R API

The MLflow R API allows you to use MLflow Tracking <tracking/>, Projects <projects/> and Models <models/>.

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

Table of Contents

`install_mlflow`

Install MLflow

Installs auxiliary dependencies of MLflow (e.g. the MLflow CLI). As a one-time setup step, you must run install_mlflow() to install these dependencies before calling other MLflow APIs.

install_mlflow(python_version = "3.7")

Arguments

Argument	Description
`python_version`	Optional Python version to use within conda environment created for installing the MLflow CLI. If unspecified, defaults to using Python 3.6

Details

install_mlflow() requires Python and Conda to be installed. See https://www.python.org/getit/ and https://docs.conda.io/projects/conda/en/latest/user-guide/install/ .

Alternatively, you can set MLFLOW_PYTHON_BIN and MLFLOW_BIN environment variables instead. MLFLOW_PYTHON_BIN should point to python executable and MLFLOW_BIN to mlflow cli executable. These variables allow you to use custom mlflow installation. Note that there may be some compatibility issues if the custom mlflow version does not match the version of the R package.

Examples

library(mlflow)
install_mlflow()

`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

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

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

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_list_experiments`

List Experiments

Gets a list of all experiments.

mlflow_list_experiments(
  view_type = c("ACTIVE_ONLY", "DELETED_ONLY", "ALL"),
  client = NULL
)

Arguments

Argument	Description
`view_type`	Qualifier for type of experiments to be returned. Defaults to `ACTIVE_ONLY`.
`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_list_registered_models`

List registered models

Retrieves a list of registered models.

mlflow_list_registered_models(
  max_results = 100,
  page_token = NULL,
  client = NULL
)

Arguments

Argument	Description
`max_results`	Maximum number of registered models to retrieve.
`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_list_run_infos`

List Run Infos

Returns a tibble whose columns contain run metadata (run ID, etc) for all runs under the specified experiment.

mlflow_list_run_infos(
  run_view_type = c("ACTIVE_ONLY", "DELETED_ONLY", "ALL"),
  experiment_id = NULL,
  client = NULL
)

Arguments

Argument	Description
`run_view_type`	Run view type.
`experiment_id`	Experiment ID. Attempts to use the active experiment if not 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_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 mlflow_load_model <#mlflow-load-mod el>__ , 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_maybe_create_conda_env`

Create conda env used by MLflow if it doesn’t already exist

mlflow_maybe_create_conda_env(python_version)

Arguments

Argument	Description
`python_version`	Python version to use within conda environment created for installing the MLflow CLI.

`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,
  no_conda = FALSE,
  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.
`no_conda`	If specified, assume that MLflow is running within a Conda environment with the necessary dependencies for the current project instead of attempting to create a new Conda environment. Only valid if running locally.
`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_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 = 4,
  static_prefix = NULL
)

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.

`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_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_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 tage.
`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)
install_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.

Files

R-api.rst

Latest commit

History

R-api.rst

File metadata and controls

R API

install_mlflow

Arguments

Details

Examples

mlflow_client

Arguments

mlflow_create_experiment

Arguments

mlflow_create_model_version

Arguments

mlflow_create_registered_model

Arguments

mlflow_delete_experiment

Arguments

mlflow_delete_model_version

Arguments

mlflow_delete_registered_model

Arguments

mlflow_delete_run

Arguments

mlflow_delete_tag

Arguments

mlflow_download_artifacts

Arguments

mlflow_end_run

Arguments

mlflow_get_experiment

Arguments

mlflow_get_latest_versions

Arguments

mlflow_get_metric_history

Arguments

mlflow_get_model_version

Arguments

mlflow_get_registered_model

Arguments

mlflow_get_run

Arguments

mlflow_get_tracking_uri

mlflow_id

Arguments

mlflow_list_artifacts

Arguments

mlflow_list_experiments

Arguments

mlflow_list_registered_models

Arguments

mlflow_list_run_infos

Arguments

mlflow_load_flavor

Arguments

mlflow_load_model

Arguments

Details

mlflow_log_artifact

Arguments

Details

mlflow_log_batch

Arguments

mlflow_log_metric

Arguments

mlflow_log_model

Arguments

mlflow_log_param

Arguments

mlflow_maybe_create_conda_env

Arguments

mlflow_param

Arguments

Examples

mlflow_predict

Arguments

mlflow_register_external_observer

`install_mlflow`

`mlflow_client`

`mlflow_create_experiment`

`mlflow_create_model_version`

`mlflow_create_registered_model`

`mlflow_delete_experiment`

`mlflow_delete_model_version`

`mlflow_delete_registered_model`

`mlflow_delete_run`

`mlflow_delete_tag`

`mlflow_download_artifacts`

`mlflow_end_run`

`mlflow_get_experiment`

`mlflow_get_latest_versions`

`mlflow_get_metric_history`

`mlflow_get_model_version`

`mlflow_get_registered_model`

`mlflow_get_run`

`mlflow_get_tracking_uri`

`mlflow_id`

`mlflow_list_artifacts`

`mlflow_list_experiments`

`mlflow_list_registered_models`

`mlflow_list_run_infos`

`mlflow_load_flavor`

`mlflow_load_model`

`mlflow_log_artifact`

`mlflow_log_batch`

`mlflow_log_metric`

`mlflow_log_model`

`mlflow_log_param`

`mlflow_maybe_create_conda_env`

`mlflow_param`

`mlflow_predict`

`mlflow_register_external_observer`

`mlflow_rename_experiment`

`mlflow_rename_registered_model`

`mlflow_restore_experiment`

`mlflow_restore_run`

`mlflow_rfunc_serve`

`mlflow_run`

`mlflow_save_model.crate`

`mlflow_search_runs`

`mlflow_server`

`mlflow_set_experiment`

`mlflow_set_experiment_tag`

`mlflow_set_tag`

`mlflow_set_tracking_uri`

`mlflow_source`

`mlflow_start_run`

`mlflow_transition_model_version_stage`

`mlflow_ui`

`mlflow_update_model_version`

`mlflow_update_registered_model`