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+---
+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 15.6 as an [Alpha](../../../policy/alpha-beta-support.md#alpha-features) release [with a flag](../../../administration/feature_flags.md) named `ml_experiment_tracking`. Disabled by default.
+
+DISCLAIMER:
+MLFlow Client Integration is an experimental feature being developed by the Incubation Engineering Department,
+and will receive significant changes over time.
+
+[MLFlow](https://mlflow.org/) is one of the most popular open source tools for Machine Learning Experiment Tracking.
+GitLabs 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 MFlow Client Integration
+
+Complete this task to enable MFlow 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**.
+
+1. Set the tracking URI and token environment variables on the host that runs the code (your local environment, CI pipeline, or remote host).
+
+   For example:
+
+   ```shell
+   export MLFLOW_TRACKING_URI="http://<your gitlab endpoint>/api/v4/projects/<your project id>/ml/mlflow"
+   export MLFLOW_TRACKING_TOKEN="<your_access_token>"
+   ```
+
+1. If your training code contains the call to `mlflow.set_tracking_uri()`, remove it.
+
+When running the training code, MLFlow will create experiments, runs, log parameters, metrics,
+and artifacts on GitLab.
+
+After experiments are logged, they are listed under `/<your project>/-/ml/experiments`. Runs are registered as Model Candidates,
+that can be explored by selecting an experiment.
+
+## 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, tags are ExperimentTags and RunTags are ignored.
+- MLFLow Model Registry is not supported.
+
+## Supported methods and caveats
+
+This is a list of methods we support 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).
+
+### `set_experiment`
+
+Accepts both experiment_name and experiment_id
+
+### `start_run()`
+
+- Nested runs have not been tested.
+- `run_name` is not supported
+
+### `log_param()`, `log_params()`, `log_metric()`, `log_metrics()`
+
+Work as defined by the documentation
+
+### `log_artifact()`, `log_artifacts()`
+
+`artifact_path` must be empty string.
+
+### `log_model()`
+
+This is an experimental method in MLFlow, and partial support is offered. It stores the model artifacts, but does
+not log the model information. The `artifact_path` parameter must be set to `''`, because Generic Packages do not support folder
+structure.