Search Results for "python linux" - Page 8
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Showing 516 open source projects for "python linux"
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Peroxide
Rust numeric library with high performance and friendly syntax
Rust numeric library contains linear algebra, numerical analysis, statistics and machine learning tools with R, MATLAB, Python-like macros. Peroxide uses a 1D data structure to represent matrices, making it straightforward to integrate with BLAS (Basic Linear Algebra Subprograms). This means that Peroxide can guarantee excellent performance for linear algebraic computations by leveraging the optimized routines provided by BLAS. For users familiar with numerical computing libraries like... -
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Django Notebook
Django + shell_plus + Jupyter notebooks made easy
Django + shell_plus + Jupyter notebooks made easy. A Jupyter notebook with access to objects from the Django ORM is a powerful tool to introspect data and run ad-hoc queries. Built-in integration with the imported objects from django-extensions shell_plus. Saves the state between sessions so you don't need to remember what you did. Inheritance diagrams on any object, including ORM models. -
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Professional Programming
A collection of learning resources for curious software engineers
Professional Programming is a long-running, curated collection of learning resources aimed at helping software engineers grow into well-rounded professionals. It goes far beyond basic “learn to code” material and covers topics like system design, debugging, testing, performance, security, architecture, and software craftsmanship. The list is organized by themes such as coding, design, operations, communication, and career, making it easy to dive into specific aspects of engineering practice.... -
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DeepXDE
A library for scientific machine learning & physics-informed learning
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms. Physics-informed neural network (PINN). Solving different problems. Solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving... -
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nghttp2
HTTP/2 C Library and tools
nghttp2 is an implementation of HTTP/2 and its header compression algorithm HPACK in C. The framing layer of HTTP/2 is implemented as a form of reusable C library. On top of that, we have implemented HTTP/2 client, server and proxy. We have also developed a load test and benchmarking tool for HTTP/2. We have participated in httpbis working group since HTTP/2 draft-04, which is the first implementation draft. Since then we have updated nghttp2 library constantly to the latest specification... -
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FairChem
FAIR Chemistry's library of machine learning methods for chemistry
FAIRChem is a unified library for machine learning in chemistry and materials, consolidating data, pretrained models, demos, and application code into a single, versioned toolkit. Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations,... -
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Anomalib
An anomaly detection library comprising state-of-the-art algorithms
Anomalib is an open-source deep learning library focused on anomaly detection and localization tasks, collecting state-of-the-art algorithms and tools under one modular framework. It provides implementations of leading anomaly detection methods drawn from current research, as well as a full set of utilities for training, evaluating, benchmarking, and deploying these models on both public and private datasets. Anomalib emphasizes flexibility and reproducibility: you can use its simple APIs to... -
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CoreNet
CoreNet: A library for training deep neural networks
CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. Its distributed... -
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Papis
Powerful and highly extensible command-line based document
Papis is a powerful and highly extensible CLI document and bibliography manager. With Papis, you can search your library for books and papers, add documents and notes, import and export to and from other formats, and much much more. Papis uses a human-readable and easily hackable .yaml file to store each entry's bibliographical data. It strives to be easy to use while providing a wide range of features. And for those who still want more, Papis makes it easy to write scripts that extend its... -
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HelloGitHub
Share interesting, entry-level open source projects on GitHub
HelloGitHub shares interesting, entry-level open source projects on GitHub. It is updated and released in the form of a monthly magazine on the 28th of every month. The content includes interesting, entry-level open-source projects, open-source books, practical projects, enterprise-level projects, etc., so that you can feel the charm of open source in a short time and fall in love with open source! At first, I just wanted to collect interesting, high-quality, and easy-to-use projects that I... -
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XGBoost
Scalable and Flexible Gradient Boosting
XGBoost is an optimized distributed gradient boosting library, designed to be scalable, flexible, portable and highly efficient. It supports regression, classification, ranking and user defined objectives, and runs on all major operating systems and cloud platforms. XGBoost works by implementing machine learning algorithms under the Gradient Boosting framework. It also offers parallel tree boosting (GBDT, GBRT or GBM) that can quickly and accurately solve many data science problems.... -
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EKS Best Practices
A best practices guide for day 2 operations
The Amazon EKS Best Practices Guide is a public repository containing comprehensive documentation and guidance for operating production-grade Kubernetes clusters on AWS’s managed service, Amazon EKS. Rather than a code library, it serves as a reference catalogue of patterns, anti-patterns, checklists and architectures across domains such as security, reliability, scalability, networking, cost optimization and hybrid cloud deployments. The repository is maintained by AWS but open to... -
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Computer Science Flash Cards
Mini website for testing both general CS knowledge and enforce coding
This repository collects concise flash cards that cover the core ideas of a traditional computer science curriculum with a focus on interview readiness. The cards distill topics like time and space complexity, classic data structures, algorithmic paradigms, operating systems, networking, and databases into short, testable prompts. They are designed for spaced-repetition style study so you can cycle frequently through fundamentals until recall feels automatic. Many cards point at canonical... -
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Double Conversion
Efficient binary-decimal & decimal-binary conversion routines for IEEE
Double Conversion is a high-performance C++ library that provides precise and efficient binary-decimal and decimal-binary conversion routines for IEEE 754 double-precision floating-point numbers. Originally extracted from the V8 JavaScript engine, it was refactored into a standalone library to make its robust number conversion algorithms easily reusable in other projects. The library ensures consistent and accurate results for converting between double values and their string... -
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Flax
Flax is a neural network library for JAX
Flax is a flexible neural-network library for JAX that embraces functional programming while offering ergonomic module abstractions. Its design separates pure computation from state by threading parameter collections and RNGs explicitly, enabling reproducibility, transformation, and easy experimentation with JAX transforms like jit, pmap, and vmap. Modules define parameterized computations, but initialization and application remain side-effect free, which pairs naturally with JAX’s staging... -
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RLax
Library of JAX-based building blocks for reinforcement learning agents
RLax (pronounced “relax”) is a JAX-based library developed by Google DeepMind that provides reusable mathematical building blocks for constructing reinforcement learning (RL) agents. Rather than implementing full algorithms, RLax focuses on the core functional operations that underpin RL methods—such as computing value functions, returns, policy gradients, and loss terms—allowing researchers to flexibly assemble their own agents. It supports both on-policy and off-policy learning, as well as... -
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Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained... -
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Theseus
A library for differentiable nonlinear optimization
Theseus is a library for differentiable nonlinear optimization that lets you embed solvers like Gauss-Newton or Levenberg–Marquardt inside PyTorch models. Problems are expressed as factor graphs with variables on manifolds (e.g., SE(3), SO(3)), so classical robotics and vision tasks—bundle adjustment, pose graph optimization, hand–eye calibration—can be written succinctly and solved efficiently. Because solves are differentiable, you can backpropagate through optimization to learn cost... -
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Prompt Engineering Interactive Tutorial
Anthropic's Interactive Prompt Engineering Tutorial
Prompt-eng-interactive-tutorial is a comprehensive, hands-on tutorial that teaches the craft of prompt engineering with Claude through guided, executable lessons. It starts with the anatomy of a good prompt and moves into techniques that deliver the “80/20” gains—separating instructions from data, specifying schemas, and setting evaluation criteria. The course leans heavily on realistic failure modes (ambiguity, hallucination, brittle instructions) and shows how to iteratively debug prompts... -
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Courses (Anthropic)
Anthropic's educational courses
Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured... -
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DeepEP
DeepEP: an efficient expert-parallel communication library
DeepEP is a communication library designed specifically to support Mixture-of-Experts (MoE) and expert parallelism (EP) deployments. Its core role is to implement high-throughput, low-latency all-to-all GPU communication kernels, which handle the dispatching of tokens to different experts (or shards) and then combining expert outputs back into the main data flow. Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP... -
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Public APIs
A collective list of free APIs
public-apis is a collaboratively maintained repository that provides an extensive, categorized list of publicly available APIs for developers. Curated by community contributors and the team at APILayer, it serves as a centralized resource for discovering APIs across a wide range of domains, including data, machine learning, weather, entertainment, and finance. The project aims to make API exploration and integration more accessible by offering a single, organized index of open and... -
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django-split-settings
Organize Django settings into multiple files and directories
Organize Django settings into multiple files and directories. Easily override and modify settings. Use wildcards in settings file paths and mark settings files as optional. Managing Django’s settings might be tricky. There are severals issues which are encountered by any Django developer along the way. First one is caused by the default project structure. Django clearly offers us a single settings.py file. It seams reasonable at the first glance. And it is actually easy to use just after the... -
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Avalanche
End-to-End Library for Continual Learning based on PyTorch
Avalanche is an end-to-end Continual Learning library based on Pytorch, born within ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms. Avalanche can help Continual Learning researchers in several ways. This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major... -
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Opacus
Training PyTorch models with differential privacy
Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. Vectorized per-sample gradient computation that is 10x faster than micro batching. Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source,...
