SlideShare a Scribd company logo
1 of 15
Download to read offline
Chainer v2 α
Seiya Tokui @ Preferred Networks
Chainer Meetup #04 | Feb. 23, 2017
(MinPy)
Era of dynamic graph frameworks
Chainer as a pioneer in this field
• 2015/06: v1.0
• 2015/09: v1.3 (CuPy)
• 2015/11: v1.5 (Link/Chain, CuPy in Cython)
• 2016/06: [MinPy]
• 2016/07: v1.11 (Trainer)
• 2017/01: [PyTorch, TensorFlow Fold]
• 2017/02: v2.0a
Libraries on top of Chainer
• ChainerRL (beta): https://github.com/pfnet/chainerrl
Reinforcement learning
• ChainerMN (to appear)
Multi-node distributed learning
Release plan
• Feb. 23: v2.0.0a1
• Mar. xx: v2.0.0b1
• Apr. xx: v2.0.0
How to use Chainer v2.0.0a1
pip install chainer –-pre
pip install cupy
Documentation: http://docs.chainer.org/en/v2.0.0a1/
CuPy Documentation: http://cupy.docs.chainer.org/
Features of v2.0.0a1
• CuPy is separated into an independent package
• Unified configuration / training mode
• Removed deprecated/obsolete APIs
• Interface improvements
CuPy is separated
• CuPy is now a separate project!
• https://github.com/cupy/cupy
• At the moment, the development of CuPy is still taking place
at Chainer v1
• Changes are merged to cupy/cupy after each minor release
• In the future, any changes that break compatibilities will be made in
the cupy repository
Unified configuration
Thread-local-like object to configure Chainer
• chainer.config: thread-local configuration
• chainer.global_config: process-wide configuration
• Users usually only have to touch chainer.config.
Built-in configuration entries
• debug
• enable_backprop
• train
• type_check
• (use_cudnn, deterministic: to be added)
Training mode
• All functions/classes that have training/test mode
distinctions now use chainer.config.train flag
• Evaluator automatically switches the flag
→ No need to pass train/test flags manually anymore!
Switching configuration
with chainer.using_config(‘train’, False):
... # code runs in test mode
with chainer.using_config(‘debug’, True):
... # code runs in debug mode
Removed/modified APIs
• Array-creation functions in chainer.cuda module
• FunctionSet
• wscale option of links and scale option of init_weight
(specify weight initializers instead)
• force_tuple option of F.split_axis is now set to True by
default
• Some minor updates
Major features planned for beta and final releases
• Optimizer with UpdateRule
Can specify hyperparameters for each parameter (e.g. learning rate,
hook functions)
• Uninitialized variable
Used to implement the parameter-shape placeholder
• Remove volatile flag
Use chainer.config.enable_backprop flag instead
• PyCharm-friendly Link/Chain APIs
Chainer v2 alpha

More Related Content

What's hot

Towards Chainer v1.5
Towards Chainer v1.5Towards Chainer v1.5
Towards Chainer v1.5Seiya Tokui
 
On the benchmark of Chainer
On the benchmark of ChainerOn the benchmark of Chainer
On the benchmark of ChainerKenta Oono
 
CuPy: A NumPy-compatible Library for GPU
CuPy: A NumPy-compatible Library for GPUCuPy: A NumPy-compatible Library for GPU
CuPy: A NumPy-compatible Library for GPUShohei Hido
 
Notes from 2016 bay area deep learning school
Notes from 2016 bay area deep learning school Notes from 2016 bay area deep learning school
Notes from 2016 bay area deep learning school Niketan Pansare
 
Comparison of deep learning frameworks from a viewpoint of double backpropaga...
Comparison of deep learning frameworks from a viewpoint of double backpropaga...Comparison of deep learning frameworks from a viewpoint of double backpropaga...
Comparison of deep learning frameworks from a viewpoint of double backpropaga...Kenta Oono
 
FCN-Based 6D Robotic Grasping for Arbitrary Placed Objects
FCN-Based 6D Robotic Grasping for Arbitrary Placed ObjectsFCN-Based 6D Robotic Grasping for Arbitrary Placed Objects
FCN-Based 6D Robotic Grasping for Arbitrary Placed ObjectsKusano Hitoshi
 
Automatically Fusing Functions on CuPy
Automatically Fusing Functions on CuPyAutomatically Fusing Functions on CuPy
Automatically Fusing Functions on CuPyPreferred Networks
 
PyTorch crash course
PyTorch crash coursePyTorch crash course
PyTorch crash courseNader Karimi
 
Distributed Multi-GPU Computing with Dask, CuPy and RAPIDS
Distributed Multi-GPU Computing with Dask, CuPy and RAPIDSDistributed Multi-GPU Computing with Dask, CuPy and RAPIDS
Distributed Multi-GPU Computing with Dask, CuPy and RAPIDSPeterAndreasEntschev
 
CUDA by Example : Atomics : Notes
CUDA by Example : Atomics : NotesCUDA by Example : Atomics : Notes
CUDA by Example : Atomics : NotesSubhajit Sahu
 
Parallelization using open mp
Parallelization using open mpParallelization using open mp
Parallelization using open mpranjit banshpal
 
OpenMP Tutorial for Beginners
OpenMP Tutorial for BeginnersOpenMP Tutorial for Beginners
OpenMP Tutorial for BeginnersDhanashree Prasad
 
Presentation on Shared Memory Parallel Programming
Presentation on Shared Memory Parallel ProgrammingPresentation on Shared Memory Parallel Programming
Presentation on Shared Memory Parallel ProgrammingVengada Karthik Rangaraju
 
Intro to OpenMP
Intro to OpenMPIntro to OpenMP
Intro to OpenMPjbp4444
 

What's hot (20)

Towards Chainer v1.5
Towards Chainer v1.5Towards Chainer v1.5
Towards Chainer v1.5
 
Deep parking
Deep parkingDeep parking
Deep parking
 
On the benchmark of Chainer
On the benchmark of ChainerOn the benchmark of Chainer
On the benchmark of Chainer
 
Chainer v4 and v5
Chainer v4 and v5Chainer v4 and v5
Chainer v4 and v5
 
CuPy: A NumPy-compatible Library for GPU
CuPy: A NumPy-compatible Library for GPUCuPy: A NumPy-compatible Library for GPU
CuPy: A NumPy-compatible Library for GPU
 
Notes from 2016 bay area deep learning school
Notes from 2016 bay area deep learning school Notes from 2016 bay area deep learning school
Notes from 2016 bay area deep learning school
 
Comparison of deep learning frameworks from a viewpoint of double backpropaga...
Comparison of deep learning frameworks from a viewpoint of double backpropaga...Comparison of deep learning frameworks from a viewpoint of double backpropaga...
Comparison of deep learning frameworks from a viewpoint of double backpropaga...
 
FCN-Based 6D Robotic Grasping for Arbitrary Placed Objects
FCN-Based 6D Robotic Grasping for Arbitrary Placed ObjectsFCN-Based 6D Robotic Grasping for Arbitrary Placed Objects
FCN-Based 6D Robotic Grasping for Arbitrary Placed Objects
 
Automatically Fusing Functions on CuPy
Automatically Fusing Functions on CuPyAutomatically Fusing Functions on CuPy
Automatically Fusing Functions on CuPy
 
Introduction to Chainer
Introduction to ChainerIntroduction to Chainer
Introduction to Chainer
 
PyTorch crash course
PyTorch crash coursePyTorch crash course
PyTorch crash course
 
Distributed Multi-GPU Computing with Dask, CuPy and RAPIDS
Distributed Multi-GPU Computing with Dask, CuPy and RAPIDSDistributed Multi-GPU Computing with Dask, CuPy and RAPIDS
Distributed Multi-GPU Computing with Dask, CuPy and RAPIDS
 
OpenMP
OpenMPOpenMP
OpenMP
 
CUDA by Example : Atomics : Notes
CUDA by Example : Atomics : NotesCUDA by Example : Atomics : Notes
CUDA by Example : Atomics : Notes
 
Parallelization using open mp
Parallelization using open mpParallelization using open mp
Parallelization using open mp
 
MPI n OpenMP
MPI n OpenMPMPI n OpenMP
MPI n OpenMP
 
OpenMP Tutorial for Beginners
OpenMP Tutorial for BeginnersOpenMP Tutorial for Beginners
OpenMP Tutorial for Beginners
 
Presentation on Shared Memory Parallel Programming
Presentation on Shared Memory Parallel ProgrammingPresentation on Shared Memory Parallel Programming
Presentation on Shared Memory Parallel Programming
 
openmp
openmpopenmp
openmp
 
Intro to OpenMP
Intro to OpenMPIntro to OpenMP
Intro to OpenMP
 

Viewers also liked

Facebookの人工知能アルゴリズム「memory networks」について調べてみた
Facebookの人工知能アルゴリズム「memory networks」について調べてみたFacebookの人工知能アルゴリズム「memory networks」について調べてみた
Facebookの人工知能アルゴリズム「memory networks」について調べてみた株式会社メタップスホールディングス
 
Estimating structured vector autoregressive models
Estimating structured vector autoregressive modelsEstimating structured vector autoregressive models
Estimating structured vector autoregressive modelsAkira Tanimoto
 
Dropout Distillation
Dropout DistillationDropout Distillation
Dropout DistillationShotaro Sano
 
Icml読み会 deep speech2
Icml読み会 deep speech2Icml読み会 deep speech2
Icml読み会 deep speech2Jiro Nishitoba
 
A Neural Attention Model for Sentence Summarization [Rush+2015]
A Neural Attention Model for Sentence Summarization [Rush+2015]A Neural Attention Model for Sentence Summarization [Rush+2015]
A Neural Attention Model for Sentence Summarization [Rush+2015]Yuta Kikuchi
 
Meta-Learning with Memory Augmented Neural Network
Meta-Learning with Memory Augmented Neural NetworkMeta-Learning with Memory Augmented Neural Network
Meta-Learning with Memory Augmented Neural NetworkYusuke Watanabe
 
サルでもわかるディープラーニング入門 (2017年) (In Japanese)
サルでもわかるディープラーニング入門 (2017年) (In Japanese)サルでもわかるディープラーニング入門 (2017年) (In Japanese)
サルでもわかるディープラーニング入門 (2017年) (In Japanese)Toshihiko Yamakami
 
Learning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsLearning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsTakuya Akiba
 
ICML2016読み会 概要紹介
ICML2016読み会 概要紹介ICML2016読み会 概要紹介
ICML2016読み会 概要紹介Kohei Hayashi
 
最近のDeep Learning (NLP) 界隈におけるAttention事情
最近のDeep Learning (NLP) 界隈におけるAttention事情最近のDeep Learning (NLP) 界隈におけるAttention事情
最近のDeep Learning (NLP) 界隈におけるAttention事情Yuta Kikuchi
 
論文紹介 Pixel Recurrent Neural Networks
論文紹介 Pixel Recurrent Neural Networks論文紹介 Pixel Recurrent Neural Networks
論文紹介 Pixel Recurrent Neural NetworksSeiya Tokui
 
Introduction to Chainer
Introduction to ChainerIntroduction to Chainer
Introduction to ChainerShunta Saito
 

Viewers also liked (12)

Facebookの人工知能アルゴリズム「memory networks」について調べてみた
Facebookの人工知能アルゴリズム「memory networks」について調べてみたFacebookの人工知能アルゴリズム「memory networks」について調べてみた
Facebookの人工知能アルゴリズム「memory networks」について調べてみた
 
Estimating structured vector autoregressive models
Estimating structured vector autoregressive modelsEstimating structured vector autoregressive models
Estimating structured vector autoregressive models
 
Dropout Distillation
Dropout DistillationDropout Distillation
Dropout Distillation
 
Icml読み会 deep speech2
Icml読み会 deep speech2Icml読み会 deep speech2
Icml読み会 deep speech2
 
A Neural Attention Model for Sentence Summarization [Rush+2015]
A Neural Attention Model for Sentence Summarization [Rush+2015]A Neural Attention Model for Sentence Summarization [Rush+2015]
A Neural Attention Model for Sentence Summarization [Rush+2015]
 
Meta-Learning with Memory Augmented Neural Network
Meta-Learning with Memory Augmented Neural NetworkMeta-Learning with Memory Augmented Neural Network
Meta-Learning with Memory Augmented Neural Network
 
サルでもわかるディープラーニング入門 (2017年) (In Japanese)
サルでもわかるディープラーニング入門 (2017年) (In Japanese)サルでもわかるディープラーニング入門 (2017年) (In Japanese)
サルでもわかるディープラーニング入門 (2017年) (In Japanese)
 
Learning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsLearning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for Graphs
 
ICML2016読み会 概要紹介
ICML2016読み会 概要紹介ICML2016読み会 概要紹介
ICML2016読み会 概要紹介
 
最近のDeep Learning (NLP) 界隈におけるAttention事情
最近のDeep Learning (NLP) 界隈におけるAttention事情最近のDeep Learning (NLP) 界隈におけるAttention事情
最近のDeep Learning (NLP) 界隈におけるAttention事情
 
論文紹介 Pixel Recurrent Neural Networks
論文紹介 Pixel Recurrent Neural Networks論文紹介 Pixel Recurrent Neural Networks
論文紹介 Pixel Recurrent Neural Networks
 
Introduction to Chainer
Introduction to ChainerIntroduction to Chainer
Introduction to Chainer
 

Similar to Chainer v2 alpha

[DLHacks]Introduction to ChainerCV
[DLHacks]Introduction to ChainerCV[DLHacks]Introduction to ChainerCV
[DLHacks]Introduction to ChainerCVDeep Learning JP
 
Democratizing machine learning on kubernetes
Democratizing machine learning on kubernetesDemocratizing machine learning on kubernetes
Democratizing machine learning on kubernetesDocker, Inc.
 
Divide and stress: the journey to component load test
Divide and stress: the journey to component load testDivide and stress: the journey to component load test
Divide and stress: the journey to component load testJuan Pedro Escalona Rueda
 
Building a Modular Server Platform with OSGi - Harshana Eranga Martin, Dileep...
Building a Modular Server Platform with OSGi - Harshana Eranga Martin, Dileep...Building a Modular Server Platform with OSGi - Harshana Eranga Martin, Dileep...
Building a Modular Server Platform with OSGi - Harshana Eranga Martin, Dileep...mfrancis
 
Building a Modular Server Platform with OSGi
Building a Modular Server Platform with OSGiBuilding a Modular Server Platform with OSGi
Building a Modular Server Platform with OSGiDileepa Jayakody
 
Kubernetes 1.16 and rancher 2.3 enhancements
Kubernetes 1.16 and rancher 2.3 enhancementsKubernetes 1.16 and rancher 2.3 enhancements
Kubernetes 1.16 and rancher 2.3 enhancementsSaiyam Pathak
 
Spring Framework 3.2 - What's New
Spring Framework 3.2 - What's NewSpring Framework 3.2 - What's New
Spring Framework 3.2 - What's NewSam Brannen
 
Building a server platform with os gi
Building a server platform with os giBuilding a server platform with os gi
Building a server platform with os giDileepa Jayakody
 
Cfg Mgmtcamp 2015 - Releases
Cfg Mgmtcamp 2015 - ReleasesCfg Mgmtcamp 2015 - Releases
Cfg Mgmtcamp 2015 - ReleasesCFEngine
 
Kubernetes 101 Workshop
Kubernetes 101 WorkshopKubernetes 101 Workshop
Kubernetes 101 WorkshopVishal Biyani
 
from ai.backend import python @ pycontw2018
from ai.backend import python @ pycontw2018from ai.backend import python @ pycontw2018
from ai.backend import python @ pycontw2018Chun-Yu Tseng
 
CloudStack Tooling Ecosystem – Kiran Chavala, ShapeBlue
CloudStack Tooling Ecosystem – Kiran Chavala, ShapeBlueCloudStack Tooling Ecosystem – Kiran Chavala, ShapeBlue
CloudStack Tooling Ecosystem – Kiran Chavala, ShapeBlueShapeBlue
 
Distributed Tensorflow with Kubernetes - data2day - Jakob Karalus
Distributed Tensorflow with Kubernetes - data2day - Jakob KaralusDistributed Tensorflow with Kubernetes - data2day - Jakob Karalus
Distributed Tensorflow with Kubernetes - data2day - Jakob KaralusJakob Karalus
 
Angular 2 overview
Angular 2 overviewAngular 2 overview
Angular 2 overviewJesse Warden
 
Ceph Day Shanghai - CeTune - Benchmarking and tuning your Ceph cluster
Ceph Day Shanghai - CeTune - Benchmarking and tuning your Ceph cluster Ceph Day Shanghai - CeTune - Benchmarking and tuning your Ceph cluster
Ceph Day Shanghai - CeTune - Benchmarking and tuning your Ceph cluster Ceph Community
 
Everything as Code with Azure DevOps
Everything as Code with Azure DevOpsEverything as Code with Azure DevOps
Everything as Code with Azure DevOpsVenura Athukorala
 
Scalamen and OT
Scalamen and OTScalamen and OT
Scalamen and OTgetch123
 

Similar to Chainer v2 alpha (20)

[DLHacks]Introduction to ChainerCV
[DLHacks]Introduction to ChainerCV[DLHacks]Introduction to ChainerCV
[DLHacks]Introduction to ChainerCV
 
Democratizing machine learning on kubernetes
Democratizing machine learning on kubernetesDemocratizing machine learning on kubernetes
Democratizing machine learning on kubernetes
 
Divide and stress: the journey to component load test
Divide and stress: the journey to component load testDivide and stress: the journey to component load test
Divide and stress: the journey to component load test
 
Building a Modular Server Platform with OSGi - Harshana Eranga Martin, Dileep...
Building a Modular Server Platform with OSGi - Harshana Eranga Martin, Dileep...Building a Modular Server Platform with OSGi - Harshana Eranga Martin, Dileep...
Building a Modular Server Platform with OSGi - Harshana Eranga Martin, Dileep...
 
Building a Modular Server Platform with OSGi
Building a Modular Server Platform with OSGiBuilding a Modular Server Platform with OSGi
Building a Modular Server Platform with OSGi
 
Kubernetes 1.16 and rancher 2.3 enhancements
Kubernetes 1.16 and rancher 2.3 enhancementsKubernetes 1.16 and rancher 2.3 enhancements
Kubernetes 1.16 and rancher 2.3 enhancements
 
Spring Framework 3.2 - What's New
Spring Framework 3.2 - What's NewSpring Framework 3.2 - What's New
Spring Framework 3.2 - What's New
 
Caffe2
Caffe2Caffe2
Caffe2
 
Building a server platform with os gi
Building a server platform with os giBuilding a server platform with os gi
Building a server platform with os gi
 
Ember - introduction
Ember - introductionEmber - introduction
Ember - introduction
 
Microservices in Java
Microservices in JavaMicroservices in Java
Microservices in Java
 
Cfg Mgmtcamp 2015 - Releases
Cfg Mgmtcamp 2015 - ReleasesCfg Mgmtcamp 2015 - Releases
Cfg Mgmtcamp 2015 - Releases
 
Kubernetes 101 Workshop
Kubernetes 101 WorkshopKubernetes 101 Workshop
Kubernetes 101 Workshop
 
from ai.backend import python @ pycontw2018
from ai.backend import python @ pycontw2018from ai.backend import python @ pycontw2018
from ai.backend import python @ pycontw2018
 
CloudStack Tooling Ecosystem – Kiran Chavala, ShapeBlue
CloudStack Tooling Ecosystem – Kiran Chavala, ShapeBlueCloudStack Tooling Ecosystem – Kiran Chavala, ShapeBlue
CloudStack Tooling Ecosystem – Kiran Chavala, ShapeBlue
 
Distributed Tensorflow with Kubernetes - data2day - Jakob Karalus
Distributed Tensorflow with Kubernetes - data2day - Jakob KaralusDistributed Tensorflow with Kubernetes - data2day - Jakob Karalus
Distributed Tensorflow with Kubernetes - data2day - Jakob Karalus
 
Angular 2 overview
Angular 2 overviewAngular 2 overview
Angular 2 overview
 
Ceph Day Shanghai - CeTune - Benchmarking and tuning your Ceph cluster
Ceph Day Shanghai - CeTune - Benchmarking and tuning your Ceph cluster Ceph Day Shanghai - CeTune - Benchmarking and tuning your Ceph cluster
Ceph Day Shanghai - CeTune - Benchmarking and tuning your Ceph cluster
 
Everything as Code with Azure DevOps
Everything as Code with Azure DevOpsEverything as Code with Azure DevOps
Everything as Code with Azure DevOps
 
Scalamen and OT
Scalamen and OTScalamen and OT
Scalamen and OT
 

More from Seiya Tokui

Chainer/CuPy v5 and Future (Japanese)
Chainer/CuPy v5 and Future (Japanese)Chainer/CuPy v5 and Future (Japanese)
Chainer/CuPy v5 and Future (Japanese)Seiya Tokui
 
Learning stochastic neural networks with Chainer
Learning stochastic neural networks with ChainerLearning stochastic neural networks with Chainer
Learning stochastic neural networks with ChainerSeiya Tokui
 
深層学習フレームワーク Chainer の開発と今後の展開
深層学習フレームワーク Chainer の開発と今後の展開深層学習フレームワーク Chainer の開発と今後の展開
深層学習フレームワーク Chainer の開発と今後の展開Seiya Tokui
 
Differences of Deep Learning Frameworks
Differences of Deep Learning FrameworksDifferences of Deep Learning Frameworks
Differences of Deep Learning FrameworksSeiya Tokui
 
Overview of Chainer and Its Features
Overview of Chainer and Its FeaturesOverview of Chainer and Its Features
Overview of Chainer and Its FeaturesSeiya Tokui
 
生成モデルの Deep Learning
生成モデルの Deep Learning生成モデルの Deep Learning
生成モデルの Deep LearningSeiya Tokui
 
Chainer Development Plan 2015/12
Chainer Development Plan 2015/12Chainer Development Plan 2015/12
Chainer Development Plan 2015/12Seiya Tokui
 
Deep Learningの基礎と応用
Deep Learningの基礎と応用Deep Learningの基礎と応用
Deep Learningの基礎と応用Seiya Tokui
 
Chainerの使い方と自然言語処理への応用
Chainerの使い方と自然言語処理への応用Chainerの使い方と自然言語処理への応用
Chainerの使い方と自然言語処理への応用Seiya Tokui
 
論文紹介 Compressing Neural Networks with the Hashing Trick
論文紹介 Compressing Neural Networks with the Hashing Trick論文紹介 Compressing Neural Networks with the Hashing Trick
論文紹介 Compressing Neural Networks with the Hashing TrickSeiya Tokui
 
深層学習フレームワークChainerの紹介とFPGAへの期待
深層学習フレームワークChainerの紹介とFPGAへの期待深層学習フレームワークChainerの紹介とFPGAへの期待
深層学習フレームワークChainerの紹介とFPGAへの期待Seiya Tokui
 
論文紹介 Semi-supervised Learning with Deep Generative Models
論文紹介 Semi-supervised Learning with Deep Generative Models論文紹介 Semi-supervised Learning with Deep Generative Models
論文紹介 Semi-supervised Learning with Deep Generative ModelsSeiya Tokui
 
Recurrent Neural Networks
Recurrent Neural NetworksRecurrent Neural Networks
Recurrent Neural NetworksSeiya Tokui
 
Deep learning実装の基礎と実践
Deep learning実装の基礎と実践Deep learning実装の基礎と実践
Deep learning実装の基礎と実践Seiya Tokui
 
Deep Learning技術の今
Deep Learning技術の今Deep Learning技術の今
Deep Learning技術の今Seiya Tokui
 
NIPS2013読み会 DeViSE: A Deep Visual-Semantic Embedding Model
NIPS2013読み会 DeViSE: A Deep Visual-Semantic Embedding ModelNIPS2013読み会 DeViSE: A Deep Visual-Semantic Embedding Model
NIPS2013読み会 DeViSE: A Deep Visual-Semantic Embedding ModelSeiya Tokui
 
ICML2013読み会 Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
ICML2013読み会 Local Deep Kernel Learning for Efficient Non-linear SVM PredictionICML2013読み会 Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
ICML2013読み会 Local Deep Kernel Learning for Efficient Non-linear SVM PredictionSeiya Tokui
 
Deep Learningの技術と未来
Deep Learningの技術と未来Deep Learningの技術と未来
Deep Learningの技術と未来Seiya Tokui
 

More from Seiya Tokui (20)

Chainer/CuPy v5 and Future (Japanese)
Chainer/CuPy v5 and Future (Japanese)Chainer/CuPy v5 and Future (Japanese)
Chainer/CuPy v5 and Future (Japanese)
 
Learning stochastic neural networks with Chainer
Learning stochastic neural networks with ChainerLearning stochastic neural networks with Chainer
Learning stochastic neural networks with Chainer
 
深層学習フレームワーク Chainer の開発と今後の展開
深層学習フレームワーク Chainer の開発と今後の展開深層学習フレームワーク Chainer の開発と今後の展開
深層学習フレームワーク Chainer の開発と今後の展開
 
Differences of Deep Learning Frameworks
Differences of Deep Learning FrameworksDifferences of Deep Learning Frameworks
Differences of Deep Learning Frameworks
 
Overview of Chainer and Its Features
Overview of Chainer and Its FeaturesOverview of Chainer and Its Features
Overview of Chainer and Its Features
 
生成モデルの Deep Learning
生成モデルの Deep Learning生成モデルの Deep Learning
生成モデルの Deep Learning
 
Chainer Development Plan 2015/12
Chainer Development Plan 2015/12Chainer Development Plan 2015/12
Chainer Development Plan 2015/12
 
Deep Learningの基礎と応用
Deep Learningの基礎と応用Deep Learningの基礎と応用
Deep Learningの基礎と応用
 
Chainerの使い方と自然言語処理への応用
Chainerの使い方と自然言語処理への応用Chainerの使い方と自然言語処理への応用
Chainerの使い方と自然言語処理への応用
 
論文紹介 Compressing Neural Networks with the Hashing Trick
論文紹介 Compressing Neural Networks with the Hashing Trick論文紹介 Compressing Neural Networks with the Hashing Trick
論文紹介 Compressing Neural Networks with the Hashing Trick
 
深層学習フレームワークChainerの紹介とFPGAへの期待
深層学習フレームワークChainerの紹介とFPGAへの期待深層学習フレームワークChainerの紹介とFPGAへの期待
深層学習フレームワークChainerの紹介とFPGAへの期待
 
論文紹介 Semi-supervised Learning with Deep Generative Models
論文紹介 Semi-supervised Learning with Deep Generative Models論文紹介 Semi-supervised Learning with Deep Generative Models
論文紹介 Semi-supervised Learning with Deep Generative Models
 
Recurrent Neural Networks
Recurrent Neural NetworksRecurrent Neural Networks
Recurrent Neural Networks
 
Deep learning実装の基礎と実践
Deep learning実装の基礎と実践Deep learning実装の基礎と実践
Deep learning実装の基礎と実践
 
Deep Learning技術の今
Deep Learning技術の今Deep Learning技術の今
Deep Learning技術の今
 
NIPS2013読み会 DeViSE: A Deep Visual-Semantic Embedding Model
NIPS2013読み会 DeViSE: A Deep Visual-Semantic Embedding ModelNIPS2013読み会 DeViSE: A Deep Visual-Semantic Embedding Model
NIPS2013読み会 DeViSE: A Deep Visual-Semantic Embedding Model
 
ICML2013読み会 Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
ICML2013読み会 Local Deep Kernel Learning for Efficient Non-linear SVM PredictionICML2013読み会 Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
ICML2013読み会 Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
 
Deep Learningの技術と未来
Deep Learningの技術と未来Deep Learningの技術と未来
Deep Learningの技術と未来
 
Tprimal agh
Tprimal aghTprimal agh
Tprimal agh
 
rinko2011-agh
rinko2011-aghrinko2011-agh
rinko2011-agh
 

Recently uploaded

IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessPixlogix Infotech
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?Igalia
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?Antenna Manufacturer Coco
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CVKhem
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxKatpro Technologies
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024Results
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Neo4j
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 

Recently uploaded (20)

IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 

Chainer v2 alpha

  • 1. Chainer v2 α Seiya Tokui @ Preferred Networks Chainer Meetup #04 | Feb. 23, 2017
  • 2. (MinPy) Era of dynamic graph frameworks
  • 3. Chainer as a pioneer in this field • 2015/06: v1.0 • 2015/09: v1.3 (CuPy) • 2015/11: v1.5 (Link/Chain, CuPy in Cython) • 2016/06: [MinPy] • 2016/07: v1.11 (Trainer) • 2017/01: [PyTorch, TensorFlow Fold] • 2017/02: v2.0a
  • 4. Libraries on top of Chainer • ChainerRL (beta): https://github.com/pfnet/chainerrl Reinforcement learning • ChainerMN (to appear) Multi-node distributed learning
  • 5. Release plan • Feb. 23: v2.0.0a1 • Mar. xx: v2.0.0b1 • Apr. xx: v2.0.0
  • 6. How to use Chainer v2.0.0a1 pip install chainer –-pre pip install cupy Documentation: http://docs.chainer.org/en/v2.0.0a1/ CuPy Documentation: http://cupy.docs.chainer.org/
  • 7. Features of v2.0.0a1 • CuPy is separated into an independent package • Unified configuration / training mode • Removed deprecated/obsolete APIs • Interface improvements
  • 8. CuPy is separated • CuPy is now a separate project! • https://github.com/cupy/cupy • At the moment, the development of CuPy is still taking place at Chainer v1 • Changes are merged to cupy/cupy after each minor release • In the future, any changes that break compatibilities will be made in the cupy repository
  • 9. Unified configuration Thread-local-like object to configure Chainer • chainer.config: thread-local configuration • chainer.global_config: process-wide configuration • Users usually only have to touch chainer.config.
  • 10. Built-in configuration entries • debug • enable_backprop • train • type_check • (use_cudnn, deterministic: to be added)
  • 11. Training mode • All functions/classes that have training/test mode distinctions now use chainer.config.train flag • Evaluator automatically switches the flag → No need to pass train/test flags manually anymore!
  • 12. Switching configuration with chainer.using_config(‘train’, False): ... # code runs in test mode with chainer.using_config(‘debug’, True): ... # code runs in debug mode
  • 13. Removed/modified APIs • Array-creation functions in chainer.cuda module • FunctionSet • wscale option of links and scale option of init_weight (specify weight initializers instead) • force_tuple option of F.split_axis is now set to True by default • Some minor updates
  • 14. Major features planned for beta and final releases • Optimizer with UpdateRule Can specify hyperparameters for each parameter (e.g. learning rate, hook functions) • Uninitialized variable Used to implement the parameter-shape placeholder • Remove volatile flag Use chainer.config.enable_backprop flag instead • PyCharm-friendly Link/Chain APIs