11/20/2023 0 Comments Matlab for machine learning![]() ![]() | Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Base Learning| Net = trainNetwork(trainImages,trainAngles,layers,options) options = trainingOptions( 'sgdm', 'InitialLearnRate',0.001. I'm just going to do the training step here, not the full example. I'm going to experiment with this option using the "Train a Convolutional Neural Network for Regression" example. You can use this option to try some network training and prediction computations to measure the practical GPU impact on deep learning on your own computer. The choices are: 'auto', 'cpu', 'gpu', 'multi-gpu', and 'parallel'. Many of the deep learning functions in Neural Network Toolbox and other products now support an option called 'ExecutionEnvironment'. And the Tesla cards are intended as high-performance cards for compute servers in double-precision applications." Comparing CPU and GPU speed for deep learning ![]() The Titan is kind of a souped-up version of GeForce that does have remote desktop support. The GeForce cards are the cheapest ones with decent compute performance, but you have to keep in mind that they don't work if you are using remote desktop software. "Well, for deep learning, you can probably focus just on three lines of cards: GeForce, Titan, and Tesla. I asked Ben for a little help understanding the wide variety of GPU cards made by NVIDIA. If you're interested, you can drill into the GPUBench report for more details, like this: For image processing and deep learning, single-precision speed is more important than double-precision speed.Īnd the Titan Xp is blazingly fast at single-precision computation, with a whopping 11,000 GFLOPS for matrix multiplication with large matrices. So, why did Ben tell me that my card was so good for deep learning? It's because of the right-hand column of the report, which focuses on single-precision computation. My Titan Xp card is better than my CPU ("Host PC" in the table above) for double precision computing, but it's definitely slower than the top cards listed. The FFT and backslash benchmarks, on the other hand, involve more of a mixture of computation and I/O, so the reported GFLOP rates are lower. The matrix multiplication benchmark is best at measuring pure computation speed, and so it has the highest GFLOP numbers. The report includes three different computational benchmarks: MTimes (matrix multiplication), backslash (linear system solving), and FFT. The report shows the best double precision cards at the top because that is most important for general MATLAB computing. Some cards excel at double precision, and some do better at single precision. The report measures computational speed for both double-precision and single-precision floating point. 1 GFLOP is roughly 1 billion floating point operations per second. You can get it from the MATLAB Central File Exchange. The next thing Ben and I discussed was the output of GPUBench, a GPU performance measurement tool maintained by Ben's team. "The difference between a high end card and a low end card within the same generation often comes down to the number of chips available." GPUBench The MultiprocessorCount is effectively the number of chips on the GPU. There's one other number, though, that might be helpful to you when comparing GPUs. The other information provided by gpuDevice is mostly useful to the developers writing low-level GPU computation routines, or for troubleshooting. The sixth generation is known as Pascal." As of the R2017b release, GPU computing with MATLAB and Parallel Computing Toolbox requires a ComputeCapability of at least 3.0. And ComputeCapability refers to the generation of computation capability supported by this card. "An Index of 1 means that the NVIDIA driver thinks this GPU is the most powerful one installed on your computer. You've got a pretty good GPU there - a lot better than the one I've got, at least for deep learning." (I'll explain this comment below.) I asked Ben to walk me through the output of gpuDevice on my computer. The function gpuDevice tells you about your GPU hardware. Comparing CPU and GPU speed for deep learning.Getting information about your GPU card.MATLAB ® makes data science easy with tools to access and preprocess data, build machine learning and predictive models, and deploy models to enterprise IT systems. ![]()
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