Gradio vs Streamlit vs Dash vs Flask | Towards Data Science
Photo by Cesar Carlevarino Aragon on Unsplash Introduction Machine 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
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‚¤
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Pythonã®Web frameworkã®ãƒ‘フォーマンス比較 (Django, Flask, responder, FastAPI, japronto) - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? 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„ãªperformanceã‚’
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
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, user_ratings in top_n.items(): user_ratings.sort(key=lambda
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
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