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After decompression, delete the original file to avoid affecting data reading during quantization: rm -f val.jsonl.zst Set DATASET_ID to the directory containing the val.jsonl file: DATASET_ID = "<local-dir>" Configure the quantization algorithm.
Call the API for deleting a training job to delete the job if it is no longer needed. Request body: URI: DELETE https://{ma_endpoint}/v2/{project_id}/training-jobs/{training_job_id} Request header: X-Auth-Token →MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...
Allow" }, { "Action": [ "smn:*:*" ], "Effect": "Allow" }, { "Action": [ "modelarts:pool:create", "modelarts:pool:update", "modelarts:pool:delete
If the error message "Host key verification failed" is displayed when you perform the SSH container test on the host machine, delete the ~/.ssh/known_host file from the host machine and try again. Use VS Code SSH to connect to the container environment.
After adding a tag, you can view, modify, or delete the tag on the notebook instance details page. You can select a predefined TMS tag from the tag drop-down list or customize a tag. Predefined tags are available to all service resources that support tags.
After importing the data, you can add or delete labels during data labeling. After setting the parameters, click Submit. Creating a Dataset (Table) Log in to the ModelArts management console. In the navigation pane on the left, choose Asset Management > Datasets. Click Create.
Stop and delete the notebook instance if it is not required. For details, see Development Environment. Creating a Notebook Instance Log in to the ModelArts console.
To reduce the final image size, delete intermediate files such as TAR packages when building each layer. For details about how to clear the cache, see conda clean. Refer to the following example.
When you perform operations on a training job, for example, obtain information of, update, or delete a training job, you can use job_instance.job_id to obtain the ID of the training job.
If this parameter is left blank, all sample labels are deleted. metadata No SampleMetadata object Key-value pair of the sample metadata attribute. sample_id No String Sample ID. sample_type No Integer Sample type.
If UID 1000 or GID 100 in the base image has been used by another user or user group, delete the user or user group. The user and user group have been added to the Dockerfile in this case. You can directly use them.
Call the API for deleting a notebook instance to delete the instance that is no longer needed. Prerequisites You have obtained the endpoints of IAM and ModelArts.
DELETE: DevServer instances are deleted in batches.
To reduce the final image size, delete intermediate files such as TAR packages when building each layer. For details about how to clear the cache, see conda clean. Refer to the following example.
To reduce the final image size, delete intermediate files such as TAR packages when building each layer. For details about how to clear the cache, see conda clean. Refer to the following example.
You can purchase or delete such an ECS at any time. Cluster Flavor Pool Name Enter a name. The name can contain 4 to 30 characters starting with a lowercase letter and not ending with a hyphen (-). Only lowercase letters, digits, and hyphens (-) are allowed.
The content of the training boot file main.py is as follows (if you need to execute a single-node and single-PU training job, delete the code for distributed reconstruction): import datetime import inspect import os import pickle import random import logging import argparse import
Why Can I View the Deleted Dedicated Resource Pools That Failed to Be Created on the ModelArts Console? After a dedicated resource pool is deleted on the console, the backend releases the resources used by the pool. It takes several minutes to release the resources, during which the
