# PC Requirements

Both PCs for 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. and PCs for deployment purposes require a fast multi-core CPU, large system memory (RAM), and a strong NVIDIA® GPU. Training PCs require larger GPU and system memory, and a large amount of free SSD space.

Requirement | Description
CPU | Intel Core i7 or higher. The Blue Locate tool is sensitive to clock speed rates, particularly with complex model A specific spatial arrangement of a set of features (Blue Locate and Blue Read tools only.) During a post-processing step, the Blue Locate and Blue Read tools can fit all of the features detected in an image to the models defined for the tool. The overall pose and identity of the model is then returned. matching applications. If a single core performs multiple tasks, processing time can increase. Therefore, use a sufficient amount of resources or allocate only one task to a single core.
GPU | Cognex recommends using a GPU for higher performance and for access to Standard tools. You can use Legacy mode tools without a GPU, but you cannot process Standard and Standard tools. VisionPro Deep Learning has the following GPU requirements: VisionPro Deep Learning 4.0 supports all NVIDIA® CUDA®-enabled GPUs with compute capability of 6.1 and higher. For more information about GPU compute capability, see the NVIDIA website. 10 GB or larger GPU RAM is required for a training PC. 8 GB or larger GPU RAM is required for a deployment PC. Note: For training and processing Standard tools, Cognex recommends more than 10 GB. Cognex recommends using a GPU driver version from 528.02 to 532.03, with the following considerations: The tested driver version is 528.02. Driver versions from 532.03 to 546.01 are not recommended. You can use a driver version higher than 546.01 if necessary. When using a version higher than 546.01, you must disable Shared GPU memory usage in the NVIDIA Control Panel. For more information, see Disable Shared GPU Memory. Note: Using driver versions beyond 532.03 can result in slower training and processing times in Standard mode. Visit the NVIDIA Driver Downloads website to find the right driver version for your GPU. For more information about selecting GPUs for your application, see Guidelines for GPU Selection.
System memory (RAM) | VisionPro Deep Learning has the following minimum system memory requirements: 64 GB RAM or larger is required for a training PC. 32 GB RAM or larger is required for a deployment PC.
System storage | SSD with 200 GB or more free space is required for a training PC.
PCIe lanes | Minimum x8 PCIe lanes. A x16 PCIe lane can reduce cycle time by approximately 10 ms, relative to a PCIe x8 (based on a 5 MP image).
Power supply | 1.25 times larger than the system and GPU power requirements.
USB 2.0 | USB port for a permanent Security Key connection via USB 2.0.