General Information
This document provides technical information for migration from Chainer to PyTorch. General InformationConcepts and components in both frameworksArray LibraryChainer uses NumPy/CuPy (xp.ndarray) as an array library, and wraps them as chainer.Variable to support autograd. Similarly, PyTorch uses ATen (at::Tensor (C++)) as an array library ("tensor library" in PyTorch terms), and wraps it as torch::
Released Chainer/CuPy v6.0.0
Released Chainer/CuPy v6.0.0 By Seiya Tokui May 16, 2019 In Announcement We have released Chainer and CuPy v6.0.0 today! This is a major release that introduces several new features. Full updates can be found in the release notes: Chainer, CuPy. ChainerX The biggest update is the introduction of ChainerX. It is a fast and portable ndarray engine with autograd support written in C++ with a very thi
ChainerX移行ガイド - Qiita
chainerx 対応：コード変æ›éƒ¨åˆ†æ—©è¦‹è¡¨ ã¨ã†ã¨ã†Chainer version 6.0.0 ãŒãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¾ã—ãŸã。本記事ã§ã¯ã“ã‚Œã¾ã§ã® Chainer v5 以å‰ã¾ã§ã«æ›¸ã‹ã‚Œã¦ã„ãŸãƒ¢ãƒ‡ãƒ«è¨“練用コードを〠Chainer v6 ã§å°Žå…¥ã•ã‚ŒãŸChainerXã¸å¯¾å¿œã•ã›ã‚‹ãŸã‚ã®å¤‰æ›´ç‚¹ã«ã¤ã„ã¦ã¾ã¨ã‚ã¾ã™ã€‚ （上記コードã®å¤‰æ›ã‚’ã—ãªãã¦ã‚‚今ã¾ã§é€šã‚Š v5 ã®ã‚³ãƒ¼ãƒ‰ã‚’ v6ã§å‹•ä½œã•ã›ã‚‹ã“ã¨ã¯å¯èƒ½ã§ã™ã€‚） 本題ã«é£›ã³ãŸã„方㯠v5 ã®ã‚³ãƒ¼ãƒ‰ã‚’ v6 ã®ChainerX対応コードã«å¤‰æ›ã™ã‚‹ ã‹ã‚‰ã”覧ãã ã•ã„。 ChainerX ã¨ã¯ 先日Chainer version 6.0.0 ãŒæ£å¼ãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¾ã—ãŸã€‚ Released Chainer/CuPy v6.0.0 #Chainer/#CuPy v6.0.0 をリリースã—ã¾ã—ãŸã€‚変更点ã®æ¦‚è¦ã¯ãƒ–ãƒã‚°ã‚’ã”覧ãã ã•ã„。 https://t.co/1k7
Chainerã§Tensor Coreを使ã£ã¦ã¿ã‚‹ - Qiita
計算速度ãŒä¸€æ°—ã«8å€é€Ÿããªã‚‹ã‚‰ã—ã„Tensor CoreãŒä½¿ãˆã‚‹ã¨ã„ã†ã“ã¨ã§ã€æ˜¨å¹´9月ã«Turing世代ã®GPUã‚’è²·ã£ã¦ã¿ãŸã‚‚ã®ã®ã€Tensor CoreãŒç°¡å˜ã«ä½¿ãˆã¦ä¸€æ°—ã«æ—©ããªã‚‹ã‚ã‘ã§ã‚‚ãªãã€ã—ã°ã‚‰ãTensor CoreãŒä½¿ãˆã¦ã„ã‚‹ã‹ã©ã†ã‹ã‚‚よãã‚ã‹ã‚‰ãªã„状態ã§ã—ãŸã€‚今回一部修æ£ã‚‚ã‚ã£ã¦ã€Chainer v7.0.0a1ã§Tensor CoreãŒä½¿ãˆã‚‹ã“ã¨ãŒã»ã¼ç¢ºèªã§ãã¾ã—ãŸã€‚ ã¾ãŸã€Google Colaboratoryã®GPUã‚‚Tesla T4ã¨Turing世代ã¨ãªã‚Šã€Tensor CoreãŒä½¿ãˆãã†ãªã®ã§è©¦ã—ã¦ã¿ã¾ã—ãŸã€‚ 環境 環境 GPU Cuda Coreæ•° FP32 FP16
ディープラーニング入門：Chainer ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«
Chainer ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ« æ•°å¦ã®åŸºç¤Žã€ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°è¨€èªž Python ã®åŸºç¤Žã‹ã‚‰ã€æ©Ÿæ¢°å¦ç¿’・ディープラーニングã®ç†è«–ã®åŸºç¤Žã¨ã‚³ãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã¾ã§ã‚’幅広ã解説 ※Chainerã®é–‹ç™ºã¯ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ãƒ¢ãƒ¼ãƒ‰ã«å…¥ã‚Šã¾ã—ãŸã€‚詳ã—ãã¯ã“ã¡ã‚‰ã‚’ã”覧ãã ã•ã„。 何ã‹ã‚‰å¦ã¶ã¹ãã‹è¿·ã‚ãªã„ ディープラーニングをå¦ã¶ã«ã¯ã€å¤§å¦ã§å¦ã¶ãƒ¬ãƒ™ãƒ«ã®æ•°å¦ã‚„ Python ã«ã‚ˆã‚‹ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã®çŸ¥è˜ã«åŠ ãˆã¦ã€ Chainer ã®ã‚ˆã†ãªãƒ‡ã‚£ãƒ¼ãƒ—ラーニングフレームワークã®ä½¿ã„æ–¹ã¾ã§ã€å¹…広ã„知è˜ãŒå¿…è¦ã¨ãªã‚Šã¾ã™ã€‚ 本ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã¯ã€åˆå¦è€…ã«ã‚ˆãã‚る「ã¾ãšä½•ã‚’å¦ã¹ã°è‰¯ã„ã‹ã€ãŒåˆ†ã‹ã‚‰ãªã„〠ã¨ã„ã†å•é¡Œã‚’解決ã™ã‚‹ãŸã‚ã«è¨è¨ˆã•ã‚Œã¾ã—ãŸã€‚ åˆå¦è€…ã¯ã€Œã¾ãšä½•ã‚’ã€ãã—ã¦ã€Œæ¬¡ã«ä½•ã‚’ã€ã¨è¿·ã†ã“ã¨ãªãã€å¿…è¦ãªçŸ¥è˜ã‚’é †ç•ªã«å¦ç¿’ã§ãã¾ã™ã€‚ å‰æ知è˜ã‹ã‚‰è§£èª¬ ã“ã®ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã¯ã€Chainer ãªã©ã®ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングフレームワークを使ã£ãŸãƒ—ãƒã‚°
kubeflowã§Chainerã—ã¦ã¿ã‚‹ - Qiita
Kubeflowã§Chainerã‚’å‹•ã‹ã™ç’°å¢ƒã‚’ã¤ãã£ã¦ã¿ã¾ã™ã€‚ 途ä¸ã¾ã§ã¯Kubeflowã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«æ‰‹é †ã«ãªã£ã¦ã¾ã™ã€‚ https://www.kubeflow.org/docs/started/getting-started-gke/ Kubeflow？ Kubernetes上ã§æ©Ÿæ¢°å¦ç¿’環境を動ã‹ã™ã‚‚ã®ã§ã™ã€‚ ã“ã®ã¸ã‚“見ã¦ã‚‚らã£ãŸæ–¹ãŒã„ã„ã‹ã‚‚ https://www.slideshare.net/shunyaueta/kubeflow-devfest18 Chainer? 日本ã®Preferred NetworksãŒä¸»å°Žã§é–‹ç™ºã—ã¦ã„ã‚‹ã€æ©Ÿæ¢°å¦ç¿’ã®OSSライブラリã§ã™ã€‚ https://chainer.org/ 自分ãŒæ‰€å±žã™ã‚‹AlpacaJapanæ ªå¼ä¼šç¤¾ã§ã¯ã€Chainerã®ã‚¨ãƒãƒ³ã‚¸ã‚§ãƒªã‚¹ãƒˆãŒã„ã¦ChainerãŒã‚ˆã使ã‚ã‚Œã¦ã„ã‚‹ã¿ãŸã„ãªã®ã§ã€ã“ã®ç’°å¢ƒã‚’ã¤ãã£ã¦ã¿ã¾ã—ãŸã€‚ https:
è³¢ã„パラメータ探索: Optuna入門 with Chainer - Qiita
Preferred Networksã®ãƒ‘ラメータ探索ライブラリOptunaを使ã£ã¦ã¿ãŸã‚‰ä¾¿åˆ©ã ã£ãŸã®ã§ã€‚ ã¯ã˜ã‚㫠機械å¦ç¿’を上手ã使ã†ã«ã¯ã€ãƒã‚¤ãƒ‘ーパラメータã®è¨å®šãŒé‡è¦ã«ãªã‚Šã¾ã™ã€‚ ã—ã‹ã—ã€æ‰‹å‹•ã§ã„ã¡ã„ã¡ãƒ‘ラメータを探ã™ã®ã¯è†¨å¤§ãªåŠ´åŠ›ã‚’å¿…è¦ã¨ã—ã¾ã™ã€‚ ã¾ãŸã€ã‚°ãƒªãƒƒãƒ‰ã‚µãƒ¼ãƒã«ã‚ˆã‚ŠæŽ¢ç´¢ã§ãるパラメータ数ã«ã‚‚é™ç•ŒãŒã‚ã‚Šã¾ã™ã€‚ è³¢ã; ã™ãªã‚ã¡çŸæ™‚é–“ã§è‰¯è³ªãªãƒ‘ラメータを発見ã™ã‚‹ãŸã‚ã®è‡ªå‹•åŒ–ã‚’å°Žå…¥ã—ã¾ã™ã€‚ Optunaを使ã†ã“ã¨ã§ã€ç°¡å˜ã«è‡ªå‹•åŒ–ã•ã‚ŒãŸãƒ‘ラメータ探索ãŒã§ãã¾ã™ã€‚ Optunaã¨ã¯ ãƒã‚¤ãƒ‘ーパラメータã®æœ€é©åŒ–フレームワーク 主è¦æ©Ÿèƒ½: 1. Define-by-Run æ—¢å˜ã®ãƒ•ãƒ¬ãƒ¼ãƒ ワークã¯æ¤œç´¢ç©ºé–“ã¨ç›®çš„関数を別々ã«å®šç¾©ã—ã¾ã™ã€‚ Optunaã¯ã€ã‚µãƒ¼ãƒã‚¹ãƒšãƒ¼ã‚¹ã¯ç›®çš„関数内ã§å®šç¾©ã•ã‚Œã€ã™ã¹ã¦ã®ãƒã‚¤ãƒ‘ーパラメータã¯å®Ÿè¡Œæ™‚ã«å®šç¾©ã•ã‚Œã¾ã™ã€‚ã“ã®æ©Ÿèƒ½ã«ã‚ˆã‚Šã€Optunaã§æ›¸ã‹ã‚ŒãŸã‚³ãƒ¼ãƒ‰ã¯ã‚ˆã‚Šãƒ¢
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ChainerX + ChainerMN integration on MNIST by keisukefukuda · Pull Request #7844 · chainer/chainer
chainer.dataset.cache_or_load_file — Chainer 7.8.1 documentation
Configuring Chainer — Chainer 7.8.1 documentation
Support NHWC format in convolution by anaruse · Pull Request #1885 · cupy/cupy
cupy.prof.TimeRangeDecorator — CuPy 13.3.0 documentation
chainer-test/docker.py at master · chainer/chainer-test
Support batched update of weights by optimizer by anaruse · Pull Request #5841 · chainer/chainer
Step-by-Step Troubleshooting — Chainer 7.8.1 documentation
Chainer ã§ã®ãƒ—ãƒãƒ•ã‚¡ã‚¤ãƒªãƒ³ã‚°ã‚’ã¡ã‚‡ã£ã¨æ¥½ã«ã™ã‚‹è©±
https://www.cc.u-tokyo.ac.jp/events/lectures/111/20190124-2.pdf
GitHub - lazykyama/chainer_profutil: Chainer utility tool for GPU profiling by nvprof.
chainercv/chainercv/transforms/image/random_erasing.py at 26b0f0ecac3f79e708f6142cc585a56ab73c050f · chainer/chainercv
optuna/chainer_integration.py at master · optuna/optuna · GitHub
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