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![Release history
• 2015/06: v1.0.0
• 2015/09: v1.3.0 (CuPy)
• 2015/11: v1.5.0 (Link/Chain, CuPy in Cython)
• 2016/06: [MinPy]
• 2016/07: v1.11.0 (Trainer)
• 2017/01: [PyTorch, TensorFlow Fold]
• 2017/02: v2.0.0a
• 2017/04: v2.0.0b
• 2017/05: v1.24.0 (Last v1 release)](https://image.slidesharecdn.com/20170519stapy-170522131314/75/Chainer-12-2048.jpg)
![NumPy-like API accelerated with CUDA
# CPU
x_cpu = numpy.array([1, 2, 3])
l2_cpu = numpy.linalg.norm(x_cpu)
# GPU
x_gpu = cupy.array([1, 2, 3])
l2_gpu = cupy.linalg.norm(x_gpu)
CuPy will be an independent project from Chainer from Chainer v2.
>150 NumPy functions are supported](https://image.slidesharecdn.com/20170519stapy-170522131314/75/Chainer-21-2048.jpg)
Uploaded byKenta Oono
深層学習フレームワーク概要とChainerの事例紹介
Deep learning framework Chainer was introduced. Chainer allows defining neural networks as Python programs for flexible construction. It supports both CPU and GPU computation and various deep learning libraries have been developed on top of Chainer like ChainerRL for reinforcement learning. The development team maintains and improves Chainer through frequent releases and community events.
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深層学習フレームワーク概要とChainerの事例紹介
- 1. 深層学習フレームワーク概要と Chainerの事例紹介 Stapy x 理研AIPオープンソース研究会 @理化学研究所 ⾰新知能統合研究センター 2017年5⽉19⽇ 株式会社Preferred Networks ⼤野健太 [email protected]
- 2. 大野 健太 • twitter:@delta2323_ • 経歴 • 数学専攻（修⼠） → 2011.4 PFI → 2014.10 PFN • 担当分野 • バイオプロジェクト • Chainerコアチーム • インターン・採⽤チーム 2
- 3.
- 4. • 2014年3⽉設⽴ • 本社：東京アメリカ⼦会社：カリフォルニア州サンマテオ • 社員数：約80名（8割以上はエンジニア・リサーチャー） • 事業内容：深層学習の産業、特に産業⽤ロボット・交通・バイオ ヘルスケアへの応⽤ 4 YouTube Channel preferredjp Factory Robot Healthcare Automotive
- 5. Warring states periodof deep learning frameworks 5
- 6. Technology stack ofa DL framework 6 name functions example Graphical visualization DIGITS, TensorBoard Machine learning workflow management Dataset prep, Save/Load Training loop Keras, TF slim Computational graph(CG) management Build/Optimize CGs Forward/Back prop Theano, TensorFlow Torch.nn Multi-dimensional array processing High-level array manipulation NumPy, CuPy Eigen, Torch (core) Numerical computation Matrix operation Convolution BLAS(OpenBLAS, MKL), cuBLAS, cuDNN, MKL DNN Computational device CPU, GPU, TPU, FPGA
- 7. üconstructing NNs asa Python programming üdynamic NN construction üCPU/GPU agnostic code with CuPy 7 Mission Speed up research and development of deep learning and its applications. Features Flexible and intuitive description of complex NNs by http://chainer.org *NN = Neural Network
- 8.
- 9. f g x fg Static graph construction Data feed x yf zg Dynamic graph construction Define-And-Run (Most frameworks) Define-By-Run (Chainer) 9 x yf x z Static Dynamic Optimization ✓ △ Flexibility △ ✓
- 10. 10 (MinPy) Era of dynamicgraph frameworks
- 11. Construct NNs as Pythonprogramming class MLP(Link): def __int__(self): super(MLP, self).__init__( l1=Linear(784, 1000), l2=Linear(1000, 1000), l3=Linear(1000, 10)) def __call__(x): h1 = F.relu(self.l1(x)) h2 = F.relu(self.l2(l1)) return self.l3(h2) Linear l1 ｘ W bias ReLU Linear l2 h1 W bias ReLU Linear l3 h2 W bias 11
- 12. Release history • 2015/06:v1.0.0 • 2015/09: v1.3.0 (CuPy) • 2015/11: v1.5.0 (Link/Chain, CuPy in Cython) • 2016/06: [MinPy] • 2016/07: v1.11.0 (Trainer) • 2017/01: [PyTorch, TensorFlow Fold] • 2017/02: v2.0.0a • 2017/04: v2.0.0b • 2017/05: v1.24.0 (Last v1 release)
- 13. Chainer v2 • Firstmajor version up that breaks backward compatibility. • Important features (almost fixed) • CuPy separation • Unified configuration (chainer.config, esp. train mode) • train argument is removed from many functions • Variable updated: Parameter class, uninitialized var, volatile removed • Funcion.retain_inputs and retain_outputs to reduce memory usage • New-style parameter/child link registration (just setting them as an attribute) • UpdateRule customized for each parameter • Extention.initialize added, invoke_before_training removed • No duplicated memory between training graph and evaluation graph • Input size is made optional in many links (L.Linear(100)) • wscale option is removed from many links 13 Will release on May 30th 2017
- 14. Libraries on topof Chainer ChainerRL (beta): Reinforcement learning ChainerMN (v0.1.0): Multi-node distributed learning ChainerCV (v0.4.5): Computer vision
- 15. ChainerRL 15 ü Implement latestdeep reinforcement learning algorithms ü Work with OpenAI Gym
- 16. ChainerMN 16 ü Distributed deeplearning with MPI, NCCL ü approx. 100x speed with 128 GPUs
- 17. ChainerCV 17 ü Dataset WrapperFor well-known CV datasets (CUB, Pascal VOC) ü Dataset transformer (random crop, random flop) ü Implements typical workflow in CV
- 18. Application of Chainer PaintsChainer: Line draw colorization tool PonanzaChainer : Shogi AI with Deep Learning
- 19.
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- 21. NumPy-like API acceleratedwith CUDA # CPU x_cpu = numpy.array([1, 2, 3]) l2_cpu = numpy.linalg.norm(x_cpu) # GPU x_gpu = cupy.array([1, 2, 3]) l2_gpu = cupy.linalg.norm(x_gpu) CuPy will be an independent project from Chainer from Chainer v2. >150 NumPy functions are supported
- 22. Development team (asof May 2017) • Chainer, CuPy • Core development team: approx. 10 members • Reviewer team: approx. 10 members • Chainer user group: approx. 5 members • Chainer RL, Chainer MN, Chainer CV: 2, 3 members for each • Paints Chainer: approx. 10 members 22 * some members overlap
- 23. CI 23 Travis CI Run allCPU tests of all PRs Jenkins installation test
- 24. CI 24 Jenkins dairy test(internal): Run all tests with various configuration
- 25. Community activities 25 Chainer meetup(#0 - #4) #5 will be held in June 10th 2017 Deep learning mini course @UCSF application for biology Google group (ja, en), Slack (ja, en), Twitter (ja, en)
- 26. Conclusion • Chainer isa Python-based deep learning framework that leverages flexible and intuitive description of NNs. • Many libraries and services are being developed on top of Chainer (ChainerRL/MN/CV, PaintsChainer, PonanzaChainer, CuPy). • Introduced the development and the user-group teams of Chainer 26
- 27. Try Chainer !http://chainer.org 27 Seiya Tokui Kenta Oono Yuya Unno Ryosuke Okuta Chainer core development team Brian Vogel Gentaro Watanabe Shunta Saito Daisuke Nishino and many contributors ! Contact: [email protected] Google Group: Chainer User Group