AI News, II: Running a Deep Learning (Dream) Machine

II: Running a Deep Learning (Dream) Machine

As a PhD student in Deep Learning, as well as running my own consultancy, building machine learning products for clients I’m used to working in the cloud and will keep doing so for production-oriented systems/algorithms.

There are however huge drawbacks to cloud-based systems for more research oriented tasks where you mainly want to try out various algorithms and architectures, to iterate and move fast.

To make this possible I decided to custom design and build my own system specifically tailored for Deep Learning, stacked full with GPUs.

With some xconfig modding I got it somehow to work on higher resolutions than 800x600, using 352.30 as the graphical driver.

Ok final step: we’re getting to the fun part: Choosing which Deep Learning libraries to work with is a matter of personal preference, as well as domain given.

It’s pretty basic, but if you’re just starting out it’s an easy way to train some ConvNets and build some image classifiers.

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