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        • IAM - ModelArts

          IAM - ModelArts

          Allow" }, { "Action": [ "smn:*:*" ], "Effect": "Allow" }, { "Action": [ "modelarts:pool:create", "modelarts:pool:update", "modelarts:pool:delete

          Help > ModelArts > Best Practices > Permissions Management > Permission Management Mechanisms
        • Using PyTorch to Create a Training Job (New-Version Training) - ModelArts

          Using PyTorch to Create a Training Job (New-Version Training) - ModelArts

          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...

          Help > ModelArts > API Reference > Use Cases
        • Configuring the Software Environment on the NPU Server - ModelArts

          Configuring the Software Environment on the NPU Server - ModelArts

          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.

          Help > ModelArts > ModelArts User Guide (Lite Server) > Configuring Lite Server Resources > Configuring the Software Environment
        • Step 2 Creating a Custom Image - ModelArts

          Step 2 Creating a Custom Image - ModelArts

          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.

          Help > ModelArts > Image Management > Using Custom Images in Notebook Instances > Creating a Custom Image on an ECS and Using It in Notebook
        • Creating a ModelArts Dataset - ModelArts

          Creating a ModelArts Dataset - ModelArts

          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.

          Help > ModelArts > ModelArts User Guide (Standard) > Preparing and Processing Data
        • Enabling Lite Cluster Resources - ModelArts

          Enabling Lite Cluster Resources - ModelArts

          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.

          Help > ModelArts > ModelArts User Guide (Lite Cluster)
        • Running a Single-Node Multi-Card GPU-based Training Job on ModelArts Standard - ModelArts

          Running a Single-Node Multi-Card GPU-based Training Job on ModelArts Standard - ModelArts

          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.

          Help > ModelArts > Best Practices > Model Training > Running a GPU-based Training Job on ModelArts Standard
        • Creating a Training Job - ModelArts

          Creating a Training Job - ModelArts

          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.

          Help > ModelArts > SDK Reference > Training Management (New Version) > Training Jobs
        • Creating a Custom Image in a Notebook Instance Using Dockerfile - ModelArts

          Creating a Custom Image in a Notebook Instance Using Dockerfile - ModelArts

          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.

          Help > ModelArts > ModelArts User Guide (Standard) > Creating a Custom Image for ModelArts Standard > Creating a Custom Image for Inference
        • Sample Code of Distributed Training - ModelArts

          Sample Code of Distributed Training - ModelArts

          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

          Help > ModelArts > Model Development (To Be Offline) > Distributed Training
        • Updating Labels of Team Labeling Samples in Batches - ModelArts

          Updating Labels of Team Labeling Samples in Batches - ModelArts

          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.

          Help > ModelArts > API Reference > Historical APIs > Data Management (Old Version)
        • Creating a Dataset (Old Version) - ModelArts

          Creating a Dataset (Old Version) - ModelArts

          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.

          Help > ModelArts > User Guide (Senior AI Engineers) > Data Management (Old Version to Be Terminated)
        • Creating a Custom Image on ECS and Using It - ModelArts

          Creating a Custom Image on ECS and Using It - ModelArts

          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.

          Help > ModelArts > ModelArts User Guide (Standard) > Creating a Custom Image for ModelArts Standard > Creating a Custom Image for a Notebook Instance
        • Creating a Development Environment Instance - ModelArts

          Creating a Development Environment Instance - ModelArts

          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.

          Help > ModelArts > API Reference > Use Cases
        • Creating a Standard Dedicated Resource Pool - ModelArts

          Creating a Standard Dedicated Resource Pool - 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 (-).

          Help > ModelArts > ModelArts User Guide (Standard) > ModelArts Standard Resource Management
        • Running a Single-Node Single-Card GPU-based Training Job on ModelArts Standard - ModelArts

          Running a Single-Node Single-Card GPU-based Training Job on ModelArts Standard - ModelArts

          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.

          Help > ModelArts > Best Practices > Model Training > Running a GPU-based Training Job on ModelArts Standard
        • Running a Multi-Node Multi-Card GPU-based Training Job on ModelArts Standard - ModelArts

          Running a Multi-Node Multi-Card GPU-based Training Job on ModelArts Standard - ModelArts

          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.

          Help > ModelArts > Best Practices > Model Training > Running a GPU-based Training Job on ModelArts Standard
        • Creating a Multiple-Node Multi-PU Distributed Training Job (DistributedDataParallel) - ModelArts

          Creating a Multiple-Node Multi-PU Distributed Training Job (DistributedDataParallel) - ModelArts

          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

          Help > ModelArts > ModelArts User Guide (Standard) > Using ModelArts Standard to Train Models > Distributed Model Training
        • Why Is a Dedicated Resource Pool That Fails to Be Created Still Displayed on the Console After It Is Deleted? - ModelArts

          Why Is a Dedicated Resource Pool That Fails to Be Created Still Displayed on the Console After It Is Deleted? - ModelArts

          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

          Help > ModelArts > FAQs > Resource Pools
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