Adding GPU testing to Github workflows

This issue is open to discuss different options for adding GPU build and test to Github workflows.

To enable this feature, SINGA must provide a real or virtual machine with GPU as host machine for running the workflow. Then use the [self-hosted runner](https://docs.github.com/en/actions/hosting-your-own-runners/about-self-hosted-runners) feature of Github Actions. See also [this MLOps video tutorial](https://www.youtube.com/watch?v=rVq-SCNyxVc).

The team need to take some decisions:

1. Which machine(s) should we use? (e.g. virtual machine(s) on AWS, dedicated server(s) at NUS, ..)
2. Which operating systems we test on? (Only Linux? or also Mac).
3. When we run the GPU build and test workflow? (with every pull request? once per day at night? once per week? only on master branch?, ...)
4. Should we keep the machines always on? or use them only when the scheduled test is running and shut down them when there is no workflow runs? Assuming we run the GPU build and test only at scheduled time.
5. Should we add workflows to run [examples](https://github.com/apache/singa/tree/master/examples) and test the Jupyter notebooks? note that some examples may take hours or days to complete the training. But automating the test of examples can be very useful to speed up the development.

What do you think?