Showing 14 open source projects for "fast performance"

View related business solutions
  • Build Agents and Models on One Platform Icon
    Build Agents and Models on One Platform

    Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

    Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
    Start Free
  • $300 Free Credits to Build on Google Cloud Icon
    $300 Free Credits to Build on Google Cloud

    New customers can spin up VMs, build with AI, and query data at no cost.

    Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
    Start Free
  • 1
    FastAPI Python

    FastAPI Python

    FastAPI framework, high performance, easy to learn, fast to code

    FastAPI framework, high performance, easy to learn, fast to code, ready for production. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints.
    Downloads: 5 This Week
    Last Update:
    See Project
  • 2
    FastAPI

    FastAPI

    FastAPI framework, high performance, easy to learn, fast to code

    FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs. Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs. Get production-ready code.
    Downloads: 43 This Week
    Last Update:
    See Project
  • 3
    Sanic

    Sanic

    Async Python 3.6+ web server/framework

    Build fast, run fast with Sanic! Sanic is a Python 3.6+ web server and web framework designed to go fast. It provides a way to get a highly performant HTTP server up and running fast, while also making it easy to build, expand, and eventually scale. Sanic aspires to be as simple as possible while delivering the performance that you require.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 4
    Django Ninja

    Django Ninja

    Fast, Async-ready, Openapi, type hints based framework

    Django Ninja is a web framework for building APIs with Django and Python 3.6+ type hints. Designed to be easy to use and intuitive. Very high performance thanks to Pydantic and async support. Type hints and automatic docs lets you focus only on business logic. Based on the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema. Django friendly (obviously) has good integration with the Django core and ORM. Used by multiple companies on live projects.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Host LLMs in Production With On-Demand GPUs Icon
    Host LLMs in Production With On-Demand GPUs

    NVIDIA L4 GPUs. 5-second cold starts. Scale to zero when idle.

    Deploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
    Start Free
  • 5
    Zendriver

    Zendriver

    A blazing fast, async-first, undetectable webscraping

    Zendriver is a modern Python web automation and scraping framework that leverages the Chrome DevTools Protocol to provide fast, asynchronous control over real browser instances. Unlike traditional tools that rely on Selenium or WebDriver, Zendriver communicates directly with the browser through CDP, enabling higher performance and more precise control over browser behavior. The framework is designed to be difficult to detect by anti-bot systems, making it suitable for advanced scraping and automation use cases where stealth is important. ...
    Downloads: 1 This Week
    Last Update:
    See Project
  • 6
    bitnet.cpp

    bitnet.cpp

    Official inference framework for 1-bit LLMs

    bitnet.cpp is the official open-source inference framework and ecosystem designed to enable ultra-efficient execution of 1-bit large language models (LLMs), which quantize most model parameters to ternary values (-1, 0, +1) while maintaining competitive performance with full-precision counterparts. At its core is bitnet.cpp, a highly optimized C++ backend that supports fast, low-memory inference on both CPUs and GPUs, enabling models such as BitNet b1.58 to run without requiring enormous compute infrastructure. The project’s focus on extreme quantization dramatically reduces memory footprint and energy consumption compared with traditional 16-bit or 32-bit LLMs, making it practical to deploy advanced language understanding and generation models on everyday machines. ...
    Downloads: 5 This Week
    Last Update:
    See Project
  • 7
    BentoML

    BentoML

    Unified Model Serving Framework

    BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference...
    Downloads: 2 This Week
    Last Update:
    See Project
  • 8
    Python Outlier Detection

    Python Outlier Detection

    A Python toolbox for scalable outlier detection

    PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly detection. PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural...
    Downloads: 4 This Week
    Last Update:
    See Project
  • 9
    TensorFlow Model Garden

    TensorFlow Model Garden

    Models and examples built with TensorFlow

    The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development. To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Demo Series - Small Business Backup By Veeam Icon
    Demo Series - Small Business Backup By Veeam

    Learn how to protect your Microsoft 365 data, with simple, actionable tips today.

    Watch this on-demand demo series and learn how to protect your Microsoft 365 data with clear, simple, actionable steps that are easy to implement for businesses of all sizes.
    Watch Demo Series
  • 10
    Pyrogram

    Pyrogram

    Elegant, modern and asynchronous Telegram MTProto API framework

    ...Easy: Makes the Telegram API simple and intuitive, while still allowing advanced usages. Elegant: Low-level details are abstracted and re-presented in a more convenient way. Fast: Boosted up by TgCrypto, a high-performance cryptography library written in C. Type-hinted: Types and methods are all type-hinted, enabling excellent editor support. Async: Fully asynchronous (also usable synchronously if wanted, for convenience). Powerful: Full access to Telegram's API to execute any official client action and more.
    Downloads: 9 This Week
    Last Update:
    See Project
  • 11
    ClassyVision

    ClassyVision

    An end-to-end PyTorch framework for image and video classification

    Classy Vision is a PyTorch-based framework designed for large-scale training and deployment of state-of-the-art image and video classification models. Developed by Facebook Research, it serves as an end-to-end system that simplifies the process of training at scale, reducing redundancy and friction in moving from research to production. Unlike traditional computer vision libraries that focus solely on modular components, Classy Vision provides a complete and unified framework, featuring...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 12
    Yandex Tank

    Yandex Tank

    Load and performance benchmark tool

    Yandex.Tank is an extensible open-source load testing tool for advanced Linux users which is especially good as a part of an automated load testing suite. Different load generators are supported. Evgeniy Mamchits' phantom is a very fast (100 000+ RPS) shooter written in C++ (default) JMeter is an extendable and widely known one. BFG is a Python-based generator that allows you to write your load scenarios in Python. Experimental Golang generator: pandora. Performance analytics backend service: Overload. Store and analyze your test results online. Several ammo formats are supported like plain URL list or access.log. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 13
    Wally

    Wally

    Distributed Stream Processing

    ...Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. The primary API for Wally is written in Pony. Wally applications are written using this Pony API.
    Downloads: 1 This Week
    Last Update:
    See Project
  • 14

    Shovel Library

    Simple graphics, keyboard and mouse library with a C interface

    is a collection of ultra-simple routines I've found useful for making small interactive graphics applications. === Functions include === * Window creation * 32-bit RGBA bitmap creation * Fast software based drawing routines (pixels, lines, text etc) * Mouse and keyboard input === Details === * Written in C * Python bindings provided * Permissive BSD licence * Win32 version currently. Linux and Mac planned. === Performance === Running on Windows XP on an Intel Core i3 530 (3.4 GHz): * Putpixel - 31 million per second * Rectangle fill - 11 billion pixels per second * Text render - 11 million characters per second (8 point, fixed width font)
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next