Click on Supported, Click To Start next to either Short Self-test or Extended Self-test.If everything looks fine, but you still believe the device has a problem, you can launch S.M.A.R.T.tab to see an overview of the device's condition based on current S.M.A.R.T. Choose the HDD or SSD you want to check from the list on the left.tests, but you can use a free third-party tool like HD Sentinel for that. Unfortunately, Windows doesn't come with tools for running S.M.A.R.T. banner, which stands for Self-Monitoring, Analysis, and Reporting Technology. Support for Windows and Linux with the latest NVIDIA data center and mobile GPUs.All modern SSDs and HDDs come with self-checking capabilities you can use to analyze their overall 'health' based on a series of different metrics.Support for fusion of memory-limited operations like pointwise and reduction with math-limited operations like convolution and matmul.Support for FP32, FP16, BF16 and TF32 floating point formats and INT8, and UINT8 integer formats.Optimized kernels for computer vision and speech models including ResNet, ResNext, EfficientNet, EfficientDet, SSD, MaskRCNN, Unet, VNet, BERT, GPT-2, Tacotron2 and WaveGlow.Tensor Core acceleration for all popular convolutions including 2D, 3D, Grouped, Depth-wise separable, and Dilated with NHWC and NCHW inputs and outputs.For access to NVIDIA optimized deep learning framework containers that have cuDNN integrated into frameworks, visit NVIDIA GPU CLOUD to learn more and get started.ĭownload cuDNN Developer Guide Forums Latest Release Notes cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PaddlePaddle, PyTorch, and TensorFlow. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning.

cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.ĭeep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks.

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