Data Science Workshop (DSW) provides a cloud-based integrated development environment (IDE) for AI development. DSW includes various built-in development environments, which allow users who are familiar with Notebook or VSCode to quickly start developing models. In addition, DSW supports a wide range of heterogeneous computing resources, lets you mount datasets from Object Storage Service (OSS), NAS, and CPFS, provides various pre-installed open source framework images, and supports instance lifecycle management. These features help you develop models efficiently.
Product overview
Benefits
Flexible and easy to use DSW provides various built-in development environments. It supports open source framework images such as PyTorch and TensorFlow. It also provides heterogeneous computing resources, including public resource groups, dedicated resource groups, and Lingjun resources. | One-stop service DSW provides DLC and EAS tools to support the entire AI development lifecycle, from data processing and debugging to model training and deployment. | Fine-grained management DSW supports lifecycle management configurations such as scheduled shutdown and idle shutdown to help you save costs. The workspace feature allows for global resource allocation and reclamation. | Scenario-based practice The Notebook Gallery provides tutorials and examples for cutting-edge fields such as large language models (LLMs) and AI-Generated Content (AIGC). You can quickly get started or perform custom development. |
Core features
Creation and management | When you create a DSW instance, you can select the instance resource type, mount a dataset, and use a custom image. | |
You can use the console to access the rich features of DSW and perform common operations such as stopping, releasing, and modifying the configuration of instances. | ||
You can associate a RAM role to access other cloud resources from within the instance using temporary Security Token Service (STS) credentials. This avoids the need to configure a long-term AccessKey and reduces the risk of key leakage. | ||
Model development environment | You can manage and install third-party Python packages or software. | |
You can use the TensorBoard plugin to visualize metrics and information during model training. | ||
DSW provides a rich collection of Notebook examples for popular models and cutting-edge fields such as large language models (LLMs) and AIGC. You can run them with a single click or use them for custom development. | ||
After a model is built, you can use PAI-EAS to deploy it as an online service. This lets you invoke the model in other applications and use features such as auto scaling, versioning, and resource monitoring. | ||
Data management | You can mount a dataset or an OSS path to expand instance storage, store data persistently, and read data files. | |
You can read OSS data files in a DSW instance using an API or an SDK. | ||
You can transfer data and models between your local machine and the instance. | ||
Network configuration | An SSH remote connection provides a local development experience while allowing you to use the powerful computing power of DSW. | |
You can create an Internet NAT gateway and attach an Elastic IP Address (EIP) to the virtual private cloud (VPC) where the instance is located to improve the network upload and download speeds of the instance. | ||
You can access services running in an instance from within a VPC or over the Internet. This is useful for model testing and validation. | ||
You can configure Global Accelerator (GA) for DSW to accelerate pulling container images (such as docker.io images) or models (such as huggingface.co models) from outside China. |
Billing
Compute instances
You can select public resources or dedicated resources (general computing resources or Lingjun resources) as your instance resource type. Different billing methods apply to different resource types.
Instance type | Billing method | Billable item | Billing rules | Stop billing |
Public resources | Pay-as-you-go | The duration of the DSW instance service (the duration for which public resources are occupied). | If you use public resources to create a DSW instance, you are billed based on the service duration of the DSW instance. |
Important Stop the instance manually or configure scheduled shutdown. For more information, see Manage DSW instances. |
Dedicated resources (general computing resources or Lingjun resources) | Subscription | The quantity and subscription duration of the purchased node specifications. | You purchase dedicated resources on a subscription basis. You are charged based on the quantity and subscription duration of the purchased node specifications. For more information, see Billing of AI computing resources. | Not applicable |
System disks
Billing method | Billable item | Billing rules | Stop billing |
Pay-as-you-go | The capacity and usage duration of the system disk. | After you scale out a system disk, you are billed for the capacity that exceeds the free quota and for the usage duration. | Delete the DSW instance. |
For more information about billing, see Billing of Data Science Workshop (DSW). To view your billing information, see View your bills.
Quick Start
The Quick Start for Data Science Workshop (DSW) document uses the MNIST handwriting recognition example to help you quickly understand and start using DSW.
FAQ
If you encounter issues such as instance startup or stop failures, billing questions, problems with free trial resources, remote connection failures, slow download speeds, or problems accessing DSW over the Internet, see FAQ about DSW.