102 lines
5.2 KiB
Markdown
102 lines
5.2 KiB
Markdown
---
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stage: Create
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group: Incubation
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info: Machine Learning Experiment Tracking is a GitLab Incubation Engineering program. No technical writer assigned to this group.
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---
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# MLflow client integration **(FREE)**
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> [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.
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NOTE:
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Model experiment tracking is an [experimental feature](../../../policy/alpha-beta-support.md).
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Refer to <https://gitlab.com/gitlab-org/gitlab/-/issues/381660> for feedback and feature requests.
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[MLflow](https://mlflow.org/) is a popular open source tool for Machine Learning Experiment Tracking.
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GitLab works as a backend to the MLflow Client, [logging experiments](../ml/experiment_tracking/index.md).
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Setting up your integrations requires minimal changes to existing code.
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GitLab plays the role of a MLflow server. Running `mlflow server` is not necessary.
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## Enable MLflow client integration
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Prerequisites:
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- A [personal access token](../../../user/profile/personal_access_tokens.md) for the project, with minimum access level of `api`.
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- 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**.
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To enable MLflow client integration:
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1. Set the tracking URI and token environment variables on the host that runs the code.
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This can be your local environment, CI pipeline, or remote host. For example:
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```shell
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export MLFLOW_TRACKING_URI="http://<your gitlab endpoint>/api/v4/projects/<your project id>/ml/mlflow"
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export MLFLOW_TRACKING_TOKEN="<your_access_token>"
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```
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1. If your training code contains the call to `mlflow.set_tracking_uri()`, remove it.
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When running the training code, MLflow creates experiments, runs, log parameters, metrics, metadata
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and artifacts on GitLab.
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After experiments are logged, they are listed under `/<your project>/-/ml/experiments`.
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Runs are registered as:
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- Model Candidates, which can be explored by selecting an experiment.
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- Tags, which are registered as metadata.
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## Associating a candidate to a CI/CD job
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> [Introduced](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/119454) in GitLab 16.1.
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If your training code is being run from a CI/CD job, GitLab can use that information to enhance
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candidate metadata. To do so, add the following snippet to your training code within the run
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execution context:
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```python
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with mlflow.start_run(run_name=f"Candidate {index}"):
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# Your training code
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# Start of snippet to be included
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if os.getenv('GITLAB_CI'):
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mlflow.set_tag('gitlab.CI_JOB_ID', os.getenv('CI_JOB_ID'))
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# End of snippet to be included
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```
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## Supported MLflow client methods and caveats
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GitLab supports these methods from the MLflow client. Other methods might be supported but were not
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tested. More information can be found in the [MLflow Documentation](https://www.mlflow.org/docs/1.28.0/python_api/mlflow.html).
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| Method | Supported | Version Added | Comments |
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|--------------------------|------------------|----------------|----------|
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| `get_experiment` | Yes | 15.11 | |
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| `get_experiment_by_name` | Yes | 15.11 | |
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| `set_experiment` | Yes | 15.11 | |
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| `get_run` | Yes | 15.11 | |
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| `start_run` | Yes | 15.11 | |
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| `log_artifact` | Yes with caveat | 15.11 | (15.11) `artifact_path` must be empty string. Does not support directories.
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| `log_artifacts` | Yes with caveat | 15.11 | (15.11) `artifact_path` must be empty string. Does not support directories.
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| `log_batch` | Yes | 15.11 | |
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| `log_metric` | Yes | 15.11 | |
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| `log_metrics` | Yes | 15.11 | |
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| `log_param` | Yes | 15.11 | |
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| `log_params` | Yes | 15.11 | |
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| `log_figure` | Yes | 15.11 | |
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| `log_image` | Yes | 15.11 | |
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| `log_text` | Yes with caveat | 15.11 | (15.11) Does not support directories.
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| `log_dict` | Yes with caveat | 15.11 | (15.11) Does not support directories.
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| `set_tag` | Yes | 15.11 | |
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| `set_tags` | Yes | 15.11 | |
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| `set_terminated` | Yes | 15.11 | |
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| `end_run` | Yes | 15.11 | |
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| `update_run` | Yes | 15.11 | |
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| `log_model` | Partial | 15.11 | (15.11) Saves the artifacts, but not the model data. `artifact_path` must be empty.
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## Limitations
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- The API GitLab supports is the one defined at MLflow version 1.28.0.
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- API endpoints not listed above are not supported.
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- During creation of experiments and runs, ExperimentTags are stored, even though they are not displayed.
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- MLflow Model Registry is not supported.
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