Using Frequently-used Frameworks to Train Models

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.
• If you select Frequently-used for Algorithm Source, prepare the training script and upload it to the OBS directory.
• At least one empty folder has been created in 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.
• In the training script, the Data Source and Training Output Path parameters must be set to the OBS path. To perform read and write operations in the path, use MoXing APIs.

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, Specify Name and Description according to actual requirements. For Version, the system automatically creates a version number, which is named according to a certain rule, for example, V0001 and V0002. The version number cannot be changed.
   2. Set job parameters, including the data source, algorithm source, and more. For details, see Table 1.
      Figure 1 Frequently-used as the algorithm source
      

      Table 1 Job parameters

      Parameter	Sub-Parameter	Description
      One-Click Configuration	N/A	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	Frequently-used	Select an AI engine and its version and specify Code Directory and Boot File. The framework selected for the AI engine must be the same as the one you select when compiling training code. For example, if TensorFlow is used in your training code, select TensorFlow when you create a training job. If your model requires Python dependencies, place the dependency package and its configuration file in the code directory based on the requirements defined in ModelArts. For details, see How Do I Create a Training Job When a Dependency Package is Referenced in a Model.
      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. For a training job, you can select multiple datasets by clicking . To delete a dataset, click in the row for it.
      	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. If you set Algorithm Source to Frequently-used, you can select multiple data storage paths for a training job by clicking . To delete a data storage path, click in the row for it.
      Training Output Path	N/A	Select a path for storing the training result. NOTE: To minimize errors, select an empty directory for Training Output Path. Do not select the directory used for storing the dataset for Training Output Path.
      Running Parameter	N/A	Set the command line parameters in the code based on the algorithm code logic. Make sure that the parameter names are the same as those in the code. For example, train_steps = 10000, where train_steps is a passing parameter in code.
      Job Log Path	N/A	Select a path for storing log files generated during job running.

   3. Select resources for the training job.

      Table 2 Resource parameters

      Parameter	Description
      Resource Pool	Select resource pools for the job. 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.
      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? NOTE: If GPU resources are used in training code, you must select a GPU cluster when selecting a resource pool. Otherwise, the training job may fail.
      Specifications	Select a resource flavor based on the resource type.
      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. Select a computing mode based on the actual requirements. When Frequently-used of Algorithm Source is set to Caffe, only standalone training is supported, that is, Compute Nodes must be set to 1. For other options of Frequently-used, you can select the standalone or distributed mode based on service requirements.

   4. Configure Notification and select whether to save the training job parameters.
      Figure 2 Configuring notifications for the training job
      

      Table 3 Parameters related to notification and parameter configuration saving

      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 job 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.