Open Source Python Libraries - Page 17
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Python Libraries
View 74 business solutionsBrowse free open source Python Libraries and projects below. Use the toggles on the left to filter open source Python Libraries by OS, license, language, programming language, and project status.
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The concept of model interpretability in the field of machine learning is still new, largely subjective, and, at times, controversial. Model interpretation is the ability to explain and validate the decisions of a predictive model to enable fairness, accountability, and transparency in algorithmic decision-making. The library has embraced object-oriented and functional programming paradigms as deemed necessary to provide scalability and concurrency while keeping code brevity in mind. -
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Sparse Attention
"Generating Long Sequences with Sparse Transformers" examples
Sparse Attention is OpenAI’s code release for the Sparse Transformer model, introduced in the paper Generating Long Sequences with Sparse Transformers. It explores how modifying the self-attention mechanism with sparse patterns can reduce the quadratic scaling of standard transformers, making it possible to model much longer sequences efficiently. The repository provides implementations of sparse attention layers, training code, and evaluation scripts for benchmark datasets. It highlights both fixed and learnable sparsity patterns that trade off computational cost and model expressiveness. By enabling tractable training on longer contexts, the project opened the door to applications in large-scale text and image generation. Though archived, it remains a key reference for efficient transformer research, influencing many later architectures that aim to extend sequence length while reducing compute. -
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Spyder notebook plugin
Jupyter notebook integration with Spyder
Spyder plugin to use Jupyter notebooks inside Spyder. Currently, it supports basic functionality such as creating new notebooks, opening any notebook in your filesystem and saving notebooks at any location. You can also use Spyder's file switcher to easily switch between notebooks and open an IPython console connected to the kernel of a notebook to inspect its variables in the Variable Explorer. -
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Spyne
A transport agnostic sync/async RPC library
Spyne is a Python RPC toolkit that makes it easy to expose online services that have a well-defined API using multiple protocols and transports. It integrates with popular Python web frameworks as well as libraries like SQLAlchemy to keep your code as DRY as possible. Spyne aims to save the protocol implementers the hassle of implementing their own remote procedure call api and the application programmers the hassle of jumping through hoops just to expose their services using multiple protocols and transports. In other words, Spyne is a framework for building distributed solutions that strictly follow the MVC pattern, where Model = spyne.model, View = spyne.protocol and Controller = user code. Spyne comes with the implementations of popular transport, protocol and interface document standards along with a well-defined API that lets you build on existing functionality. -
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StarsAndClown
Github Star Gathering Treatment List
StarsAndClown is a repository by the same maintainer that seems intended as a lighthearted “ranking / listing” project, possibly gathering interesting or amusing GitHub repositories, trending topics, or community “stars” — perhaps with a humorous or satirical twist given the name. The concept suggests a curated (or semi-automated) list of GitHub repos worth noting: whether because of popularity, novelty, or community interest — giving “people who eat grapes” (i.e. spectators) a way to enjoy and laugh along with the broader open-source ecosystem. For users browsing GitHub casually or seeking entertainment rather than strictly utility, StarsAndClown offers a curated feed of repositories that stand out — sometimes for good reason, sometimes for quirky appeal. As a public listing, it helps surface interesting corners of GitHub that mainstream ranking systems may neglect, offering a “pop-culture catalogue” of software rather than purely technical resources. -
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Strawberry GraphQL
A GraphQL library for Python that leverages type annotations
Python GraphQL library based on dataclasses. Strawberry's friendly API allows to create GraphQL API rather quickly, the debug server makes it easy to quickly test and debug. Django and ASGI support allow having your API deployed in production in a matter of minutes. The quick start method provides a server and CLI to get going quickly. Strawberry comes with a mypy plugin that enables statically type-checking your GraphQL schema. A Django view is provided for adding a GraphQL endpoint to your application. To support graphql Subscriptions over WebSockets you need to provide a WebSocket enabled server. Create a GraphQL schema defining a User type and a single query field user that will return a hardcoded user. -
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Switchboard
A feature flipper library for Pyramid and Pylons apps.
Switchboard is a port of Gargoyle, a feature flipper for Django apps, to the Pyramid or Pylons stack (including Turbogears). Originally used to selectively roll out changes to the SourceForge site, the library lets you easily control whether a particular change (a switch) is active. You can make switches active for a certain percentage of visitors, all visitors to a particular host in a cluster, or if a particular string is present in the query string. Furthermore you can easily create your own conditions to do fancier things like geo-targeting, specific users, etc. In short, Switchboard turns you into a continuous deployment ninja. -
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TF Quant Finance
High-performance TensorFlow library for quantitative finance
TF Quant Finance is a high-performance library of quantitative finance components built on TensorFlow, aimed at research and production workloads. It implements pricing engines, risk measures, stochastic models, optimizers, and random number generators that are differentiable and vectorized for accelerators. Users can value options and fixed-income instruments, simulate paths, fit curves, and calibrate models while leveraging TensorFlow’s jit compilation and automatic differentiation. The codebase is organized as modular math and finance primitives so you can combine building blocks or target end-to-end examples. It includes Bazel builds, tests, and example notebooks to accelerate learning and adoption in real workflows. With hardware acceleration and differentiable models, it enables modern techniques like gradient-based calibration and end-to-end learning of market dynamics. -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. Effortless device placement for using multiple CPU/GPU. The high-level API currently supports the most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, etc. -
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Teach Me Quantum
Practical Course on Quantum Information Science and Quantum Computing
A university-level course on Quantum Computing and Quantum Information Science that incorporates IBM Q Experience and Qiskit. This course is adequate for general audiences without prior knowledge on Quantum Mechanics and Quantum Computing (see prior knowledge), has an estimated average duration of 10 weeks at 3h/week (see duration), and is meant to be the entrypoint into the Quantum World. -
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Tenacity Python
Retrying library for Python
Tenacity is a Python library that enables automatic retrying of functions with customizable strategies. It replaces the now-deprecated retrying library and supports exponential backoff, fixed delays, stop and wait conditions, and exception filtering. Useful for network operations, API calls, or any unstable process, Tenacity helps increase reliability in Python applications by handling transient failures gracefully and robustly. -
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TensorFlow Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
TensorFlow Examples is a comprehensive repository of example implementations, tutorials, and reference code intended to help newcomers and intermediate learners dive into TensorFlow quickly. It contains both Jupyter notebooks and raw source code, covering a broad range of tasks: from basic machine-learning and neural-network models to more advanced use cases, using both TensorFlow v1 and v2 APIs. For clarity and educational value, each example is accompanied by explanatory comments or markdown cells to illustrate what the code does and why — a design that makes it especially suitable for self-learners or students following along with real data. Besides raw implementations, the repo often shows best practices using higher-level constructs (e.g. dataset pipelines, estimators, layers) which reflect modern TensorFlow workflows rather than only textbook-style code. -
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TensorFlow World
Simple and ready-to-use tutorials for TensorFlow
This repository aims to provide simple and ready-to-use tutorials for TensorFlow. The explanations are present in the wiki associated with this repository. There are different motivations for this open source project. TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Deep Learning is in very high interest these days - there's a crucial need for rapid and optimized implementations of the algorithms and architectures. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility in designing highly modular models which can also be a disadvantage for beginners since a lot of the pieces must be considered together when creating the model. -
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TensorNetwork
A library for easy and efficient manipulation of tensor networks
TensorNetwork is a high-level library for building and contracting tensor networks—graphical factorizations of large tensors that underpin many algorithms in physics and machine learning. It abstracts networks as nodes and edges, then compiles efficient contraction orders across multiple numeric backends so users can focus on model structure rather than index bookkeeping. Common network families (MPS/TT, PEPS, MERA, tree networks) are expressed with concise APIs that encourage experimentation and comparison. The library provides automatic path finding and cost estimation, exposing when contractions will explode in memory and suggesting better orders. Because it supports backends such as NumPy, TensorFlow, PyTorch, and JAX, the same model can run on CPUs, GPUs, or TPUs with minimal code changes. Tutorials and visualization helpers make it easier to understand how network topology affects expressive power and computational cost. -
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Tentacles is a Object-Relational Mapping (ORM) written in Python. It's main concept is to manipulate stored datas as you do for python data structures.
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TextBlob
TextBlob is a Python library for processing textual data
Simple, Pythonic, text processing, Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both. Supports word inflection (pluralization and singularization) and lemmatization, as well as spelling correction. Add new models or languages through extensions. Also, it comes with a WordNet integration. If you only intend to use TextBlob’s default models (no model overrides), you can pass the lite argument. This downloads only those corpora needed for basic functionality. TextBlob is also available as a conda package. -
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The Flatware Engine aims to be a cross-platform, resolution-independent engine and toolset for developing 2D games (side scroller, isometric, etc.)
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The Reactive Extensions for Python
Reactive extensions for Python
RxPY is a library for composing asynchronous and event-based programs using observable collections and pipable query operators in Python. A library for composing asynchronous and event-based programs using observable collections and query operator functions in Python. Reactive Extensions for Python (RxPY) is a set of libraries for composing asynchronous and event-based programs using observable sequences and pipable query operators in Python. Using Rx, developers represent asynchronous data streams with Observables, query asynchronous data streams using operators, and parameterize concurrency in data/event streams using Schedulers. RxPY is a fairly complete implementation of Rx with more than 120 operators, and over 1300 passing unit-tests. RxPY is mostly a direct port of RxJS, but also borrows a bit from RxNET and RxJava in terms of threading and blocking operators. -
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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 weights, feature extractors, or initialization networks end-to-end. The implementation supports batched optimization on GPU, robust losses, damping strategies, and custom factors, making it practical for real-time systems. Helper packages provide geometry primitives and utilities for composing priors, relative constraints, and measurement models. Theseus bridges the gap between classical optimization and deep learning, enabling hybrid systems that learn components. -
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Top Deep Learning Projects
A list of popular github projects related to deep learning
TopDeepLearning is a curated index of the most popular GitHub projects related to deep learning, ranked by their star count. Rather than being a library itself, it serves as a curated roadmap and reference guide for anyone exploring the deep learning ecosystem — from beginners to experienced practitioners. By aggregating high-star projects across frameworks (TensorFlow, PyTorch), tools (computer vision, NLP, reinforcement learning), tutorials, and research code, it helps users quickly discover reputable and well-maintained repositories. This way one can survey state-of-the-art projects, find learning resources, or pick stable libraries for production — without manually sifting through hundreds of repos. The repository is openly licensed under MIT, making it easy to fork, extend, or contribute updates (e.g. adding newer projects or reordering by recent popularity). -
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Transformers4Rec
Transformers4Rec is a flexible and efficient library
Transformers4Rec is an advanced recommendation system library that leverages Transformer models for sequential and session-based recommendations. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners. Traditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior. Generally, the next user interaction is related to the sequence of the user's previous choices. In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the sequential recommendation task. -
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That project aims at studying and comparing typical airline IT methods, for instance RM-related algorithms. It works from a Unix/Linux/Mac command-line, and exposes basic APIs. It is being developed in C++, with Python wrappers for some components.
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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, letting practitioners swap components without rewriting the whole stack. Examples and reference configs demonstrate end-to-end runs for common model families, helping teams reproduce baselines before customizing. Tunix also leans into research ergonomics: logging, checkpointing, and metrics are built in, and the code is written to be hackable rather than monolithic. Overall it aims to shorten the path from an off-the-shelf base model to a well-aligned, task-ready model using scalable JAX primitives. -
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Tygamusic
A pygame music lib.
This lib was produced while I was programming an other program/game. I was tired of pygame's bad system of handling playlists and the management of music in general. With this lib I want to create an layer that allows you to interact with the music, how you would expect it. Currently featuring: -Playlist -Normal pausing and resuming (played time isn’t lost when new song is loaded) -Automatic recognition of songs and adding them to a separate list -
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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 modeling experiments. The repository includes automated downloaders with checksum verification, fine-grained controls to fetch only selected modalities or super-categories, and a lightweight Python API for loading frames, geometry, and splats on demand. Metadata is indexed in SQLite for quick queries at scale, and helper builders handle alignment, undistortion, frame extraction from videos, and cropping around the object.
