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I am working in a company but I am new in the field. We have a central server which is faster than my laptop (of course). So my goal is that I still use my laptop to do python analysis for machine learning but actual computation is happening in the central server so that the computing power is stronger. I am not considering any cloud service as we have a fast server computer.

But I am clueless in this yet. Can anyone advise me how to do this?

Thank you very much in advance.

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  • $\begingroup$ You can work with a very small data on laptop for development (say just 20k samples) and check-in the code. On sever; run the same training with whole data. $\endgroup$ May 9, 2019 at 11:26
  • $\begingroup$ This is a standard situation, everybody codes and genarates the pilot on their laptop and then sends it to the headnode of the cluster for farming to the slave nodes via a queuing system. The Jupyter notebook may be a good workaround $\endgroup$
    – M__
    Jun 8, 2019 at 19:03

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I have a similar set-up; I run both local and remote installations of Jupyter. On the server I have JupyterHub running, into which i can ssh. Locally I have Anaconda set up etc.

If you are meticulous in mirroring any custom libraries across the system (or even better package them for pip installs) this works quite well.

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