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
Adding the Evaluation Code After a training job is executed, ModelArts automatically evaluates your model and provides optimization diagnosis and suggestions. For details, see Viewing the Evaluation Result. When you use a built-in algorithm to create a training job, you can view the
Stopping or Deleting a Job Stopping a Training Job In the training job list, click Stop in the Operation column for a training job in the Running state to stop a running training job. After the training job is stopped, its billing stops on ModelArts. If you have selected Save Training
Figure 5 Notebook Job Definitions tab Figure 6 Configuring a scheduled job Parent topic: Using a Notebook Instance for AI Development Through JupyterLab
Training Management (Old Version ) Introduction to Model Training Frequently-used Frameworks Creating a Training Job Stopping or Deleting a Job Managing Training Job Versions Viewing Job Details Managing Job Parameters Adding the Evaluation Code Managing Visualization Jobs
Searching for a Workflow Procedure On the workflow list page, you can use the search box to quickly search for workflows based on workflow properties. Log in to the ModelArts console. In the navigation pane, choose Development Workspace > Workflow. In the search box above the workflow
Managing a Workflow Starting a Workflow Log in to the ModelArts console. In the navigation pane, choose Development Workspace > Workflow. You can run a workflow in any of the following ways: On the workflow list page, click Start in the Operation column. In the displayed dialog box
Creating Resources This best practice uses a VPC, an SFS Turbo HPC file system, an OBS bucket, and a ModelArts resource pool. To achieve optimal acceleration performance, you are advised to select the same region and AZ for the SFS Turbo HPC file system and ModelArts resource pool
Basic Configurations Configuring Network Passthrough Between ModelArts and SFS Turbo Configuring SFS Turbo and OBS Interworking Configuring Auto Data Export from SFS Turbo to OBS Configuring the SFS Turbo Data Eviction Policy Parent topic: Implementation Procedure
Creating a Training Job Create a ModelArts training job based on the SFS Turbo shared file storage. Log in to the ModelArts console. In the navigation pane, choose Training Management > Training Jobs. Click Create Training Job in the upper right corner. On the displayed page, set
Publishing a Workflow Publishing a Workflow to ModelArts Publishing a Workflow to AI Gallery Parent topic: Workflow Development Command Reference
Performing Text and Speech Analysis Configuring Text Analysis Searching for Inspection Results Based on Keywords Viewing the Word Frequency Collecting Text Analysis Indicator Statistics Configuring Custom Indicators Managing Indicator Categories Parent topic: Managing AI Inspections
MoXing Commands in a Notebook Instance MoXing Framework Functions Using MoXing in Notebook Introducing MoXing Framework Mapping Between mox.file and Local APIs and Switchover Sample Code for Common Operations Sample Code for Advanced MoXing Usage Parent topic: Using Notebook for AI
Confirming Hard Examples In a labeling task that processes a large amount of data, auto labeling results cannot be directly used for training because the labeled images are insufficient at the initial stage of labeling. It takes a lot of time and manpower to adjust and confirm all
Introduction to Data Management ModelArts provides both new and old versions of datasets. This section describes the dataset and data management functions of the old version. Datasets of the old version are to be taken offline. You are advised to use datasets of the new version and
Managing Dataset Versions After labeling data, you can publish the dataset to multiple versions for management. For the published versions, you can view the dataset version updates, set the current version, and delete versions. For details about dataset versions, see About Dataset
Parent topic: Using a Notebook Instance for AI Development Through JupyterLab
Unified Model Management For an ExeML project, after the model training is complete, the generated model is automatically displayed on the AI Application Management > AI Applications page. See the following figure. The model name is automatically generated by the system.
Figure 2 Stopping an instance Parent topic: Using a Notebook Instance for AI Development Through JupyterLab
Figure 3 Clicking SHUT DOWN to stop an instance Parent topic: Using a Notebook Instance for AI Development Through JupyterLab
Speech Labeling Model training requires a large amount of labeled data. Therefore, before the model training, label the unlabeled audio files. ModelArts enables you to label audio files in batches by one click. In addition, you can modify the labels of audio files, or remove their
