5 dépôts
Tools for identifying and fixing performance bottlenecks.
Explore 5 awesome GitHub repositories matching part of an awesome list · Performance Profiling. Refine with filters or upvote what's useful.
py-spy is a sampling profiler and process debugger for Python. It allows for the analysis of running processes to identify performance bottlenecks and diagnose hanging programs without requiring code changes or restarts. The tool operates by reading the memory of a running process from the outside, which enables non-invasive sampling and state collection without pausing execution. It can resolve binary symbols to capture performance data from native extensions written in compiled languages and generate visual flame graphs for both native extensions and subprocesses. The project provides capa
Sampling profiler for Python applications.
pyinstrument is a statistical sampling profiler for Python that records the call stack at regular intervals to identify performance bottlenecks with low overhead. It tracks wall-clock time, including I/O and external service calls, and provides specialized profiling for asynchronous programs by attributing time spent awaiting tasks to the calling function. The project converts captured execution data into interactive HTML reports, JSON, and flamecharts. It includes a call stack visualizer to simplify the analysis of execution paths and supports the profiling of individual cells within interac
Call stack profiler for Python code.
Panda3D est un moteur de jeu multiplateforme et un système de rendu graphique 3D développé pour Python et C++. Il fonctionne comme un framework complet pour construire des applications 3D interactives, fournissant un simulateur physique en temps réel et un outil de pipeline d'assets 3D spécialisé. Le moteur se distingue en combinant un cœur C++ haute performance avec des liaisons de langage Python interopérables. Il utilise une architecture de graphe de scène pour organiser les objets 3D et fournit un système de conversion d'assets basé sur pipeline pour optimiser les modèles et textures pour le chargement au runtime. Sa surface de capacités inclut des primitives graphiques de bas niveau pour l'exécution de shaders GPU personnalisés, le rendu audio 3D et l'intégration de réalité virtuelle pour la connectivité des périphériques et trackers. Le framework dispose également d'outils pour le packaging de binaires d'applications multiplateformes et le profilage de performance de trame basé sur le réseau. Le projet fournit une couche d'abstraction matérielle unifiée pour assurer un comportement cohérent à travers différents systèmes d'exploitation et environnements matériels.
Provides a network-based profiling system to track frame time distribution and identify performance bottlenecks.
Django Silk is a profiling and inspection toolset for Django applications designed to capture SQL queries, HTTP request data, and execution timing for diagnostics. It functions as a performance profiler and debugging middleware that records runtime execution data to provide a comprehensive overview of application behavior. The system includes a database profiler for identifying slow operations through detailed timing data and an HTTP request inspector for reviewing headers, bodies, and network traffic via a web interface. It allows for the reproduction of specific server requests through gene
Live profiling and request inspection.
Detect N+1s in your Django app.
N+1 query detection with user-friendly errors.