Open Source Python Software - Page 73
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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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DAO
The Standard DAO Framework, including Whitepaper
An open‑source Solidity-based framework (“Standard DAO Framework”) to help deploy decentralized autonomous organizations (DAOs) on Ethereum by encoding governance and decision-making in smart contract code. -
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DC-TTS
TensorFlow Implementation of DC-TTS: yet another text-to-speech model
DC-TTS is a TensorFlow implementation of the DC-TTS architecture, a fully convolutional text-to-speech system designed to be efficiently trainable while producing natural speech. It follows the “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention” paper, but the author adapts and extends the design to make it practical for real experiments. The model is split into two networks: Text2Mel, which maps text to mel-spectrograms, and SSRN (spectrogram super-resolution network), which converts low-resolution mel-spectrograms into high-resolution magnitude spectrograms suitable for waveform synthesis. Training scripts, data loaders, and hyperparameter configurations are provided to reproduce results on several datasets, including LJ Speech for English, a Korean single-speaker dataset, and audiobook data from Nick Offerman and Kate Winslet. -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
DINOv2 is a self-supervised vision learning framework that produces strong, general-purpose image representations without using human labels. It builds on the DINO idea of student–teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints. -
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Data science blogs
A curated list of data science blogs
Data Science Blogs is a curated repository that aggregates a wide range of high-quality blogs and resources related to data science, machine learning, and analytics into a single organized collection. It serves as a discovery platform for practitioners, researchers, and learners who want to stay updated with industry trends, techniques, and insights without manually searching for reliable sources. The repository includes links to personal blogs, professional publications, and educational resources, often accompanied by RSS feeds for easy subscription and content tracking. By organizing these resources in a centralized and structured format, it reduces the friction associated with finding relevant and trustworthy information in a rapidly evolving field. The project is community-driven, allowing contributors to expand and maintain the list as new blogs emerge and existing ones evolve. -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and optionally the number of initial training steps. We can also feed in an image as an optimization goal, instead of only priming the generator network. Deepdaze will then render its own interpretation of that image. The regular mode for texts only allows 77 tokens. If you want to visualize a full story/paragraph/song/poem, set create_story to True. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
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DeepLearning Tutorial
Deep Learning Tutorial, Excellent Articles, Deep Learning Tutorial
DeepLearning is an open-source repository that aggregates tutorials, articles, and educational resources related to deep learning and machine learning. The project is designed as a knowledge collection that helps beginners understand neural networks, deep learning architectures, and fundamental machine learning concepts. It contains curated learning materials covering topics such as feedforward neural networks, activation functions, backpropagation algorithms, optimization methods, and convolutional neural networks. The repository organizes these materials into structured tutorials and references that allow readers to explore deep learning concepts progressively. Many of the resources include explanations of common model architectures used in computer vision and artificial intelligence. The project also collects recommended articles and educational documents that expand on deep learning theory and practical implementation strategies. -
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DeepMozart
Audio generation using diffusion models
Audio generation using diffusion models in PyTorch. The code is based on the audio-diffusion-pytorch repository. -
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DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of arts, democratizing multi-billion-parameter model training such that many deep learning scientists can explore bigger and better models. Sparse attention of DeepSpeed powers an order-of-magnitude longer input sequence and obtains up to 6x faster execution comparing with dense transformers. -
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DeepSeek VL2
Mixture-of-Experts Vision-Language Models for Advanced Multimodal
DeepSeek-VL2 is DeepSeek’s vision + language multimodal model—essentially the next-gen successor to their first vision-language models. It combines image and text inputs into a unified embedding / reasoning space so that you can query with text and image jointly (e.g. “What’s going on in this scene?” or “Generate a caption appropriate to context”). The model supports both image understanding (vision tasks) and multimodal reasoning, and is likely used as a component in agent systems to process visual inputs as context for downstream tasks. The repository includes evaluation results (e.g. image/text alignment scores, common VL benchmarks), configuration files, and model weights (where permitted). While the internal architecture details are not fully documented publicly, the repo suggests that VL2 introduces enhancements over prior vision-language models (e.g. better scaling, cross-modal attention, more robust alignment) to improve grounding and multimodal understanding. -
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Determined
Determined, deep learning training platform
The fastest and easiest way to build deep learning models. Distributed training without changing your model code. Determined takes care of provisioning machines, networking, data loading, and fault tolerance. Build more accurate models faster with scalable hyperparameter search, seamlessly orchestrated by Determined. Use state-of-the-art algorithms and explore results with our hyperparameter search visualizations. Interpret your experiment results using the Determined UI and TensorBoard, and reproduce experiments with artifact tracking. Deploy your model using Determined's built-in model registry. Easily share on-premise or cloud GPUs with your team. Determined’s cluster scheduling offers first-class support for deep learning and seamless spot instance support. Check out examples of how you can use Determined to train popular deep learning models at scale. -
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Diffusion for World Modeling
Learning agent trained in a diffusion world model
Diffusion for World Modeling is an experimental reinforcement learning system that trains intelligent agents inside a simulated environment generated by a diffusion-based world model. The project introduces the idea of using diffusion models, commonly used for image generation, to simulate the dynamics of an environment and predict future states based on previous observations and actions. Instead of interacting directly with a real environment, the reinforcement learning agent learns within a generative model that produces frames representing the environment. This approach allows training to occur in a simulated world that captures detailed visual dynamics while reducing the need for costly interactions with real environments. The system has been applied to tasks such as Atari game simulations and demonstrations involving complex environments like first-person shooter games. -
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DocsGPT
Private AI agents, assistants and search over your own documents
DocsGPT is an open-source (MIT) platform for private AI agents, assistants and enterprise search over your own documents, built by Arc53. It ingests PDFs, Office files, scanned pages (OCR), web pages and repositories, syncs Google Drive, SharePoint, Confluence, GitHub and S3, and answers with citations to the source passage. Agents call tools, APIs and MCP servers with per-action approval; a visual builder chains AI steps, conditions and code into workflows; agents can produce DOCX, XLSX, PPTX and PDF files. Models: OpenAI, Anthropic, Google and others, or local via Ollama, llama.cpp or vLLM. Vectors: pgvector, Qdrant, Milvus, Elasticsearch, MongoDB Atlas or FAISS. Install with one line (curl -fsSL https://docs.ac/install | bash), or deploy with Docker Compose or Kubernetes, including fully air-gapped. Ships an OpenAI-compatible API, an MCP server, a chat widget, OIDC SSO, SCIM, guardrails and an audit log. -
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Dr0p1t-Framework
A framework that create an advanced stealthy dropper
Dr0p1t-Framework is a penetration testing tool designed to generate advanced and stealthy droppers capable of delivering and executing payloads on target systems while evading detection mechanisms. A dropper is a type of malware used to download and install additional malicious software, and this framework focuses on making that process more flexible and difficult to detect. It provides a wide range of modules that allow users to customize payload delivery, persistence mechanisms, and execution methods. The framework includes features such as antivirus evasion, privilege escalation, and system persistence, enabling it to maintain access on compromised systems. It also incorporates techniques to avoid forensic analysis, such as self-deletion and cleaning traces after execution. The generated executables are optimized to be small and efficient, improving their ability to bypass security controls. -
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DreamerV3
Mastering Diverse Domains through World Models
DreamerV3 is an open-source implementation of a reinforcement learning algorithm that uses world models to train intelligent agents capable of learning complex behaviors across many environments. The system works by building an internal model of the environment and then using that model to simulate possible future outcomes of actions, allowing the agent to learn from imagined experiences rather than only from real interactions. This approach enables the algorithm to efficiently learn policies for decision-making tasks that would otherwise require enormous amounts of data or computational resources. DreamerV3 was designed as a general reinforcement learning framework that can solve diverse tasks using the same configuration of hyperparameters across many environments. In research demonstrations, the algorithm has been shown to perform strongly across more than one hundred control tasks and complex simulated environments. -
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Dynamic Routing Between Capsules
A PyTorch implementation of the NIPS 2017 paper
Dynamic Routing Between Capsules is a PyTorch implementation of the Capsule Network architecture originally proposed to address limitations in traditional convolutional neural networks. Capsule networks aim to improve how neural models represent spatial hierarchies and relationships between objects within images. Instead of scalar neuron activations, capsules output vectors that encode both the presence of features and their spatial properties such as orientation or pose. The repository implements the dynamic routing algorithm between capsules, which allows lower-level features to route their outputs to higher-level structures that best represent the detected patterns. This approach enables the model to capture part-to-whole relationships in visual data more effectively than standard CNNs. The project serves primarily as a research implementation that demonstrates how capsule networks can be built and trained using modern deep learning frameworks. -
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E2B Cookbook
Examples of using E2B
E2B Cookbook is an open-source collection of example projects, guides, and reference implementations demonstrating how to build applications using the E2B platform. The repository acts as a practical learning resource for developers who want to integrate AI agents with secure cloud execution environments that allow large language models to run code and interact with tools. The examples illustrate how developers can build AI workflows capable of performing tasks such as data analysis, code execution, and application generation inside isolated sandbox environments. E2B itself provides secure Linux-based sandboxes that enable AI systems to safely run generated code and interact with real computing resources without compromising the host environment. The cookbook organizes examples across multiple frameworks and model providers, allowing developers to experiment with integrations involving models from OpenAI, Anthropic, and other ecosystems. -
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E2M
E2M converts various file types (doc, docx, epub, html, htm, url
E2M is a SourceForge mirror of the e2m open-source project, which focuses on providing tools or services designed to convert or process content between different formats or systems. Projects with similar naming conventions typically emphasize automation workflows where input data from one environment is transformed into another representation or output structure. The mirrored repository allows users to access the project’s codebase independently from its original hosting platform while preserving the development history and release artifacts. Systems like e2m often serve as middleware components that connect different software systems or facilitate data processing pipelines. By acting as a transformation layer, the software can support workflows such as converting data formats, integrating services, or bridging incompatible systems. The mirror hosted on SourceForge ensures that developers can continue accessing the project even if the primary repository becomes unavailable. -
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ERAlchemy
Entity Relation Diagrams generation tool
ERAlchemy is a tool that generates Entity-Relationship (ER) diagrams from databases or SQLAlchemy models and vice versa. It’s useful for database documentation, reverse engineering, and understanding complex schemas. ERAlchemy can export diagrams in formats like Graphviz and Mermaid, making it easy to include in reports or markdown files. -
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EasyAuth
Create a centralized Authentication and Authorization token server
Create a centralized Authentication and Authorization token server. Easily secure FastAPI endpoints based on Users, Groups, Roles or Permissions with very little database usage. Centralized Auth - Single location for Users & Permissions to share across apps. Granular Endpoint Security - Verify user identity, and define explicitly who and what each user or group of users may access. Admin GUI - easy management of users, permissions, tokens, OAuth, and more! Advanced JWT - Token Based Client authorization with built-in invalidation capabilities. Google Oauth - Easy to configure google login. Integrated Login & Cookie Management - Users are not just authenticated and authorized, they are re-directed on token expiration to login pages via the cookie system and sent back to the last location afterward. -
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EasyRL
Reinforcement learning (RL) tutorial series
easy-rl is a beginner-friendly reinforcement learning (RL) tutorial series and framework developed by Datawhale China. It provides educational resources and implementations of various RL algorithms to help new researchers and practitioners learn RL concepts. -
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EduCDM
The Model Zoo of cognitive diagnosis models
The Model Zoo of Cognitive Diagnosis Models, including classic Item Response Ranking (IRT), Multidimensional Item Response Ranking (MIRT), Deterministic Input, Noisy "And" model(DINA), and advanced Fuzzy Cognitive Diagnosis Framework (FuzzyCDF), Neural Cognitive Diagnosis Model (NCDM), Item Response Ranking framework (IRR), Incremental Cognitive Diagnosis (ICD) and Knowledge-association baesd extension of NeuralCD (KaNCD). Cognitive diagnosis model (CDM) for intelligent educational systems is a type of model that infers students' knowledge states from their learning behaviors (especially exercise response logs). Typically, the input of a CDM could be the students' response logs of items (i.e., exercises/questions), the Q-matrix that denotes the correlation between items and knowledge concepts (skills). The output is the diagnosed student knowledge states, such as students' abilities and students' proficiencies on each knowledge concepts. -
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Eigent
The Open Source Cowork Desktop to Unlock Your Exceptional Productivity
Eigent is an open-source cowork desktop application designed to help you build, manage, and deploy a custom AI workforce. It enables multiple specialized AI agents to collaborate in parallel, turning complex workflows into automated, end-to-end tasks. Built on the CAMEL-AI multi-agent framework, Eigent emphasizes productivity, flexibility, and transparent system design. You can run Eigent fully locally for maximum privacy and data control, or choose a cloud-connected experience for quick access. The platform supports a wide range of AI models and integrates powerful tools through the Model Context Protocol (MCP). With human-in-the-loop controls and enterprise-ready features, Eigent balances automation with oversight and security. -
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Example Streamlit
Example Streamlit app that you can fork to test out share.streamlit.io
streamlit-example is an open source sample app created by the Streamlit team to demonstrate how to quickly build and deploy applications with Streamlit. The repository contains a minimal Python app (streamlit_app.py) that can be customized by editing the source file. It is designed for use with share.streamlit.io, allowing developers to fork the repo and instantly deploy their own interactive app. The project includes basic dependencies defined in requirements.txt and supports containerized development via .devcontainer. As a teaching and testing resource, it provides a foundation for experimenting with Streamlit’s rapid prototyping features. -
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Excel MCP Server
A Model Context Protocol server for Excel file manipulation
The Excel MCP Server is a Python-based implementation of the Model Context Protocol that provides Excel file manipulation capabilities without requiring Microsoft Excel installation. It enables workbook creation, data manipulation, formatting, and advanced Excel features.
