Help Center/ ModelArts/ Function Overview
功能总览

Function

  • DevEnviron

    • During AI development, it is challenging to set up a development environment, select an AI framework and algorithm, debug code, install software, or accelerate hardware. To resolve these issues, ModelArts offers notebook for simplified development.

      Both old and new versions of ModelArts notebook are available. Compared with the old version, the new version offers more optimized functions. Use the new version. The following describes the functions of the new-version notebook.

      Released in: EU-Dublin

  • Algorithm Management

    • You can upload locally developed algorithms or algorithms developed using other tools to ModelArts for unified management. You can use the algorithm you have created or subscribed to quickly create a training job on ModelArts and obtain the desired model.

      Released in: EU-Dublin

      Introduction to Algorithm Preparation

      Introduction to Custom Script

  • Training Management

    • All the algorithms developed locally or using other tools can be uploaded to ModelArts for unified management. You can also subscribe to algorithms in AI Gallery to build models. In AI Gallery, both the built-in algorithms officially released by ModelArts and the algorithms shared by other users are available for you to subscribe to.

      You can use the algorithms either you have created or you have subscribed to to quickly create training jobs on ModelArts.

      Released in: EU-Dublin

    • Using Subscribed Algorithms to Develop Models

      • Both officially released algorithms and custom algorithms shared by developers are available in ModelArts AI Gallery. You can use these algorithms to build models without writing any code.

    • Using Custom Algorithms to Develop Models

      • If the algorithms that are available to subscribe to cannot meet service requirements or you want to migrate local algorithms to the cloud for training, use the training engines built in ModelArts to create algorithms. This is also known as using a custom script to create algorithms.

        ModelArts offers almost all mainstream AI engines. These built-in engines are pre-loaded with some additional Python packages, such as NumPy. You can also use the requirements.txt file in the code directory to install dependency packages.

        Custom Image Specifications for Training Jobs

    • Using Custom Images to Develop Models

      • The built-in training engines and the algorithms that can be subscribed to apply to most training scenarios. In certain scenarios, ModelArts allows you to create custom images to train models. Custom images can be used in the cloud only after they are uploaded to the Software Repository for Container (SWR).

        Customizing an image requires a deep understanding of containers. Use this method only if the algorithms that are available to subscribe to and the built-in training engines cannot meet your requirements.

  • Model Management

    • ModelArts allows you to deploy models as AI applications and centrally manages these applications. The models can be locally deployed or obtained from training jobs.

      ModelArts also enables you to convert models and deploy them on different devices, such as Arm devices.

      Released in: EU-Dublin

    • Importing a Meta Model from OBS Through Manual Configurations

    • Importing Models from Custom Images

    • Model Package Specifications

      • When you create an AI application in AI Application Management, if the meta model is imported from OBS or a container image, ensure the model package complies with specifications:

        Edit the inference code and configuration file for subsequent inference deployment.

        Note: A model trained using a built-in algorithm has had the inference code and configuration file configured. You do not need to configure them anymore.

        Model Package Specifications