# NVIDIA® GPU Guidelines

VisionPro Deep Learning uses the GPU during the development of your application, typically with the training Training is the process that your tool, which is a neural network, is learning about the features (pixels) based on the labels you made. For example, a tool will learn the defect/normal pixels in each image based on the defect/normal labels you drew. The goal of the tool Training is learning enough to give the correct inspection results of whether an unseen image is defective or not. The key to training is to ensure that you include all possible variations within your training set, and that your images are accurately labeled. Training times vary by the application, tool setup and the GPU in the PC being used to train the network. of tools. In addition, a GPU can also be used during runtime deployment, where it increases the performance of runtime workspaces. The choices made during GPU selection directly affect the processing speed of the tool, but not tool accuracy or behavior.

The following NVIDIA® device features affect the speed of VisionPro Deep Learning:

Feature | Definition | Importance
--- | --- | ---
CUDA Core | The standard NVIDIA parallel processing unit. | YES. The number of CUDA cores is the standard measure of NVIDIA GPU processing. The more CUDA cores, the faster the VisionPro Deep Learning processing and training.
ECC Memory | Error correcting code (ECC) memory involves hardware support for verifying that memory read/writes do not contain errors. | NO. Due to the huge number of computations involved in training and processing neural networks, the likelihood of a memory error affecting a tool result is very low.
TCC | Tesla Compute Cluster (TCC) driver mode A high-performance driver that is optimized for computational use of an NVIDIA GPU. | YES. Whenever possible, you should select cards that support the TCC driver mode, and they should be enabled.
Tensor Core | Full-precision, mixed-precision (and eventually integer math) parallel processing unit that is dedicated to matrix multiply operations. | YES. Introduced in version VisionPro Deep Learning 3.2.0, VisionPro Deep Learning automatically takes advantage of Tensor Cores for faster processing and training, as long as the user has a Standard or Advanced license. The number of Tensor cores is related to processing speed in low-precision mode only. These cores do not affect standard precision processing or training speed.
Tensor RT | The NVIDIA framework for optimizing (by using low-precision and integer math) runtime performance of TensorFlow, Caffe and other standard framework networks running on a GPU with Tensor Cores. | NO. VisionPro Deep Learning uses a proprietary network architecture that is not compatible with Tensor RT.

Note: Deep Learning-optimized memory is enabled by default and improves performance by overriding the standard NVIDIA GPU memory management system. Performance improvement is not as significant on GPUs that use the TCC driver.