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.

Table of Contents

Create Experiment
List Experiments
Get Experiment
Delete Experiment
Restore Experiment
Update Experiment
Create Run
Mlflow Runs Delete
Mlflow Runs Restore
Get Run
Log Metric
Log Batch
Set Tag
Log Param
Get Metric History
Search Runs
List Artifacts
Update Run
Data Structures

Create Experiment

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

Get metadata for an experiment and a list of runs for the experiment. This RPC will work 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	Returns experiment details.
runs	An array of RunInfo	All (max limit to be imposed) active runs associated with this experiment.

Delete Experiment

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

Mark an experiment and associated runs, params, metrics, … etc 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/preview/mlflow/experiments/restore`	`POST`

Restore an experiment marked for deletion. This also restores associated metadata, runs, metrics, and params. 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`	Identifier to get an experiment This field is required.

Update Experiment

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

Updates an 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 will be changed to this. The new name must be unique.

Create Run

Endpoint	HTTP Method
`2.0/preview/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.
run_name	`STRING`	Human readable name for the run. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.runName` run tag instead.
source_type	SourceType	Originating source for the run. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.source.type` run tag instead.
source_name	`STRING`	String descriptor for the run’s source. For example, name or description of a notebook, or the URL or path to a project. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.source.name` run tag instead.
entry_point_name	`STRING`	Name of the project entry point associated with the current run, if any. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.project.entryPoint` run tag instead.
start_time	`INT64`	Unix timestamp of when the run started in milliseconds.
source_version	`STRING`	Git commit hash of the source code used to create run. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.source.git.commit` run tag instead.
tags	An array of RunTag	Additional metadata for run.
parent_run_id	`STRING`	ID of the parent run which started this run. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.parentRunId` run tag instead.

Response Structure

Field Name	Type	Description
run	Run	The newly created run.

Mlflow Runs Delete

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

This operation will mark the run for deletion.

Request Structure

Field Name	Type	Description
run_id	`STRING`	This field is required.

Mlflow Runs Restore

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

Restore a deleted run.

Request Structure

Field Name	Type	Description
run_id	`STRING`	This field is required.

Get Run

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

Get metadata, params, tags, and metrics for a run. Only the last logged value for each metric is returned.

Request Structure

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

Response Structure

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

Log Metric

Endpoint	HTTP Method
`2.0/preview/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_uuid	`STRING`	ID of the run under which to log the metric. This field is required.
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.

Log Batch

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

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

Metrics, params, and tags may be written 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 may 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 may not 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 would be invalid. The following limits also apply to metric, param, and tag keys & values:

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

Request Structure

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

Set Tag

Endpoint	HTTP Method
`2.0/preview/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_uuid	`STRING`	ID of the run under which to set the tag. This field is required.
key	`STRING`	Name of the tag. Maximum size is 255 bytes. This field is required.
value	`STRING`	String value of the tag being logged. Maximum size is 5000 bytes. This field is required.

Log Param

Endpoint	HTTP Method
`2.0/preview/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_uuid	`STRING`	ID of the run under which to log the param. This field is required.
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/preview/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_uuid	`STRING`	ID of the run from which to fetch metric values. This field is required.
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/preview/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.
anded_expressions	An array of SearchExpression	This field is deprecated and will be removed in MLflow 1.0. Expressions describing runs (AND-ed together when filtering runs). See ‘filter’ for a simpler on-the-wire representation of these expressions.
filter	`STRING`	A filter expression over params, metrics, and tags, allowing returning a subset of runs. The syntax is a subset of SQL which allows only ANDing together binary operations between a param/metric/tag and a constant. Example: `metrics.rmse < 1 and params.model_class = 'LogisticRegression'` You can also select columns with spaces by using backticks or double quotes: metrics.`model class` = 'LinearRegression' and tags."user name" = 'Tomas' Supported operators are =, !=, >, >=, <, <=, and LIKE. LIKE syntax: `params.model_class LIKE 'Linear%'` ‘filter’ may not be provided when anded_expressions is present; an INVALID_PARAMETER_VALUE error will be returned if both are specified. If both ‘filter’ and ‘anded_expressions’ are absent, all runs part of the given experiments will be returned.
run_view_type	ViewType	Whether to display only active, only deleted, or all runs. Defaults to only active runs.

Response Structure

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

List Artifacts

Endpoint	HTTP Method
`2.0/preview/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_uuid	`STRING`	ID of the run whose artifacts to list.
path	`STRING`	Filter artifacts matching this path (a relative path from the root artifact directory).

Response Structure

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

Update Run

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

Update run metadata.

Request Structure

Field Name	Type	Description
run_uuid	`STRING`	ID of the run to update. This field is required.
status	RunStatus	Updated status of the run.
end_time	`INT64`	Unix timestamp of when the run ended in milliseconds.

Response Structure

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

Data Structures

DoubleClause

Field Name	Type	Description
comparator	`STRING`	OneOf (“>”, “>=”, “==”, “!=”, “<=”, “<”)
value	`DOUBLE`	Float value for comparison.

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

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.

FloatClause

Field Name	Type	Description
comparator	`STRING`	OneOf (“>”, “>=”, “==”, “!=”, “<=”, “<”)
value	`FLOAT`	Float value for comparison.

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.

MetricSearchExpression

Field Name Type Description

float OR double

FloatClause OR DoubleClause

If float, [Deprecated in 0.7.0, to be removed in future version] Float clause for comparison. Use ‘double’ instead.

If double, double clause of comparison

key STRING Metric key for search.

Param

Param associated with a run.

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

ParameterSearchExpression

Field Name	Type	Description
`string`	StringClause	If `string`, string clause for comparison.
key	`STRING`	Param key for search.

Run

A single run.

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

RunData

Run data (metrics, params, etc).

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_uuid	`STRING`	Unique identifier for the run.
experiment_id	`STRING`	The experiment ID.
name	`STRING`	Human readable name that identifies this run. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.runName` run tag instead.
source_type	SourceType	Source type. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.source.type` run tag instead.
source_name	`STRING`	Source identifier: GitHub URL, name of notebook, name of job, etc. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.source.name` run tag instead.
user_id	`STRING`	User who initiated the run.
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.
source_version	`STRING`	Git commit hash of the code used for the run. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.source.git.commit` run tag instead.
entry_point_name	`STRING`	Name of the entry point for the run. This field is deprecated and will be removed in MLflow 1.0. Use the `mlflow.project.entryPoint` run tag instead.
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.

Tag keys that start with mlflow. are reserved for internal use. The following tags are set automatically by MLflow:

Key	Description
`mlflow.runName`	Human readable name that identifies this run.
`mlflow.parentRunId`	The ID of the parent run, if this is a nested run.
`mlflow.source.type`	The source type, one of SourceType.
`mlflow.source.name`	Source identifier: GitHub URL, name of notebook, name of job, etc.
`mlflow.source.git.commit`	Commit hash of the executed code, if in a git repository.
`mlflow.source.git.branch`	Name of the branch of the executed code, if in a git repository.
`mlflow.source.git.repoURL`	URL that the executed code was clone from.
`mlflow.project.env`	One of “docker” or “conda”, indicating the runtime context used by the mlflow project.
`mlflow.project.entryPoint`	Name of the project entry point associated with the current run, if any.
`mlflow.docker.image.name`	Name of the Docker image used to execute this run.
`mlflow.docker.image.id`	ID of the Docker image used to execute this run.

SearchExpression

Field Name Type Description

metric OR parameter

MetricSearchExpression OR ParameterSearchExpression

If metric, a metric search expression.

If parameter, a parameter search expression.

StringClause

Field Name	Type	Description
comparator	`STRING`	OneOf (“==”, “!=”, “~”)
value	`STRING`	String value for comparison.

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.

SourceType

Source that generated a run.

Name	Description
NOTEBOOK	Databricks notebook environment.
JOB	Scheduled or Run Now job.
PROJECT	As a prepackaged project: either a Docker image or GitHub source, etc.
LOCAL	Local run: Using CLI, IDE, or local notebook.
UNKNOWN	Unknown source type.

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.