Open Source Python Software - Page 64
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Python Software
Browse free open source Python Software and projects below. Use the toggles on the left to filter open source Python Software by OS, license, language, programming language, and project status.
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Scanpy
Single-cell analysis in Python
Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells. -
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SciSpaCy
A full spaCy pipeline and models for scientific/biomedical documents
ScispaCy is a spaCy extension optimized for processing biomedical and scientific text, providing domain-specific NLP models for tasks like named entity recognition (NER) and dependency parsing. -
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Seeker
Accurately Locate Smartphones using Social Engineering
Seeker is an open source project that demonstrates how to obtain precise location information from devices using social engineering and web-based techniques. The tool sets up a phishing page that asks for location permissions, allowing GPS and other device data to be shared if the user consents. It can capture latitude, longitude, accuracy, altitude, direction, and even speed, with results displayed in a terminal. The project supports both manual deployment and tunneling services like Ngrok for external access. While primarily intended as an educational resource on security awareness, it highlights the risks of exposing geolocation data online. Its simplicity and effectiveness have made it a popular project in cybersecurity learning circles. -
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Segments.ai
Segments.ai Python SDK
Multi-sensor labeling platform for robotics and autonomous vehicles. The platform for fast and accurate multi-sensor data annotation. Label in-house or with an external workforce. Intuitive labeling interfaces for images, videos, and 3D point clouds (lidar and RGBD). Obtain segmentation labels, vector labels, and more. Our labeling interfaces are set up to label fast and precise. Powerful ML assistance lets you label faster and reduce costs. Integrate data labeling into your existing ML pipelines and workflows using our simple yet powerful Python SDK. Onboard your own workforce or use one of our workforce partners. Our management tools make it easy to label and review large datasets together. Now, Segments.ai is providing a data labeling backbone to help robotics and AV companies build better datasets. -
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
The de facto standard open-source platform for rapidly deploying machine learning models on Kubernetes. Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment. Built on Kubernetes, runs on any cloud and on-premises. Framework agnostic, supports top ML libraries, toolkits and languages. Advanced deployments with experiments, ensembles and transformers. Our open-source framework makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes. -
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SeleniumBase
A framework for browser automation and testing with Selenium
SeleniumBase automatically handles common WebDriver actions such as launching web browsers before tests, saving screenshots during failures, and closing web browsers after tests. SeleniumBase lets you customize test runs from the command line. SeleniumBase uses simple syntax for commands. pytest includes automatic test discovery. If you don't specify a specific file or folder to run, pytest will automatically search through all subdirectories for tests to run. No More Flaky Tests! SeleniumBase methods automatically wait for page elements to finish loading before interacting with them (up to a timeout limit). This means you no longer need random time.sleep() statements in your scripts. SeleniumBase includes an automated/manual hybrid solution called MasterQA, which speeds up manual testing by having automation perform all the browser actions while the manual tester handles validation. -
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Semantix
Non-Pydantic, Non-JSON Schema, efficient AutoPrompting
Semantix empowers developers to infuse meaning into their code through enhanced variable typing (semantic typing). By leveraging the power of large language models (LLMs) behind the scenes, Semantix transforms ordinary functions into intelligent, context-aware operations without explicit LLM calls. -
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SetFit
Efficient few-shot learning with Sentence Transformers
SetFit is an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers. It achieves high accuracy with little labeled data - for instance, with only 8 labeled examples per class on the Customer Reviews sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples. -
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Shennina
Automating Host Exploitation with AI
Shennina is an automated host exploitation framework. The mission of the project is to fully automate the scanning, vulnerability scanning/analysis, and exploitation using Artificial Intelligence. Shennina is integrated with Metasploit and Nmap for performing the attacks, as well as being integrated with an in-house Command-and-Control Server for exfiltrating data from compromised machines automatically. Shennina scans a set of input targets for available network services, uses its AI engine to identify recommended exploits for the attacks, and then attempts to test and attack the targets. If the attack succeeds, Shennina proceeds with the post-exploitation phase. The AI engine is initially trained against live targets to learn reliable exploits against remote services. Shennina also supports a "Heuristics" mode for identfying recommended exploits. -
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Shiptest
The Shiptest Codebase
Shiptest is an open source fork of Space Station 13 that replaces the traditional single-station gameplay with multiple player-controlled ships. Instead of being confined to one station, players can design, operate, and explore with their own ships in a shared space environment. The repository contains full source code, assets, and maps to host or develop servers. Shiptest introduces new mechanics around ship construction, navigation, and resource management, creating a sandbox that emphasizes exploration and collaboration. Its modular design allows for diverse playstyles, from engineering and trade to combat and survival. The project is actively updated by its community, pushing SS13 gameplay in a fresh, experimental direction. -
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Solid Python
A comprehensive gradient-free optimization framework written in Python
Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them. -
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Spaceship Generator
A Blender script to procedurally generate 3D spaceships
A Blender script to procedurally generate 3D spaceships from a random seed. Install Blender 2.80 or greater. Download newest add_mesh_SpaceshipGenerator.zip from the Releases section. Under Edit, Preferences, Add-ons, Install, open the downloaded ZIP file. Under Edit, Preferences, Add-ons enable the "Add Mesh Spaceship Generator" script (search for "spaceship"). Add a spaceship in the 3D View under Add, Mesh, Spaceship. Expand the Spaceship tab that appears in the bottom left of the viewport to adjust procedural generation settings. Build the hull. Extrude the front/rear faces several times, adding random translation/scaling/rotation along the way. Add asymmetry to the hull: Pick random faces and extrude them out in a similar manner, reducing in scale each time. Categorize each face by its orientation and generate details on it such as engines, antenna, weapon turrets, lights etc. -
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Spilo
Highly available elephant herd: HA PostgreSQL cluster using Docker
Spilo is a Docker-based HA PostgreSQL cluster built on Patroni and heavily optimized for Kubernetes environments. It includes components for failover, streaming replication, backups, and connection pooling. Spilo is used in production by Zalando and is designed to provide a resilient, self-healing Postgres cluster with minimal manual intervention. -
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Stable Baselines3
PyTorch version of Stable Baselines
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details. -
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Stable Diffusion Version 2
High-Resolution Image Synthesis with Latent Diffusion Models
Stable Diffusion (the stablediffusion repo by Stability-AI) is an open-source implementation and reference codebase for high-resolution latent diffusion image models that power many text-to-image systems. The repository provides code for training and running Stable Diffusion-style models, instructions for installing dependencies (with notes about performance libraries like xformers), and guidance on hardware/driver requirements for efficient GPU inference and training. It’s organized as a practical, developer-focused toolkit: model code, scripts for inference, and examples for using memory-efficient attention and related optimizations are included so researchers and engineers can run or adapt the model for their own projects. The project sits within a larger ecosystem of Stability AI repositories (including inference-only reference implementations like SD3.5 and web UI projects) and the README points users toward compatible components, recommended CUDA/PyTorch versions. -
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Stable Diffusion WebUI Forge
Stable Diffusion WebUI Forge is a platform on top of Stable Diffusion
Stable Diffusion WebUI Forge is a performance- and feature-oriented fork of the popular AUTOMATIC1111 interface that experiments with new backends, memory optimizations, and UX improvements. It targets heavy users and researchers who push large models, control nets, and high-resolution pipelines where default settings can become bottlenecks. The fork typically introduces toggles for scheduler behavior, attention implementations, caching, and precision modes to reach better speed or quality on given hardware. It also focuses on stability during long sessions, aiming to reduce out-of-memory failures and provide clearer diagnostics when they occur. The UI surfaces advanced options in a way that remains recognizable to WebUI users, so migration costs are low while gaining experimental features. In practice, Forge serves as a proving ground for ideas that may later influence upstream tools, giving power users early access to cutting-edge techniques. -
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Starlette
The little ASGI framework that shines
Starlette is a lightweight ASGI framework/toolkit, which is ideal for building async web services in Python. It is production-ready and gives you a lightweight, low-complexity HTTP web framework. WebSocket support. In-process background tasks. Startup and shutdown events. Test client built on httpx. CORS, GZip, Static Files, streaming responses. Session and Cookie support. 100% test coverage. 100% type annotated codebase. Few hard dependencies. Compatible with asyncio and trio backends. -
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Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
Predicting stock prices can be a challenging task as it often does not follow any specific pattern. However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks, which are a type of recurrent neural network (RNN) capable of remembering information over a long period of time. This makes them extremely useful for predicting stock prices. Predicting stock prices is a complex task, as it is influenced by various factors such as market trends, political events, and economic indicators. The fluctuations in stock prices are driven by the forces of supply and demand, which can be unpredictable at times. To identify patterns and trends in stock prices, deep learning techniques can be used for machine learning. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that is specifically designed for sequence modeling and prediction. -
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Substra
Low-level Python library used to interact with a Substra network
An open-source framework supporting privacy-preserving, traceable federated learning and machine learning orchestration. Offers a Python SDK, high-level FL library (SubstraFL), and web UI to define datasets, models, tasks, and orchestrate secure, auditable collaborations. -
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Super Easy AI Installer Tool
Application that simplifies the installation of AI-related projects
"Super Easy AI Installer Tool" is a user-friendly application that simplifies the installation process of AI-related repositories for users. The tool is designed to provide an easy-to-use solution for accessing and installing AI repositories with minimal technical hassle to none the tool will automatically handle the installation process, making it easier for users to access and use AI tools. "Super Easy AI Installer Tool" is currently in early development phase and may have a few bugs. But remains a great solution for users with minimal technical knowledge or expertise. Fixes underway. A tool that can generate animations and music from text, ideal for producing short videos and GIFs, as well as creating brief cinematic scenes. -
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SuperDuperDB
Integrate, train and manage any AI models and APIs with your database
Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. Just using Python. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Train models on your data in your datastore simply by querying without additional ingestion and pre-processing. -
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Synapse
Matrix reference homeserver
Matrix is an ambitious new ecosystem for open federated Instant Messaging and VoIP. Everything in Matrix happens in a room. Rooms are distributed and do not exist on any single server. Rooms can be located using convenience aliases like #matrix:matrix.org or #test:localhost:8448. Synapse is currently in rapid development, but as of version 0.5 we believe it is sufficiently stable to be run as an internet-facing service for real usage! Create and manage fully distributed chat rooms with no single points of control or failure. Eventually-consistent cryptographically secure synchronization of room state across a global open network of federated servers and services. Send and receive extensible messages in a room with (optional) end-to-end encryption. Use 3rd Party IDs (3PIDs) such as email addresses, phone numbers, Facebook accounts to authenticate, identify and discover users on Matrix. -
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System Design Primer
Learn how to design large-scale systems
System Design Primer is a curated, open source collection of resources that helps engineers learn how to design large-scale systems. The project is structured as a comprehensive guide covering core system design concepts, trade-offs, and patterns necessary for building scalable, reliable, and maintainable systems. It offers both theoretical foundations—such as scalability principles, the CAP theorem, and consistency models—and practical exercises, including real-world system design interview questions with sample solutions, diagrams, and code. The repository also contains study guides for short, medium, and long interview timelines, allowing learners to focus on both breadth and depth depending on their preparation needs. In addition, it includes flashcard decks designed to reinforce learning through spaced repetition, making it easier to retain key system design knowledge. -
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TRFL
TensorFlow Reinforcement Learning
TRFL, developed by Google DeepMind, is a TensorFlow-based library that provides a collection of essential building blocks for reinforcement learning (RL) algorithms. Pronounced “truffle,” it simplifies the implementation of RL agents by offering reusable components such as loss functions, value estimation tools, and temporal difference (TD) learning operators. The library is designed to integrate seamlessly with TensorFlow, allowing users to define differentiable RL objectives and train models using standard optimization routines. TRFL supports both CPU and GPU TensorFlow environments, though TensorFlow itself must be installed separately. It exposes clean, modular APIs for various RL methods including Q-learning, policy gradient, and actor-critic algorithms, among others. Each function returns not only the computed loss tensor but also a detailed structure containing auxiliary information like TD errors and targets. -
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples.