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When it was first introduced, the name was an acronym for Compute Unified Device Architecture, [4] but Nvidia later dropped the common use of the acronym and now rarely expands it. [5] CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [6]
TensorFlow serves as a core platform and library for machine learning. TensorFlow's APIs use Keras to allow users to make their own machine-learning models. [33] [43] In addition to building and training their model, TensorFlow can also help load the data to train the model, and deploy it using TensorFlow Serving. [44]
Some very heavily optimized pipelines have yielded speed increases of several hundred times the original CPU-based pipeline on one high-use task. A simple example would be a GPU program that collects data about average lighting values as it renders some view from either a camera or a computer graphics program back to the main program on the CPU ...
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by ...
The following code-block defines a neural network with linear layers using the nn module. import torch from torch import nn # Import the nn sub-module from PyTorch class NeuralNetwork ( nn . Module ): # Neural networks are defined as classes def __init__ ( self ): # Layers and variables are defined in the __init__ method super () . __init__ ...
The number of threads in a block is limited, but grids can be used for computations that require a large number of thread blocks to operate in parallel and to use all available multiprocessors. CUDA is a parallel computing platform and programming model that higher level languages can use to exploit parallelism.
For example, in CIFAR-10, images are only of size 32×32×3 (32 wide, 32 high, 3 color channels), so a single fully connected neuron in the first hidden layer of a regular neural network would have 32*32*3 = 3,072 weights. A 200×200 image, however, would lead to neurons that have 200*200*3 = 120,000 weights.
TechPowerUp GPU-Z (or just GPU-Z) is a lightweight utility designed to provide information about video cards and GPUs. [2] The program displays the specifications of Graphics Processing Unit (often shortened to GPU) and its memory; also displays temperature, core frequency, memory frequency, GPU load and fan speeds.