CML Experiment Tracking through MLflow API

CML’s experiment tracking features allow you to use the MLflow client library for logging parameters, code versions, metrics, and output files when running your machine learning code. The MLflow library is available in CML Sessions without you having to install it. CML also provides a UI for later visualizing the results. MLflow tracking lets you log and query experiments using the following logging functions:

mlflow.create_experiment() creates a new experiment and returns its ID. Runs can be launched under the experiment by passing the experiment ID to mlflow.start_run.
Cloudera recommends that you create an experiment to organize your runs. You can also create experiments using the UI.
mlflow.set_experiment() sets an experiment as active. If the experiment does not exist, mlflow.set_experiment creates a new experiment. If you do not wish to use the set_experiment method, a default experiment is selected.
Cloudera recommends that you set the experiment using mlflow.set_experiment.
mlflow.start_run() returns the currently active run (if one exists), or starts a new run and returns a mlflow.ActiveRun object usable as a context manager for the current run. You do not need to call start_run explicitly; calling one of the logging functions with no active run automatically starts a new one.
mlflow.end_run() ends the currently active run, if any, taking an optional run status.
mlflow.active_run() returns a mlflow.entities.Run object corresponding to the currently active run, if any.
note
You cannot access currently-active run attributes (parameters, metrics, etc.) through the run returned by mlflow.active_run. In order to access such attributes, use the mlflow.tracking.MlflowClient as follows:
```
client = mlflow.tracking.MlflowClient() 
data = client.get_run(mlflow.active_run().info.run_id).data
```
mlflow.log_param() logs a single key-value parameter in the currently active run. The key and value are both strings. Use mlflow.log_params() to log multiple parameters at once.
mlflow.log_metric() logs a single key-value metric for the current run. The value must always be a number. MLflow remembers the history of values for each metric. Use mlflow.log_metrics() to log multiple metrics at once.
Parameters:
- key - Metric name (string)
- value - Metric value (float). Note that some special values such as +/- Infinity may be replaced by other values depending on the store. For example, the SQLAlchemy store replaces +/- Infinity with max / min float values.
- step - Metric step (int). Defaults to zero if unspecified.
Syntax - mlflow.log_metrics(metrics: Dict[str, float], step: Optional[int] = None) → None
mlflow.set_tag() sets a single key-value tag in the currently active run. The key and value are both strings. Use mlflow.set_tags() to set multiple tags at once.
mlflow.log_artifact() logs a local file or directory as an artifact, optionally taking an artifact_path to place it within the run’s artifact URI. Run artifacts can be organized into directories, so you can place the artifact in a directory this way.
mlflow.log_artifacts() logs all the files in a given directory as artifacts, again taking an optional artifact_path.
mlflow.get_artifact_uri() returns the URI that artifacts from the current run should be logged to.

For more information on MLflow API commands used for tracking, see MLflow Tracking.