Creating an Algorithm

Prerequisites

• Training data is available. You can create a dataset in ModelArts or upload an existing dataset used for training to the OBS directory.
• Your training script has been uploaded to an OBS directory. For details about how to develop a training script, see Developing a Custom Script.
• At least one empty folder has been created in OBS for storing the training output.

Accessing the Algorithm Creation Page

1. Log in to the ModelArts console and choose Algorithm Management in the navigation pane on the left.
2. On the My algorithm tab, click Create. The Create Algorithm page is displayed.

Setting Basic Information

Enter basic information, including Name and Description.

Figure 1 Setting basic information

Setting the Boot Mode

Select a preset image to create an algorithm.

Set Image, Code Directory, and Boot File based on the algorithm code. Ensure that the framework of the AI image you select is the same as the one you use for editing algorithm code. For example, if TensorFlow is used for editing algorithm code, select a TensorFlow image when you create an algorithm.

Table 1 Parameters

Parameter	Description
Boot Mode > Preset image	AI images supported by the new-version training are displayed by default. For details, see Overview.
Code Directory	OBS path for storing the algorithm code. The files required for training, such as the training code, dependency installation packages, and pre-generated models, are uploaded to the code directory. Do not store training data in the code directory. When the training job starts, the data stored in the code directory will be downloaded to the backend. A large amount of training data may lead to a download failure. After you create the training job, ModelArts downloads the code directory and its subdirectories to the container. Take OBS path obs://obs-bucket/training-test/demo-code as an example. The content in the OBS path will be automatically downloaded to ${MA_JOB_DIR}/demo-code in the training container, and demo-code (customizable) is the last-level directory of the OBS path. NOTE: Any programming language is supported. The total number of both files and folders cannot exceed 1,000. The total size of files cannot exceed 5 GB.
Boot File	The file must be stored in the code directory and end with .py. ModelArts supports boot files edited only in Python. The boot file in the code directory is used to start a training job.

Figure 2 Using a custom script to create an algorithm

Configuring Pipelines

A preset image-based algorithm obtains data from an OBS bucket or dataset for model training. The training output is stored in an OBS bucket. The input and output parameters in your algorithm code must be parsed to enable data exchange between ModelArts and OBS. For details about how to develop code for training on ModelArts, see Developing a Custom Script.

When you use a preset image to create an algorithm, configure the input and output parameters defined in the algorithm code.

• Input configurations

  Table 2 Input configurations

  Parameter	Description
  Parameter Name	Set this parameter based on the data input parameter in your algorithm code. The code path parameter must be the same as the training input parameter parsed in your algorithm code. Otherwise, the algorithm code cannot obtain the input data. For example, If you use argparse in the algorithm code to parse data_url into the data input, set the data input parameter to data_url when creating the algorithm.
  Description	Customize the description of the input parameter.
  Obtained from	Select a source of the input parameter, Hyperparameters (default) or Environment variables.
  Constraints	Enable this parameter to specify the input source. The default source is a storage path. This parameter is optional.
  Add	Add multiple input data sources based on your algorithm.

  Figure 3 Input configurations
  
• Output configurations

  Table 3 Output configurations

  Parameter	Description
  Parameter Name	Set this parameter based on the data output parameter in your algorithm code. The code path parameter must be the same as the training output parameter parsed in your algorithm code. Otherwise, the algorithm code cannot obtain the output path. For example, if you use argparse in the algorithm code to parse train_url into the data output, set the data output parameter to train_url when creating the algorithm.
  Description	Customize the description of the output parameter.
  Obtained from	Select a source of the output parameter, Hyperparameters (default) or Environment variables.
  Add	Add multiple output data paths based on your algorithm.

  Figure 4 Output configurations
  

Configuring Hyperparameters

When you use a preset image to create an algorithm on ModelArts, you can customize hyperparameters so you can review or modify them anytime. Defined hyperparameters are displayed in the boot command and passed to your boot file as CLI parameters.

1. Import hyperparameters.

   You can click Add hyperparameter to manually add hyperparameters.

   Figure 5 Adding hyperparameters
   
2. Edit hyperparameters. For details, see Table 4.

   Table 4 Hyperparameter parameters

   Parameter	Description
   Name	Enter the hyperparameter name. Enter 1 to 64 characters. Only letters, digits, hyphens (-), and underscores (_) are allowed.
   Type	Select a data type of the hyperparameter. The value can be String, Integer, Float, or Boolean
   Default	Set the default value of the hyperparameter. This value will be used for training jobs by default.
   Restrain	Click Restrain and set the range of the default value or enumerated value in the dialog box displayed.
   Required	Select Yes or No. If you select No, you can delete the hyperparameter on the training job creation page when using this algorithm to create a training job. If you select Yes, you cannot delete the hyperparameter on the training job creation page when using this algorithm to create a training job.
   Description	Enter the description of the hyperparameter. Only letters, digits, spaces, hyphens (-), underscores (_), commas (,), and periods (.) are allowed.

Supported Policies

Only the pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64 and tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64 images are available for auto search.

Adding Training Constraints

You can add training constraints of the algorithm based on your needs.

• Resource Type: Select the required resource types.
• Multicard Training: Choose whether to support multi-card training.
• Distributed Training: Choose whether to support distributed training.

Figure 6 Training constraints

Previewing the Runtime Environment

When creating an algorithm, click the arrow on in the lower right corner of the page to know the paths of the code directory, boot file, and input and output data in the training container.

Figure 7 Preview Runtime Environment

Follow-Up Operations

After an algorithm is created, use it to create a training job. For details, see Creating a Training Job.