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MLflow client compatibility

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NOTE: Model registry and model experiment tracking are experiments. Provide feedback for model experiment tracking. Provide feedback for model registry.

MLflow is a popular open source tool for Machine Learning experiment tracking. GitLab Model experiment tracking and GitLab Model registry are compatible with the MLflow client. The setup 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, project, or group access token with at least the Developer role and the api permission.
  • The project ID. To find the project ID:
    1. On the left sidebar, select Search or go to and find your project.
    2. Select Settings > General.

To use MLflow client compatibility from a local environment:

  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:

    export MLFLOW_TRACKING_URI="<your gitlab endpoint>/api/v4/projects/<your project id>/ml/mlflow"
    export MLFLOW_TRACKING_TOKEN="<your_access_token>"
  2. If the training code contains the call to mlflow.set_tracking_uri(), remove it.

In the model registry, you can copy the tracking URI from the overflow menu in the top right by selecting the vertical ellipsis ({ellipsis_v}).

Model experiments

When running the training code, MLflow client can be used to create experiments, runs, models, model versions, log parameters, metrics, metadata and artifacts on GitLab.

After experiments are logged, they are listed under /<your project>/-/ml/experiments.

Runs are registered as candidates, which can be explored by selecting an experiment, model, or model version.

Associating a candidate to a CI/CD job

If your training code is being run from a CI/CD job, GitLab can use that information to enhance candidate metadata. To associate a candidate to a CI/CD job:

  1. In the Project CI variables, include the following variables:

    • MLFLOW_TRACKING_URI: "<your gitlab endpoint>/api/v4/projects/<your project id>/ml/mlflow"
    • MLFLOW_TRACKING_TOKEN: <your_access_token>
  2. In your training code within the run execution context, add the following code snippet:

    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

Model registry

You can also manage models and model versions by using the MLflow client. Models are registered under /<your project>/-/ml/models.

Models

Creating a model

client = MlflowClient()
model_name = '<your_model_name>'
description = 'Model description'
model = client.create_registered_model(model_name, description=description)

Notes

  • create_registered_model argument tags is ignored.
  • name must be unique within the project.
  • name cannot be the name of an existing experiment.

Fetching a model

client = MlflowClient()
model_name = '<your_model_name>'
model = client.get_registered_model(model_name)

Updating a model

client = MlflowClient()
model_name = '<your_model_name>'
description = 'New description'
client.update_registered_model(model_name, description=description)

Deleting a model

client = MlflowClient()
model_name = '<your_model_name>'
client.delete_registered_model(model_name)

Logging candidates to a model

Every model has an associated experiment with the same name prefixed by [model]. To log a candidate/run to the model, use the experiment passing the correct name:

client = MlflowClient()
model_name = '<your_model_name>'
exp = client.get_experiment_by_name(f"[model]{model_name}")
run = client.create_run(exp.experiment_id)

Model version

Creating a model version

client = MlflowClient()
model_name = '<your_model_name>'
description = 'Model version description'
model_version = client.create_model_version(model_name, source="", description=description)

If the version parameter is not passed, it will be auto-incremented from the latest uploaded version. You can set the version by passing a tag during model version creation. The version must follow SemVer format.

client = MlflowClient()
model_name = '<your_model_name>'
version = '<your_version>'
tags = { "gitlab.version": version }
client.create_model_version(model_name, version, description=description, tags=tags)

Notes

  • Argument run_id is ignored. Every model version behaves as a Candidate/Run. Creating a mode version from a run is not yet supported.
  • Argument source is ignored. GitLab will create a package location for the model version files.
  • Argument run_link is ignored.
  • Argument await_creation_for is ignored.

Updating a model

client = MlflowClient()
model_name = '<your_model_name>'
version = '<your_version>'
description = 'New description'
client.update_model_version(model_name, version, description=description)

Fetching a model version

client = MlflowClient()
model_name = '<your_model_name>'
version = '<your_version>'
client.get_model_version(model_name, version)

Getting latest versions of a model

client = MlflowClient()
model_name = '<your_model_name>'
client.get_latest_versions(model_name)

Notes

  • Argument stages is ignored.
  • Versions are ordered by last created.

Logging metrics and parameters to a model version

Every model version is also a candidate/run, allowing users to log parameters and metrics. The run ID can either be found at the Model version page in GitLab, or by using the MLflow client:

client = MlflowClient()
model_name = '<your_model_name>'
version = '<your_version>'
model_version = client.get_model_version(model_name, version)
run_id = model_version.run_id

# Your training code

client.log_metric(run_id, '<metric_name>', '<metric_value>')
client.log_param(run_id, '<param_name>', '<param_value>')
client.log_batch(run_id, metric_list, param_list, tag_list)

Logging artifacts to a model version

GitLab creates a package that can be used by the MLflow client to upload files.

client = MlflowClient()
model_name = '<your_model_name>'
version = '<your_version>'
model_version = client.get_model_version(model_name, version)
run_id = model_version.run_id

# Your training code

client.log_artifact(run_id, '<local/path/to/file.txt>', artifact_path="")
client.log_figure(run_id, figure, artifact_file="my_plot.png")
client.log_dict(run_id, my_dict, artifact_file="my_dict.json")
client.log_image(run_id, image, artifact_file="image.png")

Artifacts will then be available under https/<your project>/-/ml/models/<model_id>/versions/<version_id>.

Linking a model version to a CI/CD job

Similar to candidates, it is also possible to link a model version to a CI/CD job:

client = MlflowClient()
model_name = '<your_model_name>'
version = '<your_version>'
model_version = client.get_model_version(model_name, version)
run_id = model_version.run_id

# Your training code

if os.getenv('GITLAB_CI'):
    client.set_tag(model_version.run_id, 'gitlab.CI_JOB_ID', os.getenv('CI_JOB_ID'))

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. The MlflowClient counterparts of the methods below are also supported with the same caveats.

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 (16.3) If a name is not provided, the candidate receives a random nickname.
search_runs Yes 15.11 (16.4) experiment_ids supports only a single experiment ID with order by column or metric.
log_artifact Yes with caveat 15.11 (15.11) artifact_path must be empty. Does not support directories.
log_artifacts Yes with caveat 15.11 (15.11) artifact_path must be empty. 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.

Other MLflowClient methods:

Method Supported Version added Comments
create_registered_model Yes with caveats 16.8 See notes
get_registered_model Yes 16.8
delete_registered_model Yes 16.8
update_registered_model Yes 16.8
create_model_version Yes with caveats 16.8 See notes
get_model_version Yes 16.8
get_latest_versions Yes with caveats 16.8 See notes
update_model_version Yes 16.8
create_registered_model Yes 16.8
create_registered_model Yes 16.8

Limitations

  • The API GitLab supports is the one defined at MLflow version 2.7.1.
  • MLflow client methods not listed above are not supported.
  • During creation of experiments and runs, ExperimentTags are stored, even though they are not displayed.