Collaborative Calculation and Data Science

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VIDEO: https://youtu.be/NkNx6tx3nu0

LINK: https://github.com/sagemathinc/cocalc-howto/blob/main/onprem.md


We add an on-prem  compute server running on my Macbook Pro laptop to a CoCalc (https://cocalc.com) project, and using the compute server via a Jupyter notebook and a terminal.  This involves creating an Ubuntu 22.04 virtual machine via multipass, and pasting a line of code into the VM to connect it to CoCalc.


<img width="1462" alt="image" src="https://github.com/sagemathinc/cocalc/assets/1276278/3aed1b8a-4af9-4379-aa67-9ee6eaf72de2">

After using a compute server running on my laptop, I create another compute server running on Lambda cloud (https://lambdalabs.com/).  This involves renting a powerful server with an H100 GPU, waiting a few minutes for it to boot up, then pasting in a line of code.  The compute server gets configured, starts up, and we are able to confirm that the H100 is available.  We then type "conda install -y pytorch" to install pytorch, and use Claude3 to run a demo involving the GPU and train a toy model.

Running On-Prem Compute Servers on CoCalc (Video)

New GPU Cloud Integration with Hyperstack! 

Using VS Code on CoCalc

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