Module
Cuda
cuda
Role
Description

CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform 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.

Machine learning education
The pre-configured and ready-to-use runtime environment for the CS231n course - Convolutional Neural Networks for Visual Recognition, Stanford University, Spring 2017. It includes latest versions of Python 3, TensorFlow, PyTorch, CUDA, and cuDNN. The software stack is optimized for running on NVIdia GPU.
stanford-cs231n-course:1617spring, tensorflow:1.4.0, pytorch:0.3.0, keras:2.1.2, python:3.6.3, cuda:9.0.176, cudnn:7.0.5, cuda_only-nvidia_drivers:384.98
Machine learning education
The pre-configured and ready-to-use runtime environment for the CS231n course - Convolutional Neural Networks for Visual Recognition, Stanford University, Spring 2017. It includes latest versions of Python 2, TensorFlow, PyTorch, CUDA, and cuDNN. The software stack is optimized for running on NVIdia GPU.
stanford-cs231n-course:1617spring, tensorflow:1.4.0, pytorch:0.3.0, keras:2.1.2, python:2.7.14, cuda:9.0.176, cudnn:7.0.5, cuda_only-nvidia_drivers:384.98
Machine learning education
The pre-configured and ready-to-use runtime environment for the CS231n course - Convolutional Neural Networks for Visual Recognition, Stanford University, Spring 2017. It includes original (old) versions of Python, TensorFlow, PyTorch, CUDA, and cuDNN, used in the course. The software stack is optimized for running on NVIdia GPU.
stanford-cs231n-course:1617spring, tensorflow:1.0.1, pytorch:0.1.11, keras:2.1.2, python:3.5.4, cuda:8.0.61, cudnn:5.1.10, cuda_only-nvidia_drivers:384.98