This topic provides an overview of Data Science Workshop (DSW) 1.0. DSW of Machine Learning Platform for AI is an Integrated Development Environment (IDE) on the cloud. It provides interactive development environments for developers of different levels.
DSW integrates with open-source JupyterLab and provides plug-ins for customized development. You can directly launch Notebook to write, debug, and run Python code without O&M configurations. DSW also provides a variety of computing resources and supports heterogeneous data sources. DSW offers all-in-one machine learning services. You can use EASCMD to deploy models trained in DSW as RESTful APIs to provide online services.
- Supports resource monitoring in real time. DSW can monitor CPU/GPU usage during algorithm development.
- Supports more than one source data, such as MaxCompute, Object Storage Service (OSS), and Network Attacked System (NAS) file systems.
- Allows you to write and run SQL statements.
- Supports multiple resource types, including a variety of CPUs and GPU models.
- Allows you to switch among different types of resources, which reduces resource usage costs.
- Provides built-in big data development packages and algorithm libraries, and allows you to install third-party libraries.
|②||Top navigation pane and shortcut tools|
|⑤||Resource usage graphs|
|Resource type||Resource specification||Region|
Note A DSW instance deployed in the China (Beijing) region supports up to eight GPUs.
|pai.medium.1xm40||GPU M40||China (Shanghai)|
|pai.large.2core4g||2 CPU Core+4 GB||
|pai.xlarge.4core8g||4 CPU Core+8 GB|
|pai.2xlarge.8core16g||8 CPU Core+16 GB|
|pai.4xlarge.16core32g||16 CPU Core+32 GB|
|pai.6xlarge.24core48g||24 CPU Core+48 GB|
|Subscription||TensorFlow||2.0 and 1.12.3|
|Pay-as-you-go||TensorFlow||2.0 and 1.14.0|