Search Results for "bastille-linux" - Page 7
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Showing 482 open source projects for "bastille-linux"
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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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Tunix
A JAX-native LLM Post-Training Library
Tunix is a JAX-native library for post-training large language models, bringing supervised fine-tuning, reinforcement learning–based alignment, and knowledge distillation into one coherent toolkit. It embraces JAX’s strengths—functional programming, jit compilation, and effortless multi-device execution—so experiments scale from a single GPU to pods of TPUs with minimal code changes. The library is organized around modular pipelines for data loading, rollout, optimization, and evaluation,... -
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LangExtract
A Python library for extracting structured information
LangExtract is a Python library developed by Google that leverages large language models (LLMs) to extract structured information from unstructured text—such as clinical notes, research papers, or literary works—based on user-defined instructions. It is designed to transform free-form text into reliable, schema-constrained data while maintaining traceability back to the source material. Each extracted entity is precisely grounded in its original context, allowing visual inspection and... -
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MuJoCo Playground
An open source library for GPU-accelerated robot learning
MuJoCo Playground, developed by Google DeepMind, is a GPU-accelerated suite of simulation environments for robot learning and sim-to-real research, built on top of MuJoCo MJX. It unifies a range of control, locomotion, and manipulation tasks into a consistent and scalable framework optimized for JAX and Warp backends. The project includes classic control benchmarks from dm_control, advanced quadruped and bipedal locomotion systems, and dexterous as well as non-prehensile manipulation setups.... -
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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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Multimodal
TorchMultimodal is a PyTorch library
This project, also known as TorchMultimodal, is a PyTorch library for building, training, and experimenting with multimodal, multi-task models at scale. The library provides modular building blocks such as encoders, fusion modules, loss functions, and transformations that support combining modalities (vision, text, audio, etc.) in unified architectures. It includes a collection of ready model classes—like ALBEF, CLIP, BLIP-2, COCA, FLAVA, MDETR, and Omnivore—that serve as reference... -
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GenAI Processors
GenAI Processors is a lightweight Python library
GenAI Processors is a lightweight Python library for building modular, asynchronous, and composable AI pipelines around Gemini. Its central abstraction is the Processor, a unit of work that consumes an asynchronous stream of parts (text, images, audio, JSON) and produces another stream, making it natural to chain operations and keep everything streaming end-to-end. Processors can be composed sequentially (to build multi-step flows) or in parallel (to fan-out work and merge results), which... -
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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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Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions... -
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WTForms
A flexible forms validation and rendering library for Python
WTForms is a flexible forms validation and rendering library for Python web development. It can work with whatever web framework and template engine you choose. It supports data validation, CSRF protection, internationalization (I18N), and more. There are various community libraries that provide closer integration with popular frameworks. WTForms is designed to work with any web framework and template engine. There are a number of community-provided libraries that make integrating with... -
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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,... -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support,... -
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Amazon Braket Python Schemas
A library that contains schemas for Amazon Braket
Amazon Braket Python Schemas is an open source library that contains the schemas for Braket, including intermediate representations (IR) for Amazon Braket quantum tasks and offers serialization and deserialization of those IR payloads. Think of the IR as the contract between the Amazon Braket SDK and Amazon Braket API for quantum programs. Schemas for the S3 results of each quantum task. Schemas for the device capabilities of each device. The preferred way to get Amazon Braket Python Schemas... -
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JC
CLI tool and python library
CLI tool and python library that converts the output of popular command-line tools and file types to JSON or Dictionaries. This allows piping of output to tools like jq and simplifying automation scripts. jc JSONifies the output of many CLI tools and file types for easier parsing in scripts. This allows further command-line processing of output with tools like jq or jello by piping commands. The JC parsers can also be used as python modules. In this case, the output will be a python... -
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Watchdog
Python library and shell utilities to monitor filesystem events
Python API library and shell utilities to monitor file system events. A simple program that uses watchdog to monitor directories specified as command-line arguments and logs events generated. Watchdog comes with an optional utility script called watchmedo. Please type watchmedo --help at the shell prompt to know more about this tool. You can use the shell-command subcommand to execute shell commands in response to events. watchmedo can read tricks.yaml files and execute tricks within them in... -
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Boltons
250+ constructs, recipes, and snippets which extend the Python library
Boltons is a set of pure-Python utilities in the same spirit as, and yet conspicuously missing from, the standard library. Due to the nature of utilities, application developers might want to consider other integration options. Boltons is tested against Python 2.6-2.7, 3.4-3.7, and PyPy. The majority of boltons strive to be “good enough” for a wide range of basic uses, leaving advanced use cases to Python’s myriad specialized 3rd-party libraries. In many cases the respective boltons module... -
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Python-Spider
Python3 web crawler practice
Python-Spider is a repository intended to teach or provide examples for writing web spiders / crawlers in Python — part of a broader learning and resource collection by its author. The code and documentation are oriented toward beginners or intermediate learners who want to learn how to fetch, parse, and extract data from websites programmatically. As part of the author’s public learning-path repositories, python-spider likely includes examples of HTTP requests, HTML parsing, maybe... -
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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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Playground Cheatsheet for Python
Playground and cheatsheet for learning Python
learn-python is another repository by Oleksii Trekhleb that serves as both a playground and an interactive cheatsheet for learning Python. It contains numerous Python scripts organized by topic (lists, dictionaries, loops, functions, classes, modules, etc.), each with code examples, explanations, test assertions, and links to further readings. The design supports “learn by doing”: you can modify the code, run the tests, see how behavior changes, and thus internalize Python language features,... -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is... -
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UCO3D
Uncommon Objects in 3D dataset
uCO3D is a large-scale 3D vision dataset and toolkit centered on turn-table videos of everyday objects drawn from the LVIS taxonomy. It provides about 170,000 full videos per object instance rather than still frames, along with per-video annotations including object masks, calibrated camera poses, and multiple flavors of point clouds. Each sequence also ships with a precomputed 3D Gaussian Splat reconstruction, enabling fast, differentiable rendering workflows and modern implicit/point-based... -
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fvcore
Collection of common code shared among different research projects
fvcore is a lightweight utility library that factors out common performance-minded components used across Facebook/Meta computer-vision codebases. It provides numerics and loss layers (e.g., focal loss, smooth-L1, IoU/GIoU) implemented for speed and clarity, along with initialization helpers and normalization layers for building PyTorch models. Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code. A...
