Performance Optimization Python Packages

fastmssql

High performance async Mssql library for Python.

django-trackings

A Django app that tracks your queries to help optimize them. See Qorme for automated ORM optimization: https://qorme.com?ref=dj-tracker

codegreen

Know the true energy cost of your code

django-eagle

Catch wasted eager loads in your Django ORM queries.

autozyme

Autonomous multi-agent framework to optimize scientific software

onediff

OneDiff: An out-of-the-box acceleration library for diffusion models.

akenoai

AkenoAI-Lib is a high-performance library for making raw HTTP requests, powered by aiohttp. It provides a simple yet powerful syntax for handling API requests efficiently, ensuring speed and flexibility.

onediffx

OneDiff: An out-of-the-box acceleration library for diffusion models.

ado-core

A framework for designing, executing and analysing experiment campaigns

reactive-caching

Caching module for class properties, with automatic invalidation based on attribute changes.

ado-vllm-performance

A framework for designing, executing and analysing experiment campaigns

fastmssql-dbproj

High performance async Mssql library for Python.

asyncio-keyed-lock

Per-key asyncio locking with automatic cleanup and graceful-shutdown support. Zero dependencies.

profine

Profine automatically profiles and optimizes PyTorch training jobs on real GPUs, delivering measurable speedups and lower GPU costs before teams waste days tuning configs by hand.

autotuning-methodology

This software package accompanies the paper "A Methodology for Comparing Auto-Tuning Optimization Algorithms" (https://doi.org/10.1016/j.future.2024.05.021), making the guidelines in the methodology easy to apply.

labelmev2

Image Polygonal Annotation with Python - V2 with 45x Faster Performance

residue-protocol

Bare-Metal AVX2 Inference Shield for LLMs

llm-autobatch

Turn single LLM calls into fast micro-batches. Rust core, Python API.

batch-inference

Batch Inference

hybrid-cloud-navigator

A new package that helps users understand and navigate the complexities of hybrid cloud environments in 2025. Users can input their specific hybrid cloud challenges, such as data gravity (the tendency

lvmthin-helper

lvmthin-helper automates LVM Thin Provisioning management by analyzing user inputs and current setups, offering optimized configurations and troubleshooting steps for efficient storage.

pdperf

A static performance linter that detects slow Pandas anti-patterns before they reach production.

labelme-enhanced

Image annotation tool with 45x faster loading, enhanced zoom (5000%), multiple point addition, polygon merge, and dark mode. Enhanced fork of wkentaro/labelme optimized for large image annotation workflows.