R API

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

Crate a function to share with another process

crate() creates functions in a self-contained environment (technically, a child of the base environment). This has two advantages:

  • They can easily be executed in another process.
  • Their effects are reproducible. You can run them locally with the same results as on a different process.

Creating self-contained functions requires some care, see section below.

crate(.fn, ...)

Arguments

Argument Description
.fn A fresh formula or function. “Fresh” here means that they should be declared in the call to crate(). See examples if you need to crate a function that is already defined. Formulas are converted to purrr-like lambda functions using [rlang::as_function()].
... Arguments to declare in the environment of .fn. If a name is supplied, the object is assigned to that name. Otherwise the argument is automatically named after itself.

Examples 

# You can create functions using the ordinary notation:
crate(function(x) stats::var(x))

# Or the formula notation:
crate(~stats::var(.x))

# Declare data by supplying named arguments. You can test you have
# declared all necessary data by calling your crated function:
na_rm <- TRUE
fn <- crate(~stats::var(.x, na.rm = na_rm))
try(fn(1:10))

# Arguments are automatically named after themselves so that the
# following are equivalent:
crate(~stats::var(.x, na.rm = na_rm), na_rm = na_rm)
crate(~stats::var(.x, na.rm = na_rm), na_rm)

# However if you supply a complex expression, do supply a name!
crate(~stats::var(.x, na.rm = na_rm), !na_rm)
crate(~stats::var(.x, na.rm = na_rm), na_rm = na_rm)

# For small data it is handy to unquote instead. Unquoting inlines
# objects inside the function. This is less verbose if your
# function depends on many small objects:
fn <- crate(~stats::var(.x, na.rm = !!na_rm))
fn(1:10)

# One downside is that the individual sizes of unquoted objects
# won't be shown in the crate printout:
fn


# The function or formula you pass to crate() should defined inside
# the crate() call, i.e. you can't pass an already defined
# function:
fn <- function(x) toupper(x)
try(crate(fn))

# If you really need to crate an existing function, you can
# explicitly set its environment to the crate environment with the
# set_env() function from rlang:
crate(rlang::set_env(fn))

Is an object a crate?

Is an object a crate?

is_crate(x)

Arguments

Argument Description
x An object to test.

Active Run

Retrieves the active run.

mlflow_active_run()

MLflow Command

Runs a generic MLflow command through the command-line interface.

mlflow_cli(..., background = FALSE, echo = TRUE,
  stderr_callback = NULL)

Arguments

Argument Description
... The parameters to pass to the command line.
background Should this command be triggered as a background task? Defaults to FALSE .
echo Print the standard output and error to the screen? Defaults to TRUE , does not apply to background tasks.
stderr_callback NULL, or a function to call for every chunk of the standard error.

Value

A processx task.

Examples 

list("\n", "library(mlflow)\n", "mlflow_install()\n", "\n", "mlflow_cli(\"server\", \"--help\")\n")

Create Experiment - Tracking Client

Creates an MLflow experiment.

mlflow_client_create_experiment(client, name, artifact_location = NULL)

Arguments

Argument Description
client An mlflow_client object.
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.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

Create Run

Create a new run within an experiment. A run is usually a single execution of a machine learning or data ETL pipeline.

mlflow_client_create_run(client, experiment_id, user_id = NULL,
  run_name = NULL, source_type = NULL, source_name = NULL,
  entry_point_name = NULL, start_time = NULL, source_version = NULL,
  tags = NULL)

Arguments

Argument Description
client An mlflow_client object.
experiment_id Unique identifier for the associated experiment.
user_id User ID or LDAP for the user executing the run.
run_name Human readable name for run.
source_type Originating source for this run. One of Notebook, Job, Project, Local, or Unknown.
source_name String descriptor for source. For example, name or description of the notebook, or job name.
entry_point_name Name of the entry point for the run.
start_time Unix timestamp of when the run started in milliseconds.
source_version Git version of the source code used to create run.
tags Additional metadata for run in key-value pairs.

Details

MLflow uses runs to track Param, Metric, and RunTag, associated with a single execution.

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

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_client_delete_experiment(client, experiment_id)

Arguments

Argument Description
client An mlflow_client object.
experiment_id ID of the associated experiment. This field is required.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

Delete a Run

Delete a Run

mlflow_client_delete_run(client, run_id)

Arguments

Argument Description
client An mlflow_client object.
run_id Run ID.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

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_client_download_artifacts(client, run_id, path)

Arguments

Argument Description
client An mlflow_client object.
run_id Run ID.
path Relative source path to the desired artifact.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

Get Experiment by Name

Gets metadata for an experiment by name.

mlflow_client_get_experiment_by_name(client, name)

Arguments

Argument Description
client An mlflow_client object.
name The experiment name.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

Get Experiment

Gets metadata for an experiment and a list of runs for the experiment.

mlflow_client_get_experiment(client, experiment_id)

Arguments

Argument Description
client An mlflow_client object.
experiment_id Identifer to get an experiment.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

Get Run

Gets metadata, params, tags, and metrics for a run. In the case where multiple metrics with the same key are logged for the run, returns only the value with the latest timestamp. If there are multiple values with the latest timestamp, returns the maximum of these values.

mlflow_client_get_run(client, run_id)

Arguments

Argument Description
client An mlflow_client object.
run_id Run ID.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

List Artifacts

Gets a list of artifacts.

mlflow_client_list_artifacts(client, run_id, path = NULL)

Arguments

Argument Description
client An mlflow_client object.
run_id Run ID.
path The run’s relative artifact path to list from. If not specified, it is set to the root artifact path

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

List Experiments

Gets a list of all experiments.

mlflow_client_list_experiments(client, view_type = c("ACTIVE_ONLY",
  "DELETED_ONLY", "ALL"))

Arguments

Argument Description
client An mlflow_client object.
view_type Qualifier for type of experiments to be returned. Defaults to ACTIVE_ONLY.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

Log Artifact

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

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

Arguments

Argument Description
client An mlflow_client object.
run_id Run ID.
path The file or directory to log as an artifact.
artifact_path Destination path within the run’s artifact URI.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

When logging to Amazon S3, ensure that the user has a proper policy attach to it.

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.

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. Backend will keep track of historical values along with timestamps.

mlflow_client_log_metric(client, run_id, key, value, timestamp = NULL)

Arguments

Argument Description
client An mlflow_client object.
run_id Run ID.
key Name of the metric.
value Float value for the metric being logged.
timestamp Unix timestamp in milliseconds at the time metric was logged.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

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_client_log_param(client, run_id, key, value)

Arguments

Argument Description
client An mlflow_client object.
run_id Run ID.
key Name of the parameter.
value String value of the parameter.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

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_client_restore_experiment(client, experiment_id)

Arguments

Argument Description
client An mlflow_client object.
experiment_id ID of the associated experiment. This field is required.

Details

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

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

Restore a Run

Restore a Run

mlflow_client_restore_run(client, run_id)

Arguments

Argument Description
client An mlflow_client object.
run_id Run ID.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

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_client_set_tag(client, run_id, key, value)

Arguments

Argument Description
client An mlflow_client object.
run_id Run ID.
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.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

Terminate a Run

Terminates a run.

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

Arguments

Argument Description
client An mlflow_client object.
run_id Unique identifier for the run.
status Updated status of the run. Defaults to FINISHED.
end_time Unix timestamp of when the run ended in milliseconds.
run_id Run ID.

Details

The Tracking Client family of functions require an MLflow client to be specified explicitly. These functions allow for greater control of where the operations take place in terms of services and runs, but are more verbose compared to the Fluent API.

Initialize an MLflow Client

Initializes an MLflow client.

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().

Create Experiment

Creates an MLflow experiment.

mlflow_create_experiment(name, artifact_location = 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.

Details

The fluent API family of functions operate with an implied MLflow client determined by the service set by mlflow_set_tracking_uri(). For operations involving a run it adopts the current active run, or, if one does not exist, starts one through the implied service.

End a Run

Ends an active MLflow run (if there is one).

mlflow_end_run(status = c("FINISHED", "SCHEDULED", "FAILED", "KILLED"))

Arguments

Argument Description
status Updated status of the run. Defaults to FINISHED.

Details

The fluent API family of functions operate with an implied MLflow client determined by the service set by mlflow_set_tracking_uri(). For operations involving a run it adopts the current active run, or, if one does not exist, starts one through the implied service.

Get Remote Tracking URI

Gets the remote tracking URI.

mlflow_get_tracking_uri()

Install MLflow

Installs MLflow for individual use.

mlflow_install()

Details

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/.

Examples 

list("\n", "library(mlflow)\n", "mlflow_install()\n")

Load MLflow Model Flavor

Loads an MLflow model flavor, to be used by package authors to extend the supported MLflow models.

mlflow_load_flavor(model_path)

Arguments

Argument Description
model_path The path to the MLflow model wrapped in the correct class.

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_path, flavor = NULL, run_id = NULL)

Arguments

Argument Description
model_path Path to the MLflow model. The path is relative to the run with the given run-id or local filesystem path without run-id.
flavor Optional flavor specification. Can be used to load a particular flavor in case there are multiple flavors available.
run_id Optional MLflow run-id. If supplied model will be fetched from MLflow tracking server.

Log Artifact

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

mlflow_log_artifact(path, artifact_path = NULL)

Arguments

Argument Description
path The file or directory to log as an artifact.
artifact_path Destination path within the run’s artifact URI.

Details

The fluent API family of functions operate with an implied MLflow client determined by the service set by mlflow_set_tracking_uri(). For operations involving a run it adopts the current active run, or, if one does not exist, starts one through the implied service.

When logging to Amazon S3, ensure that the user has a proper policy attach to it.

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.

Log Metric

Logs a metric for this 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. Backend will keep track of historical values along with timestamps.

mlflow_log_metric(key, value, timestamp = NULL)

Arguments

Argument Description
key Name of the metric.
value Float value for the metric being logged.
timestamp Unix timestamp in milliseconds at the time metric was logged.

Details

The fluent API family of functions operate with an implied MLflow client determined by the service set by mlflow_set_tracking_uri(). For operations involving a run it adopts the current active run, or, if one does not exist, starts one through the implied service.

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(fn, artifact_path)

Arguments

Argument Description
fn The serving function that will perform a prediction.
artifact_path Destination path where this MLflow compatible model will be saved.

Log Parameter

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

mlflow_log_param(key, value)

Arguments

Argument Description
key Name of the parameter.
value String value of the parameter.

Details

The fluent API family of functions operate with an implied MLflow client determined by the service set by mlflow_set_tracking_uri(). For operations involving a run it adopts the current active run, or, if one does not exist, starts one through the implied service.

Read Command-Line Parameter

Reads a command-line parameter.

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 the parameter. Required if default is not set. If specified, must be one of “numeric”, “integer”, or “string”.
description Optional description for the parameter.

Predict over MLflow Model Flavor

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

mlflow_predict_flavor(model, data)

Arguments

Argument Description
model The loaded MLflow model flavor.
data A data frame to perform scoring.

Generate Prediction with MLflow Model

Generates a prediction with an MLflow model.

mlflow_predict_model(model, data)

Arguments

Argument Description
model MLflow model.
data Dataframe to be scored.

Restore Snapshot

Restores a snapshot of all dependencies required to run the files in the current directory.

mlflow_restore_snapshot()

Predict using RFunc MLflow Model

Performs prediction using an RFunc MLflow model from a file or data frame.

mlflow_rfunc_predict(model_path, run_uuid = NULL, input_path = NULL,
  output_path = NULL, data = NULL, restore = FALSE)

Arguments

Argument Description
model_path The path to the MLflow model, as a string.
run_uuid Run ID of run to grab the model from.
input_path Path to JSON or CSV file to be used for prediction.
output_path JSON or CSV file where the prediction will be written to.
data Data frame to be scored. This can be used for testing purposes and can only be specified when input_path is not specified.
restore Should mlflow_restore_snapshot() be called before serving?

Examples 

list("\n", "library(mlflow)\n", "\n", "# save simple model which roundtrips data as prediction\n", "mlflow_save_model(function(df) df, \"mlflow_roundtrip\")\n", "\n", "# save data as json\n", "jsonlite::write_json(iris, \"iris.json\")\n", "\n", "# predict existing model from json data\n", "mlflow_rfunc_predict(\"mlflow_roundtrip\", \"iris.json\")\n")

Serve an RFunc MLflow Model

Serves an RFunc MLflow model as a local web API.

mlflow_rfunc_serve(model_path, run_uuid = NULL, host = "127.0.0.1",
  port = 8090, daemonized = FALSE, browse = !daemonized,
  restore = FALSE)

Arguments

Argument Description
model_path The path to the MLflow model, as a string.
run_uuid ID of run to grab the model from.
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?
restore Should mlflow_restore_snapshot() be called before serving?

Examples 

list("\n", "library(mlflow)\n", "\n", "# save simple model with constant prediction\n", "mlflow_save_model(function(df) 1, \"mlflow_constant\")\n", "\n", "# serve an existing model over a web interface\n", "mlflow_rfunc_serve(\"mlflow_constant\")\n", "\n", "# request prediction from server\n", "httr::POST(\"http://127.0.0.1:8090/predict/\")\n")

Run in MLflow

Wrapper for mlflow run.

mlflow_run(entry_point = NULL, uri = ".", version = NULL,
  param_list = NULL, experiment_id = NULL, mode = NULL,
  cluster_spec = NULL, git_username = NULL, git_password = NULL,
  no_conda = FALSE, storage_dir = NULL)

Arguments

Argument Description
entry_point Entry point within project, defaults to main if not specified.
uri A directory containing modeling scripts, defaults to the current directory.
version Version of the project to run, as a Git commit reference for Git projects.
param_list A list of parameters.
experiment_id ID of the experiment under which to launch the run.
mode Execution mode to use for run.
cluster_spec Path to JSON file describing the cluster to use when launching a run on Databricks.
git_username Username for HTTP(S) Git authentication.
git_password Password for HTTP(S) Git authentication.
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 mode 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.

Save MLflow Keras Model Flavor

Saves model in MLflow Keras flavor.

list(list("mlflow_save_flavor"), list("keras.engine.training.Model"))(x,
  path = "model", r_dependencies = NULL, conda_env = NULL)

Arguments

Argument Description
x The serving function or model that will perform a prediction.
path Destination path where this MLflow compatible model will be saved.
r_dependencies Optional vector of paths to dependency files to include in the model, as in r-dependencies.txt or conda.yaml .
conda_env Path to Conda dependencies file.

Value

This function must return a list of flavors that conform to the MLmodel specification.

Save MLflow Model Flavor

Saves model in MLflow flavor, to be used by package authors to extend the supported MLflow models.

mlflow_save_flavor(x, path = "model", r_dependencies = NULL,
  conda_env = NULL)

Arguments

Argument Description
x The serving function or model that will perform a prediction.
path Destination path where this MLflow compatible model will be saved.
r_dependencies Optional vector of paths to dependency files to include in the model, as in r-dependencies.txt or conda.yaml .
conda_env Path to Conda dependencies file.

Value

This function must return a list of flavors that conform to the MLmodel specification.

Save Model for MLflow

Saves model in MLflow format that can later be used for prediction and serving.

mlflow_save_model(x, path = "model", r_dependencies = NULL,
  conda_env = NULL)

Arguments

Argument Description
x The serving function or model that will perform a prediction.
path Destination path where this MLflow compatible model will be saved.
r_dependencies Optional vector of paths to dependency files to include in the model, as in r-dependencies.txt or conda.yaml .
conda_env Path to Conda dependencies file.

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.

Set Experiment

Sets an experiment as the active experiment. If the experiment does not exist, creates an experiment with provided name.

mlflow_set_experiment(experiment_name)

Arguments

Argument Description
experiment_name Name of experiment to be activated.

Details

The fluent API family of functions operate with an implied MLflow client determined by the service set by mlflow_set_tracking_uri(). For operations involving a run it adopts the current active run, or, if one does not exist, starts one through the implied service.

Set Tag

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

mlflow_set_tag(key, value)

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.

Details

The fluent API family of functions operate with an implied MLflow client determined by the service set by mlflow_set_tracking_uri(). For operations involving a run it adopts the current active run, or, if one does not exist, starts one through the implied service.

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.

Dependencies Snapshot

Creates a snapshot of all dependencies required to run the files in the current directory.

mlflow_snapshot()

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.

Start Run

Starts a new run within an experiment, should be used within a with block.

mlflow_start_run(run_uuid = NULL, experiment_id = NULL,
  source_name = NULL, source_version = NULL, entry_point_name = NULL,
  source_type = "LOCAL")

Arguments

Argument Description
run_uuid 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_uuid 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.
source_name Name of the source file or URI of the project to be associated with the run. Defaults to the current file if none provided.
source_version Optional Git commit hash to associate with the run.
entry_point_name Optional name of the entry point for to the current run.
source_type Integer enum value describing the type of the run (“local”, “project”, etc.).

Details

The fluent API family of functions operate with an implied MLflow client determined by the service set by mlflow_set_tracking_uri(). For operations involving a run it adopts the current active run, or, if one does not exist, starts one through the implied service.

Examples 

list("\n", "with(mlflow_start_run(), {\n", "  mlflow_log(\"test\", 10)\n", "})\n")

Run MLflow User Interface

Launches the MLflow user interface.

mlflow_ui(x, ...)

Arguments

Argument Description
x An mlflow_client object.
... Optional arguments passed to mlflow_server() when x is a path to a file store.

Examples 

list("\n", "library(mlflow)\n", "mlflow_install()\n", "\n", "# launch mlflow ui locally\n", "mlflow_ui()\n", "\n", "# launch mlflow ui for existing mlflow server\n", "mlflow_set_tracking_uri(\"http://tracking-server:5000\")\n", "mlflow_ui()\n")

Uninstall MLflow

Uninstalls MLflow by removing the Conda environment.

mlflow_uninstall()

Examples 

list("\n", "library(mlflow)\n", "mlflow_install()\n", "mlflow_uninstall()\n")