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If ModelArts is no longer used, stop or delete the services running in ModelArts and delete the data stored in OBS and EVS. Clearing Storage Data ModelArts data is stored in OBS. To stop storage billing, switch to the OBS console and delete the data and directories.
On the Training Jobs page, click Delete in the Operation column. In the displayed dialog box, click OK to delete the training job. Go to OBS and delete the OBS bucket and files used by the training job.
Table 2 Query Parameters Parameter Mandatory Type Description delete_source No Boolean Whether to delete the sample source files. Options: true: Delete the sample source files. false: Do not delete the sample source files. (Default value) label_type No Integer Label type.
Request Parameters None Response Parameters None Example Requests Delete the authorization of a specified user. DELETE https://{endpoint}/v2/{project_id}/authorizations?
Deleting an Algorithm Function This API is used to delete an algorithm. Debugging You can debug this API through automatic authentication in API Explorer or use the SDK sample code generated by API Explorer.
Deleting a Dataset Function This API is used to delete a dataset without deleting the source data of the dataset. Debugging You can debug this API through automatic authentication in API Explorer or use the SDK sample code generated by API Explorer.
Deleting a Dataset Delete a dataset based on the dataset ID. delete_dataset(session, dataset_id) Sample Code Delete a dataset. from modelarts.session import Session from modelarts.dataset import Dataset session = Session() Dataset.delete_dataset(session, dataset_id="68ZXdK6CZwgvUICOOdC
URI DELETE /v1/{project_id}/services/{resource_id}/tags/delete Table 1 Path Parameters Parameter Mandatory Type Description project_id Yes String Project ID.
Locate the the target resource pool in the list and choose > Delete in the Operation column. In the Delete Dedicated Resource Pool dialog box, enter DELETE in the text box and click OK.
Locate the row that contains the target resource pool, choose More > Delete in the Operation column. In the Delete Dedicated Resource Pool dialog box, enter DELETE in the text box and click OK.
URI DELETE /v2/{project_id}/training-jobs/{training_job_id} Table 1 Path Parameters Parameter Mandatory Type Description project_id Yes String Project ID.
Options: 0: Delete the label. 1: Delete the label and the samples that only contain this label, but do not delete source files. 2: Delete the label and the samples that only contain this label and also delete source files.
Options: false: Do not delete the source file. (Default value) true: Delete the source file. (Note: This operation may affect the dataset versions or other datasets that have used these files.
URI DELETE /v2/{project_id}/datasets/{dataset_id}/versions/{version_id} Table 1 Path Parameters Parameter Mandatory Type Description dataset_id Yes String Dataset ID. project_id Yes String Project ID.
URI DELETE /v2/{project_id}/processor-tasks/{task_id} Table 1 Path Parameters Parameter Mandatory Type Description project_id Yes String Project ID.
URI DELETE /v1/{project_id}/dev-servers/{id} Table 1 Path Parameters Parameter Mandatory Type Description id Yes String DevServer ID project_id Yes String Project ID. For details, see Obtaining a Project ID and Name.
Deleting a Dataset Version Delete a specified dataset version. dataset.delete_version(version_id) Sample Code Delete a specified dataset version. from modelarts.session import Session from modelarts.dataset import Dataset session = Session() dataset = Dataset(session, dataset_id)
Deleting a Labeling Team Function This API is used to delete a labeling team. Debugging You can debug this API through automatic authentication in API Explorer or use the SDK sample code generated by API Explorer.
Deleting an AI application Function This interface is used to delete an AI application based on the AI application ID.
Example Requests Delete a node pool.
