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Allow" }, { "Action": [ "smn:*:*" ], "Effect": "Allow" }, { "Action": [ "modelarts:pool:create", "modelarts:pool:update", "modelarts:pool:delete
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...
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.
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.
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.
You can create or delete your resources at any time. CCE Cluster N/A Choose an existing CCE cluster from the drop-down list. Click Create Cluster on the right to create a cluster if none is available.
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.
Decompress model.zip and delete it. # Decompress the ZIP file. unzip model.zip Figure 3 Decompressing model.zip on the Terminal Open a new IPYNB file, start the image creation script, and specify the paths to the Dockerfile and image.
The content of the training boot file main.py is as follows (if you need to execute a single-node and single-card training job, delete the code for distributed reconstruction): import datetime import inspect import os import pickle import random import logging import argparse import
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.
You can perform the following operations on the dataset: label data, publish dataset versions, manage dataset versions, modify the dataset, import data, and delete the dataset.
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.
You can purchase or delete such an ECS at any time. Cluster Specifications Cluster Name Enter a name. Only lowercase letters, digits, and hyphens (-) are allowed. The value must start with a lowercase letter and cannot end with a hyphen (-).
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.
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 Is a Dedicated Resource Pool That Fails to Be Created Still Displayed on the Console After It Is Deleted? 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
