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
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.
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)