Devops - jupyter automation
on the jupyter notebook from the CPU server, when the new "GPU kernel" is launched, the calculation must take place on the GPU server.After calculating the GPU, the server must be turned off again.You can use runpod.io, vast.ai and others.It is important that all output from a single notebook cell is also issued in the jupyter notebook on the CPU server.We work with the training of LMM models that weigh 20 or 50 GB.And of course, the model itself should be on that GPU server, because.During the training they will be uploaded to the schedule of the map.How this can work: Models are on the GPU server to create a start script that connects to the CPU server and copies from there all the project files to start through the cli jupyter and enter such a cell - the output to transfer to the CPU server and the output to the jupyter at the end of the work to turn off the GPU server This is just an idea - there may be ready solutions for this.API for incl/vylk.GPU servers have runpod and vast.
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1 day65 USD
210 1 day65 USDThe task is interesting, but never done the same outside the AWS services, GCP ...
I’m ready to do, I can’t co-orientate in time, sorry.
Price $7 per hour
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Frankfurt am Main