Data Science Workshop (DSW) of Platform for AI (PAI) is a one-stop Integrated Development Environment (IDE) for AI tailored for algorithm developers. DSW integrates multiple development environments, such as Notebook, VSCode, and Terminal, for coding, debugging, and task running. DSW provides various heterogeneous computing resources and open-source images and supports mounting of datasets of the Object Storage Service (OSS), File Storage NAS (NAS), and Cloud Parallel File Storage (CPFS) types. You can manage the lifecycle of DSW instances and use DSW for development in an easy and efficient manner.
Advantages
Flexibility and ease of use
DSW provides built-in development environments, such as Notebook, VSCode, and Terminal, to meet various development requirements.
DSW provides images of multiple open-source frameworks such as PyTorch and TensorFlow, and supports custom images.
DSW provides various heterogeneous computing resources, including public resource groups, dedicated resource groups, and Lingjun resources. You can flexibly configure and manage resources in DSW.
DSW supports the writing and execution of R language and SQL statements on top of Python.
One-stop service
DSW allows you to mount file systems, such as OSS, NAS, and CPFS file systems, and access MaxCompute data.
DSW provides Deep Learning Containers (DLC) and Elastic Algorithm Service (EAS) tools to implement a full AI development pipeline from data processing, coding, debugging, model training, to model deployment.
DSW provides the AI coding assistant Tongyi Lingma to improve coding efficiency.
Fine-grained management
DSW allows you to configure scheduled stop for an instance or auto stop for idle instances to reduce costs.
DSW provides real-time monitoring of CPU, GPU, and memory usage to help you analyze the resource usage in real time.
A workspace administrator can allocate global resources and configure resource reclamation strategies.
Scenario-based tutorials
DSW provides Notebook Gallery as a content platform for developers. You can use the tutorials for large language model (LLM) and AI content generation-related industries in Notebook Gallery to quickly get started with development.
Usage process
Instance creation and access | Before you use DSW, grant the operation account the required creation and development permissions. | |
During instance creation, you can select a resource type, attach a dataset, and select a custom image based on your business requirements. | ||
You can access a DSW instance in the PAI console in a simple manner and use the features of DSW. | ||
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 | You can manage and change the lifecycle and configurations of an instance, such as configuring shutdown policies and optimizing instance costs. | |
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. | ||
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. | ||
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 | 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. | |
You can read OSS or MaxCompute data files from an instance by using an API or SDK. | ||
You can transmit data and models between on-premises machines and instances. | ||
After you build a model, you can use this feature to enable DSW to provide services over the Internet. | ||
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 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. | |
The TensorBoard plugin is provided to display the metrics and relevant information during model training in a visualized manner. | ||
DSW is integrated with open-source JupyterLab. You can install the R kernel on a DSW instance to run R scripts for data analysis. |
Billing
Compute instance
You can select public resources and dedicated resources such as general computing or Lingjun resources. Different resources use different billing methods.
Resource type | Billing method | Billable item | Billing rule | How to stop billing |
Public resources | Pay-as-you-go | Service duration of a DSW instance (the duration for which a DSW instance occupies public resources) | If you use public resources to create a DSW instance, you are charged based on the service duration of the DSW instance. |
Important You must manually stop an instance or configure scheduled stop. For more information, see Manage DSW instances. |
Dedicated resources (general computing or Lingjun resources) | Subscription | Number of nodes of an instance type and purchase duration | If you purchase subscription dedicated resources, you are charged based on the number of nodes of an instance type and purchase duration. For more information, see Billing of AI computing resources. | None. |
System disk
Billing method | Billable item | Billing rule | How to stop billing |
Pay-as-you-go | System disk capacity and usage duration | After you expand the system disk capacity, you are charged for the capacity that exceeds the free quota and usage duration. | Delete a DSW instance. |
For more information, see Billing of DSW.