Package
![Pytorch-0 Pytorch](/ic/packages/min/pytorch.png)
pytorch-0.3.0--python-2.7.14--cuda-9.0.176--cudnn-7.0.3--openblas-0.2.19
Module
Version
Flavor
python-2.7.14--cuda-9.0.176--cudnn-7.0.3--openblas-0.2.19
Next versions
Other flavors
python-3.4.7--cuda-8.0.61--cudnn-7.0.3--openblas-0.2.19, python-3.4.7--openblas-0.2.19, python-3.5.4--cuda-8.0.61--cudnn-7.0.3--openblas-0.2.19, python-3.4.7--cuda-9.1.85--cudnn-7.0.5--openblas-0.2.19, python-3.6.3--openblas-0.2.19, python-3.5.4--cuda-9.1.85--cudnn-7.0.5--openblas-0.2.19, python-3.6.3--cuda-8.0.61--cudnn-7.0.3--openblas-0.2.19, python-2.7.14--cuda-8.0.61--cudnn-6.0.21--openblas-0.2.19, python-2.7.14--cuda-9.1.85--cudnn-7.0.5--openblas-0.2.19, python-3.6.3--cuda-9.0.176--cudnn-7.0.3--openblas-0.2.19
Role
Description
PyTorch, an open source machine learning library for Python.
Web site
![machine_learning_education Machine learning education](/ic/constructors/max/machine_learning_education.png)
The runtime environment constructor for the machine learning and deep learning tutorials and courses.
![Pytorch03_python2_cuda9 Machine learning](/ic/appliances/max/machine_learning.png)
A pre-configured and fully integrated software stack with PyTorch, an open source machine learning library, and Python 2.7. It provides a stable and tested execution environment for training, inference, or running as an API service. The stack can be easily integrated into continuous integration and deployment workflows. It is designed for short and long-running high-performance tasks and optimized for running on NVidia GPU.
pytorch:0.3.0, python:2.7.14, cuda:9.0.176, cudnn:7.0.5, cuda_only-nvidia_drivers:384.111, selfmanagement_preset, development_preset:1
![Course_stanford_cs231n_1617spring_python2_cuda9_latest Course stanford](/ic/appliances/max/course_stanford.png)
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, and PyTorch. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
stanford-cs231n-course:1617spring, tensorflow:1.5.0, pytorch:0.3.0, keras:2.1.2, python:2.7.14, cuda:9.0.176, cudnn:7.0.5, cuda_only-nvidia_drivers:384.111