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[D] How do you run your CPU-intensive ML tasks?

Hello

To be frank, I need a piece of advice. I intend to run a long task (preferably within a Jupyter Notebook) bound mainly by CPU. More specifically I want to run TPOT on a fairly large visual dataset. Now, as I do no physically own any machine suited for this kind of endeavor, I resort to the cloud. And, as I have some experience with AWS, I instinctively opted for AWS SageMaker to host some JupyterLab instance. One pain-point most of you have already experience is the fact that you need to keep the JupyerLab (or Notebook) window open at all times with a working connection in order not to lose the cell outputs. Keeping all that in mind, I left my computer open while I was gone for a couple of hours only to come back and realize my AWS auth token expired automatically and I was logged out from their platform. This also invalidated the connection with SageMaker notebook instance and made me conscious of all that hosting money that went down the drain.

Anyway, how do you guys run your ML tasks? Especially those on Jupyter Notebooks and CPU-intensive?

Side note: did anyone experiment with an EC2 instance hosting their Jupyter instance & not relying on AWS’ authentication?

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.