Machine Learning Platform for AI (PAI) of Alibaba Cloud supports deep learning frameworks and provides GPU computing clusters with various features. You can use deep learning algorithms based on these frameworks and hardware resources.

Prerequisites

A project is created. For more information, see Create a project.

Background information

Deep learning supports the following frameworks: MXNet 0.9.5, Caffe rc3, ,TensorFlow 1.4, and TensorFlow 1.8. TensorFlow and MXNet support custom coding in Python. Caffe supports customized net files.

Before you use a deep learning framework to train models, you must upload your data to Alibaba Cloud Object Storage Service (OSS). The algorithms read data from specified OSS directories when you run the algorithms. If you use algorithms to access OSS data in the same region, no costs are generated for the traffic. However, if you use algorithms to access OSS data in other regions, costs are generated for the traffic.
Note GPU clusters are deployed only in the China (Shanghai) and China (Beijing) regions for PAI.

Enable deep learning

To use deep learning, you must select By usage in the Open GPU column for a specific project.

  1. Log on to the PAI console.
  2. In the left-side navigation pane, choose Model Training > Studio-Modeling Visualization.
  3. On the PAI Visualization Modeling page, find the created project and select By usage in the Open GPU column.

    The projects for which you have selected By usage are allocated to a public resource pool. This way, the projects can use the underlying GPU computing resources.