R API

The MLflow R API allows you to use MLflow Tracking, Projects and Models.

Prerequisites

To use the MLflow R API, you must install the MLflow Python package.

pip install mlflow

Installing with an Available Conda Environment example:

conda create -n mlflow-env python
conda activate mlflow-env
pip install mlflow

The above provided commands create a new Conda environment named mlflow-env, specifying the default Python version. It then activates this environment, making it the active working environment. Finally, it installs the MLflow package using pip, ensuring that MLflow is isolated within this environment, allowing for independent Python and package management for MLflow-related tasks.

Optionally, you can set the MLFLOW_PYTHON_BIN and MLFLOW_BIN environment variables to specify the Python and MLflow binaries to use. By default, the R client automatically finds them using Sys.which('python') and Sys.which('mlflow').

export MLFLOW_PYTHON_BIN=/path/to/bin/python
export MLFLOW_BIN=/path/to/bin/mlflow

You can use the R API to start the user interface, create experiment and search experiments, save models, run projects and serve models among many other functions available in the R API.

build_context_tags_from_databricks_job_info

Get information from a Databricks job execution context

Parses the data from a job execution context when running on Databricks in a non-interactive mode. This function extracts relevant data that MLflow needs in order to properly utilize the MLflow APIs from this context.

build_context_tags_from_databricks_job_info(job_info)

Arguments

Argument

Description

job_info

The job-related metadata from a running Databricks job

Value

A list of tags to be set by the run context when creating MLflow runs in the current Databricks Job environment

build_context_tags_from_databricks_notebook_info

Get information from Databricks Notebook environment

Retrieves the notebook id, path, url, name, version, and type from the Databricks Notebook execution environment and sets them to a list to be used for setting the configured environment for executing an MLflow run in R from Databricks.

build_context_tags_from_databricks_notebook_info(notebook_info)

Arguments

Argument

Description

notebook_info

The configuration data from the Databricks Notebook environment

Value

A list of tags to be set by the run context when creating MLflow runs in the current Databricks Notebook environment

mlflow_client

Initialize an MLflow Client

Initializes and returns an MLflow client that communicates with the tracking server or store at the specified URI.

mlflow_client(tracking_uri = NULL)

Arguments

Argument

Description

tracking_uri

The tracking URI. If not provided, defaults to the service set by mlflow_set_tracking_uri().

mlflow_create_experiment

Create Experiment

Creates an MLflow experiment and returns its id.

mlflow_create_experiment(
  name,
  artifact_location = NULL,
  client = NULL,
  tags = NULL
)

Arguments

Argument

Description

name

The name of the experiment to create.

artifact_location

Location where all artifacts for this experiment are stored. If not provided, the remote server will select an appropriate default.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

tags

Experiment tags to set on the experiment upon experiment creation.

mlflow_create_model_version

Create a model version

Create a model version

mlflow_create_model_version(
  name,
  source,
  run_id = NULL,
  tags = NULL,
  run_link = NULL,
  description = NULL,
  client = NULL
)

Arguments

Argument

Description

name

Register model under this name.

source

URI indicating the location of the model artifacts.

run_id

MLflow run ID for correlation, if source was generated by an experiment run in MLflow Tracking.

tags

Additional metadata.

run_link

MLflow run link - This is the exact link of the run that generated this model version.

description

Description for model version.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_create_registered_model

Create registered model

Creates a new registered model in the model registry

mlflow_create_registered_model(
  name,
  tags = NULL,
  description = NULL,
  client = NULL
)

Arguments

Argument

Description

name

The name of the model to create.

tags

Additional metadata for the registered model (Optional).

description

Description for the registered model (Optional).

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_delete_experiment

Delete Experiment

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

mlflow_delete_experiment(experiment_id, client = NULL)

Arguments

Argument

Description

experiment_id

ID of the associated experiment. This field is required.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_delete_model_version

Delete a model version

Delete a model version

mlflow_delete_model_version(name, version, client = NULL)

Arguments

Argument

Description

name

Name of the registered model.

version

Model version number.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_delete_registered_model

Delete registered model

Deletes an existing registered model by name

mlflow_delete_registered_model(name, client = NULL)

Arguments

Argument

Description

name

The name of the model to delete

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_delete_run

Delete a Run

Deletes the run with the specified ID.

mlflow_delete_run(run_id, client = NULL)

Arguments

Argument

Description

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_delete_tag

Delete Tag

Deletes a tag on a run. This is irreversible. Tags are run metadata that can be updated during a run and after a run completes.

mlflow_delete_tag(key, run_id = NULL, client = NULL)

Arguments

Argument

Description

key

Name of the tag. Maximum size is 255 bytes. This field is required.

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_download_artifacts

Download Artifacts

Download an artifact file or directory from a run to a local directory if applicable, and return a local path for it.

mlflow_download_artifacts(path, run_id = NULL, client = NULL)

Arguments

Argument

Description

path

Relative source path to the desired artifact.

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_end_run

End a Run

Terminates a run. Attempts to end the current active run if run_id is not specified.

mlflow_end_run(
  status = c("FINISHED", "FAILED", "KILLED"),
  end_time = NULL,
  run_id = NULL,
  client = NULL
)

Arguments

Argument

Description

status

Updated status of the run. Defaults to FINISHED. Can also be set to “FAILED” or “KILLED”.

end_time

Unix timestamp of when the run ended in milliseconds.

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_get_experiment

Get Experiment

Gets metadata for an experiment and a list of runs for the experiment. Attempts to obtain the active experiment if both experiment_id and name are unspecified.

mlflow_get_experiment(experiment_id = NULL, name = NULL, client = NULL)

Arguments

Argument

Description

experiment_id

ID of the experiment.

name

The experiment name. Only one of name or experiment_id should be specified.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_get_latest_versions

Get latest model versions

Retrieves a list of the latest model versions for a given model.

mlflow_get_latest_versions(name, stages = list(), client = NULL)

Arguments

Argument

Description

name

Name of the model.

stages

A list of desired stages. If the input list is NULL, return latest versions for ALL_STAGES.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_get_metric_history

Get Metric History

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

mlflow_get_metric_history(metric_key, run_id = NULL, client = NULL)

Arguments

Argument

Description

metric_key

Name of the metric.

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_get_model_version

Get a model version

Get a model version

mlflow_get_model_version(name, version, client = NULL)

Arguments

Argument

Description

name

Name of the registered model.

version

Model version number.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_get_registered_model

Get a registered model

Retrieves a registered model from the Model Registry.

mlflow_get_registered_model(name, client = NULL)

Arguments

Argument

Description

name

The name of the model to retrieve.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_get_run

Get Run

Gets metadata, params, tags, and metrics for a run. Returns a single value for each metric key: the most recently logged metric value at the largest step.

mlflow_get_run(run_id = NULL, client = NULL)

Arguments

Argument

Description

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_get_tracking_uri

Get Remote Tracking URI

Gets the remote tracking URI.

mlflow_get_tracking_uri()

mlflow_id

Get Run or Experiment ID

Extracts the ID of the run or experiment.

mlflow_id(object)
list(list("mlflow_id"), list("mlflow_run"))(object)
list(list("mlflow_id"), list("mlflow_experiment"))(object)

Arguments

Argument

Description

object

An mlflow_run or mlflow_experiment object.

mlflow_list_artifacts

List Artifacts

Gets a list of artifacts.

mlflow_list_artifacts(path = NULL, run_id = NULL, client = NULL)

Arguments

Argument

Description

path

The run’s relative artifact path to list from. If not specified, it is set to the root artifact path

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_load_flavor

Load MLflow Model Flavor

Loads an MLflow model using a specific flavor. This method is called internally by mlflow_load_model , but is exposed for package authors to extend the supported MLflow models. See https://mlflow.org/docs/latest/models.html#storage-format for more info on MLflow model flavors.

mlflow_load_flavor(flavor, model_path)

Arguments

Argument

Description

flavor

An MLflow flavor object loaded by mlflo w_load_model , with class loaded from the flavor field in an MLmodel file.

model_path

The path to the MLflow model wrapped in the correct class.

mlflow_load_model

Load MLflow Model

Loads an MLflow model. MLflow models can have multiple model flavors. Not all flavors / models can be loaded in R. This method by default searches for a flavor supported by R/MLflow.

mlflow_load_model(model_uri, flavor = NULL, client = mlflow_client())

Arguments

Argument

Description

model_uri

The location, in URI format, of the MLflow model.

flavor

Optional flavor specification (string). Can be used to load a particular flavor in case there are multiple flavors available.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

Details

The URI scheme must be supported by MLflow - i.e. there has to be an MLflow artifact repository corresponding to the scheme of the URI. The content is expected to point to a directory containing MLmodel. The following are examples of valid model uris:

  • file:///absolute/path/to/local/model

  • file:relative/path/to/local/model

  • s3://my_bucket/path/to/model

  • runs:/<mlflow_run_id>/run-relative/path/to/model

  • models:/<model_name>/<model_version>

  • models:/<model_name>/<stage>

For more information about supported URI schemes, see the Artifacts Documentation at https://www.mlflow.org/docs/latest/tracking.html#artifact-stores.

mlflow_log_artifact

Log Artifact

Logs a specific file or directory as an artifact for a run.

mlflow_log_artifact(path, artifact_path = NULL, run_id = NULL, client = NULL)

Arguments

Argument

Description

path

The file or directory to log as an artifact.

artifact_path

Destination path within the run’s artifact URI.

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

Details

When logging to Amazon S3, ensure that you have the s3:PutObject, s3:GetObject, s3:ListBucket, and s3:GetBucketLocation permissions on your bucket.

Additionally, at least the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables must be set to the corresponding key and secrets provided by Amazon IAM.

mlflow_log_batch

Log Batch

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.

mlflow_log_batch(
  metrics = NULL,
  params = NULL,
  tags = NULL,
  run_id = NULL,
  client = NULL
)

Arguments

Argument

Description

metrics

A dataframe of metrics to log, containing the following columns: “key”, “value”, “step”, “timestamp”. This dataframe cannot contain any missing (‘NA’) entries.

params

A dataframe of params to log, containing the following columns: “key”, “value”. This dataframe cannot contain any missing (‘NA’) entries.

tags

A dataframe of tags to log, containing the following columns: “key”, “value”. This dataframe cannot contain any missing (‘NA’) entries.

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_log_metric

Log Metric

Logs a metric for a run. Metrics key-value pair that records a single float measure. During a single execution of a run, a particular metric can be logged several times. The MLflow Backend keeps track of historical metric values along two axes: timestamp and step.

mlflow_log_metric(
  key,
  value,
  timestamp = NULL,
  step = NULL,
  run_id = NULL,
  client = NULL
)

Arguments

Argument

Description

key

Name of the metric.

value

Float value for the metric being logged.

timestamp

Timestamp at which to log the metric. Timestamp is rounded to the nearest integer. If unspecified, the number of milliseconds since the Unix epoch is used.

step

Step at which to log the metric. Step is rounded to the nearest integer. If unspecified, the default value of zero is used.

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_log_model

Log Model

Logs a model for this run. Similar to mlflow_save_model() but stores model as an artifact within the active run.

mlflow_log_model(model, artifact_path, ...)

Arguments

Argument

Description

model

The model that will perform a prediction.

artifact_path

Destination path where this MLflow compatible model will be saved.

...

Optional additional arguments passed to mlflow_save_model() when persisting the model. For example, conda_env = /path/to/conda.yaml may be passed to specify a conda dependencies file for flavors (e.g. keras) that support conda environments.

mlflow_log_param

Log Parameter

Logs a parameter for a run. Examples are params and hyperparams used for ML training, or constant dates and values used in an ETL pipeline. A param is a STRING key-value pair. For a run, a single parameter is allowed to be logged only once.

mlflow_log_param(key, value, run_id = NULL, client = NULL)

Arguments

Argument

Description

key

Name of the parameter.

value

String value of the parameter.

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_param

Read Command-Line Parameter

Reads a command-line parameter passed to an MLflow project MLflow allows you to define named, typed input parameters to your R scripts via the mlflow_param API. This is useful for experimentation, e.g. tracking multiple invocations of the same script with different parameters.

mlflow_param(name, default = NULL, type = NULL, description = NULL)

Arguments

Argument

Description

name

The name of the parameter.

default

The default value of the parameter.

type

Type of this parameter. Required if default is not set. If specified, must be one of “numeric”, “integer”, or “string”.

description

Optional description for the parameter.

Examples

# This parametrized script trains a GBM model on the Iris dataset and can be run as an MLflow
# project. You can run this script (assuming it's saved at /some/directory/params_example.R)
# with custom parameters via:
# mlflow_run(entry_point = "params_example.R", uri = "/some/directory",
#   parameters = list(num_trees = 200, learning_rate = 0.1))
install.packages("gbm")
library(mlflow)
library(gbm)
# define and read input parameters
num_trees <- mlflow_param(name = "num_trees", default = 200, type = "integer")
lr <- mlflow_param(name = "learning_rate", default = 0.1, type = "numeric")
# use params to fit a model
ir.adaboost <- gbm(Species ~., data=iris, n.trees=num_trees, shrinkage=lr)

mlflow_predict

Generate Prediction with MLflow Model

Performs prediction over a model loaded using mlflow_load_model() , to be used by package authors to extend the supported MLflow models.

mlflow_predict(model, data, ...)

Arguments

Argument

Description

model

The loaded MLflow model flavor.

data

A data frame to perform scoring.

...

Optional additional arguments passed to underlying predict methods.

mlflow_register_external_observer

Register an external MLflow observer

Registers an external MLflow observer that will receive a register_tracking_event(event_name, data) callback on any model tracking event such as “create_run”, “delete_run”, or “log_metric”. Each observer should have a register_tracking_event(event_name, data) callback accepting a character vector event_name specifying the name of the tracking event, and data containing a list of attributes of the event. The callback should be non-blocking, and ideally should complete instantaneously. Any exception thrown from the callback will be ignored.

mlflow_register_external_observer(observer)

Arguments

Argument

Description

observer

The observer object (see example)

Examples

library(mlflow)

observer <- structure(list())
observer$register_tracking_event <- function(event_name, data) {
print(event_name)
print(data)
}
mlflow_register_external_observer(observer)

mlflow_rename_experiment

Rename Experiment

Renames an experiment.

mlflow_rename_experiment(new_name, experiment_id = NULL, client = NULL)

Arguments

Argument

Description

new_name

The experiment’s name will be changed to this. The new name must be unique.

experiment_id

ID of the associated experiment. This field is required.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_rename_registered_model

Rename a registered model

Renames a model in the Model Registry.

mlflow_rename_registered_model(name, new_name, client = NULL)

Arguments

Argument

Description

name

The current name of the model.

new_name

The new name for the model.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_restore_experiment

Restore Experiment

Restores 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.

mlflow_restore_experiment(experiment_id, client = NULL)

Arguments

Argument

Description

experiment_id

ID of the associated experiment. This field is required.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

Details

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

mlflow_restore_run

Restore a Run

Restores the run with the specified ID.

mlflow_restore_run(run_id, client = NULL)

Arguments

Argument

Description

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_rfunc_serve

Serve an RFunc MLflow Model

Serves an RFunc MLflow model as a local REST API server. This interface provides similar functionality to mlflow models serve cli command, however, it can only be used to deploy models that include RFunc flavor. The deployed server supports standard mlflow models interface with /ping and /invocation endpoints. In addition, R function models also support deprecated /predict endpoint for generating predictions. The /predict endpoint will be removed in a future version of mlflow.

mlflow_rfunc_serve(
  model_uri,
  host = "127.0.0.1",
  port = 8090,
  daemonized = FALSE,
  browse = !daemonized,
  ...
)

Arguments

Argument

Description

model_uri

The location, in URI format, of the MLflow model.

host

Address to use to serve model, as a string.

port

Port to use to serve model, as numeric.

daemonized

Makes httpuv server daemonized so R interactive sessions are not blocked to handle requests. To terminate a daemonized server, call httpuv::stopDaemonizedServer() with the handle returned from this call.

browse

Launch browser with serving landing page?

...

Optional arguments passed to mlflow_predict().

Details

The URI scheme must be supported by MLflow - i.e. there has to be an MLflow artifact repository corresponding to the scheme of the URI. The content is expected to point to a directory containing MLmodel. The following are examples of valid model uris:

  • file:///absolute/path/to/local/model

  • file:relative/path/to/local/model

  • s3://my_bucket/path/to/model

  • runs:/<mlflow_run_id>/run-relative/path/to/model

  • models:/<model_name>/<model_version>

  • models:/<model_name>/<stage>

For more information about supported URI schemes, see the Artifacts Documentation at https://www.mlflow.org/docs/latest/tracking.html#artifact-stores.

Examples

library(mlflow)

# save simple model with constant prediction
mlflow_save_model(function(df) 1, "mlflow_constant")

# serve an existing model over a web interface
mlflow_rfunc_serve("mlflow_constant")

# request prediction from server
httr::POST("http://127.0.0.1:8090/predict/")

mlflow_run

Run an MLflow Project

Wrapper for the mlflow run CLI command. See https://www.mlflow.org/docs/latest/cli.html#mlflow-run for more info.

mlflow_run(
  uri = ".",
  entry_point = NULL,
  version = NULL,
  parameters = NULL,
  experiment_id = NULL,
  experiment_name = NULL,
  backend = NULL,
  backend_config = NULL,
  env_manager = NULL,
  storage_dir = NULL
)

Arguments

Argument

Description

uri

A directory containing modeling scripts, defaults to the current directory.

entry_point

Entry point within project, defaults to main if not specified.

version

Version of the project to run, as a Git commit reference for Git projects.

parameters

A list of parameters.

experiment_id

ID of the experiment under which to launch the run.

experiment_name

Name of the experiment under which to launch the run.

backend

Execution backend to use for run.

backend_config

Path to JSON file which will be passed to the backend. For the Databricks backend, it should describe the cluster to use when launching a run on Databricks.

env_manager

If specified, create an environment for the project using the specified environment manager. Available options are ‘local’, ‘virtualenv’, and ‘conda’.

storage_dir

Valid only when backend is local. MLflow downloads artifacts from distributed URIs passed to parameters of type path to subdirectories of storage_dir.

Value

The run associated with this run.

Examples

# This parametrized script trains a GBM model on the Iris dataset and can be run as an MLflow
# project. You can run this script (assuming it's saved at /some/directory/params_example.R)
# with custom parameters via:
# mlflow_run(entry_point = "params_example.R", uri = "/some/directory",
#   parameters = list(num_trees = 200, learning_rate = 0.1))
install.packages("gbm")
library(mlflow)
library(gbm)
# define and read input parameters
num_trees <- mlflow_param(name = "num_trees", default = 200, type = "integer")
lr <- mlflow_param(name = "learning_rate", default = 0.1, type = "numeric")
# use params to fit a model
ir.adaboost <- gbm(Species ~., data=iris, n.trees=num_trees, shrinkage=lr)

mlflow_save_model.crate

Save Model for MLflow

Saves model in MLflow format that can later be used for prediction and serving. This method is generic to allow package authors to save custom model types.

list(list("mlflow_save_model"), list("crate"))(model, path, model_spec = list(), ...)
mlflow_save_model(model, path, model_spec = list(), ...)
list(list("mlflow_save_model"), list("H2OModel"))(model, path, model_spec = list(), conda_env = NULL, ...)
list(list("mlflow_save_model"), list("keras.engine.training.Model"))(model, path, model_spec = list(), conda_env = NULL, ...)
list(list("mlflow_save_model"), list("xgb.Booster"))(model, path, model_spec = list(), conda_env = NULL, ...)

Arguments

Argument

Description

model

The model that will perform a prediction.

path

Destination path where this MLflow compatible model will be saved.

model_spec

MLflow model config this model flavor is being added to.

...

Optional additional arguments.

conda_env

Path to Conda dependencies file.

mlflow_search_experiments

Search Experiments

Search for experiments that satisfy specified criteria.

mlflow_search_experiments(
  filter = NULL,
  experiment_view_type = c("ACTIVE_ONLY", "DELETED_ONLY", "ALL"),
  max_results = 1000,
  order_by = list(),
  page_token = NULL,
  client = NULL
)

Arguments

Argument

Description

filter

A filter expression used to identify specific experiments. The syntax is a subset of SQL which allows only ANDing together binary operations. Examples: “attribute.name = ‘MyExperiment’”, “tags.problem_type = ‘iris_regression’”

experiment_view_type

Experiment view type. Only experiments matching this view type are returned.

max_results

Maximum number of experiments to retrieve.

order_by

List of properties to order by. Example: “attribute.name”.

page_token

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

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_search_registered_models

List registered models

Retrieves a list of registered models.

mlflow_search_registered_models(
  filter = NULL,
  max_results = 100,
  order_by = list(),
  page_token = NULL,
  client = NULL
)

Arguments

Argument

Description

filter

A filter expression used to identify specific registered models. The syntax is a subset of SQL which allows only ANDing together binary operations. Example: “name = ‘my_model_name’ and tag.key = ‘value1’”

max_results

Maximum number of registered models to retrieve.

order_by

List of registered model properties to order by. Example: “name”.

page_token

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

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_search_runs

Search Runs

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

mlflow_search_runs(
  filter = NULL,
  run_view_type = c("ACTIVE_ONLY", "DELETED_ONLY", "ALL"),
  experiment_ids = NULL,
  order_by = list(),
  client = NULL
)

Arguments

Argument

Description

filter

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.

run_view_type

Run view type.

experiment_ids

List of string experiment IDs (or a single string experiment ID) to search over. Attempts to use active experiment if not specified.

order_by

List of properties to order by. Example: “metrics.acc DESC”.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_server

Run MLflow Tracking Server

Wrapper for mlflow server.

mlflow_server(
  file_store = "mlruns",
  default_artifact_root = NULL,
  host = "127.0.0.1",
  port = 5000,
  workers = NULL,
  static_prefix = NULL,
  serve_artifacts = FALSE
)

Arguments

Argument

Description

file_store

The root of the backing file store for experiment and run data.

default_artifact_root

Local or S3 URI to store artifacts in, for newly created experiments.

host

The network address to listen on (default: 127.0.0.1).

port

The port to listen on (default: 5000).

workers

Number of gunicorn worker processes to handle requests (default: 4).

static_prefix

A prefix which will be prepended to the path of all static paths.

serve_artifacts

A flag specifying whether or not to enable artifact serving (default: FALSE).

mlflow_set_experiment_tag

Set Experiment Tag

Sets a tag on an experiment with the specified ID. Tags are experiment metadata that can be updated.

mlflow_set_experiment_tag(key, value, experiment_id = NULL, client = NULL)

Arguments

Argument

Description

key

Name of the tag. All storage backends are guaranteed to support key values up to 250 bytes in size. This field is required.

value

String value of the tag being logged. All storage backends are guaranteed to support key values up to 5000 bytes in size. This field is required.

experiment_id

ID of the experiment.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_set_experiment

Set Experiment

Sets an experiment as the active experiment. Either the name or ID of the experiment can be provided. If the a name is provided but the experiment does not exist, this function creates an experiment with provided name. Returns the ID of the active experiment.

mlflow_set_experiment(
  experiment_name = NULL,
  experiment_id = NULL,
  artifact_location = NULL
)

Arguments

Argument

Description

experiment_name

Name of experiment to be activated.

experiment_id

ID of experiment to be activated.

artifact_location

Location where all artifacts for this experiment are stored. If not provided, the remote server will select an appropriate default.

mlflow_set_model_version_tag

Set Model version tag

Set a tag for the model version. When stage is set, tag will be set for latest model version of the stage. Setting both version and stage parameter will result in error.

mlflow_set_model_version_tag(
  name,
  version = NULL,
  key = NULL,
  value = NULL,
  stage = NULL,
  client = NULL
)

Arguments

Argument

Description

name

Registered model name.

version

Registered model version.

key

Tag key to log. key is required.

value

Tag value to log. value is required.

stage

Registered model stage.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_set_tag

Set Tag

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

mlflow_set_tag(key, value, run_id = NULL, client = NULL)

Arguments

Argument

Description

key

Name of the tag. Maximum size is 255 bytes. This field is required.

value

String value of the tag being logged. Maximum size is 500 bytes. This field is required.

run_id

Run ID.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_set_tracking_uri

Set Remote Tracking URI

Specifies the URI to the remote MLflow server that will be used to track experiments.

mlflow_set_tracking_uri(uri)

Arguments

Argument

Description

uri

The URI to the remote MLflow server.

mlflow_source

Source a Script with MLflow Params

This function should not be used interactively. It is designed to be called via Rscript from the terminal or through the MLflow CLI.

mlflow_source(uri)

Arguments

Argument

Description

uri

Path to an R script, can be a quoted or unquoted string.

mlflow_start_run

Start Run

Starts a new run. If client is not provided, this function infers contextual information such as source name and version, and also registers the created run as the active run. If client is provided, no inference is done, and additional arguments such as start_time can be provided.

mlflow_start_run(
  run_id = NULL,
  experiment_id = NULL,
  start_time = NULL,
  tags = NULL,
  client = NULL,
  nested = FALSE
)

Arguments

Argument

Description

run_id

If specified, get the run with the specified UUID and log metrics and params under that run. The run’s end time is unset and its status is set to running, but the run’s other attributes remain unchanged.

experiment_id

Used only when run_id is unspecified. ID of the experiment under which to create the current run. If unspecified, the run is created under a new experiment with a randomly generated name.

start_time

Unix timestamp of when the run started in milliseconds. Only used when client is specified.

tags

Additional metadata for run in key-value pairs. Only used when client is specified.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

nested

Controls whether the run to be started is nested in a parent run. TRUE creates a nest run.

Examples

with(mlflow_start_run(), {
mlflow_log_metric("test", 10)
})

mlflow_transition_model_version_stage

Transition ModelVersion Stage

Transition a model version to a different stage.

mlflow_transition_model_version_stage(
  name,
  version,
  stage,
  archive_existing_versions = FALSE,
  client = NULL
)

Arguments

Argument

Description

name

Name of the registered model.

version

Model version number.

stage

Transition model_version to this stage.

archive_existing_versions

(Optional)

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_ui

Run MLflow User Interface

Launches the MLflow user interface.

mlflow_ui(client, ...)

Arguments

Argument

Description

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

...

Optional arguments passed to mlflow_server() when x is a path to a file store.

Examples

library(mlflow)

# launch mlflow ui locally
mlflow_ui()

# launch mlflow ui for existing mlflow server
mlflow_set_tracking_uri("http://tracking-server:5000")
mlflow_ui()

mlflow_update_model_version

Update model version

Updates a model version

mlflow_update_model_version(name, version, description, client = NULL)

Arguments

Argument

Description

name

Name of the registered model.

version

Model version number.

description

Description of this model version.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.

mlflow_update_registered_model

Update a registered model

Updates a model in the Model Registry.

mlflow_update_registered_model(name, description, client = NULL)

Arguments

Argument

Description

name

The name of the registered model.

description

The updated description for this registered model.

client

(Optional) An MLflow client object returned from mlflow_client . If specified, MLflow will use the tracking server associated with the passed-in client. If unspecified (the common case), MLflow will use the tracking server associated with the current tracking URI.