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NVIDIA’s standard libraries made it very easy to establish the first deep learning libraries in CUDA, while there were no such powerful standard libraries for AMD’s Open CL.
Right now, there are just no good deep learning libraries for AMD cards – so NVIDIA it is.
TL; DR Having a fast GPU is a very important aspect when one begins to learn deep learning as this allows for rapid gain in practical experience which is key to building the expertise with which you will be able to apply deep learning to new problems.
Without this rapid feedback it just takes too much time to learn from one’s mistakes and it can be discouraging and frustrating to go on with deep learning.
Even if some Open CL libraries would be available in the future I would stick with NVIDIA: The thing is that the GPU computing or GPGPU community is very large for CUDA and rather small for Open CL.
Thus, in the CUDA community, good open source solutions and solid advice for your programming is readily available.
With a good, solid GPU, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours.
So making the right choice when it comes to buying a GPU is critical.
The only deep learning library which currently implements efficient algorithms across GPUs and across computers is CNTK which uses Microsoft’s special parallelization algorithms of 1-bit quantization (efficient) and block momentum (very efficient).I quickly found that it is not only very difficult to parallelize neural networks on multiple GPUs efficiently, but also that the speedup was only mediocre for dense neural networks.Small neural networks could be parallelized rather efficiently using data parallelism, but larger neural networks like I used in the Partly Sunny with a Chance of Hashtags Kaggle competition received almost no speedup.I naively optimized parallel algorithms for a range of problems, only to find that even with optimized custom code parallelism on multiple GPUs does not work well, given the effort that you have to put in .You need to be very aware of your hardware and how it interacts with deep learning algorithms to gauge if you can benefit from parallelization in the first place.
With GPUs I quickly learned how to apply deep learning on a range of Kaggle competitions and I managed to earn second place in the Partly Sunny with a Chance of Hashtags Kaggle competition using a deep learning approach, where it was the task to predict weather ratings for a given tweet.