PyTorchã¨TensorBoardã§Optunaãƒã‚°ã®å¯è¦–化 - livlea blog
ã¯ã˜ã‚ã« Deep Learningã®ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯é–‹ç™ºã§ã¯ã€å¯è¦–化ã«matplotlibを使ã†ã“ã¨ãŒå¤šã„ã¨æ€ã„ã¾ã™ãŒã€TensorBoardも有用ã§ã™ã€‚TensorFlowを使ã†å ´åˆã¯å¯è¦–化手段ã¨ã—ã¦TensorBoardを使ãˆã°è‰¯ã„ã®ã§ã™ãŒã€PyTorchã®å ´åˆã¯ã©ã†ã™ã‚Œã°è‰¯ã„ã®ã§ã—ょã†ã‹ï¼Ÿã“ã‚Œã¾ã§ã¯tensorboardXã¨ã„ã†PyTorchã‹ã‚‰TensorBoardを使ãˆã‚‹ã‚ˆã†ã«ã—ãŸãƒ©ã‚¤ãƒ–ラリを使ã†äººãŒå¤šã‹ã£ãŸã¨æ€ã„ã¾ã™ãŒã€PyTorch v1.1.0ã‹ã‚‰TensorBoardãŒã‚ªãƒ•ã‚£ã‚·ãƒ£ãƒ«ã§ã‚µãƒãƒ¼ãƒˆã•ã‚Œã‚‹ã‚ˆã†ã«ãªã£ãŸã®ã§ã€PyTorchã§ã‚‚TensorBoardを使ãˆã°è‰¯ã„ã®ã§ã™ï¼å…¬å¼ã‚µãƒãƒ¼ãƒˆã¯ã€ã‚„ã¯ã‚Šã‚ã‚ŠãŒãŸã„ã§ã™ã。 ã¾ãŸDeep Learningã®æ€§èƒ½å‘上ã«ãŠã„ã¦ã¯ã€æ³¥è‡ã„ãƒã‚¤ãƒ‘ーパラメータãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ãŒæ¬ ã‹ã›ã¾ã›ã‚“ãŒã€Preferred NetworksãŒé–‹ç™ºã—OSSã¨ã—ã¦
ä¸è€ã«ãŠã‘ã‚‹Optunaを利用ã—ãŸåˆ†æ•£ãƒã‚¤ãƒ‘ーパラメータ最é©åŒ– - 今æ‘秀明（åå¤å±‹å¤§å¦ Optuna講習会）
2020/10/23 (金)ã®åå¤å±‹å¤§å¦ä¸»å‚¬ã€NVIDIA・PFN共催ã®ã€ŒOptuna講習会ã€ã®è³‡æ–™ã§ã™ã€‚Read less
ãƒã‚¤ãƒ‘ーパラメーター最é©åŒ–フレームワークOptunaã®å®Ÿè£…解説 | | AI tech studio
AI Lab AutoMLãƒãƒ¼ãƒ ã®èŠç”°ã§ã™ (GitHub: @c-bata)。 ãƒã‚¤ãƒ‘ーパラメーター最é©åŒ–ã¯ã€æ©Ÿæ¢°å¦ç¿’モデルãŒãã®æ€§èƒ½ã‚’発æ®ã™ã‚‹ãŸã‚ã«é‡è¦ãªãƒ—ãƒã‚»ã‚¹ã®1ã¤ã§ã™ã€‚Pythonã®ãƒã‚¤ãƒ‘ーパラメーター最é©åŒ–ライブラリã¨ã—ã¦æœ‰å㪠Optuna [1] ã¯ã€æ§˜ã€…ãªæœ€é©åŒ–アルゴリズムã«å¯¾å¿œã—ã¤ã¤ã‚‚ã€ä½¿ã„ã‚„ã™ãè¨è¨ˆçš„ã«ã‚‚優れãŸã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã§ã™ã€‚本記事ã§ã¯Optunaã®å†…部実装ã«ã¤ã„ã¦ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢çš„ãªå´é¢ã‚’ä¸å¿ƒã«è§£èª¬ã—ã¾ã™ã€‚ Optunaã®å†…部実装をç†è§£ã™ã‚‹ãŸã‚ã«ã¯ã€ä¸»è¦ã‚³ãƒ³ãƒãƒ¼ãƒãƒ³ãƒˆã®å½¹å‰²ã¨å…¨ä½“ã®å‹•ä½œã®æµã‚Œã‚’押ã•ãˆã‚‹å¿…è¦ãŒã‚ã‚Šã¾ã™ã€‚ã—ã‹ã—Optunaã®é–‹ç™ºã¯æ´»ç™ºã§ã€ã‚³ãƒ¼ãƒ‰é‡ã‚‚多ããªã‚Šã€å…¨ä½“ã®æµã‚Œã‚’コードã‹ã‚‰èªã¿å–ã‚‹ã“ã¨ã¯é›£ã—ããªã£ã¦ãã¾ã—ãŸã€‚ãã“ã§ä»Šå›žMinitunaã¨ã„ã†å°ã•ãªãƒ—ãƒã‚°ãƒ©ãƒ を用æ„ã—ã¾ã—ãŸã€‚Minitunaã«ã¯å…¨éƒ¨ã§3ã¤ã®versionãŒã‚ã‚Šã€ãã‚Œãžã‚Œ100è¡Œã€200è¡Œ
Using Optuna to Optimize PyTorch Lightning Hyperparameters
This post uses pytorch-lightning v0.6.0 (PyTorch v1.3.1)and optuna v1.1.0. PyTorch Lightning + Optuna!Optuna is a hyperparameter optimization framework applicable to machine learning frameworks and black-box optimization solvers. PyTorch Lightning provides a lightweight PyTorch wrapper for better scaling with less code. Combining the two of them allows for automatic tuning of hyperparameters to fi
Optunaを使ã£ãŸRocksDBã®ãƒ‘フォーマンスãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°
README.md æ¦‚è¦ Optunaã¨ã„ã†ãƒã‚¤ãƒ‘ーパラメータ最é©åŒ–ツールを使ã£ã¦ã€RocksDB(組ã¿è¾¼ã¿DB・KVS)ã®ãƒ‘フォーマンスãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ã‚’試ã—ã¦ã¿ãŸéš›ã®çµæžœãƒ¡ãƒ¢ 対象ã¨ãªã‚‹ãƒ¯ãƒ¼ã‚¯ãƒãƒ¼ãƒ‰ã«å¯¾ã—ã¦ã€æœ€é©ãªæ€§èƒ½ã‚’発æ®ã™ã‚‹ãƒ‘ラメータ群を自動ã§è¦‹ã¤ã‘出ã™ã®ãŒç›®çš„ çµæžœã¨ã—ã¦ã¯ã€ãƒ‡ãƒ•ã‚©ãƒ«ãƒˆãƒ‘ラメータをãã®ã¾ã¾ä½¿ã£ãŸå ´åˆã«æ¯”ã¹ã¦ã€ã‹ãªã‚Šè‰¯ã„性能ãŒå¾—られるパラメータ群を見ã¤ã‘ã‚‹ã“ã¨ãŒã§ããŸ: デフォルトã§ã®ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ã®æ‰€è¦æ™‚é–“: 372秒 Optunaã«ã‚ˆã‚‹æœ€é©åŒ–後ã®ãƒ‘ラメータã§ã®æ‰€è¦æ™‚é–“: 30秒 モãƒãƒ™ãƒ¼ã‚·ãƒ§ãƒ³ RocksDBã«ã¯ã€ã‚«ã‚¹ã‚¿ãƒžã‚¤ã‚ºã§ãるパラメータ群ãŒå¤šæ•°ã‚る（数å～数百？） 自分ã®åˆ©ç”¨ç”¨é€”ã«æœ€é©ãªãƒ‘ラメータ群を人手ã§èª¿ã¹ã‚‹ã®ã¯çµæ§‹å¤§å¤‰ RocksDBã¯ã€ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ã‚¬ã‚¤ãƒ‰ã‚’å«ã‚ã¦ã€ã‹ãªã‚Šãƒ‰ã‚ュメントãŒå……実ã—ã¦ã„ã‚‹ãŒã€ãã‚Œã§ã‚‚ä¸æ…£ã‚Œãªäººã«ã¯æ•·å±…ãŒé«˜ã„ ↑ã®ã‚ˆã†ãªçŠ¶æ³ã¯ã€Ro
Optuna: A Define-by-Run Hyperparameter Optimization Framework
ã€è¬›æ¼”資料】æ±å·¥å¤§ã§8/5-6開催ã®ã€Œç¬¬1回 ディープラーニング分散å¦ç¿’ãƒãƒƒã‚«ã‚½ãƒ³ã€ã§ã€PFNエンジニアã®æŸ³ç€¬åˆ©å½¦ãŒTSUBAME3.0上ã§ã®Optunaã®åˆ©ç”¨æ–¹æ³•ã‚’紹介ã—ã¾ã—ãŸã€‚Read less
Optuna+LightGBMã§ãƒã‚¤ãƒ‘ーパラメータãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ã—ãªãŒã‚‰ãƒ¢ãƒ‡ãƒ«ã‚’ä¿å˜ã—ã€èªã¿è¾¼ã‚€æ–¹æ³• - Qiita
import lightgbm as lgb import optuna, os, uuid, pickle from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score import numpy as np def train_optuna(): data = load_breast_cancer() X_train, X_test, y_train, y_test = train_test_split(data["data"], data["target"], test_size=0.3, random_state=19) def objectives(trial
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2019å¹´6月28æ—¥ã®æ˜Žæ²»å¤§å¦ã§ã®è¬›ç¾©è³‡æ–™ã§ã™ã€‚ ã§ãã‚‹ã ã‘æ•°å¼ã‚’使ã‚ãšã«ã€Žæ©Ÿæ¢°å¦ç¿’ã®ãŠã•ã‚‰ã„ã€ã€Žè‡ªå‹•ãƒã‚¤ãƒ‘ーパラメタ最é©åŒ–ã€ã€ŽOptuna ã®ä½¿ã„æ–¹ã€ã€Žãƒ™ã‚¤ã‚ºæœ€é©åŒ–ã®å¿œç”¨äº‹ä¾‹ã€ã«ã¤ã„ã¦èª¬æ˜Žã—ã¦ã„ã¾ã™ã€‚ â—Optuna : https://github.com/pfnet/optunaRead less
Optuna 㨠Chainer ã§æ·±å±¤å¦ç¿’ã®ãƒã‚¤ãƒ‘ーパラメータ自動最é©åŒ– - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? ã¯ã˜ã‚ã« Optuna ã¯ã€ã‚ªãƒ¼ãƒ—ンソースã®ãƒã‚¤ãƒ‘ーパラメータ自動最é©åŒ–ツールã§ã™ã€‚ã“ã®è¨˜äº‹ã§ã¯ã€Optuna 㨠Chainer を用ã„ã¦ã€å®Ÿéš›ã«æ·±å±¤å¦ç¿’（ディープラーニング）ã®ãƒã‚¤ãƒ‘ーパラメータ最é©åŒ–ã‚’è¡Œã†æ–¹æ³•ã‚’説明ã—ã¾ã™ã€‚Optuna ã® examples ã«ã¯ã•ã¾ã–ã¾ãªãƒ•ãƒ¬ãƒ¼ãƒ ワークã§ã®ä¾‹ãŒè¼‰ã£ã¦ã„ã¾ã™ãŒã€ãƒ‰ã‚ュメントã¨ã—ã¦èª¬æ˜Žã¯ã•ã‚Œã¦ã„ãªã„よã†ãªã®ã§ã€ç§ãªã‚Šã«ã‚¢ãƒ¬ãƒ³ã‚¸ã‚’åŠ ãˆãŸã‚‚ã®ã‚’紹介ã—ã¾ã™ã€‚ 内容ã¨ã—ã¦ã¯ï¼š ãƒã‚¤ãƒ‘ーパラメータã¨ã¯ä½•ã‹ï¼Ÿ Optuna ã§ã‚·ãƒ³ãƒ—ルãªæœ€é©åŒ– Optuna + Chainer ã§æ·±å±¤å¦ç¿’ã®ãƒã‚¤ãƒ‘ー
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GitHub - optuna/optuna-integration: Extended functionalities for Optuna in combination with third-party libraries.
optuna-integration/optuna_integration/pytorch_distributed.py at main · optuna/optuna-integration
Prototyping a Faster Optuna Implementation in Rust
optuna.integration.tensorboard — Optuna 3.1.0 documentation
optuna-examples/tfkeras/tfkeras_integration.py at main · optuna/optuna-examples
Memory increase over trials · Issue #1178 · optuna/optuna
Optuna + Keras ㌠GPU ã®ãƒ¡ãƒ¢ãƒªã‚’食ã„ã¤ã¶ã™ - Qiita
Add `MPIStudy` to optimize distributed neural network training with Horovod by toshihikoyanase · Pull Request #1341 · optuna/optuna
Using Optuna to Optimize PyTorch Hyperparameters
Optuna vs Hyperopt: Which Hyperparameter Optimization Library Should You Choose?
It search only small `parameter` area when using pruning. · Issue #270 · optuna/optuna
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