Search Results for "python library" - Page 16
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Showing 2736 open source projects for "python library"
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Amazon Braket PennyLane Plugin
A plugin for allowing Xanadu PennyLane to use Amazon Braket devices
The Amazon Braket PennyLane plugin offers two Amazon Braket quantum devices to work with PennyLane. The Amazon Braket Python SDK is an open-source library that provides a framework to interact with quantum computing hardware devices and simulators through Amazon Braket. PennyLane is a machine learning library for optimization and automatic differentiation of hybrid quantum-classical computations. Once the Pennylane-Braket plugin is installed, the provided Braket devices can be accessed straight... -
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plexe
Build a machine learning model from a prompt
..., versioned, and deployed, with reports to explain performance and limitations. The project supports both a Python library and a managed cloud option, meeting teams wherever they prefer to run workloads. The overall goal is to compress the path from idea to usable model while keeping humans in the loop for review and adjustment. -
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AWS SDK for pandas
Easy integration with Athena, Glue, Redshift, Timestream, Neptune
aws-sdk-pandas (formerly AWS Data Wrangler) bridges pandas with the AWS analytics stack so DataFrames flow seamlessly to and from cloud services. With a few lines of code, you can read from and write to Amazon S3 in Parquet/CSV/JSON/ORC, register tables in the AWS Glue Data Catalog, and query with Amazon Athena directly into pandas. The library abstracts efficient patterns like partitioning, compression, and vectorized I/O so you get performant data lake operations without hand-rolling... -
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Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a high-level neural network API for specifying complex, hierarchical models at both finite and infinite width, built in Python on top of JAX and XLA. It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels... -
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Granite TSFM
Foundation Models for Time Series
granite-tsfm collects public notebooks, utilities, and serving components for IBM’s Time Series Foundation Models (TSFM), giving practitioners a practical path from data prep to inference for forecasting and anomaly-detection use cases. The repository focuses on end-to-end workflows: loading data, building datasets, fine-tuning forecasters, running evaluations, and serving models. It documents the currently supported Python versions and points users to where the core TSFM models are hosted... -
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Tiktoken
tiktoken is a fast BPE tokeniser for use with OpenAI's models
tiktoken is a high-performance, tokenizer library (based on byte-pair encoding, BPE) designed for use with OpenAI’s models. It handles encoding and decoding text to token IDs efficiently, with minimal overhead. Because tokenization is a fundamental step in preparing text for models, tiktoken is optimized for speed, memory, and correctness in model contexts (e.g. matching OpenAI’s internal tokenization). The repo supports multiple encodings (e.g. “cl100k_base”) and lets users switch encoding... -
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
... that produce your data by automatically generating test cases for them. Integrate seamlessly with the Python ecosystem. Overcome the initial hurdle of defining a schema by inferring one from clean data, then refine it over time. Identify the critical points in your data pipeline, and validate data going in and out of them. Build confidence in the quality of your data by defining schemas for complex data objects. -
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dm_control
DeepMind's software stack for physics-based simulation
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics. The MuJoCo Python bindings support three different OpenGL rendering backends: EGL (headless, hardware-accelerated), GLFW (windowed, hardware-accelerated), and OSMesa (purely software-based). At least one of these three backends must be available in order render through... -
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Vedo
A python module for scientific analysis of 3D data
A lightweight and powerful python module for scientific analysis and visualization of 3d objects. Inspired by the vpython manifesto "3D programming for ordinary mortals", vedo makes it easy to work with 3D pointclouds, meshes and volumes, in just a few lines of code, even for less experienced programmers. vedo is based on VTK and numpy, with no other dependencies. Import meshes from VTK format, STL, Wavefront OBJ, 3DS, Dolfin-XML, Neutral, GMSH, OFF, PCD (PointCloud). Export meshes as ASCII... -
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Tweepy
Twitter for Python
An easy-to-use Python library for accessing the Twitter API. You can also use Git to clone the repository from GitHub to install the latest development version. The easiest way to install the latest version from PyPI is by using pip. Twitter requires all requests to use OAuth for authentication. The API class provides access to the entire twitter RESTful API methods. Each method can accept various parameters and return responses. When we invoke an API method most of the time returned back to us... -
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Click
Python composable command line interface toolkit
Click is a Python package for creating beautiful command line interfaces in a composable way with as little code as necessary. It’s the “Command Line Interface Creation Kit”. It’s highly configurable but comes with sensible defaults out of the box. It aims to make the process of writing command line tools quick and fun while also preventing any frustration caused by the inability to implement an intended CLI API. Click in three points, arbitrary nesting of commands, automatic help page... -
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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 representations... -
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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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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... -
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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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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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Pearl
A Production-ready Reinforcement Learning AI Agent Library
Pearl is a production-ready reinforcement learning and contextual bandit agent library built for real-world sequential decision making. It is organized around modular components—policy learners, replay buffers, exploration strategies, safety modules, and history summarizers—that snap together to form reliable agents with clear boundaries and strong defaults. The library implements classic and modern algorithms across two regimes: contextual bandits (e.g., LinUCB, LinTS, SquareCB, neural bandits... -
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Flow Matching
A PyTorch library for implementing flow matching algorithms
flow_matching is a PyTorch library implementing flow matching algorithms in both continuous and discrete settings, enabling generative modeling via matching vector fields rather than diffusion. The underlying idea is to parameterize a flow (a time-dependent vector field) that transports samples from a simple base distribution to a target distribution, and train via matching of flows without requiring score estimation or noisy corruption—this can lead to more efficient or stable generative... -
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PyTorch3D
PyTorch3D is FAIR's library of reusable components for deep learning
PyTorch3D is a comprehensive library for 3D deep learning that brings differentiable rendering, geometric operations, and 3D data structures into the PyTorch ecosystem. It’s designed to make it easy to build and train neural networks that work directly with 3D data such as meshes, point clouds, and implicit surfaces. The library provides fast GPU-accelerated implementations of rendering pipelines, transformations, rasterization, and lighting—making it possible to compute gradients through full... -
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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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Auto Bangumi
Automated Bangumi episode downloader and organizer with Web UI
Auto_Bangumi is a fully automated tool for downloading, organizing, and tracking anime (Bangumi) episodes using RSS feeds and download clients like qBittorrent. It offers a modern Web UI for managing subscriptions, custom filtering rules, automatic file renaming, and subtitle matching. Designed for anime fans, it streamlines the process of staying up-to-date with seasonal shows by integrating feed parsing, downloading, and library organization. -
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FastUI
Build better UIs faster
FastUI is a library that lets developers build interactive user interfaces for FastAPI applications using Pydantic models. It automatically generates frontend components based on data schemas and endpoint logic, reducing the need for manual UI development. Designed to be type-safe, reactive, and fast, FastUI streamlines the creation of web dashboards, admin panels, and internal tools within a FastAPI backend. -
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EvoTorch
Advanced evolutionary computation library built on top of PyTorch
EvoTorch is an evolutionary optimization framework built on top of PyTorch, developed by NNAISENSE. It is designed for large-scale optimization problems, particularly those that require evolutionary algorithms rather than gradient-based methods.
