Prerequisites

• Data has been prepared. Specifically, you have created an available dataset in ModelArts, or you have uploaded the dataset used for training to the OBS directory.
• At least one empty folder has been created on OBS for storing the training output.
• The account is not in arrears because resources are consumed when training jobs are running.
• The OBS directory you use and ModelArts are in the same region.

Precautions

In the dataset directory specified for a training job, the names of the files (such as the image file, audio file, and label file) containing data used for training contain 0 to 255 characters. If the names of certain files in the dataset directory contain over 255 characters, the training job will ignore these files and use data in the valid files for training. If the names of all files in the dataset directory contain over 255 characters, no data is available for the training job and the training job fails.

Creating a Training Job

1. Log in to the ModelArts management console. In the left navigation pane, choose Training Management > Training Jobs. By default, the system switches to the Training Jobs page.
2. In the upper left corner of the training job list, click Create to switch to the Create Training Job page.
3. Set related parameters and click Next.
   1. Set the basic information, including Billing Mode, Name, Version, and Description. Billing Mode supports only Pay-per-use. The Version information is automatically generated by the system and named in an ascending order of V001, V002, and so on. You cannot manually modify it.
      Specify Name and Description according to actual requirements.

      Figure 1 Setting basic information about the training job
      
   2. Set job parameters, including the data source, algorithm source, and more. For details, see Table 1.
      Figure 2 Built-in as the algorithm source
      

      Table 1 Job parameter description

      Parameter	Sub-Parameter	Description
      One-Click Configuration	-	If you have saved job parameter configurations in ModelArts, click One-Click Configuration and select an existing job parameter configuration as prompted to quickly complete parameter setting for the job.
      Algorithm Source	Built-in	Select a built-in algorithm in ModelArts. For details, see Introduction to Built-in Algorithms.
      Data Source	Dataset	Select an available dataset and its version from the ModelArts Data Management module. Dataset: Select an existing dataset from the drop-down list. If no dataset is available in ModelArts, no result will be displayed in the drop-down list. Version: Select a version according to the Dataset setting.
      Data Source	Data path	Select the training data from your OBS bucket. On the right of the Data path text box, click Select. In the dialog box that is displayed, select an OBS folder for storing data. The dataset must meet the requirements of different types of built-in algorithms. For details, see Requirements on Datasets.
      Running Parameter	-	After you select a built-in algorithm, the running parameters that are set by default are displayed based on the selected algorithm. You can modify the parameters based on the actual requirements. For details about the running parameters of different algorithms, see Algorithms and Their Running Parameters. You can also use the default values to create a training job. If the training result is unsatisfactory, you can optimize the parameters.
      Training Output Path	-	Storage path of the training result NOTE: To avoid errors, you are advised to select an empty directory for Training Output Path. Do not select the directory used for storing the dataset for Training Output Path.
      Job Log Path	-	Select a path for storing log files generated during job running.

   3. Select resources for the training job.
      Figure 3 Selecting resources for the training job
      

      Table 2 Resource parameters

      Parameter	Description
      Resource Pool	Select resource pools for the job. For training jobs, Public resource pools and Dedicated resource pools are available. Instances in the public resource pool can be of the CPU or GPU type. Pricing standards for resource pools with different instance types are different. For details, see Product Pricing Details. For details about how to create a dedicated resource pool, see Resource Pools.
      Type	If Resource Pool is set to Public resource pools, select a resource type. Available resource types are CPU and GPU. The GPU resource delivers better performance, and the CPU resource is more cost effective. If the selected algorithm has been defined to use the CPU or GPU, the resource type is automatically displayed on the page. Select the resource type as required. The data disk capacity varies depending on the resource type. For details, see What Are Sizes of the /cache Directories for GPU and CPU Resources in the Training Environment?.
      Specifications	Select a resource flavor based on the resource type. In the resource flavor list, the flavor marked with Limited-time free is a free flavor. You can use the flavor to experience the training job function of ModelArts free of charge. For details about the precautions for using the flavor, see Experiencing AI Development Lifecyle for Free.
      Compute Nodes	Set the number of compute nodes. If you set Compute Nodes to 1, the standalone computing mode is used. If you set Compute Nodes to a value greater than 1, the distributed computing mode is used. Currently, only the modelarts.bm.gpu.8v100 flavor supports distributed training.

   4. Configure Notification and select whether to save the parameters of the training job.
      Figure 4 Configuring notifications for the training job
      

      Table 3

      Parameter	Description
      Notification	Select the resource pool status to be monitored from the event list, and SMN sends a notification message when the event occurs. This parameter is optional. You can choose whether to enable subscription based on actual requirements. If you enable subscription, set the following parameters as required: Topic: indicates the topic name. You can create a topic on the SMN console. Event: indicates the event to be subscribed to. The options are OnJobRunning, OnJobSucceeded, and OnJobFailed, indicating that training is in progress, successful, and failed, respectively.
      Saving Training Parameters	If you select this option, the parameter settings of the current training will be saved to facilitate subsequent job creation. Select Save Training Parameters and specify Configuration Name and Description. After a training job is created, you can switch to the Job Parameters tab page to view your saved job parameter settings. For details, see Managing Job Parameters.

   5. After setting the parameters, click Next.
4. Confirm that the information is correct on the Confirm page that is displayed and click Submit. Generally, training jobs run for a period of time, which may be several minutes or tens of minutes depending on the amount of your selected data and resources.
   

   After a training job is created, it is started immediately. During the running, you will be charged based on your selected resources.

   You can switch to the training job list to view the basic information about training jobs. In the training job list, Status of the newly created training job is Initializing. If the status changes to Successful, the training job ends and the model generated is stored in the location specified by Training Output Path. If the status of a training job changes to Running failed. Click the name of the training job and view the job logs. Troubleshoot the fault based on the logs.