자동 로깅은 모델 학습 실행의 파라미터 및 측정항목을 Vertex AI Experiments에 자동으로 로깅하는 Vertex AI SDK의 기능입니다. 이 기능을 사용하면 이 데이터를 수동으로 로깅할 필요가 없으므로 시간과 노력을 절약할 수 있습니다. 자동 로깅은 파라미터 및 측정항목 로깅만 지원합니다.
데이터 자동 로깅
Vertex AI Experiments에 데이터를 자동 로깅하는 데에는 두 가지 옵션이 있습니다.
Vertex AI SDK는 사용자를 위해 ExperimentRun 리소스를 자동으로 만듭니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(UTC)"],[],[],null,["# Autolog data to an experiment run\n\nAutologging is a feature in the Vertex AI SDK that automatically\nlogs parameters and metrics from model-training runs to\nVertex AI Experiments. This can save time and effort by eliminating the\nneed to manually log this data. Autologging only supports parameter\nand metric logging.\n\nAutolog data\n------------\n\nThere are two options for autologging data to Vertex AI Experiments.\n\n1. Let the Vertex AI SDK automatically create [ExperimentRun](https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.ExperimentRun?_gl=1*m6thp1*_ga*NDY0ODI5NjQ5LjE2ODIxMjU5Nzc.*_ga_4LYFWVHBEB*MTY4MjM2MDE5NC40LjEuMTY4MjM2MDcxNy4wLjAuMA..#google_cloud_aiplatform_ExperimentRun) resources for you. \n2. Specify the ExperimentRun resource that you'd like autologged parameters and metrics to be written to. \n\n### Auto-created\n\n\nThe Vertex AI SDK for Python handles creating ExperimentRun resources for you.\nAutomatically created ExperimentRun resources will have a run name in the following format:\n`{ml-framework-name}-{timestamp}-{uid}`,\nfor example: \"tensorflow-2023-01-04-16-09-20-86a88\". \n\nThe following sample uses the [`init`](/python/docs/reference/aiplatform/latest/google.cloud.aiplatform#google_cloud_aiplatform_init) method,\nfrom the `aiplatform` [Package\nfunctions](/python/docs/reference/aiplatform/latest/google.cloud.aiplatform#functions).\n\n### Python\n\n from typing import Optional, Union\n\n from google.cloud import aiplatform\n\n\n def autologging_with_auto_run_creation_sample(\n experiment_name: str,\n project: str,\n location: str,\n experiment_tensorboard: Optional[Union[str, aiplatform.Tensorboard]] = None,\n ):\n aiplatform.init(\n experiment=experiment_name,\n project=project,\n location=location,\n experiment_tensorboard=experiment_tensorboard,\n )\n\n aiplatform.autolog()\n\n # Your model training code goes here\n\n aiplatform.autolog(disable=True)\n\n- `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.\n- `experiment_tensorboard`: (Optional) Provide a name for your Vertex AI TensorBoard instance.\n- `project`: . You can find these Project IDs in the Google Cloud console [welcome](https://console.cloud.google.com/welcome) page.\n- `location`: See [List of available locations](/vertex-ai/docs/general/locations)\n\n### User-specified\n\n\nProvide your own ExperimentRun names and have metrics and parameters\nfrom multiple model-training runs logged to the same ExperimentRun. Any metrics from model\nto the current run set by calling `aiplatform.start_run(\"your-run-name\")` until\n`aiplatform.end_run()` is called. \n\nThe following sample uses the [`init`](/python/docs/reference/aiplatform/latest/google.cloud.aiplatform#google_cloud_aiplatform_init) method,\nfrom the `aiplatform` [Package functions](/python/docs/reference/aiplatform/latest/google.cloud.aiplatform#functions).\n\n### Python\n\n from typing import Optional, Union\n\n from google.cloud import aiplatform\n\n\n def autologging_with_manual_run_creation_sample(\n experiment_name: str,\n run_name: str,\n project: str,\n location: str,\n experiment_tensorboard: Optional[Union[str, aiplatform.Tensorboard]] = None,\n ):\n aiplatform.init(\n experiment=experiment_name,\n project=project,\n location=location,\n experiment_tensorboard=experiment_tensorboard,\n )\n\n aiplatform.autolog()\n\n aiplatform.start_run(run=run_name)\n\n # Your model training code goes here\n\n aiplatform.end_run()\n\n aiplatform.autolog(disable=True)\n\n- `experiment_name`: Provide the name of your experiment.\n- `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.\n- `project`: . You can find these Project IDs in the Google Cloud console [welcome](https://console.cloud.google.com/welcome) page.\n- `location`: See [List of available locations](/vertex-ai/docs/general/locations)\n- `experiment_tensorboard`: (Optional) Provide a name for your Vertex AI TensorBoard instance.\n\nVertex AI SDK autologging uses MLFlow's autologging in its implementation.\nEvaluation metrics and parameters from the following frameworks are logged\nto your ExperimentRun when autologging is enabled.\n\n- Fastai\n- Gluon\n- Keras\n- LightGBM\n- Pytorch Lightning\n- Scikit-learn\n- Spark\n- Statsmodels\n- XGBoost\n\nView autologged parameters and metrics\n--------------------------------------\n\nUse the Vertex AI SDK for Python\nto [compare runs](/vertex-ai/docs/experiments/compare-analyze-runs#compare-runs) and get\nruns data.\nThe [Google Cloud console](/vertex-ai/docs/experiments/compare-analyze-runs#console-compare-analyze-runs)\nprovides an easy way to compare these runs.\n\nRelevant notebook sample\n------------------------\n\n- [Autolog data](/vertex-ai/docs/experiments/user-journey/uj-autologging)\n\nBlog post\n---------\n\n- [How you can automate ML experiment tracking with Vertex AI Experiments\n autologging](https://cloud.google.com/blog/products/ai-machine-learning/effortless-tracking-of-your-vertex-ai-model-training)"]]
