PAI-DSW（Data Science Workshop） is a deep learning development environment provided on the cloud for algorithm developers at different levels. DSW has integrated JupyterLab, and provided some customized features. Developers can open notebook to write code and debug directly. Also, you can read data from MaxCompute table, NAS and OSS. After tranning, models can be deployed to PAI EAS model serving by using EASCMD.
The user interface contains the files section on the left side, the code editing section in the middle, and the resource search section on the right side. More details about DSW Environment, please refer to DSW Environment。
- Real-time resource monitoring, visual display of CPU / GPU usage while algorithm development
- Built-in common data science and algorithm libraries, and support custom installation of third-party libraries
- Multi-source data access, including MaxCompute, OSS and NAS
- SQL is supported in ipynb
- Provide a variety options of resource type, including CPU and GPU.
- Flexible switching of various resources to effectively reduce the cost of use
China (Beijing) China (Hangzhou) China (Shanghai) China (Shenzhen) Singapore and India.
Support pre-paid (annual and monthly) and post-paid (pay-as-you-go), you can choose the payment method when creating a DSW instance. Product pricing and billing methods can refer to the document：PAI-DSW Pricing
Tips：The prepaid billing DSW instance in Beijing and Shanghai and the postpaid M40 in Shanghai do not support networking, and postpaid for other available regions support networking.
About Switching：Currently, the DSW resource switching function only supports instances of the post-paid instance。
|Resource Type||Resource Details||Supported Regions|
|pai.medium.1xv100||GPU V100||China (Beijing),China (Shanghai),Singapore|
|pai.medium.1xp100||GPU P100||All Regions|
|pai.medium.1xm40||GPU M40||China (Shanghai)|
|pai.large.2core4g||CPU 2Core4GB||All Regions|
|pai.xlarge.4core8g||CPU 4Core8GV||All Regions|
|pai.2xlarge.8core16g||CPU 8Core16GB||All Regions|
|pai.4xlarge.16core32g||CPU 16Core32GB||All Regions|
|pai.6xlarge.24core48g||CPU 24Core48GB||All Regions|
How to add storage capacity by mounting NAS, please refer to Document.