Open Source Python Software - Page 79
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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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Ricks-Lab GPU Utilities
A set of utilities for monitoring and customizing GPU performance
A set of utilities for monitoring GPU performance and modifying control settings. In order to get the maximum capability of these utilities, you should be running with a kernel that provides support for the GPUs you have installed. If using AMD GPUs, installing the latest AMD GPU driver or ROCm package may provide additional capabilities. If you have Nvidia GPUs installed, you should have Nvidia-smi installed in order for the utility reading of the cards to be possible. Writing to GPUs is currently only possible for compatible AMD GPUs on systems with appropriate kernel versions with the AMD ppfeaturemask set to enable this capability. -
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Roxy-WI
Web interface for managing Haproxy, Nginx, Apache and Keepalived
For those who need a convenient interface for managing all services in one place. Roxy-WI was created for people who want to have a fault-tolerant infrastructure, but do not want to plunge deep into the details of setting up and creating a cluster based on HAProxy, NGINX, Apache, and Keepalived. Use Roxy-WI to build a high available cluster for a couple of clicks: install HAProxy, NGINX, Apache, Keepalived, and its exporters, and carry out the initial configuration for the services. Collect download statistics in one place. Choose one of the three available monitoring options or use them all. If there are any problems, you will be informed immediately. -
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SENAITE LIMS
SENAITE Meta Package
SENAITE is a beautiful trigonal, oil-green to greenish-black crystal, with almost the hardness of a diamond. Although the crystal is described with a complex formula, it still has clear and straight shapes. Therefore, it reflects nicely the complexity of the LIMS, while providing a modern, intuitive, and friendly UI/ UX. Amongst other functionalities, SENAITE comes with highly-customizable workflows to drive users through the analytical process, easy-to-use UI for data registration, automatic import of results, data validation, and transition constraints. SENAITE can be easily integrated with instruments by using off-the-shell interfaces for data import and export. Custom interfacing is supported too. Import instrument results and avoid human errors in the carrying-over process. Reduce the turnaround time on results report delivery. Assign priorities to samples and due dates for tests, plan and assign the daily work by using worksheets, and keep track of delayed tests immediately. -
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SSD
A PyTorch Implementation of Single Shot MultiBox Detector
SSD is a PyTorch implementation of the Single Shot MultiBox Detector, a well-known object detection architecture introduced in the original SSD paper. It is built to help users train, evaluate, and experiment with object detection models using PyTorch rather than the original Caffe implementation. The repository includes the major components needed for an object detection workflow, including training scripts, evaluation scripts, demos, and utility modules. It supports commonly used benchmark datasets such as PASCAL VOC and MS COCO, and it also provides scripts to simplify downloading and setting up those datasets. For training visibility, the project includes support for Visdom so users can monitor loss in real time through a browser-based interface. Its structure makes it useful both as a reference implementation for learning SSD and as a base for custom experimentation in detection research or practical computer vision projects. -
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SageAttention
NeurIPS2025 Spotlight] Quantized Attention
SageAttention is an open-source optimization library designed to accelerate the attention mechanism used in transformer-based neural networks. Since attention operations are often the most computationally expensive component of modern AI models, SageAttention introduces quantization techniques that significantly reduce computational overhead while preserving model accuracy. The system achieves this by using low-precision numerical formats such as INT4, FP8, or INT8 to represent key matrices within the attention computation. These optimizations allow models to perform matrix operations faster and consume less memory during inference. SageAttention is designed to function as a plug-and-play replacement for standard attention implementations, enabling developers to accelerate existing models without modifying their architecture. -
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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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Scientific Visualization
An open access book on scientific visualization using python
The Scientific Visualization book is a freely available open-access textbook that introduces how to produce effective scientific visualizations using Python, focusing especially on leveraging the popular plotting library Matplotlib (and related tools). It goes beyond simple plotting tutorials and emphasizes design principles: how to choose colors, layout subplots, annotate graphs, and present data in a way that is both accurate and visually compelling. As such, it serves as a guide for researchers, data scientists, and academic authors who need to create publication-quality figures or explanatory graphics, rather than quick exploratory plots. It includes extensive examples that demonstrate best practices — for instance handling multiple subplots, combining line plots with scatter/density overlays, or rendering high-resolution vector graphics for print. -
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Scout Suite
Multi-cloud security auditing tool
Scout Suite is an open-source multi-cloud security-auditing tool, which enables security posture assessment of cloud environments. Using the APIs exposed by cloud providers, Scout Suite gathers configuration data for manual inspection and highlights risk areas. Rather than going through dozens of pages on the web consoles, Scout Suite presents a clear view of the attack surface automatically. Scout Suite was designed by security consultants/auditors. It is meant to provide a point-in-time security-oriented view of the cloud account it was run in. Once the data has been gathered, all users may be performed offline. Our self-service cloud account monitoring platform, NCC Scout, is a user-friendly SaaS providing you with the ability to constantly monitor your public cloud accounts, allowing you to check they’re configured to comply with industry best practice. -
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SeedVR
Repo for SeedVR2 & SeedVR
SeedVR (from the ByteDance-Seed organization) is an open-source research and implementation repository focused on cutting-edge video restoration using diffusion transformer architectures. The project includes both the original SeedVR and its successor SeedVR2 models, which are designed to restore degraded or low-quality video content by learning to reconstruct high-fidelity frames with temporal coherence. These models leverage advanced techniques such as adaptive attention mechanisms and adversarial training to produce visually appealing results in a single inference step, pushing the boundaries of video restoration research. SeedVR’s transformer-based design allows it to handle variable frame resolutions and lengths, and its architecture is optimized to overcome traditional limitations of windowed attention in high-resolution contexts. -
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
Shumai is an experimental differentiable tensor library for TypeScript and JavaScript, developed by Facebook Research. It provides a high-performance framework for numerical computing and machine learning within modern JavaScript runtimes. Built on Bun and Flashlight, with ArrayFire as its numerical backend, Shumai brings GPU-accelerated tensor operations, automatic differentiation, and scientific computing tools directly to JavaScript developers. It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. The library supports matrix operations, gradient computation, and tensor conversions with intuitive APIs and near-native speed, thanks to Bun’s low-overhead FFI bindings. It can automatically leverage GPU acceleration on Linux (via CUDA) and CPU computation on macOS. -
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Sippts
Set of tools to audit SIP based VoIP Systems
Sippts is a set of tools to audit VoIP servers and devices using SIP protocol. Sippts is programmed in Python and it allows us to check the security of a VoIP server using SIP protocol. You can freely use, modify and distribute. If modified, please put a reference to this site. Most security tools can be used for illegal purposes, but the purpose of this tool is to check the security of your own servers and not to use to do bad things. I am not responsible for the misuse of this tool. Sippts is a set of tools to audit VoIP servers and devices using SIP protocol. Sipscan is a fast scanner for SIP services that uses multithread. Sipscan can check several IPs and port ranges and it can work over UDP or TCP. Sipexten identifies extensions on a SIP server. Also tells you if the extension line requires authentication or not. Sipexten can check several IPs and port ranges. -
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Slither
Static Analyzer for Solidity
Slither is a Solidity static analysis framework written in Python 3. It runs a suite of vulnerability detectors, prints visual information about contract details, and provides an API to easily write custom analyses. Slither enables developers to find vulnerabilities, enhance their code comprehension, and quickly prototype custom analyses. Slither is the first open-source static analysis framework for Solidity. Slither is fast and precise; it can find real vulnerabilities in a few seconds without user intervention. It is highly customizable and provides a set of APIs to inspect and analyze Solidity code easily. We use it in all of our security reviews. Now you can integrate it into your code-review process. We are open sourcing the core analysis engine of Slither. This core provides advanced static-analysis features, including an intermediate representation (SlithIR) with taint tracking capabilities on top of which complex analyses (“detectors”) can be built. -
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Splunk Attack Range
A tool that allows you to create vulnerable environments
The Splunk Attack Range is an open-source project maintained by the Splunk Threat Research Team. It builds instrumented cloud (AWS, Azure) and local environments (Virtualbox), simulates attacks, and forwards the data into a Splunk instance. This environment can then be used to develop and test the effectiveness of detections. -
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Stable Diffusion Rembg
Removes backgrounds from pictures. Extension for webui
This project is an extension for the Stable Diffusion Web UI that removes backgrounds from images directly inside the interface. It wraps popular background-removal models so creators can take a generated or uploaded image and isolate the subject with a single click. The workflow is designed to be non-destructive: you can preview, tweak thresholds, and export either a transparent PNG or a masked layer for further editing. Because it runs within the Web UI, you can chain it with other operations such as upscaling, inpainting, or ControlNet to refine edges and composites. Batch processing helps clear backgrounds from whole sets of renders, which is useful for asset pipelines, catalogs, and thumbnails. The extension aims for convenience and predictable results, sparing users from round-tripping through separate editors just to knock out a background. -
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StableSwarmUI
Multi-user UI for managing and running Stable Diffusion workflows tool
StableSwarmUI is a web-based interface designed to manage and coordinate Stable Diffusion image generation workflows in a multi-user environment. It focuses on enabling multiple users to interact with shared resources, making it suitable for collaborative or server-based deployments. It provides a centralized system where users can submit, monitor, and manage generation tasks through a browser interface. It abstracts much of the complexity involved in running diffusion models by offering a structured environment for handling prompts, outputs, and processing queues. StableSwarmUI is built to work alongside backend systems that execute the actual image generation, allowing separation between user interaction and compute workloads. It also emphasizes scalability, making it useful for setups where multiple jobs need to be processed efficiently. Overall, it serves as a coordination layer for Stable Diffusion usage rather than a standalone model implementation. -
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Step-Audio
Open-source framework for intelligent speech interaction
Step-Audio is a unified, open-source framework aimed at building intelligent speech systems that combine both comprehension and generation: it integrates large language models (LLMs) with speech input/output to handle not only semantic understanding but also rich vocal characteristics like tone, style, dialect, emotion, and prosody. The design moves beyond traditional separate-component pipelines (ASR → text model → TTS), instead offering a multimodal model that ingests speech or audio and produces speech accordingly, enabling natural dialogue, voice cloning, and expressive speech synthesis. Through its architecture, Step-Audio supports multilingual interaction, dialects, emotional tones (joy, sadness, etc.), and even more creative speech styles (like rap or singing), while allowing dynamic control over speech characteristics. It also provides a “generative data engine,” which can produce synthetic speech data (cloning voices, varying style) to support TTS training. -
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Sumo Easy Miner
The most easy, intuitive CPU miner for cryptonote-based cryptocurrency
A GUI-based Monero mining software designed for ease of use with SumoCoins. -
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TNT
A lightweight library for PyTorch training tools and utilities
TNT is a lightweight training framework developed by Meta that simplifies the process of building and managing machine learning training loops using PyTorch. The project focuses on providing a flexible yet structured environment for implementing training pipelines without the complexity of large deep learning frameworks. It introduces modular abstractions that allow developers to organize training logic into reusable components such as trainers, evaluators, and callbacks. This design helps separate concerns such as model training, evaluation, logging, and checkpointing, making machine learning experiments easier to manage. The framework is particularly useful for large-scale experiments where maintaining clear training workflows becomes increasingly important. Because it is built on top of PyTorch, the framework integrates naturally with existing deep learning models and datasets. -
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TensorFlow Machine Learning Cookbook
Code for Tensorflow Machine Learning Cookbook
TensorFlow Machine Learning Cookbook repository provides practical code examples and educational materials that accompany the book TensorFlow Machine Learning Cookbook. The repository contains numerous Python scripts and Jupyter notebooks that demonstrate how to implement machine learning algorithms and neural networks using the TensorFlow framework. Each section focuses on a different aspect of machine learning development, including tensor manipulation, model training, optimization strategies, and data processing techniques. The examples illustrate how TensorFlow operations and tensors can be used to build machine learning pipelines and perform tasks such as regression, classification, and clustering. By combining theoretical explanations with executable code, the project helps developers understand how TensorFlow algorithms operate internally while also providing working examples that can be adapted for real projects. -
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TensorFlow Quantum
Open-source Python framework for hybrid quantum-classical ml learning
TensorFlow Quantum is an open-source software framework designed for building and training hybrid quantum-classical machine learning models within the TensorFlow ecosystem. The framework enables researchers and developers to represent quantum circuits as data and integrate them directly into machine learning workflows. By combining classical deep learning techniques with quantum algorithms, the platform allows experimentation with quantum machine learning methods that may offer advantages for certain computational tasks. TensorFlow Quantum integrates with the Cirq quantum computing framework to define and manipulate quantum circuits, while leveraging TensorFlow’s infrastructure for optimization, automatic differentiation, and large-scale computation. The library also supports high-performance simulation of quantum circuits, enabling researchers to test and evaluate quantum models even without direct access to quantum hardware. -
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Termux APT Repo
Script to create Termux apt repositories
termux-apt-repo is a script designed to create APT repositories for Termux, allowing users to publish and distribute their own packages. It supports cross-compiled packages created using the Termux build setup or on-device packages created with termux-create-package. This tool facilitates the sharing and installation of custom packages within the Termux environment. -
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TerraGov Marine Corps
TGMC: TerraGov Marine Corps, a SS13 mod
TerraGov Marine Corps (TGMC) is an open source multiplayer game built on the BYOND engine, forked from the Space Station 13 (SS13) codebase. It is a tactical, role-playing game that pits groups of human marines against alien forces in large-scale, cooperative and competitive scenarios. The project focuses heavily on teamwork, coordination, and immersive gameplay, providing players with different roles such as engineers, medics, or combat marines to ensure strategic variety. TGMC offers a persistent, evolving experience where community contributions shape mechanics, balance, and lore. The codebase serves as both a live game server foundation and a development platform for contributors who want to expand gameplay features, design new mechanics, or refine existing systems. With its mixture of roleplay, tactical combat, and science fiction setting, TGMC provides a distinctive twist on the SS13 lineage. -
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Text Generation Inference
Large Language Model Text Generation Inference
Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability. -
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Text-to-LoRA (T2L)
Hypernetworks that adapt LLMs for specific benchmark tasks
Text-to-LoRA is a research project that introduces a method for dynamically adapting large language models using hypernetworks that generate LoRA parameters directly from textual descriptions. Instead of training a new LoRA adapter for every task or dataset, the system can produce task-specific adaptations based solely on a text description of the desired capability. This approach enables models to rapidly internalize new contextual knowledge without performing traditional fine-tuning steps. The project provides a reference implementation of the Doc-to-LoRA method, which allows language models to quickly encode factual information or contextual constraints into lightweight LoRA modules. Developers and researchers can experiment with how textual task descriptions can generate LoRA weights that modify model behavior in real time. -
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TextBox
A text generation library with pre-trained language models github.com
TextBox 2.0 is an up-to-date text generation library based on Python and PyTorch focusing on building a unified and standardized pipeline for applying pre-trained language models to text generation. From a task perspective, we consider 13 common text generation tasks such as translation, story generation, and style transfer, and their corresponding 83 widely-used datasets. From a model perspective, we incorporate 47 pre-trained language models/modules covering the categories of general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight models (modules). From a training perspective, we support 4 pre-training objectives and 4 efficient and robust training strategies, such as distributed data parallel and efficient generation. Compared with the previous version of TextBox, this extension mainly focuses on building a unified, flexible, and standardized framework for better supporting PLM-based text generation models.
