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As part of the data science team, you want to try different modeling approaches
during experimentation phase.To guarantee reproducibility, each approach has
different parameters that you need to manually track. Vertex AI SDK for Python
autologging, which is a one-line code SDK capability leveraging MLflow,
provides automatic metrics and parameters tracking associated with your
Vertex AI Experiments and experiment runs.
Notebook: Vertex AI Experiments Autologging
In the "Vertex AI Experiments: Autologging" notebook,
you'll learn how to use Vertex AI Experiments to:
Enable autologging in the Vertex AI SDK for Python.
Train scikit-learn model and see the resulting experiment run with metrics
and parameters autologged to Vertex AI Experiments without setting
an experiment run.
Train TensorFlow model, check autologged metrics and parameters to
Vertex AI Experiments by manually setting an experiment run with
aiplatform.start_run() and aiplatform.end_run().
Disable autologging in the Vertex AI SDK for Python, train a PyTorch model and
check that none of the parameters or metrics are logged.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-29 UTC."],[],[],null,["# Autologging: Notebook\n\nAs part of the data science team, you want to try different modeling approaches\nduring experimentation phase.To guarantee reproducibility, each approach has\ndifferent parameters that you need to manually track. Vertex AI SDK for Python\nautologging, which is a one-line code SDK capability leveraging MLflow,\nprovides automatic metrics and parameters tracking associated with your\nVertex AI Experiments and experiment runs.\n\nNotebook: Vertex AI Experiments Autologging\n-------------------------------------------\n\n| To see an example of autologging in Vertex AI Experiments,\n| run the \"Vertex AI Experiments: Autologging\" notebook in one of the following\n| environments:\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments_autologging.ipynb)\n|\n|\n| \\|\n|\n| [Open in Colab Enterprise](https://console.cloud.google.com/vertex-ai/colab/import/https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fexperiments%2Fget_started_with_vertex_experiments_autologging.ipynb)\n|\n|\n| \\|\n|\n| [Open\n| in Vertex AI Workbench](https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fexperiments%2Fget_started_with_vertex_experiments_autologging.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments_autologging.ipynb)\n\nIn the \"Vertex AI Experiments: Autologging\" notebook,\nyou'll learn how to use Vertex AI Experiments to:\n\n- Enable autologging in the Vertex AI SDK for Python.\n- Train scikit-learn model and see the resulting experiment run with metrics and parameters autologged to Vertex AI Experiments without setting an experiment run.\n- Train TensorFlow model, check autologged metrics and parameters to Vertex AI Experiments by manually setting an experiment run with `aiplatform.start_run()` and `aiplatform.end_run()`.\n- Disable autologging in the Vertex AI SDK for Python, train a PyTorch model and check that none of the parameters or metrics are logged.\n\nRelevant content\n----------------\n\n- [Autolog data](/vertex-ai/docs/experiments/autolog-data#auto-created)"]]