81 lines
3.4 KiB
Markdown
81 lines
3.4 KiB
Markdown
---
|
|
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.
|