Autolog data to an experiment run

Autologging is a feature in the Vertex AI SDK that automatically logs parameters and metrics from model-training runs to Vertex AI Experiments. This can save time and effort by eliminating the need to manually log this data. Currently, autologging only supports parameter and metric logging.

Autolog data

There are two options for autologging data to Vertex AI Experiments.

  1. Let the Vertex AI SDK automatically create ExperimentRun resources for you.
  2. Specify the ExperimentRun resource that you'd like autologged parameters and metrics to be written to.

Auto-created

The Vertex AI SDK for Python handles creating ExperimentRun resources for you. Automatically created ExperimentRun resources will have a run name in the following format: {ml-framework-name}-{timestamp}-{uid}, for example: "tensorflow-2023-01-04-16-09-20-86a88".

The following sample uses the init method, from the aiplatform Package functions.

Python

from typing import Optional, Union

from google.cloud import aiplatform


def autologging_with_auto_run_creation_sample(
    experiment_name: str,
    project: str,
    location: str,
    experiment_tensorboard: Optional[Union[str, aiplatform.Tensorboard]] = None,
):
    aiplatform.init(
        experiment=experiment_name,
        project=project,
        location=location,
        experiment_tensorboard=experiment_tensorboard,
    )

    aiplatform.autolog()

    # Your model training code goes here

    aiplatform.autolog(disable=True)
  • experiment_name: Provide a name for your experiment. You can find your list of experiments in the Google Cloud console by selecting Experiments in the section nav.
  • experiment_tensorboard: (Optional) Provide a name for your Vertex AI TensorBoard instance.
  • project: Your project ID. You can find these Project IDs in the Google Cloud console welcome page.
  • location: See List of available locations

User-specified

Provide your own ExperimentRun names and have metrics and parameters from multiple model-training runs logged to the same ExperimentRun. Any metrics from model to the current run set by calling aiplatform.start_run("your-run-name") until aiplatform.end_run() is called.

The following sample uses the init method, from the aiplatform Package functions.

Python

from typing import Optional, Union

from google.cloud import aiplatform


def autologging_with_manual_run_creation_sample(
    experiment_name: str,
    run_name: str,
    project: str,
    location: str,
    experiment_tensorboard: Optional[Union[str, aiplatform.Tensorboard]] = None,
):
    aiplatform.init(
        experiment=experiment_name,
        project=project,
        location=location,
        experiment_tensorboard=experiment_tensorboard,
    )

    aiplatform.autolog()

    aiplatform.start_run(run=run_name)

    # Your model training code goes here

    aiplatform.end_run()

    aiplatform.autolog(disable=True)
  • experiment_name: Provide the name of your experiment.
  • run_name: Provide a name for your experiment run. You can find your list of experiments in the Google Cloud console by selecting Experiments in the section nav.
  • project: Your project ID. You can find these Project IDs in the Google Cloud console welcome page.
  • location: See List of available locations
  • experiment_tensorboard: (Optional) Provide a name for your Vertex AI TensorBoard instance.

Vertex AI SDK autologging uses MLFlow's autologging in its implementation. Evaluation metrics and parameters from the following frameworks are logged to your ExperimentRun when autologging is enabled.

  • Fastai
  • Gluon
  • Keras
  • LightGBM
  • Pytorch Lightning
  • Scikit-learn
  • Spark
  • Statsmodels
  • XGBoost

View autologged parameters and metrics

Use the Vertex AI SDK for Python to compare runs and get runs data. The Google Cloud console provides an easy way to compare these runs.

Relevant notebook sample

Blog post