To do this it nees to be traine on a large dataset of images. This is a complex computational process. In order for the network to work with images they are converte into three dimensional matrices tensors from cells with pixel parameters. Matrix calculations are use to work with tensors affine transformations displacements rotations and so on. The GPU handles them more efficiently. In addition there are a large number of neural network architectures that are implemente base on operations with tensors. Comparison of performance of image classification on GPU and CPU. The EfficientNet B model was use for classification.
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Source Popular imaging libraries OpenCV PyTorch TensorFlow are also more efficient on GPUs. Even taking into account the optimization of the CPU with the help of special software OpenVINO etc Performance of ML models in production GPUs are neee not only at the training stage but also when working in production. If your ML service Albania Email List works with meia data then with a video card the spee of user service will be higher. In addition models are often retraine while working with client requests. Lets say a company is developing an application that determines the name of a dish from a photo. When the service was first launche the model was often wrong. Users helpe her classify dishes. Thousands of people could use the service at the same time. However the application did not slow down as it worke on the power of the GPU. OpenCV CUDA core efficiency of models on CPU and GPU.
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Source Is it possible to train on the CPU. Not in every ML task there is a noticeable difference in spee between CPU and GPU. For models on small samples of numerical data for example for simple forecasting or regression analysis it does not BM Leads matter on which processor to work. The quality of training does not depend on the selecte type of processor. With a balance representative sample the quality of the model will not change. But the learning process will be slower. It can take months a time that companies do not always have in a competitive environment. Why containers are use for ML tasks Trends show that to run ML models it is better to use containers that can be deploye both in the clouds and on deicate servers.