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Showing 146 open source projects for "void based linux"
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Context for your AI agentsBuild data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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Easy-to-use Business Software for the Waste Management Software IndustryDOP Software’s mission is to streamline waste and recycling business’ processes by providing them with dynamic, comprehensive software and services that increase productivity and quality of performance.
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical... -
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KoboldAI
Your gateway to GPT writing
This is a browser-based front-end for AI-assisted writing with multiple local & remote AI models. It offers the standard array of tools, including Memory, Author's Note, World Info, Save & Load, adjustable AI settings, formatting options, and the ability to import existing AI Dungeon adventures. You can also turn on Adventure mode and play the game like AI Dungeon Unleashed. Stories can be played like a Novel, a text adventure game or used as a chatbot with an easy toggles to change... -
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GFPGAN
GFPGAN aims at developing Practical Algorithms
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration. Colab Demo for GFPGAN; (Another Colab Demo for the original paper model) Online demo: Huggingface (return only the cropped face) Online demo: Replicate.ai (may need to sign in, return the whole image). Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing... -
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OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant... -
Next-Gen Encryption for Post-Quantum Security | CLEAR by Quantum KnightCLEAR by Quantum Knight is a FIPS-140-3 validated encryption SDK engineered for enterprises requiring top-tier security. Offering robust post-quantum cryptography, CLEAR secures files, streaming media, databases, and networks with ease across over 30 modern platforms. Its compact design, smaller than a single smartphone image, ensures maximum efficiency and low energy consumption.
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Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no... -
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BEVFormer
Implementation of BEVFormer, a camera-only framework
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV... -
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Elephas
Distributed Deep learning with Keras & Spark
Elephas is an extension of Keras, which allows you to run distributed deep learning models at scale with Spark. Elephas currently supports a number of applications. Elephas brings deep learning with Keras to Spark. Elephas intends to keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which can be run on massive data sets. Elephas implements a class of data-parallel algorithms on top of Keras, using Spark's RDDs and data frames. Keras... -
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PyTorch Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, etc.
TLlib is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please... -
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Chainer
A flexible deep learning framework
Chainer is a Python-based deep learning framework. It provides automatic differentiation APIs based on dynamic computational graphs as well as high-level APIs for neural networks. -
All-in-one security tool helps you prevent ransomware and breaches.Blumira’s detection and response platform enables faster resolution of threats to help you stop ransomware attacks and prevent data breaches. We surface real threats, providing meaningful findings so you know what to prioritize. With our 3-step rapid response, you can automatically block known threats, use our playbooks for easy remediation, or contact our security team for additional guidance. Our responsive security team helps with onboarding, triage and ongoing consultations to continuously help your organization improve your security coverage.
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Guild AI
Experiment tracking, ML developer tools
Guild AI is an open-source experiment tracking toolkit designed to bring systematic control to machine learning workflows, enabling users to build better models faster. It automatically captures every detail of training runs as unique experiments, facilitating comprehensive tracking and analysis. Users can compare and analyze runs to deepen their understanding and incrementally improve models. Guild AI simplifies hyperparameter tuning by applying state-of-the-art algorithms through... -
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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series... -
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LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser.... -
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SageMaker Scikit-Learn Extension
A library of additional estimators and SageMaker tools based on scikit
A library of additional estimators and SageMaker tools based on scikit-learn. This project contains standalone scikit-learn estimators and additional tools to support SageMaker Autopilot. Many of the additional estimators are based on existing scikit-learn estimators. SageMaker Scikit-Learn Extension is a Python module for machine learning built on top of scikit-learn. In order to use the I/O functionalies in the sagemaker_sklearn_extension.externals module, you will also need to install the... -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing... -
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Hugging Face Transformer
CPU/GPU inference server for Hugging Face transformer models
Optimize and deploy in production Hugging Face Transformer models in a single command line. At Lefebvre Dalloz we run in-production semantic search engines in the legal domain, in the non-marketing language it's a re-ranker, and we based ours on Transformer. In that setup, latency is key to providing a good user experience, and relevancy inference is done online for hundreds of snippets per user query. Most tutorials on Transformer deployment in production are built over Pytorch and FastAPI.... -
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Face Mask Detection
Face Mask Detection system based on computer vision and deep learning
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams. Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential... -
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Scikit-Optimize
Sequential model-based optimization with a `scipy.optimize` interface
Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts. The library is built on top of NumPy, SciPy and Scikit-Learn. -
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TorchGAN
Research Framework for easy and efficient training of GANs
The torchgan package consists of various generative adversarial networks and utilities that have been found useful in training them. This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to... -
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ML workspace
All-in-one web-based IDE specialized for machine learning
...Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH. Easy to deploy on Mac, Linux, and Windows via Docker. Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.By default, the workspace container has no resource constraints and can use as much of a given resource as the host’s kernel scheduler allows. -
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U-Net Fusion RFI
U-Net for RFI Detection based on @jakeret's implementation
See original code here: https://github.com/jakeret/tf_unet Currently this project is based on Tensorflow 1.13 code base and there are no plans to transfer to TF version 2. The primary improvements to this code base include a training and evaluation framework, along with a fusion based approach to detection, combining a number of models (currently hard coded to two trained models) along with Sum Threshold as an additional "expert." Additional work is being done to add custom layers to... -
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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying... -
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Texthero
Text preprocessing, representation and visualization from zero to hero
Texthero is a python package to work with text data efficiently. It empowers NLP developers with a tool to quickly understand any text-based dataset and it provides a solid pipeline to clean and represent text data, from zero to hero. -
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DeepMosaics
Automatically remove the mosaics in images and videos, or add mosaics
Automatically remove the mosaics in images and videos, or add mosaics to them. This project is based on "semantic segmentation" and "Image-to-Image Translation". You can either run DeepMosaics via a pre-built binary package, or from source. Run time depends on the computer's performance (GPU version has better performance but requires CUDA to be installed). Different pre-trained models are suitable for different effects.[Introduction to pre-trained models]. -
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Opyrator
Turns your machine learning code into microservices with web API
Instantly turn your Python functions into production-ready microservices. Deploy and access your services via HTTP API or interactive UI. Seamlessly export your services into portable, shareable, and executable files or Docker images. Opyrator builds on open standards - OpenAPI, JSON Schema, and Python type hints - and is powered by FastAPI, Streamlit, and Pydantic. It cuts out all the pain for productizing and sharing your Python code - or anything you can wrap into a single Python... -
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CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network"
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019). The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph...
