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>Our most ambitious so far has been setting up each student in the course with a p2.xlarge machine with cuda and TensorFlow so they could do deep learning work for their final projects.

Wow! How expensive was this? Do you do any sort of shutdown/startup work or use pre-empt instances?

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The answer for this particular course was, "very expensive". Each students machine shuts down after 20 minutes of inactivity (defined as no Jupyter process currently running), so that saves money, but in this specific instance many of the students were running extremely long jobs, so that didn't really help us.

The average cost over all of the other courses was something like $2-3 per month per student. The deep learning course ended up being closer to $20 per student. Thanks to Amazon Educate almost the entire cost was covered with credit.

Interesting. Do you think your use case could be helped by doing only the training remotely on a single GPU/GPU cluster, and doing the rest of the development work on a cheaper machine? (basically just equivalent of estimator train() runs on a faster machine that quits afterward).

I wrote a tool that does that with Keras but I'm not sure if it's actually useful for real-world use cases.

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