chrishkchris commented on issue #802: URL: https://github.com/apache/singa/issues/802#issuecomment-732755673
umm... cloud service will induce recurrent spending, e.g. if on-demand g4dn.xlarge costs ~0.526USD per hour, every month the recurrent cost seems to be around 0.526USD*24(hour)*30(days)=378USD per month https://aws.amazon.com/cn/ec2/pricing/on-demand/ correct me if I am wrong > > The only question is, @naili-xing , we cannot connect our machine from outside without a VPN. So maybe it's more feasible to listen to the update of the commit from our machine, once get such an update, run the test and push the result to the Github Actions. > > If the connection between NUS self-hosted runners and Github servers is not possible because of NUS network restrictions, then another solution can be considered: self-hosting Github itself using [Github Enterprise](https://github.com/enterprise). The cost is $21 per user per month, but there are [special discounted licenses for universities](https://enterprise.github.com/faq). In this case both Github and the Github runners are hosted locally in the same network. (But again I guess we need to configure the DNS of the SINGA github repository to point to the locally hosted github, so we must have some NUS network configuration for external access but may be it is easier?). There is a free trail for Github Enterprise if we want to evaluate it on the NUS server. > > Otherwise, we just use a cloud-based solution (from Amazon/Microsoft/Google, ..) to execute the GPU Github workflows. There will be a cost for this cloud service time and resources, but it will also save the cost we need to configure and maintain our own GPU testing pipeline. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
