Package
pytorch-0.3.0--python-3.6.3--openblas-0.2.19
Module
Version
Flavor
python-3.6.3--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-2.7.14--cuda-9.0.176--cudnn-7.0.3--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-3.5.4--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
The runtime environment constructor for the machine learning and deep learning tutorials and courses.
A pre-configured and fully integrated minimal runtime environment with PyTorch, an open source machine learning library, Jupyter Notebook, a browser-based interactive notebook for programming, mathematics, and data science, and the Python programming language. The stack is optimized for running on CPU.
pytorch:0.3.0, python:3.6.3, jupyter_notebook:1.0.0, development_preset:1
A pre-configured and fully integrated software stack with PyTorch, an open source machine learning library, and Python 3.6. 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 CPU.
pytorch:0.3.0, python:3.6.3, selfmanagement_preset, development_preset:1
The pre-configured and ready-to-use runtime environment for the Fast.ai's courses Practical Deep Learning for Coders, 2018 edition, part 1. It includes Python 3.6 and PyTorch 0.3.0. The software stack is optimized for running on CPU.
fast_ai-course:2018-1
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, and PyTorch. The software stack is optimized for running on CPU.
stanford-cs231n-course:1617spring, tensorflow:1.5.0, pytorch:0.3.0, keras:2.1.2, python:3.6.3
