1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
|
---
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 <https://gitlab.com/gitlab-org/gitlab/-/issues/381660> for feedback and feature requests.
[MLflow](https://mlflow.org/) is a popular open source tool 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 a MLflow server. Running `mlflow server` is not necessary.
## 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://<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 creates experiments, runs, log parameters, metrics, metadata
and artifacts on GitLab.
After experiments are logged, they are listed under `/<your project>/-/ml/experiments`.
Runs are registered as:
- Model Candidates, which can be explored by selecting an experiment.
- Tags, which are registered as metadata.
## Associating a candidate to a CI/CD job
> [Introduced](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/119454) in GitLab 16.1.
If your training code is being run from a CI/CD job, GitLab can use that information to enhance
candidate metadata. To do so, add the following snippet to your training code within the run
execution context:
```python
with mlflow.start_run(run_name=f"Candidate {index}"):
# Your training code
# Start of snippet to be included
if os.getenv('GITLAB_CI'):
mlflow.set_tag('gitlab.CI_JOB_ID', os.getenv('CI_JOB_ID'))
# End of snippet to be included
```
## 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.
|
