Search Results for "source code to flowchart" - Page 5
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Showing 2317 open source projects for "source code to flowchart"
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The Hundred-Page Machine Learning Book
The Python code to reproduce illustrations from Machine Learning Book
The Hundred-Page Machine Learning Book is the official companion repository for The Hundred-Page Machine Learning Book written by machine learning researcher Andriy Burkov. The repository contains Python code used to generate the figures, visualizations, and illustrative examples presented in the book. Its purpose is to help readers better understand the concepts explained in the text by allowing them to run and experiment with the underlying code themselves. The book itself provides a... -
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GLM-4.7
Advanced language and coding AI model
GLM-4.7 is an advanced agent-oriented large language model designed as a high-performance coding and reasoning partner. It delivers significant gains over GLM-4.6 in multilingual agentic coding, terminal-based workflows, and real-world developer benchmarks such as SWE-bench and Terminal Bench 2.0. The model introduces stronger “thinking before acting” behavior, improving stability and accuracy in complex agent frameworks like Claude Code, Cline, and Roo Code. GLM-4.7 also advances “vibe... -
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Autograd
Efficiently computes derivatives of numpy code
Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily.... -
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CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports... -
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LLMStack
No-code multi-agent framework to build LLM Agents, workflows
LLMStack is a no-code platform for building generative AI agents, workflows and chatbots, connecting them to your data and business processes. Build tailor-made generative AI agents, applications and chatbots that cater to your unique needs by chaining multiple LLMs. Seamlessly integrate your own data, internal tools and GPT-powered models without any coding experience using LLMStack's no-code builder. Trigger your AI chains from Slack or Discord. Deploy to the cloud or on-premise. -
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Pyright
Static type checker for Python
...Type inference for function return values, instance variables, class variables, and globals. Type guards that understand conditional code flow constructs like if/else statements. Type hinting generics in standard collections. Pyright ships as both a command-line tool and a VS Code extension that provides many powerful features that help improve programming efficiency. The VS Code extension supports many time-saving language features. -
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NVIDIA Warp
A Python framework for accelerated simulation, data generation
NVIDIA Warp is a high-performance Python framework developed by NVIDIA for building and accelerating simulation, graphics, and physics-based workloads using GPU computing. It enables developers to write kernel-level code in Python that is automatically compiled into efficient CUDA kernels, combining ease of use with near-native performance. The framework is designed for applications such as robotics, reinforcement learning, physical simulation, and differentiable computing, where performance... -
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Hunyuan3D-2.1
From Images to High-Fidelity 3D Assets
Hunyuan3D-2.1 is Tencent Hunyuan’s advanced 3D asset generation system that produces high-fidelity 3D models with Physically Based Rendering (PBR) textures. It is fully open-source with released model weights, training, and inference code. It improves on prior versions by using a PBR texture pipeline (enabling realistic material effects like reflections and subsurface scattering) and allowing community fine-tuning and extension. It supports both shape generation (mesh geometry) and texture generation modules. ... -
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PyGAD
Source code of PyGAD, Python 3 library for building genetic algorithms
PyGAD is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine learning algorithms. It supports Keras and PyTorch. PyGAD supports optimizing both single-objective and multi-objective problems. PyGAD supports different types of crossover, mutation, and parent selection. PyGAD allows different types of problems to be optimized using the genetic algorithm by customizing the fitness function. -
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Checkov
Prevent cloud misconfigurations during build-time for Terraform
Checkov scans cloud infrastructure configurations to find misconfigurations before they're deployed. Checkov uses a common command-line interface to manage and analyze infrastructure as code (IaC) scan results across platforms such as Terraform, CloudFormation, Kubernetes, Helm, ARM Templates and Serverless framework. Verify changes to hundreds of supported resource types in all major cloud providers. Checkov supports developers using Terraform, Terraform plan, CloudFormation, Kubernetes,... -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. -
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NetworkX
Network analysis in Python
...Generators for classic graphs, random graphs, and synthetic networks. Nodes can be "anything" (e.g., text, images, XML records). Edges can hold arbitrary data (e.g., weights, time-series). Open source 3-clause BSD license. Well tested with over 90% code coverage. Additional benefits from Python include fast prototyping, easy to teach, and multi-platform. Find the shortest path between two nodes in an undirected graph. Python’s None object is not allowed to be used as a node. It determines whether optional function arguments have been assigned in many functions. ... -
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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. ... -
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Label Sleuth
Open source no-code system for text annotation and building of text
An open-source no-code system for text annotation and building text classifiers. No AI knowledge needed. From task definition to working model in just a few hours! While domain experts label their data, Label Sleuth automatically trains in the background-appropriate machine learning models. To avoid wasted labeling effort, Label Sleuth employs active learning techniques to guide the user in what they should be labeled next. -
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Nexent
Zero-code platform for building AI agents from natural language input
Nexent is an open source platform designed to enable users to create intelligent agents using natural language instead of traditional programming or visual orchestration tools. It focuses on a zero-code approach, allowing users to define workflows and agent behavior purely through language prompts, significantly lowering the barrier to entry for AI development. -
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srsly
Modern high-performance serialization utilities for Python
This package bundles some of the best Python serialization libraries into one standalone package, with a high-level API that makes it easy to write code that's correct across platforms and Pythons. This allows us to provide all the serialization utilities we need in a single binary wheel. Currently supports JSON, JSONL, MessagePack, Pickle, and YAML. Serialization is hard, especially across Python versions and multiple platforms. After dealing with many subtle bugs over the years (encodings,... -
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Sentry
Cross-platform application monitoring and error tracking software
Sentry is a cross-platform, self-hosted error monitoring solution that helps software teams discover, monitor and fix errors in real-time. The most users and logs will have to provide are the clues, and Sentry provides the answers. Sentry offers enhanced application performance monitoring through information-laden stack traces. It lets you build better software faster and more efficiently by showing you all issues in one place and providing the trail of events that lead to errors. It also... -
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UltraRAG
Less Code, Lower Barrier, Faster Deployment
UltraRAG 2.0 is a low-code, MCP-enabled RAG framework that aims to lower the barrier to building complex retrieval pipelines for research and production. It provides end-to-end recipes—from encoding and indexing corpora to deploying retrievers and LLMs—so users can reproduce baselines and iterate rapidly. The toolkit comes with built-in support for popular RAG datasets, large corpora, and canonical baselines, plus documentation that walks from “quick start” to debugging and case analysis. It... -
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OpenSandbox
OpenSandbox is a general-purpose sandbox platform for AI applications
OpenSandbox is a general purpose sandbox platform designed to securely run and isolate AI applications and untrusted workloads in controlled environments. The project focuses on providing a unified sandbox API that simplifies the process of executing code safely across different runtime backends. It supports multiple programming languages through SDKs, allowing developers to integrate sandbox capabilities into their systems without building custom isolation layers. The platform is built to... -
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TorchRL
A modular, primitive-first, python-first PyTorch library
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort. -
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HumanEval
Code for the paper "Evaluating Large Language Models Trained on Code"
human-eval is a benchmark dataset and evaluation framework created by OpenAI for measuring the ability of language models to generate correct code. It consists of hand-written programming problems with unit tests, designed to assess functional correctness rather than superficial metrics like text similarity. Each task includes a natural language prompt and a function signature, requiring the model to generate an implementation that passes all provided tests. The benchmark has become a... -
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py2many
Transpiler of Python to many other languages
...We can fix that by transpiring a subset of the language into a more performant, statically typed language. A second benefit is security. Writing security-sensitive code in a low-level language like C is error-prone and could lead to privilege escalation. Specialized languages such as wuffs exist to address this use case. py2many can be a more general-purpose solution to the problem where you can verify the source via unit tests before you transpile. Swift and Kotlin dominate the mobile app development workflow. ... -
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DVC
Data Version Control | Git for Data & Models
DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code. Version control machine learning models, data sets and intermediate files. DVC connects them with code and uses Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network-attached storage, or disc to store file contents. Version control machine learning models, data sets,... -
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C3
The goal of CLAIMED is to enable low-code/no-code rapid prototyping
C3 is an open-source framework designed to simplify the development and deployment of data science and machine learning workflows through reusable components and low-code development techniques. The framework focuses on enabling rapid prototyping while maintaining a path to production through automated CI/CD integration. CLAIMED provides a component-based architecture where data processing steps, models, and workflows can be packaged into reusable operators. -
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LLM Action
Technical principles related to large models
LLM-Action is a knowledge/tutorial/repository that shares principles, techniques, and real-world experience related to large language models (LLMs), focusing on LLM engineering, deployment, optimization, inference, compression, and tooling. It organizes content in domains like training, inference, compression, alignment, evaluation, pipelines, and applications. Sections covering infrastructure, engineering, and deployment. Repository templates, sample code, and resource links. Articles/code...
