create_custom_training_job_from_component 유틸리티는 제공된 컨테이너 또는 Python 구성요소를 Vertex AI에서 커스텀 작업을 실행하는 구성요소로 변환합니다. 이 작업은 커스텀 학습 작업 만들기를 단순화합니다. 제공된 구성요소의 모든 입력 및 출력은 생성된 학습 작업 연산자로 복사됩니다.
[[["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: 2025-09-09(UTC)"],[],[],null,["# CustomJob components\n\n[Custom training jobs](/vertex-ai/docs/training/create-custom-job) let you run your custom machine\nlearning (ML) training code in Vertex AI.\n\n`CustomTrainingJobOp`\n---------------------\n\nThe [`CustomTrainingJobOp`](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/custom_job.html#v1.custom_job.CustomTrainingJobOp) component exposes the full functionalities of the [`CustomJob` resource](/vertex-ai/docs/reference/rest/v1/projects.locations.customJobs), to allow both single and distributed training using a [`ContainerSpec`](/vertex-ai/docs/reference/rest/v1/CustomJobSpec#ContainerSpec) or [`PythonPackageSpec`](/vertex-ai/docs/reference/rest/v1/CustomJobSpec#PythonPackageSpec) instance.\n\n`create_custom_training_job_from_component` function\n----------------------------------------------------\n\nThe [`create_custom_training_job_from_component`](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/custom_job.html#v1.custom_job.create_custom_training_job_from_component) utility\nconverts a given container or Python component to a component that runs a\ncustom job in Vertex AI. This simplifies the creation of custom training\njobs. All inputs and outputs of the supplied component will be copied over to\nthe constructed training job operator.\n\nNote that this utility constructs a `ClusterSpec`, where the primary and all the\nworkers use the same specification, meaning all disk and machine\nspecification-related parameters will apply to all replicas. This is suitable\nfor use cases where, for example, you are training with\n[`MultiWorkerMirroredStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/MultiWorkerMirroredStrategy) or\n[`MirroredStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy).\n\nThis component does not support `CustomJob` Python package training, or\ndistributed training with different worker pool specs.\n\nAPI reference\n-------------\n\n- For component reference, see the [Google Cloud SDK reference for CustomJob components](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/custom_job.html).\n- For Vertex AI API reference, see the [`CustomJob` resource](/vertex-ai/docs/reference/rest/v1/projects.locations.customJobs) page.\n\nVersion history and release notes\n---------------------------------\n\nTo learn more about the version history and changes to the Google Cloud Pipeline Components SDK, see the [Google Cloud Pipeline Components SDK Release Notes](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/release.html).\n\nTechnical support contacts\n--------------------------\n\nIf you have any questions, reach out to\n[kubeflow-pipelines-components@google.com](mailto: kubeflow-pipelines-components@google.com)."]]
