Instance creation and access | Grant the permissions that are required to use DSW | Before you use DSW, grant the operation account the required creation and development permissions. |
Create a DSW instance | During instance creation, you can select a resource type, attach a dataset, and select a custom image based on your business requirements. |
Access a DSW instance | You can access a DSW instance in the PAI console in a simple manner and use the features of DSW. |
Connect to a DSW instance | DSW enables you to connect to an instance remotely by using SSH through an on-premises terminal or VSCode. This facilitates the running and debugging of your on-premises code in the cloud. |
Instance configuration and management | Manage DSW instances | You can manage and change the lifecycle and configurations of an instance, such as configuring shutdown policies and optimizing instance costs. |
Mount datasets or OSS paths | To expand the storage space of an instance, persist storage data, or read data files, attach a dataset to the instance and mount an OSS directory. |
DSW network configuration | To use an instance in a VPC, improve the data upload or download speed, or manage public access, you need to configure network parameters for the instance. |
Configure RAM roles for a DSW instance | You can associate a RAM role with an instance and access other cloud services from the instance by using a Security Token Service (STS) temporary credential without the need to configure AccessKey pairs. This reduces the risk of key leakage. |
Model development and deployment | Using Tongyi Lingma for development | DSW features the built-in AI coding assistant Tongyi Lingma to provide various capabilities, such as code completion and optimization and intelligent Q&A. This facilitates efficient development. |
Read data from and write data to OSS and MaxCompute | You can read OSS or MaxCompute data files from an instance by using an API or SDK. |
File upload and download | You can transmit data and models between on-premises machines and instances. |
Custom services access configuration | After you build a model, you can access the service in the instance over VPC or the Internet to test or evaluate the model. |
Deploy a model | If you want to call the model that you recently built from other applications or perform elastic scaling, version control, or resource monitoring, you can deploy the model as an online service. |
Advanced features of DSW | Notebook Gallery | Notebook Gallery provides various notebook cases, including cutting-edge areas such as LLM and AI-generated content (AIGC), and popular models, which you can run with a few clicks and optimize. |
TensorBoard: training visualization | The TensorBoard plugin is provided to display the metrics and relevant information during model training in a visualized manner. |
Install the R kernel in DSW | DSW is integrated with open-source JupyterLab. You can install the R kernel on a DSW instance to run R scripts for data analysis. |