mlflow_experiment.md

subcategory
MLflow

databricks_mlflow_experiment Resource

This resource allows you to manage MLflow experiments in Databricks.

Example Usage

data "databricks_current_user" "me" {}

resource "databricks_mlflow_experiment" "this" {
  name              = "${data.databricks_current_user.me.home}/Sample"
  artifact_location = "dbfs:/tmp/my-experiment"
  description       = "My MLflow experiment description"
}

Argument Reference

The following arguments are supported:

• name - (Required) Name of MLflow experiment. It must be an absolute path within the Databricks workspace, e.g. /Users/<some-username>/my-experiment. For more information about changes to experiment naming conventions, see mlflow docs.
• artifact_location - Path to dbfs:/ or s3:// artifact location of the MLflow experiment.
• description - The description of the MLflow experiment.

Attribute Reference

In addition to all arguments above, the following attributes are exported:

• id - ID of the MLflow experiment.

Access Control

• databricks_permissions can control which groups or individual users can Read, Edit, or Manage individual experiments.

Import

The experiment resource can be imported using the id of the experiment

terraform import databricks_mlflow_experiment.this <experiment-id>

Related Resources

The following resources are often used in the same context:

• databricks_registered_model to create Models in Unity Catalog in Databricks.
• End to end workspace management guide.
• databricks_directory to manage directories in Databricks Workpace.
• databricks_mlflow_model to create models in the workspace model registry in Databricks.
• databricks_notebook to manage Databricks Notebooks.
• databricks_notebook data to export a notebook from Databricks Workspace.
• databricks_repo to manage Databricks Repos.