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One approach is to convert the code to use parallelism. For an example of how to do it in Python using joblib see this article: http://blog.dominodatalab.com/si
by facorreia 11y ago
One approach is to convert the code to use parallelism. For an example of how to do it in Python using joblib see this article: http://blog.dominodatalab.com/simple-parallelization/ http://blog.dominodatalab.com/simple-parallelization/
Even if you can't afford a 32-core instance, you might get to use 4 cores in your laptop.
- syllogism 11y agoThe problem's pretty obviously the Python implementation...Throwing more cores at it isn't really going to help. Is it even easy to parallelise SVM training?
- moridin007 11y agoso the solution would be move from python to go? like python isn't that good for ML algorithms?
- syllogism 11y agoPython's fine if the heavy lifting is in a C extension. I use Cython for this; others prefer just numpy, maybe something like numba. But you can't just have a Python list of floats. Java, C++, Scala and Julia are all popular choices. Go is probably fine too, although I know less about it.
- Lofkin 11y agoActually list support in numba is WIP