检测到您已登录华为云国际站账号，为了您更好的体验，建议您访问国际站服务网站 https://www.huaweicloud.com/intl/zh-cn
不再显示此消息
检测到您已登录华为云国际站账号，为了您更好的体验，建议您访问国际站服务网站 https://www.huaweicloud.com/intl/zh-cn
不再显示此消息
But using data storage, importing models, or deploying services may incur fees because they rely on OBS and SWR. For details, see Billing Overview. Adding Dependency Service Authorization Access to MaaS features requires authorization from OBS and SWR.
Critical Table 6 Events for dynamic OBS mounting Event Description Severity DynamicMountStorage The OBS storage is mounted. Major DynamicUnmountStorage The OBS storage is unmounted.
If the AI application is imported from OBS or a training job, the total size of the base image, model files, code, data files, and software packages cannot exceed the limit.
Normal [model 0.0.1] OBS bucket, OBS parallel file system, or SFS Turbo mounted successfully. [%s] %s volume successfully. N/A During service deployment and running, key events can both be manually and automatically refreshed.
Normal [model 0.0.1] OBS bucket, OBS parallel file system, or SFS Turbo mounted successfully. [%s] %s volume successfully. N/A During service deployment and running, key events can both be manually and automatically refreshed.
Symptom: After the labeled data is uploaded to OBS and synchronized, the data is displayed as unlabeled. Possible causes: Automatic encryption is enabled in the OBS bucket. Solution: Create an OBS bucket and upload data again, or disable bucket encryption and upload data again.
You can call SDKs on a notebook instance to perform operations such as OBS management, job management, model management, and service management by referring to the SDK reference.
AI Gallery stores file assets in official OBS buckets. AI Gallery stores image assets in official SWR repositories. AI Gallery stores personal information of users in databases. AI Gallery encrypts sensitive personal information, such as mobile numbers and emails, in databases.
#mox.file.copy_parallel(args.data_url, local_data_path) ... # Upload the local container data to the OBS path.
If your model is trained locally or on a third-party platform, upload the model to OBS and then import the model from OBS to ModelArts.
Upload the training script mindspore-verification.py to obs://test-modelarts/ascend/demo-code/ in the OBS bucket. Upload the five Ascend startup scripts to the obs://test-modelarts/ascend/demo-code/run_ascend/ folder in the OBS bucket.
Debugging a Training Job Debugging a Model 2020-04-10 Added the following sections: OBS Management: Added OBS-related operation guide. 2019-08-20 This is the second official release. Added the following sections: Training Management: Added the local training function.
Symptom: After the labeled data is uploaded to OBS and synchronized, the data is displayed as unlabeled. Possible causes: Automatic encryption is enabled in the OBS bucket. Solution: Create an OBS bucket and upload data again, or disable bucket encryption and upload data again.
String Error code. error_msg String Error message. file_statistics FileCopyProgress object File replication progress finished_file_count Long Number of files that have been transferred. finished_file_size Long Size of the file that has been transferred, in bytes. import_path String OBS
Data and its labeling information are still stored in the OBS directory. However, this affects version management. Exercise caution when performing this operation. Parent topic: Preparing and Processing Data
Select the OBS directory where the training code file is stored. Upload code to the OBS bucket beforehand. The total size of files in the directory cannot exceed 5 GB, the number of files cannot exceed 1,000, and the folder depth cannot exceed 32.
Directory Structure of Dataset Versions Datasets are managed based on OBS directories. After a new version is published, the directory is generated based on the new version in the output dataset path. Take an image classification dataset as an example.
Prerequisites The training job has been executed, and the model has been stored in the OBS directory where the training output is stored (the input parameter is train_url).
Only OBS parallel file systems are supported. Source Path: Select the storage path of the parallel file. A cross-region OBS parallel file system cannot be selected. Mount Path: Enter the mount path of the container, for example, /obs-mount/. Select a new directory.
Name: name of the new dataset Storage Path: input path of the new dataset, that is, the OBS path where the data to be exported is stored Output Path: output path of the new dataset, that is, the output path after labeling is complete The output path cannot be the same as the storage
