Search Results for "netbeans with source code" - Page 9
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Showing 935 open source projects for "netbeans with source code"
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NSync
nsync is a C library that exports various synchronization primitives
nsync is a portable C library that provides a collection of advanced synchronization primitives designed to facilitate safe and efficient multithreaded programming. It offers reader-writer locks, condition variables, run-once initialization, waitable counters, and waitable bits for coordination and cancellation between threads. Unlike traditional pthreads-based synchronization, nsync introduces conditional critical sections, allowing developers to wait for arbitrary conditions without... -
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FairChem
FAIR Chemistry's library of machine learning methods for chemistry
FAIRChem is a unified library for machine learning in chemistry and materials, consolidating data, pretrained models, demos, and application code into a single, versioned toolkit. Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations,... -
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Google CTF
Google CTF
Google CTF is the public repository that houses most of the challenges from Google’s Capture-the-Flag competitions since 2017 and the infrastructure used to run them. It’s a learning and practice archive: competitors and educators can replay tasks across categories like pwn, reversing, crypto, web, sandboxing, and forensics. The code and binaries intentionally contain vulnerabilities—by design—so users can explore exploit chains and patching in realistic settings. The repo also includes... -
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Petastorm
Petastorm library enables single machine or distributed training
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code. Petastorm is an open-source data access library developed at Uber ATG. This library enables single machine or distributed training and evaluation of deep learning models directly from datasets in Apache Parquet format. Petastorm supports popular Python-based machine learning (ML) frameworks such as Tensorflow, PyTorch, and PySpark. ... -
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Jupyter Notebook Tools for Sphinx
Sphinx source parser for Jupyter notebooks
nbsphinx is a Sphinx extension that provides a source parser for *.ipynb files. Custom Sphinx directives are used to show Jupyter Notebook code cells (and of course their results) in both HTML and LaTeX output. Un-evaluated notebooks – i.e. notebooks without stored output cells – will be automatically executed during the Sphinx build process. -
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Ansible for DevOps
Ansible for DevOps examples
Ansible for DevOps is a collection of Ansible playbooks, roles, and infrastructure-as-code examples that accompany the book Ansible for DevOps by Jeff Geerling. Rather than being theoretical, the examples span real-world infrastructure setups: multi-server orchestration, LAMP stacks, Docker deployments, Kubernetes cluster spins, rolling updates, and security hardening. You can clone the repo and play with actual scenarios using Vagrant, VirtualBox, or cloud hosts, making it ideal for both... -
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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,... -
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IVY
The Unified Machine Learning Framework
Take any code that you'd like to include. For example, an existing TensorFlow model, and some useful functions from both PyTorch and NumPy libraries. Choose any framework for writing your higher-level pipeline, including data loading, distributed training, analytics, logging, visualization etc. Choose any backend framework which should be used under the hood, for running this entire pipeline. Choose the most appropriate device or combination of devices for your needs. DeepMind releases an... -
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Mezzanine
CMS framework for Django
...Mezzanine has a simple yet highly extensible architecture that lets you really get into the code. Apart from the features that come with Django such as MVC architecture, ORM, templating and caching, Mezzanine comes with a great many other features. This includes hierarchical page navigation, a simple drag-and-drop HTML5 forms builder with CSV export, scheduled publishing, easy page ordering, social media sharing, and so much more. -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement... -
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BackAnt CLI
Let your AI agent create clean, easy to maintain flask backends
BackAnt CLI is an AI-native backend scaffolding tool designed to generate production-ready Flask APIs from a JSON specification or a small set of CLI commands. Its main goal is to reduce the repetitive work involved in setting up layered backend projects by generating key application components automatically rather than forcing developers to assemble them by hand. The tool creates a structured backend architecture that includes routes, services, repositories, models, startup files, Docker... -
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AWS Serverless Application Model
An open-source framework for building serverless applications
The AWS Serverless Application Model (SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. With just a few lines per resource, you can define the application you want and model it using YAML. During deployment, SAM transforms and expands the SAM syntax into AWS CloudFormation syntax, enabling you to build serverless applications faster. To get started with building SAM-based... -
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Professional Programming
A collection of learning resources for curious software engineers
Professional Programming is a long-running, curated collection of learning resources aimed at helping software engineers grow into well-rounded professionals. It goes far beyond basic “learn to code” material and covers topics like system design, debugging, testing, performance, security, architecture, and software craftsmanship. The list is organized by themes such as coding, design, operations, communication, and career, making it easy to dive into specific aspects of engineering practice.... -
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troposphere
Python library to create AWS CloudFormation descriptions
The troposphere library allows for easier creation of the AWS CloudFormation JSON by writing Python code to describe the AWS resources. troposphere also includes some basic support for OpenStack resources via Heat. To facilitate catching CloudFormation or JSON errors early the library has property and type checking built into the classes. -
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shot-scraper
A command-line utility for taking automated screenshots of websites
shot-scraper is a command-line utility for taking automated screenshots of web pages using a headless browser engine. After installation, a single command can capture a full-page screenshot of a URL and save it to a file, making it ideal for documentation, monitoring, and visual regression tasks. Under the hood it uses a modern browser (installed via a one-time shot-scraper install step) and exposes options for viewport size, full-page versus clipped screenshots, and device emulation. Beyond... -
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Faker for Python
Python package that generates fake data for you
Faker is a Python package that generates fake data for you. Whether you need to bootstrap your database, create good-looking XML documents, fill-in your persistence to stress test it, or anonymize data taken from a production service, Faker is for you. Starting from version 4.0.0, Faker dropped support for Python 2 and from version 5.0.0 only supports Python 3.6 and above. If you still need Python 2 compatibility, please install version 3.0.1 in the meantime, and please consider updating... -
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Django Rules
Awesome Django authorization, without the database
rules is a tiny but powerful app providing object-level permissions to Django, without requiring a database. At its core, it is a generic framework for building rule-based systems, similar to decision trees. It can also be used as a standalone library in other contexts and frameworks. Versatile. Decorate callables to build complex graphs of predicates. Predicates can be any type of callable -- simple functions, lambdas, methods, callable class objects, partial functions, decorated functions,... -
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Django Ninja
Fast, Async-ready, Openapi, type hints based framework
Django Ninja is a web framework for building APIs with Django and Python 3.6+ type hints. Designed to be easy to use and intuitive. Very high performance thanks to Pydantic and async support. Type hints and automatic docs lets you focus only on business logic. Based on the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema. Django friendly (obviously) has good integration with the Django core and ORM. Used by multiple companies on live projects. -
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lightning AI
The most intuitive, flexible, way for researchers to build models
Build in days not months with the most intuitive, flexible framework for building models and Lightning Apps (ie: ML workflow templates) which "glue" together your favorite ML lifecycle tools. Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any... -
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kagglehub
Python library to access Kaggle resources
kagglehub is a Python library for accessing Kaggle resources directly from Python code. It provides a simple API for downloading datasets, models, competition files, and notebook outputs without requiring users to manually manage every URL or file path. The library is designed to work both inside and outside Kaggle Notebooks, with native behavior that can adapt when it runs in Kaggle’s hosted notebook environment. It is useful for machine learning workflows where data, models, and notebook... -
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HASS Configurator
Configuration UI for Home Assistant
The HASS Configurator is a small web app (you access it via a web browser) that provides a filesystem browser and text-editor to modify files on the machine the configurator is running on. It has been created to allow easy configuration of Home Assistant. It is powered by Ace editor, which supports syntax highlighting for various code/markup languages. YAML files (the default language for Home Assistant configuration files) will be automatically checked for syntax errors while editing. The... -
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Sanic
Async Python 3.6+ web server/framework
Build fast, run fast with Sanic! Sanic is a Python 3.6+ web server and web framework designed to go fast. It provides a way to get a highly performant HTTP server up and running fast, while also making it easy to build, expand, and eventually scale. Sanic aspires to be as simple as possible while delivering the performance that you require. It allows the usage of the async/await syntax added in Python 3.5, so your code is guaranteed to be non-blocking and speedy. It's also ASGI compliant,... -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before... -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support,... -
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Face Alignment
2D and 3D Face alignment library build using pytorch
Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. Build using FAN's state-of-the-art deep learning-based face alignment method. For numerical evaluations, it is highly recommended to use the lua version which uses identical models with the ones evaluated in the paper. More models will be added soon. By default, the package will use the SFD face detector. However, the users can alternatively...
