Gradio vs Streamlit vs Dash vs Flask
IntroductionMachine learning models are exciting and powerful, but they aren’t very useful by themselves. Once a model is complete, it likely has to be deployed before it can deliver any sort of value. As well, being able to deploy a preliminary model or a prototype to get feedback from other stakeholders is extremely useful. Recently, there has been an emergence of several tools that Data Scienti
Awesome Pythonï¼šç´ æ™´ã‚‰ã—ã„ Python フレームワーク・ライブラリ・ソフトウェア・リソースã®æ•°ã€… - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? 元記事: Awesome Python Awesome List in Qiita Awesome Ruby Awesome Java Awesome JavaScript Awesome Node.js Awesome Go Awesome Selenium Awesome Appium 管ç†ãƒ‘ãƒãƒ« 管ç†ã‚¤ãƒ³ã‚¿ãƒ•ã‚§ãƒ¼ã‚¹ç”¨ãƒ©ã‚¤ãƒ–ラリ ajenti - サーãƒç”¨ç®¡ç†ãƒ‘ãƒãƒ«. django-grappelli - Django 管ç†ã‚¤ãƒ³ã‚¿ãƒ¼ãƒ•ã‚§ãƒ¼ã‚¹ã®ãŸã‚ã®ã‚¸ãƒ£ã‚ºã‚¹ã‚ン. django-jet - 改良ã•ã‚ŒãŸæ©Ÿèƒ½ã‚’å‚™ãˆãŸ Django 管ç†ã‚¤
Pythonãƒãƒ¼ã‚«ãƒ«é–‹ç™ºç’°å¢ƒæ§‹ç¯‰ãƒ†ãƒ³ãƒ—レートã€Flask/Django/Jupyter with Docker + VSCode】 - Qiita
Pythonãƒãƒ¼ã‚«ãƒ«é–‹ç™ºç’°å¢ƒæ§‹ç¯‰ãƒ†ãƒ³ãƒ—レートã€Flask/Django/Jupyter with Docker + VSCode】PythonDjangoFlask開発環境新人プãƒã‚°ãƒ©ãƒžå¿œæ´ æ¦‚è¦ ãƒãƒ¼ã‚«ãƒ«ç’°å¢ƒã‚’構築ã™ã‚‹ã“ã¨ã«ã¯ã€ãã‚Œãªã‚Šã®ã‚³ã‚¹ãƒˆã‚’ä¼´ã„ã¾ã™ã€‚ã‚‚ã¡ã‚ã‚“ã“ã“ã§å¤šå°‘苦労ã—ãŸã»ã†ãŒã‚ˆã„ã¨ã„ã†æ„見ã¯ã™ã”ã分ã‹ã‚Šã¾ã™ã€‚ãŒã€ãã‚Œã§æ°—æŒã¡ãŒå‰ŠãŒã‚Œã¦ã—ã¾ã†ã®ã‚‚ã‚‚ã£ãŸã„ãªã„ã¨æ€ã†æ¬¡ç¬¬ã§ã™ã€‚ ãã“ã§ã€Flask/Django/JupyterNotebookå‘ã‘ã® Python開発環境構築を3コマンドã§å®Œäº† ã•ã›ã‚‰ã‚Œã‚‹ã‚ˆã†ãªãƒ†ãƒ³ãƒ—レートを作æˆã—ã¾ã—ãŸã€‚(cdを除ã) å‰ææ¡ä»¶ MacOSã§ã‚ã‚‹ã“㨠gitãŠã‚ˆã³makeコマンドãŒæœ‰åŠ¹ã§ã‚ã‚‹ã“ã¨(デフォルトã§æœ‰åŠ¹ãªã¯ãš) ※condaãŒã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã•ã‚Œã¦ã„ã‚‹å ´åˆã¯ã€pipã¨ã®ãƒãƒƒãƒ†ã‚£ãƒ³ã‚°ãŒç™ºç”Ÿã—ä¸é€”ã§å¤±æ•—ã™ã‚‹å¯èƒ½æ€§ãŒã‚ã‚Šã¾ã™ã€‚ ãƒãƒ¼ã‚«ãƒ«æ§‹ç¯‰ã‚¤ãƒ¡ãƒ¼ã‚¸
Pythonã®Web frameworkã®ãƒ‘フォーマンス比較 (Django, Flask, responder, FastAPI, japronto) - Qiita
Pythonã®Web frameworkã®ãƒ‘フォーマンス比較 (Django, Flask, responder, FastAPI, japronto)PythonFlaskwrk2responderFastAPI 以下ã®Python Web frameworkを使ã£ã¦å˜ç´”ãªAPIã‚’ç«‹ã¦ã¦ã€è² è·è©¦é¨“ã‚’ã—ã¦ã¿ã¾ã—ãŸã€‚ Django (2.X) Flask FastAPI responder japronto çµæžœçš„ã«ã€ã–ã£ãã‚Šã¨ä»¥ä¸‹ãŒåˆ†ã‹ã‚Šã¾ã—ãŸï¼ performanceã¯ã€Œjapronto >>> FastAPI > responder >>> Flask ~ Djangoã€ã ã¨è¨€ãˆãㆠFastAPIã¨responderã¯single workerã ã¨ç§’é–“100~1000程度ã®requestã§ã‚ã‚Œã°pythonç•Œã§åœ§å€’çš„ãªperformanceを誇るjaprontoã¨ã»ã¨ã‚“ã©åŒæ°´æº– （検
Pythonã®æŽ¨è–¦ã‚·ã‚¹ãƒ†ãƒ å‘ã‘ライブラリSurpriseã¨Flaskã§ãƒ¬ã‚³ãƒ¡ãƒ³ãƒ‡ãƒ¼ã‚·ãƒ§ãƒ³ã‚¨ãƒ³ã‚¸ãƒ³ã‚’作æˆã™ã‚‹ - Qiita
ã¾ãšã¯Surpriseã¨ã„ã†ãƒ©ã‚¤ãƒ–ラリをインストールã—ã¾ã™ã€‚ pip install scikit-surprise import json from collections import defaultdict from surprise import SVD from surprise import Dataset def get_top_n(predictions, n=10): ''' 予測セットã«åŸºã„ã¦å„ユーザã«ãƒˆãƒƒãƒ—N件ã®ãƒ¬ã‚³ãƒ¡ãƒ³ãƒ‡ãƒ¼ã‚·ãƒ§ãƒ³ã‚’è¿”ã™ã€‚ ''' # ã¾ãšå„ユーザã«äºˆæ¸¬å€¤ã‚’マップã™ã‚‹ã€‚ top_n = defaultdict(list) for uid, iid, true_r, est, _ in predictions: top_n[uid].append((iid, est)) # ãã—ã¦å„ユーザã«å¯¾ã—ã¦äºˆæ¸¬å€¤ã‚’ソートã—ã¦æœ€ã‚‚高ã„k個を返ã™ã€‚ for uid,
Pythonã§DI+モックを使ã„ãªãŒã‚‰ã€Clean Architectureã§ã‚¢ãƒ—リケーションを構築ã™ã‚‹ - Qiita
Pythonã§DI+モックを使ã„ãªãŒã‚‰ã€Clean Architectureã§ã‚¢ãƒ—リケーションを構築ã™ã‚‹PythonFlaskDIPython3CleanArchitecture æ¥å‹™ã§Pythonを使ã£ã¦ã‚¦ã‚§ãƒ–アプリケーションを実装ã™ã‚‹éš›ã€ãƒ¬ã‚¤ãƒ¤ãƒ¼æ¯Žã«é–¢å¿ƒã®åˆ†é›¢ã‚’è¡Œã„ãªãŒã‚‰é–‹ç™ºã™ã‚‹ãŸã‚ã«ã€Clean Architectureã‚’å°Žå…¥ã™ã‚‹ã“ã¨ã«ãªã‚Šã¾ã—ãŸã€‚ ãƒãƒ¼ãƒ メンãƒãƒ¼ã¸ã®ãƒŠãƒ¬ãƒƒã‚¸å…±æœ‰ã‚’å…¼ãã¦ã€æ¼¸é€²çš„型付ã‘ã¨Dependency Injectionを用ã„ãªãŒã‚‰ã€ãƒ†ã‚¹ã‚¿ãƒ“リティã®ä¼´ã£ãŸã‚¢ãƒ—リケーションを開発ã™ã‚‹ãŸã‚ã®ãƒ—ラクティスをã¾ã¨ã‚ã¾ã—ãŸã€‚ 今回ã¯Pythonを用ã„ãŸã‚µãƒ³ãƒ—ルを目的ã¨ã—ã¦ã„ã‚‹ãŸã‚ã€Clean Architectureã®è§£èª¬ã¯ç°¡æ˜“ã«æ¸ˆã¾ã›ã¾ã™ã€‚ (The Clean Architectureより引用) Clean Architectureã¯ãƒãƒãƒ¼ãƒˆãƒ»C・マーティンã«ã‚ˆã£ã¦2
ã€flask】Webサーãƒãƒ¼ã‚’https対応ã™ã‚‹æ–¹æ³• ã€ãŠå®¶IT#4】 - MotoJapan's Tech-Memo
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