Eclipse IDE – how to limit the number of of CPU cores used on a Linux PC

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In the very hot days which we experienced in Germany the last week my old ventilation cooled PC suffers a bit when all of my CPU cores get under full load. Temperatures up to 70&grad; Celsius despite an Alpenföhn ventilator are beyond my normal experience and tolerance. The culprit was Eclipse with PyDev.

I had installed the latest Eclipse version (4.32.0) some days before. And opened a new Python project based on a directory with tons of code for Machine Learning classes. As a default the PyDev validator started to check the code for problems. It does so by using all available CPU cores and threads in parallel. Yeah, Eclipse works highly parallelized with its underlying Java VM. As I had a lot of complex code the code analysis lasted for some minutes – putting an extreme load on my PC.

So, my question was: How can one limit the number of CPU cores/threads used by Eclipse?

For Linux the answer is surprisingly simple:

We can use the “taskset” command. E.g., if I wanted to use the first 4 CPU cores (or recognized threads) we would write on the command line: 

taskset -c 0-3 ./eclipse &

This works perfectly! The initial code analysis takes a bit longer – but the load on the PC got down. Happy coding!

 

Machine Learning on PCs – Use mixed precision and look out for super-convergence to save energy

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People doing Machine Learning [ML] experiments on their own Linux PCs or laptops know that the numerical training runs put a heavy load on the graphics cards and consume a lot of energy as a direct consequence. Especially in a hot summer like we have it in Germany right now, cooling of your systems may become a problem. And as energy has a high price tag here, any method to reduce the load and/or power consumption is welcome.

But I think that caring about energy consumption is a topic which we as a Linux and ML enthusiasts should keep in mind in general. Some big tech companies will probably not do it – as long as their money machinery works and as some heads follow fantasies about building small nuclear power plants for their big AI data centers. But we Opensource people would like to see more AI- and ML-services independent of the monopolists and their infrastructure, anyway. Not only for reasons of data and privacy protection.

As soon as we, however, proclaim and work for a development that favors local and resource optimized installations of AI and ML tools both for private people and companies, we have to care about side effects: We have to bring the energy consumption down for these many local installations substantially in parallel. Otherwise, centralized solutions may have a better energy efficiency than decentralized solutions.

For me as a retired person in Germany the general financial pressure is high enough to enforce a careful use of my private resources. With this post I want to draw your attention to two points which may help you, too, to save energy during your ML-experiments. (In addition to or aside of standard measures like saving certain model states during training runs to get better starting points for new runs.)

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VPN Provider with OpenVPN access – check your DNS settings to avoid DNS leaks!

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You have bought a VPN account at one of the major VPN providers. You have done so, because you e.g. have a second residence in another country. You may even have a dual citizenship.
Let us assume that you are abroad and want to work with web services offered in your home country for its citizens.

You are abroad, but electronically you may need to operate as if you were located in your home country. There are a lot of services which check the geolocation of your IP and only serve you if you call from “inside” the country, i.e. if the IP is located there.

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