Search Results for "python-libpcap" - Page 38
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Showing 17037 open source projects for "python-libpcap"
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Cloud tools for web scraping and data extractionAutomate web data collection with cloud tools that handle anti-bot measures, browser rendering, and data transformation out of the box. Extract content from any website, push to vector databases for RAG workflows, or pipe directly into your apps via API. Schedule runs, set up webhooks, and connect to your existing stack. Free tier available, then scale as you need to.
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Meta-World
Collections of robotics environments
...The environments adhere to the Gymnasium API, which makes them easy to plug into existing RL pipelines, and they support both synchronous and asynchronous vectorized execution for running many environments in parallel. Installation is done via pip, with official support for Python versions 3.8 through 3.11 on Linux and macOS, and the project is licensed under MIT to encourage broad academic and industry use. -
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mise
Dev tools, env vars, task runner
Mise (formerly RTX) is a cross-language CLI tool and task runner that manages developer tools, environment variables, and project tasks in a unified configuration (mise.toml). It handles tool installation (e.g., Node, Python, Terraform), env var profiles, and repeatable command scripting. -
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py2many
Transpiler of Python to many other languages
Python is popular, and easy to program in, but it has poor runtime performance. We can fix that by transpiring a subset of the language into a more performant, statically typed language. A second benefit is security. Writing security-sensitive code in a low-level language like C is error-prone and could lead to privilege escalation. Specialized languages such as wuffs exist to address this use case. py2many can be a more general-purpose solution to the problem where you can verify the source via unit tests before you transpile. ... -
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Wox
A cross-platform launcher that simply works
Wox is a cross-platform launcher that simply works. It's an alternative to Alfred and Launchy. -
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
...It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. The library supports matrix operations, gradient computation, and tensor conversions with intuitive APIs and near-native speed, thanks to Bun’s low-overhead FFI bindings. It can automatically leverage GPU acceleration on Linux (via CUDA) and CPU computation on macOS. -
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NeoDB
NeoDB is a self-hosted server tracking what you read/watch/listen/play
NeoDB is an open-source software and global community platform since 2021. It helps users to manage and explore collections, reviews, and ratings for various cultural products, including books, movies, music, podcasts, games, and performances. Additionally, users can share their collections, publish microblogs, and engage with others in the Fediverse. NeoDB integrates the functionalities of platforms like Goodreads, Letterboxd, RateYourMusic, and Podchaser, among others. It also supports... -
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LFortran
Official main repository for LFortran
LFortran is a modern open-source (BSD licensed) interactive Fortran compiler built on top of LLVM. It can execute the user’s code interactively to allow exploratory work (much like Python, MATLAB or Julia) as well as compile binaries with the goal of running the user’s code on modern architectures such as multi-core CPUs and GPUs. LFortran is in alpha (it is expected to not work on third-party codes and users enthusiastically participate in bug reporting and fixing). -
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PySR
High-Performance Symbolic Regression in Python and Julia
PySR is an open-source tool for Symbolic Regression: a machine learning task where the goal is to find an interpretable symbolic expression that optimizes some objective. Over a period of several years, PySR has been engineered from the ground up to be (1) as high-performance as possible, (2) as configurable as possible, and (3) easy to use. PySR is developed alongside the Julia library SymbolicRegression.jl, which forms the powerful search engine of PySR. The details of these algorithms are... -
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rwkv.cpp
INT4/INT5/INT8 and FP16 inference on CPU for RWKV language model
Besides the usual FP32, it supports FP16, quantized INT4, INT5 and INT8 inference. This project is focused on CPU, but cuBLAS is also supported. RWKV is a novel large language model architecture, with the largest model in the family having 14B parameters. In contrast to Transformer with O(n^2) attention, RWKV requires only state from the previous step to calculate logits. This makes RWKV very CPU-friendly on large context lengths. -
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MedGemma
Collection of Gemma 3 variants that are trained for performance
MedGemma is a collection of specialized open-source AI models created by Google as part of its Health AI Developer Foundations initiative, built on the Gemma 3 family of transformer models and trained for medical text and image comprehension tasks that help accelerate the development of healthcare-focused AI applications. It includes multiple variants such as a 4 billion-parameter multimodal model that can process both medical images and text and a 27 billion-parameter text-only (and... -
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NVIDIA Isaac Sim
NVIDIA Isaac Sim is an open-source application on NVIDIA Omniverse
NVIDIA Isaac Sim is a high-fidelity robotics simulation platform built on NVIDIA Omniverse to develop, test, and validate AI-driven robots in physically accurate virtual environments. It supports a wide array of robotics formats (URDF, MJCF, CAD), includes GPU-accelerated physics, and features immersive RTX rendering and multisensory simulation. Realistic physics via GPU-accelerated engines and RTX ray tracing. Multi-sensor simulation (RGB-D cameras, Lidar, Radar, IMU, contact sensors).... -
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Appfl
Advanced Privacy-Preserving Federated Learning framework
APPFL (Advanced Privacy-Preserving Federated Learning) is a Python framework enabling researchers to easily build and benchmark privacy-aware federated learning solutions. It supports flexible algorithm development, differential privacy, secure communications, and runs efficiently on HPC and multi-GPU setups. -
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Peewee-async
Asynchronous interface for peewee ORM powered by asyncio
peewee-async is an async extension for the Peewee ORM, enabling non-blocking database access in asyncio-powered Python applications. It allows you to use familiar Peewee models while benefiting from asynchronous I/O, making it ideal for web applications or services that require concurrency. -
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Piccolo
A fast, user friendly ORM and query builder which supports asyncio
Piccolo is a modern, fast, and type-safe ORM for Python, designed with developer ergonomics in mind. It provides a clean syntax for defining schemas and building queries while supporting both sync and async execution. With built-in admin tools and rich introspection, Piccolo is suitable for web apps, APIs, and small-to-medium scale backends that prioritize clarity and speed. -
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Pixoo
A library to help you make the most out of your Pixoo 64
Pixoo is a Python-based library for controlling Divoom Pixoo LED displays using Bluetooth Low Energy (BLE). It allows users to send images, animations, or text to Pixoo devices, enabling creative integrations like desktop widgets, real-time data displays, or custom artwork. -
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TalkingHeads
A library to communicate with ChatGPT, Claude, Copilot, Gemini
TalkingHeads is a Python library designed to facilitate communication with various AI chat agents, including ChatGPT, Claude, Copilot, Gemini, HuggingChat, and Pi. It provides a unified interface for interacting with these platforms, simplifying the integration of conversational AI capabilities into applications. TalkingHeads supports browser automation and offers tools to manage sessions, handle prompts, and process responses effectively. -
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Alibi Explain
Algorithms for explaining machine learning models
Alibi is a Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. -
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KerasTuner
A Hyperparameter Tuning Library for Keras
KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search... -
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TensorFlow Datasets
TFDS is a collection of datasets ready to use with TensorFlow,
TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data. Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets. -
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F·W·K
3D game engine/framework in C, with Luajit and Python bindings now
3D game framework in C, with Luajit and Python bindings now. -
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yq JSON
Command-line YAML, XML, TOML processor
Before using yq, you also have to install its dependency, jq. See the jq installation instructions for details and directions specific to your platform. On macOS, yq is also available on Homebrew use brew install python-yq. -
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Selectolax
Python binding to Modest and Lexbor engines
A fast HTML5 parser with CSS selectors using Modest and Lexbor engines. Selectolax supports two backends: Modest and Lexbor. By default, all examples use the Modest backend. Most of the features between backends are almost identical, but there are still some differences. Currently, the Lexbor backend is in beta and missing some of the features. To use lexbor, just import the parser and use it in the similar way to the HTMLParser. -
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Darts
A python library for easy manipulation and forecasting of time series
...The ML-based models can be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic forecasting. We recommend to first setup a clean Python environment for your project with at least Python 3.7 using your favorite tool (conda, venv, virtualenv with or without virtualenvwrapper). -
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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... -
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SageMaker Spark Container
Docker image used to run data processing workloads
...The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker.
