grain 0.1.1
autograd and dynamic neural networks library for D
To use this package, run the following command in your project's root directory:
Manual usage
Put the following dependency into your project's dependences section:
<p align="center"> <img height="160" src="https://raw.githubusercontent.com/ShigekiKarita/grain/master/grain.png"> </p>
grain
autograd mir and CUDA library for dynamic neural networks in D.
- documentaion https://shigekikarita.github.io/grain
- introduction (PDF) https://github.com/ShigekiKarita/grain-talk/blob/master/slide.pdf
features
- dynamic computation graph like chainer or pytorch
- statically typed tensor
Variable(T, size_t dim, alias Storage)
unlike numpy - CPU (mir) and CUDA (cublas/cudnn) backend
- extensible (i.e., user-defined) autograd function
- LDC2 (CPU/CUDA) and DMD (CPU only) support
how to run MNIST
$ dub --config=example-mnist -b=cuda-release # with cuda
$ dub --config=example-mnist -b=release # without cuda
it results as following (may take several seconds without cuda)
Running ./grain-example-mnist
loading data/train-images-idx3-ubyte.gz
loading data/train-labels-idx1-ubyte.gz
loading data/t10k-images-idx3-ubyte.gz
loading data/t10k-labels-idx1-ubyte.gz
train loss: 0.538635, acc: 0.864311
test loss: 0.299959, acc: 0.915264
train loss: 0.277901, acc: 0.920858
test loss: 0.241783, acc: 0.930589
train loss: 0.229879, acc: 0.934999
test loss: 0.206087, acc: 0.939704
train loss: 0.198716, acc: 0.943937
test loss: 0.181938, acc: 0.945613
train loss: 0.175066, acc: 0.950957
test loss: 0.163919, acc: 0.951022
how to test
$ curl -fsS https://dlang.org/install.sh | bash -s ldc-1.9.0
$ source ~/dlang/ldc-1.9.0/activate
$ dub test -b=cuda-unittest # with cuda
$ dub test # without cuda
I have tested with
- LDC1.9.0 (prebuilt binary)
- CUDA9.1 (optional)
- CUDNN7 (optional but required if you use CUDA)
- NVidia GTX760, GTX1080 (https://grain.dpldocs.info/grain.htmloptional)
links
- CUDA in D
- https://github.com/ldc-developers/ldc
- https://llvm.org/docs/NVPTXUsage.html
- https://llvm.org/docs/CompileCudaWithLLVM.html
- Referenced autograd libraries
- Pytorch https://github.com/pytorch/pytorch
- Chainer v1 https://github.com/chainer/chainer/tree/v1
todo
sorted by higher priority for me
- practical examples (MNIST, CIFAR10, WordLM). see example/
dub --config=example-mnist
- (wip)
dub --config=example-char-rnn
- more autograd functions. see source/grain/functions/ TODO
- multi GPU
- curand wrappers
- statically linked kernel module instead of ptx string
- dmd support
- double backward (implement Function.backward with Chain)
- 0.1.1 released 5 years ago
- ShigekiKarita/grain
- BSL-1.0
- Copyright © 2018, karita
- Authors:
- Dependencies:
- numir, derelict-cuda, lubeck
- Versions:
-
0.1.1 2019-Apr-28 0.1.0 2018-Nov-25 0.0.10 2018-Sep-10 0.0.9 2018-Jul-08 0.0.8 2018-Jul-04 - Download Stats:
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- Score:
- 2.1
- Short URL:
- grain.dub.pm