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Showing 1901 open source projects for "compiler python linux"
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Gen AI apps are built with MongoDB AtlasMongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
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Cloud-based help desk software with ServoDeskWhat if You Could Automate 90% of Your Repetitive Tasks in Under 30 Days? At ServoDesk, we help businesses like yours automate operations with AI, allowing you to cut service times in half and increase productivity by 25% - without hiring more staff.
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YData Synthetic
Synthetic data generators for tabular and time-series data
A package to generate synthetic tabular and time-series data leveraging state-of-the-art generative models. Synthetic data is artificially generated data that is not collected from real-world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy. This repository contains material related to Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series. It... -
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OpenVINO Training Extensions
Trainable models and NN optimization tools
OpenVINO™ Training Extensions provide a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference. When ote_cli is installed in the virtual environment, you can use the ote command line interface to perform various actions for templates related to the chosen task type, such as running, training, evaluating, exporting, etc. ote train trains a model (a particular model template) on a dataset and saves results in two files. ote... -
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Hivemind
Decentralized deep learning in PyTorch. Built to train models
Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers. Distributed training without a master node: Distributed Hash Table allows connecting computers in a decentralized network. Fault-tolerant backpropagation: forward and backward passes succeed even if some nodes are unresponsive or take too long to respond. Decentralized parameter... -
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AIF360
A comprehensive set of fairness metrics for datasets
This extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. We invite you to use and improve it. The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and R. The... -
Smart Business Texting that Generates PipelineTextUs is the leading text messaging service provider for businesses that want to engage in real-time conversations with customers, leads, employees and candidates. Text messaging is one of the most engaging ways to communicate with customers, candidates, employees and leads. 1:1, two-way messaging encourages response and engagement. Text messages help teams get 10x the response rate over phone and email. Business text messaging has become a more viable form of communication than traditional mediums. The TextUs user experience is intentionally designed to resemble the familiar SMS inbox, allowing users to easily manage contacts, conversations, and campaigns. Work right from your desktop with the TextUs web app or use the Chrome extension alongside your ATS or CRM. Leverage the mobile app for on-the-go sending and responding.
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ClearML
Streamline your ML workflow
ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams all over the world. It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments... -
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model... -
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CLIP-as-service
Embed images and sentences into fixed-length vectors
CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions. Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks. Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing. Easy-to-use. No learning curve, minimalist... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
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 an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
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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... -
Free and Open Source HR SoftwareGive your HR team the tools they need to streamline administrative tasks, support employees, and make informed decisions with the OrangeHRM free and open source HR software.
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Open Interpreter
A natural language interface for computers
Open Interpreter is an open-source tool that provides a natural-language interface for interacting with your computer. It lets large language models (LLMs) run code locally (Python, JavaScript, shell, etc.), enabling you to ask your computer to do tasks like data analysis, file manipulation, browsing, etc. in human terms (“chat with your computer”), with safeguards. Runs locally or via configured remote LLM servers/inference backends, giving flexibility to use models you trust or have... -
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PyTorch Image Models
The largest collection of PyTorch image encoders / backbones
timm (PyTorch Image Models) is a premier library hosting a vast collection of state-of-the-art image classification models and backbones such as ResNet, EfficientNet, NFNet, Vision Transformer, ConvNeXt, and more. Created by Ross Wightman and now maintained by Hugging Face, it includes pretrained weights, data loaders, augmentations, optimizers, schedulers, and reference scripts for training, evaluation, inference, and model export. It's an essential toolkit for vision research and... -
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Notte
Opensource browser using agents
Notte is an open-source browser framework that enables the development and deployment of web-based AI agents. It introduces a perception layer that transforms web pages into structured, navigable maps described in natural language, allowing agents to interact with the internet more effectively. Notte is designed for building scalable and efficient browser-based AI applications. -
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OSWorld
Benchmarking Multimodal Agents for Open-Ended Tasks
OSWorld is an open-source synthetic world environment designed for embodied AI research and multi-agent learning. It provides a richly simulated 3D world where multiple agents can interact, perform tasks, and learn complex behaviors. OSWorld emphasizes multi-modal interaction, enabling agents to process visual, auditory, and symbolic data for grounded learning in a simulated world. -
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LightZero
[NeurIPS 2023 Spotlight] LightZero
LightZero is an efficient, scalable, and open-source framework implementing MuZero, a powerful model-based reinforcement learning algorithm that learns to predict rewards and transitions without explicit environment models. Developed by OpenDILab, LightZero focuses on providing a highly optimized and user-friendly platform for both academic research and industrial applications of MuZero and similar algorithms. -
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EvoTorch
Advanced evolutionary computation library built on top of PyTorch
EvoTorch is an evolutionary optimization framework built on top of PyTorch, developed by NNAISENSE. It is designed for large-scale optimization problems, particularly those that require evolutionary algorithms rather than gradient-based methods. -
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PaLM + RLHF - Pytorch
Implementation of RLHF (Reinforcement Learning with Human Feedback)
PaLM-rlhf-pytorch is a PyTorch implementation of Pathways Language Model (PaLM) with Reinforcement Learning from Human Feedback (RLHF). It is designed for fine-tuning large-scale language models with human preference alignment, similar to OpenAI’s approach for training models like ChatGPT. -
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OpenRLHF
An Easy-to-use, Scalable and High-performance RLHF Framework
OpenRLHF is an easy-to-use, scalable, and high-performance framework for Reinforcement Learning with Human Feedback (RLHF). It supports various training techniques and model architectures. -
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Mirascope
LLM abstractions that aren't obstructions
Mirascope is a powerful, flexible, and user-friendly library that simplifies the process of working with LLMs through a unified interface that works across various supported providers, including OpenAI, Anthropic, Mistral, Gemini, Groq, Cohere, LiteLLM, Azure AI, Vertex AI, and Bedrock. Whether you're generating text, extracting structured information, or developing complex AI-driven agent systems, Mirascope provides the tools you need to streamline your development process and create... -
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BudouX
Standalone, small, language-neutral
Standalone. Small. Language-neutral. BudouX is the successor to Budou, the machine learning-powered line break organizer tool. It is standalone. It works with no dependency on third-party word segmenters such as Google cloud natural language API. It is small. It takes only around 15 KB including its machine learning model. It's reasonable to use it even on the client-side. It is language-neutral. You can train a model for any language by feeding a dataset to BudouX’s training... -
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TFX
TFX is an end-to-end platform for deploying production ML pipelines
TensorFlow Extended (TFX) is a Google-production-scale machine learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines consisting of TFX components. TFX pipelines can be orchestrated using Apache Airflow and Kubeflow Pipelines. Both the components themselves and the integrations with orchestration systems can be extended. TFX components interact with an ML Metadata backend that keeps a record of component runs, input and output artifacts, and... -
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SkyPilot
SkyPilot: Run AI and batch jobs on any infra
SkyPilot is a framework for running AI and batch workloads on any infra, offering unified execution, high cost savings, and high GPU availability. Run AI and batch jobs on any infra (Kubernetes or 12+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface. -
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imodelsX
Interpretable prompting and models for NLP
Interpretable prompting and models for NLP (using large language models). Generates a prompt that explains patterns in data (Official) Explain the difference between two distributions. Find a natural-language prompt using input-gradients. Fit a better linear model using an LLM to extract embeddings. Fit better decision trees using an LLM to expand features. Finetune a single linear layer on top of LLM embeddings. Use these just a like a sci-kit-learn model. During training, they fit better... -
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solo-learn
Library of self-supervised methods for visual representation
A library of self-supervised methods for visual representation learning powered by Pytorch Lightning. A library of self-supervised methods for unsupervised visual representation learning powered by PyTorch Lightning. We aim at providing SOTA self-supervised methods in a comparable environment while, at the same time, implementing training tricks. The library is self-contained, but it is possible to use the models outside of solo-learn. -
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Denoising Diffusion Probabilistic Model
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model. -
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MMDetection
An open source object detection toolbox based on PyTorch
MMDetection is an open source object detection toolbox that's part of the OpenMMLab project developed by Multimedia Laboratory, CUHK. It stems from the codebase developed by the MMDet team, who won the COCO Detection Challenge in 2018. Since that win this toolbox has continuously been developed and improved. MMDetection detects various objects within a given image with high efficiency. Its training speed is comparable or even faster than those of other codebases like Detectron2 and...
