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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 MLFlow Client Integration
Complete this task to 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**.
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 stored, even though they are not displayed.
- 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.
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