Hardware Support (CPU/GPU)

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Deep learning algorithms perform a lot of computations and can be very slow to train on a CPU.

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Use the EDeepLearningTool::SetEnableGPU(false) method to use the CPU with the deep learning tools.

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The deep learning tools support CPU processing for both 32-bit and 64-bit applications.

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The memory of a 32-bit application is limited to 2 GB and this can slow the training or the classification of large images.

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The 64-bit version better supports the SIMD instructions of modern CPUs and is usually faster than the 32-bit version.

Using a recent NVIDIA GPU greatly accelerates the processing speeds. Refer to the benchmarks for each tool type to compare the GPU and CPU speeds.

1.

To use an NVIDIA GPU with the deep learning tools, install the following NVIDIA libraries on your computer:

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NVIDIA CUDA® Toolkit version v11.1 (https://developer.nvidia.com/cuda-toolkit)

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NVIDIA CUDA® Deep Neural Network library (cuDNN) v8.1 for CUDA 11.1 (https://developer.nvidia.com/cudnn)

2.

According to the installation location:

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If you install the NVIDIA CUDA® Toolkit in its default location (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.1), a deep learning tool automatically finds what it needs.

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Otherwise, copy the DLLs cusolver64_11.dll, curand64_10.dll, cufft64_10.dll and cublas64_11.dll in the Open eVision DLL folder (its default location is C:\Program Files (x86)\Euresys\Open eVision X.X\Bin64\).

3.

Install the NVIDIA CUDA ® Deep Neural Network library (cuDNN) that comes as a zip archive:

a.

Unzip the files.

b.

Copy the unzipped files to the NVIDIA CUDA® Toolkit installation directory as indicated in https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows.

c.

If the NVIDIA CUDA® Toolkit is not installed in its default location, copy all the DLL files cudnn*8.dll in the Open eVision DLL folder (its default location is C:\Program Files (x86)\Euresys\Open eVision X.X\Bin64\).

4.

Use the method EDeepLearningTool::SetEnableGPU(true) to use the GPU with the deep learning tools.

Using multiple GPUs

You can use multiple GPUs for the training and the batch classification.

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In the API, to set the list of GPUs, use the EDeepLearningTool::SetGPUIndexes method.

Using multiple GPUs increases the training and batch classification speed only if these GPUs are Quadro or Tesla models with the TCC driver model (see https://docs.nvidia.com/gameworks/content/developertools/desktop/nsight/tesla_compute_cluster.htm).
Using multiple GeForce GPUs is slower than using a single one. If there are more than one GPU installed on your computer, set the index of the GPU to use with the EDeepLearningTool::SetGPUIndexes method.

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In Deep Learning Studio, to choose the processing devices, select an execution profile.

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You can configure these execution profiles to match your needs.

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GPU processing is not possible with 32-bit applications.

The image cache is the part of the memory reserved for storing images during training.

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The default size is 1 GB.

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With large dataset, increasing the image cache size may improve the training speed.

To specify the cache size in bytes:

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In the API, use the EDeepLearningTool::SetImageCacheSize method.

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In Deep Learning Studio, click on the Configure button below the Execution profile control and select Image cache in the menu.

Multicore processing

The deep learning tools support multicore processing (see Multicore Processing):

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In the API, use the multicore processing helper function from Open eVision (that is Easy ::SetMaxNumberOfProcessingThreads() with a value greater than 1).

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In Deep Learning Studio, click on the Configure button below the Execution profile control and select CPU Settings in the menu.