--- stage: Create group: Incubation info: Machine Learning Experiment Tracking is a GitLab Incubation Engineering program. No technical writer assigned to this group. --- # MLFlow client integration **(FREE)** > [Introduced](https://gitlab.com/groups/gitlab-org/-/epics/8560) in GitLab 15.11 as an [Experiment](../../../policy/alpha-beta-support.md#experiment) release [with a flag](../../../administration/feature_flags.md) named `ml_experiment_tracking`. Disabled by default. NOTE: Model experiment tracking is an [experimental feature](../../../policy/alpha-beta-support.md). Refer to for feedback and feature requests. [MLFlow](https://mlflow.org/) is one of the most popular open source tools for Machine Learning Experiment Tracking. GitLab works as a backend to the MLFlow Client, [logging experiments](../ml/experiment_tracking/index.md). Setting up your integrations requires minimal changes to existing code. GitLab plays the role of proxy server, both for artifact storage and tracking data. It reflects the MLFlow [Scenario 5](https://www.mlflow.org/docs/latest/tracking.html#scenario-5-mlflow-tracking-server-enabled-with-proxied-artifact-storage-access). ## Enable MLFlow client integration Prerequisites: - A [personal access token](../../../user/profile/personal_access_tokens.md) for the project, with minimum access level of `api`. - The project ID. To find the project ID, on the top bar, select **Main menu > Projects** and find your project. On the left sidebar, select **Settings > General**. To enable MLFlow client integration: 1. Set the tracking URI and token environment variables on the host that runs the code. This can be your local environment, CI pipeline, or remote host. For example: ```shell export MLFLOW_TRACKING_URI="http:///api/v4/projects//ml/mlflow" export MLFLOW_TRACKING_TOKEN="" ``` 1. If your training code contains the call to `mlflow.set_tracking_uri()`, remove it. When running the training code, MLFlow creates experiments, runs, log parameters, metrics, metadata and artifacts on GitLab. After experiments are logged, they are listed under `//-/ml/experiments`. Runs are registered as: - Model Candidates, which can be explored by selecting an experiment. - Tags, which are registered as metadata. ## Supported MlFlow client methods and caveats GitLab supports these methods from the MLFlow client. Other methods might be supported but were not tested. More information can be found in the [MLFlow Documentation](https://www.mlflow.org/docs/1.28.0/python_api/mlflow.html). | Method | Supported | Version Added | Comments | |--------------------------|------------------|----------------|----------| | `get_experiment` | Yes | 15.11 | | | `get_experiment_by_name` | Yes | 15.11 | | | `set_experiment` | Yes | 15.11 | | | `get_run` | Yes | 15.11 | | | `start_run` | Yes | 15.11 | | | `log_artifact` | Yes with caveat | 15.11 | (15.11) `artifact_path` must be empty string. Does not support directories. | `log_artifacts` | Yes with caveat | 15.11 | (15.11) `artifact_path` must be empty string. Does not support directories. | `log_batch` | Yes | 15.11 | | | `log_metric` | Yes | 15.11 | | | `log_metrics` | Yes | 15.11 | | | `log_param` | Yes | 15.11 | | | `log_params` | Yes | 15.11 | | | `log_figure` | Yes | 15.11 | | | `log_image` | Yes | 15.11 | | | `log_text` | Yes with caveat | 15.11 | (15.11) Does not support directories. | `log_dict` | Yes with caveat | 15.11 | (15.11) Does not support directories. | `set_tag` | Yes | 15.11 | | | `set_tags` | Yes | 15.11 | | | `set_terminated` | Yes | 15.11 | | | `end_run` | Yes | 15.11 | | | `update_run` | Yes | 15.11 | | | `log_model` | Partial | 15.11 | (15.11) Saves the artifacts, but not the model data. `artifact_path` must be empty. ## Limitations - The API GitLab supports is the one defined at MLFlow version 1.28.0. - API endpoints not listed above are not supported. - During creation of experiments and runs, ExperimentTags are stored, even though they are not displayed. - MLFlow Model Registry is not supported.