Last update: 28 July, 2017

Chainer – a deep learning framework
Chainer provides a set of features required for research and
development using deep learning such as designing neural
networks, training, and evaluation.
Designing a network Training, evaluation
Data
set

Features and Characteristics of Chainer
Powerful
☑ CUDA
☑ cuDNN
☑ NCCL
Versatile
☑ Convolutional Network
☑ Recurrent Network
☑ Many Other Components
☑ Various Optimizers
Intuitive
☑ Define-by-Run
☑ High debuggability
Supports GPU calculation using CUDA
High-speed training/inference by cuDNN
Supports a fast, multi-GPU learning using NCCL
N-dimensional Convolution, Deconvolution, Pooling, BN, etc.
RNN components such as LSTM, Bi-directional LSTM, GRU and Bi-directional GRU
Many layer definitions and various loss functions used in neural networks
Various optimizers, e.g., SGD, MomentumSGD, AdaGrad, RMSProp, Adam, etc.
Easy to write a complicated network
User-friendly error messages. Easy to debug using pure Python debuggers.
Well-abstracted common tools for various NN learning, easy to write a set of learning flows☑ Simple APIs

Popularity Growth of Chainer

Neural network = Computational graph
NN can be interpreted as a computational graph that applies
many linear and nonlinear functions to input vectors

How to handle a computational graph
A definition of
computational graph
exists apart from code
that performs
computation according
to the definition
Static
The actual code that
performs computation is
treated as a definition of
computational graph
Dynamic

Chainer is the first deep-learning framework to adopt “Define-by-Run”*
How about Chainer? → Dynamic
● Define-and-Run（static graph）
Consists of two steps: first to build a computational graph, then feed data to the
computational graph (Caffe, theano, TensorFlow, etc.)
● Define-by-Run（dynamic graph）
Describing a forward-pass computation means to construct a computational
graph for the backward computation (Chainer, DyNet, PyTorch, etc.）
* autograd adopted Define-by-Run but it was not a framework for deep learning.

Define-and-Run and Define-by-Run
# Building
x = Variable(‘x’)
y = Variable(‘y’)
z = x + 2 * y
# Evaluation
for xi, yi in data:
eval(z, (xi, yi))
# Build, evaluate at the same time
for xi, yi in data:
x = Variable(xi)
y = Variable(yi)
z = x + 2 * y
You can make a branch to change
the forward computation
depending on the data
Define-and-Run Define-by-Run

How to write a Convolutional Network
import chainer
import chainer.links as L
import chainer.functions as F
class LeNet5(chainer.Chain):
def __init__(self):
super(LeNet5, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution2D(1, 6, 5, 1)
self.conv2 = L.Convolution2D(6, 16, 5, 1)
self.conv3 = L.Convolution2D(16, 120, 4, 1)
self.fc4 = L.Linear(None, 84)
self.fc5 = L.Linear(84, 10)
• Start writing a model by inheriting Chain class
• Register parametric layers inside the init_scope
• Write forward computation in
__call__ method (no need to
write backward computation)
def __call__(self, x):
h = F.sigmoid(self.conv1(x))
h = F.max_pooling_2d(h, 2, 2)
h = F.sigmoid(self.conv2(h))
h = F.max_pooling_2d(h, 2, 2)
h = F.sigmoid(self.conv3(h))
h = F.sigmoid(self.fc4(h))
return self.fc5(h)

Training models
model = LeNet5()
model = L.Classifier(model)
# Dataset is a list! ([] to access, having __len__)
dataset = [(x1, t1), (x2, t2), ...]
# iterator to return a mini-batch retrieved from dataset
it = iterators.SerialIterator(dataset, batchsize=32)
# Optimization methods (you can easily try various methods by changing SGD to
# MomentumSGD, Adam, RMSprop, AdaGrad, etc.)
opt = optimizers.SGD(lr=0.01)
opt.setup(model)
updater = training.StandardUpdater(it, opt, device=0) # device=-1 if you use CPU
trainer = training.Trainer(updater, stop_trigger=(100, 'epoch'))
trainer.run()
For more details, refer to official examples: https://github.com/pfnet/chainer/tree/master/examples

Define-by-Run brings flexibility and intuitiveness
“Forward computation” becomes a definition of network
• Depending on data, it is easy to change a network structure
• You can define a network itself by Python code
＝The network structure can be treated as a program instead of data.
For Chainer, the “forward computation” can be written in Python
• Enables you to write a network structure freely using the syntax of Python
• Define-by-Run makes it easy to insert any process like putting a print statement between network
computations (In case of define-and-run which compiles a network, this kind of debugging is
difficult)
• Easy to reuse code of the same network for other purposes with few changes (e.g. by just adding
a conditional branch partially)
• Easy to check intermediate values and the design of the network itself using external debugging
tools etc.

Chainer v2.0.1
Significantly reduced memory consumption, organized API in response to the users feedback
Aggressive Buffer Release
to reduce the memory
consumption during
training→
CuPy has been released as an
independent library. This allows for
array operations using GPU via an
interface highly compatible with
NumPy.
https://cupy.chainer.org
https://chainer.org

CuPy
Independent library to handle all GPU calculations in Chainer
Lower cost to migrate CPU code to GPU with NumPy-compatible API
GPU-execute linear algebra algorithms such as a singular value decomposition
Rich in examples such as KMeans, Gaussian Mixture Model
import numpy as np
x = np.random.rand(10)
W = np.random.rand(10, 5)
y = np.dot(x, W)
import cupy as cp
x = cp.random.rand(10)
W = cp.random.rand(10, 5)
y = cp.dot(x, W)
GPU
https://github.com/cupy/cupy

Add-on packages for Chainer
Distribute deep learning, deep reinforcement learning, computer vision
ChainerMN (Multi-Node): additional package for distributed deep learning
High scalability (100 times faster with 128GPU)
ChainerRL: deep reinforcement learning library
DQN, DDPG, A3C, ACER, NSQ, PCL, etc. OpenAI Gym support
ChainerCV: provides image recognition algorithms, dataset wrappers
Faster R-CNN, Single Shot Multibox Detector (SSD), SegNet, etc.

ChainerMN
Chainer + Multi-Node

ChainerMN: Multi-node
Keeping the easy-to-use characteristics of Chainer as is,
ChainerMN enables to use multiple nodes which have multiple
GPUs easily to make training faster
GPU
GPU
InfiniBand
GPU
GPU
InfiniBand
MPI
NVIDIA NCCL

Destributed deep learning with ChainerMN
100x speed up with 128 GPUs

Comparison with other frameworks
ChainerMN is the fastest at the comparison of elapsed time to train
ResNet-50 on ImageNet dataset for 100 epochs (May 2017)

We confirmed that if we increase the number of nodes,
the almost same accuracy can be achieved
Speedup without dropping the accuracy

Scale-out test on Microsoft Azure

Easy-to-use API of ChainerMN
You can start using ChainerMN just by wrapping one line!
optimizer = chainer.optimizers.MomentumSGD()
optimizer = chainermn.DistributedOptimizer(
chainer.optimizers.MomentumSGD())

ARM template will be announced soon
https://github.com/mitmul/ARMTeamplate4ChainerMN
↑ Click this to make a master node ↑ Click this to make worker nodes

Scaling via web interface
You can launch a scale-set of Azure instances super easily!

ChainerRL
Chainer + Reinforcement Learning

Reinforcement Learning:
ChainerRL: Deep Reinforcement Learning Library
Train an agent which interacts with the environment to maximize
the rewards
Action
Env
Observation, Reward

Reinforcement Learning with ChainerRL
1. Create an environment
Action
Env
Observation, Reward

Distribution: Softmax, Mellowmax, Gaussian,…
Policy: Observation → Distribution of actions
2. Define an agent model
Reinforcement Learning with ChainerRL

2. Define an agent model (contd.)
Q-Function: Observation → Value of each action (expectation of the sum of future rewards)
ActionValue: Discrete, Quadratic
Reinforcement Learning with ChainerRL

Action
Env
Observation, Reward
3. Create an agent
Reinforcement Learning with ChainerRL

4. Interact with the environment!
Reinforcement Learning with ChainerRL

Algorithms provided by ChainerRL
• Deep Q-Network (Mnih et al., 2015)
• Double DQN (Hasselt et al., 2016)
• Normalized Advantage Function (Gu et al., 2016)
• (Persistent) Advantage Learning (Bellemare et al., 2016)
• Deep Deterministic Policy Gradient (Lillicrap et al., 2016)
• SVG(0) (Heese et al., 2015)
• Asynchronous Advantage Actor-Critic (Mnih et al., 2016)
• Asynchronous N-step Q-learning (Mnih et al., 2016)
• Actor-Critic with Experience Replay (Wang et al., 2017) <- NEW!
• Path Consistency Learning (Nachum et al., 2017) <- NEW!
• etc.

ChainerRL Quickstart Guide
• Define a Q-function in a Jupyter notebook and learn the Cart Pole
Balancing problem with DQN
https://github.com/pfnet/chainerrl/blob/master/examples/quickstart/quickstart.ipynb

ChainerCV
Chainer + Computer Vision

Evaluate your
model on
popular
datasets
Running and training deep-learning models easier for Computer Vision tasks
ChainerCV https://github.com/pfnet/chainercv
Datasets
Pascal VOC,
Caltech-UCSD
Birds-200-2011,
Stanford Online
Products, CamVid, etc.
Models
Faster R-CNN, SSD,
SegNet (will add more
models!)
Training
tools
Evaluation
tools
Dataset
Abstraction
Train popular
models with
your data

Start computer vision research using deep learning much easier
ChainerCV
Latest algorithms with your data
Provide complete model code, training code, inference code for segmentation
algorithms (SegNet, etc.) and object detection algorithms (Faster R-CNN, SSD,
etc.), and so on
All code is confirmed to reproduce the results
All training code and model code reproduced the experimental results shown in
the original paper
https://github.com/pfnet/chainercv

• If you want to see some
examples of ChainerCV
and the reproducing code
for some papers, please
check the official Github
repository
(chainer/chainercv)
• The right figure shows the
result of the inference code
of Faster RCNN example
• The pre-trained weights
are automatically
downloaded!
https://github.com/pfnet/chainercv
$ pip install chainercv

•
•
→
←

•
•
•

Intel Chainer

Intel Chainer with MKL-DNN Backend
CPU
CuPy
NVIDIA GPU
CUDA
cuDNN
BLAS
NumPy
Chainer
MKL-DNN
Intel Xeon/Xeon Phi
MKL

Intel Chainer with MKL-DNN Backend
MKL-DNN
• Neural Network library optimized for Intel architectures
• Supported CPUs:
✓ Intel Atom(R) processor with Intel(R) SSE4.1 support
✓ 4th, 5th, 6th and 7th generation Intel(R) Core processor
✓ Intel(R) Xeon(R) processor E5 v3 family (code named Haswell)
✓ Intel(R) Xeon(R) processor E5 v4 family (code named Broadwell)
✓ Intel(R) Xeon(R) Platinum processor family (code name Skylake)
✓ Intel(R) Xeon Phi(TM) product family x200 (code named Knights Landing)
✓ Future Intel(R) Xeon Phi(TM) processor (code named Knights Mill)
• MKL-DNN accelerates the computation of NN on the above CPUs

Intel Chainer with MKL-DNN Backend
convnet-benchmarks* result:
Intel Chainer Chainer with NumPy (MKL-Build)
Alexnet Forward 429.16 ms 5041.91 ms
Alexnet Backward 841.73 ms 5569.49 ms
Alexnet Total 1270.89 ms 10611.40 ms
~8.35x faster than NumPy backend！

Intel Chainer with MKL-DNN Backend
Intel is developing Intel Chainer as a fork of Chainer v2
https://github.com/intel/chainer

Applications using Chainer

Object Detection
https://www.youtube.com/watch?v=yNc5N1MOOt4

Semantic Segmentation
https://www.youtube.com/watch?v=lGOjchGdVQs

Ponanza Chainer
● Won the 2nd
place at The 27th
World Computer Shogi Championship
● Based on Ponanza which was the champion for two years in a row (2015, 2016)
● “Ponanza Chainer” applied Deep Learning for ordering the possible next moves for which
“Ponanza” should think ahead deeply
● “Ponanza Chainer” wins “Ponanza” with a probability of 80%
Team
PFN
Issei
Yamamoto
Akira
Shimoyama
Team
Ponanza

Paints Chainer
● Auto Sketch Colorization
● Train a neural network with
a large dataset of paintings
● It takes a line drawings as
input, and output a
colorized image!
● You can also give color hits
which indicates preferable
colors
https://paintschainer.preferred.tech

Installation of Chainer

1. Install CUDA Toolkit 8.0
https://developer.nvidia.com/cuda-downloads
2. Install cuDNN v6.0 Library
https://developer.nvidia.com/rdp/cudnn-download
3. Install NCCL for Multi-GPUs
https://github.com/NVIDIA/nccl
4. Install CuPy and Chainer
% pip install cupy
% pip install chainer
Chainer on Ubuntu
For more details, see the official installation guide:
http://docs.chainer.org/en/stable/install.html

Chainer on Windows with NVIDIA GPU
1. Install Visual C++ 2015 Build Tools
http://landinghub.visualstudio.com/visual-cpp-build-tools
2. Install CUDA Toolkit 8.0
https://developer.nvidia.com/cuda-downloads
3. Install cuDNN v6.0 Library for Windows 10
https://developer.nvidia.com/rdp/cudnn-download
Put all files under C:Program FilesNVIDIA GPU Computing ToolkitCUDAv8.0
4. Install Anaconda 4.3.1 Python 3.6 or 2.7
https://www.continuum.io/downloads
5. Add environmental variables
- Add “C:Program Files (x86)Microsoft Visual Studio 14.0VCbin” to PATH variable
- Add “C:Program Files (x86)Windows Kits10Include10.0.10240.0ucrt” to INCLUDE variable
6. Install Chainer on Anaconda Prompt
> pip install chainer

Chainer on Azure
Use Data Science Virtual Machine for Linux (Ubuntu)
•Ready for CUDA 8.0 & cuDNN 5.1
•After ssh, ”pip install --user chainer”
1
2
3

Chainer Model Export
tfchain: TensorFlow export (experimental)
Caffe-export: Caffe export (experimental)
• https://github.com/mitmul/tfchain
• Supports Linear, Convolution2D, MaxPooling2D, ReLU
• Just add @totf decorator right before the forward method of the model
• Currently closed project
• Supports Conv2D, Deconv2D, BatchNorm, ReLU, Concat, Softmax,
Reshape

External Projects for Model Portability
DLPack
• https://mil-tokyo.github.io/webdnn/
• The model conversion to run it on a web browser supports Chainer
WebDNN
• https://github.com/dmlc/dlpa
ck
• MXNet, Torch, Caffe2 have
joined to discuss the
guideline of memory layout
of tensor and the common
operator interfaces

Companies supporting Chainer

Companies supporting Chainer

Contributing to Chainer

Chainer is an open-source project.
• You can send a PR from here: https://github.com/chainer/chainer
• The development speed of Deep Learning research is super fast, therefore,
to provide the state-of-the-art technologies through Chainer, we
continuously update the development plans:
• Chainer v3.0.0 will be released on 26th
September!
• Will support gradient of gradient (higher order differentiation)
• Will add the official Windows support ensured by Microsoft
The release schedule after
v2.0.1 (4th
July)→