_CONNECTION_STATE_UNSPECIFIED

NOT_SET_UP

default state

CONNECTED

connected job: job exists, owner has ACLs, and required job parameters are present

NOT_FOUND

job was deleted OR owner had job ACLs removed

REQUIRED_PARAMETERS_CHANGED

required job parameters were changed

View type for ListExperiments query.

Name

Description

ACTIVE_ONLY

Default. Return only active experiments.

DELETED_ONLY

Return only deleted experiments.

ALL

Get all experiments.

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