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.

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()

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/ .

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)

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.

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_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 Identifer to get an 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_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_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_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 , 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

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

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

For more information about supported URI schemes, see the Artifacts Documentation at https://www.mlflow.org/docs/latest/tracking.html#supported-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#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, ...)
list(list("mlflow_save_model"), list("keras.engine.training.Model"))(model, path,
  conda_env = NULL, ...)
mlflow_save_model(model, path, ...)

Arguments

Argument Description
model The model that will perform a prediction.
path Destination path where this MLflow compatible model will be saved.
... 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_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)

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.

Examples

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

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()