While OpenCL sounds attractive because of its generality, it hasn’t performed as well as CUDA on NVIDIA GPUs, and many deep learning frameworks either don’t support OpenCL or only support it ...

CUDA is Nvidia’s API for its general purpose ... If the current progress is anything to go by, PyTorch should be as good as any deep learning framework by the time of the PyTorch 1.0 release ...

Popular deep learning frameworks are optimized for NVIDIA CUDA and CuDNN technologies. The company is now pushing the limits through the announcement of the most powerful GPU, Kubernetes ...

Nvidia runs the Deep Learning Institute (DLI), through which it helps developers learn to use frameworks to design, train, and deploy neural network-powered machine learning for a variety of ...

As with CUDA, ROCm is an ideal solution for AI applications, as some deep-learning frameworks already support a ROCm backend (e.g., TensorFlow, PyTorch, MXNet, ONNX, CuPy, and more). According to ...

TensorFlow works seamlessly on Linux, allowing developers to leverage NVIDIA CUDA and TensorRT for faster computations. PyTorch, developed by Facebook's AI Research Lab, is another popular deep ...

This would support CUDA and OpenCL and could even have an NVlink like ... “To begin with, there’s a divergence since all of the deep learning frameworks were not developed by HPC folks. Their ...

This combines pre-compiled deep learning frameworks (Caffe, Torch, Theano, and OpenBLAS) into one package aimed at Ubuntu 16.04 running on an IBM Power processor, Nvidia CUDA v8.0, and Nvidia ...

The Tensorbook makes it easy to install PyTorch, TensorFlow, Caffee, and Caffee 2 deep-learning frameworks, as well as GPU-focused applications CUDA and cuDNN, plus dev tools through Lambda's ...

Within the realm of data science, deep learning frameworks are predominantly delivered via software found in the Python ecosystem. When looking at the options in the space, it may appear to some as a ...