Torch

(Lua 프로젝트인 torch 가 아닌 Python 프로젝트는 pytorch로 이동)

Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Features

• a powerful N-dimensional array
• lots of routines for indexing, slicing, transposing, ...
• amazing interface to C, via LuaJIT
• linear algebra routines
• neural network, and energy-based models
• numeric optimization routines
• Fast and efficient GPU support
• Embeddable, with ports to iOS, Android and FPGA backends

Device query

• Stackoverflow - How to check if pytorch is using the GPU?

import torch

torch.cuda.is_available()
>>> True

torch.cuda.current_device()
>>> 0

torch.cuda.device(0)
>>> <torch.cuda.device at 0x7efce0b03be0>

torch.cuda.device_count()
>>> 1

torch.cuda.get_device_name(0)
>>> 'GeForce GTX 950M'

See also

• torch-twrl: Torch-twrl is a package that enables reinforcement learning in Torch.
• Github - pby5/ssd_torch project site
• OpenNMT: Open Source Neural Machine Translation
• iTorch: https://github.com/facebook/iTorch
  • IPython kernel for Torch with visualization and plotting.
• OpenMP
• pytorch

Favorite site

• Torch web site
• Facebook Open Source Image Recognition Tools: 딥마스트(DeepMask)와 샵마스크(SharpMask), 그리고 멀티패스넷(MultiPathNet)에 대한 내용.
• Github - convnet-benchmarks (convnet의 Deep Learning 프레임워크별 수행속도 비교)