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/ directory in the OBS bucket.
The field value is the OBS URL of the file in any of the following formats: Bucket path <obs path>{{Bucket name}}/{{Object name}}/File name, which is used to access your OBS data. You can access the path for obtaining an object in OBS. <obs path> can be obs:// or s3://.
algorithm = wf.steps.MrsJobAlgorithm( boot_file="obs://spark-sql/wordcount.py", # OBS path to the boot script parameters=[wf.AlgorithmParameters(name="run_args", value="--master,yarn-cluster")] ) inputs = wf.steps.MrsJobInput(name="mrs_input", data=wf.data.OBSPath
<obs path> can be obs:// or s3://. Share link generated by OBS, including signature information. It applies to accessing OBS data of other users. The link has a validity period. Perform operations within the period.
Solution Obtain the size of an OBS folder. mox.file.get_size('obs://bucket_name/sub_dir_0/sub_dir_1', recursive=True) Obtain the size of an OBS file. mox.file.get_size('obs://bucket_name/obs_file.txt') Parent topic: MoXing
Yes str, Placeholder input Template input path, which can be an OBS file path or OBS directory path.
Training Jobs OBS Operation Issues In-Cloud Migration Adaptation Issues Hard Faults Due to Space Limit Internet Access Issues Permission Issues GPU Issues Service Code Issues Locating Training Job Suspension Running a Training Job Failed Training Jobs Created in a Dedicated Resource
Prepare the tenant ID and IAM ID for OBS bucket configuration. Send the prepared information to Huawei technical support, who will configure an OBS bucket policy based on your information. You can upload the collected logs to the corresponding OBS bucket.
Delete the OBS bucket and data stored in OBS. Workflow: Stop training jobs and real-time services created for running workflows. Delete the OBS bucket and data stored in OBS. Notebook: Delete notebook instances. Delete the OBS bucket and data stored in OBS.
The value can be OBS or PFS. Storage Path: Set the OBS path for storing notebook data. If you want to use existing files or data, upload them to the specified OBS path.
Options: If type is set to OBS, source is an OBS path. If type is set to DATASET, source is a dataset ID. type String Type of a working path. Options: OBS: OBS path DATASET: dataset version_id String Version of a dataset. version_name String Name of a dataset version.
Obtain the manifest files of the two datasets from the OBS path set for Output Dataset Path. Create empty dataset C and select an empty OBS folder for Input Dataset Path. Import the manifest files of datasets A and B to dataset C.
# Define a dataset. dataset = wf.data.DatasetPlaceholder(name="input_dataset") # Define OBS data. obs = wf.data.OBSPlaceholder(name = "obs_placeholder_name", object_type = "directory" ) # object_type must be file or directory.
Verify OBS permissions. In the service list in the upper left corner, select OBS. The OBS management console is displayed. Click Create Bucket in the upper right corner. If this operation is successful, you have obtained OBS operation permissions. Verify SWR permissions.
If this parameter is set to True, you need to set resume to the OBS path where the trained model is stored. resume empty If predict is set to True, enter the OBS path where the TensorFlow model file is stored for inference and prediction.
The input data, output data, and cached data generated during AI development using ModelArts Standard can be stored in OBS buckets. Therefore, you are advised to create an OBS bucket before using ModelArts. You can also create an OBS bucket later when needed.
Options: If type is set to OBS, source is an OBS path. If type is set to DATASET, source is a dataset ID. type String Type of a working path. Options: OBS: OBS path DATASET: dataset version_id String Version of a dataset. version_name String Name of a dataset version.
Select an OBS path as Input Dataset PathImport Path, and select another OBS path as Output Dataset Path. Figure 1 Data import and output paths After setting the parameters, click CreateSubmit in the lower right corner to create a dataset.
Configuring Pipelines A preset image-based algorithm obtains data from an OBS bucket or dataset for model training. The training output is stored in an OBS bucket.
For details about how to upload a file using OBS Browser, see Uploading a File. Use MoXing to copy the source code uploaded to OBS to a notebook instance in the development environment.
