Tools tagged with "Distributed execution"

NVIDIA cuML is a Python machine learning library for data scientists whose scikit-learn, UMAP or HDBSCAN workloads take too long on a CPU. It runs on NVIDIA GPUs in Linux or WSL 2, on a workstation or across a cluster. The project is open source under Apache 2.0.

ONNX Runtime is an AI inference and training engine for developers adding models to apps and services. It supports deployment on a user's device or a server, including applications that need the same model format across platforms.

Augmentoolkit turns documents into datasets for training an LLM on a particular subject. It's for people building custom models around material such as research papers or fictional lore.

SwarmLLM is a ChatGPT alternative for people who want to run AI models on their own hardware. It can use a single computer or combine several machines to handle a model that won't fit on one.

AReaL is open-source reinforcement learning infrastructure for researchers and developers training LLMs and AI agents on their own compute. Its asynchronous training approach targets large reasoning models and agent applications, with support for single-node runs and distributed training. It uses the Apache 2.0 license.

olmOCR converts scanned and image-based documents into text for researchers and developers preparing LLM datasets. It handles pages whose layout makes ordinary text extraction difficult.

PyTorch Lightning is a Python framework for researchers and developers pretraining or fine-tuning AI models. It handles recurring training infrastructure while leaving the model logic in your PyTorch code.

OpenBitFun is a desktop AI agent workspace for coding and other tasks that use files and shell commands on your machine or a selected SSH host. It runs on Windows, macOS and Linux. Model requests go to the configured provider; built-in cloud connections send them to external services, and custom providers are also supported.

NeMo Automodel is a Python library for researchers and developers who want to train or adapt AI models on their own GPU workstations or clusters. Its PyTorch-based training works with Hugging Face models without converting checkpoints.

LazyLLM is a Python framework for developers building applications with multiple AI agents. It brings application workflows, model training and evaluation together so teams can refine a prototype using task-specific data.

DataTrove is a Python library for teams preparing large text datasets, including LLM training corpora. It combines reusable processing blocks with custom cleanup logic, so teams can apply their own rules across large workloads.

Pooled runs a single open model across laptops, desktops and phones in browser tabs. It lets people combine their devices' memory to use a model that wouldn't fit on one device, for shared chat or coding tasks.

Emdash is a desktop development environment for developers who want to delegate several coding tasks at once. It keeps each task's branch, terminal, conversation, and review state together so parallel work stays separate.

Fed-BioMed lets hospitals and researchers train AI models across institutions while patient data stays on each institution's own infrastructure. It's a self-hosted platform for biomedical research that needs access to data across multiple sites but faces restrictions on sharing clinical records.

PyTorch Forecasting is a Python library for developers and researchers who want to predict time series with neural networks on their own hardware. It combines ready-made forecasting architectures with data preparation, training, and evaluation tools. It's open source under the MIT license.

Ruflo is an AI agent framework for developers who want Claude Code and Codex to work as coordinated teams. It adds specialist roles and shared context for projects that involve coding, testing, architecture and documentation.

Hivemind is a Python library for researchers and teams that want to train a shared neural network across computers owned by universities, companies or volunteers. It uses PyTorch and connects participants over the internet, so contributors can pool their machines for a training job without a central master node.

Vald is a distributed vector search engine for developers building similarity search into applications. It runs on a self-hosted Kubernetes cluster and finds approximate nearest neighbors in dense vectors using NGT. It's open source under the Apache 2.0 license, with an architecture designed to spread large vector collections across multiple machines.

Code Buddy is a terminal AI coding agent for developers who want it to read a repository, make changes and run commands on their machine. It supports local models through Ollama as well as cloud providers.

VAR is an image generation research toolkit for researchers and developers studying autoregressive models as an alternative to diffusion. It generates images through next-scale prediction, building finer detail across successive image scales.

SMAC3 is a Python tool for researchers and developers tuning machine learning models or other algorithms on their own hardware. It combines Bayesian optimization with a racing mechanism that decides which configurations deserve further evaluation. It runs locally on Linux, Windows and macOS, and supports parallel evaluation on clusters through Dask.

Sonar serves language and multimodal models for developers and teams running AI applications on their own hardware. It builds on vLLM and works with Hugging Face-compatible models.

TensorRT-LLM is an inference library for developers serving LLMs on their own NVIDIA GPUs. It supports application-level inference and server deployments, with performance optimizations for workloads spread across one or several machines.

AX is a self-hosted AI agent orchestrator for teams running autonomous tasks on Kubernetes. It manages agents that retain state and need access to models, repositories and tools while they work.

Ralph TUI coordinates AI coding agents on your machine or servers as they work through a task backlog. It's for developers who want autonomous execution with progress and agent output visible in a terminal.

OLMo Core is a Python library for researchers and developers building and training OLMo language models. Its PyTorch components and official training scripts suit teams working with model training code on their own hardware or servers.

Flower is a federated AI framework for developers and researchers who want to train models across separate organizations or devices. Participants retain control of their data rather than gathering everyone's data in one place.

Soup is a local LLM fine-tuning tool for developers and teams adapting models to their own datasets. It brings training, evaluation and export into one workflow that runs on your own hardware without requiring a cloud service.

Axolotl is a free, open-source LLM fine-tuning framework for developers and researchers adapting models to their own data. It supports training on local hardware or cloud infrastructure you control.

Munder Difflin is a desktop app for developers who want coding agents to collaborate on their own machine. You give an orchestrator a request, and it divides the work among agents instead of leaving you to coordinate separate terminals.

CatBoost is an open-source machine learning library for developers and data scientists building prediction models with numerical and categorical data. Its built-in handling of categories reduces the work needed to turn non-numeric inputs into usable features. It runs computations on your own CPU or GPU and uses the Apache 2.0 license.

SWE AF is an AI coding agent system built on AgentField for developers delegating changes across a repository or several related codebases. It divides work among agents and adjusts the remaining plan when tasks run into problems.

OpenRLHF is a self-hosted Python framework for researchers and teams training language models with human feedback or custom rewards. It supports fine-tuning and reinforcement learning on your own GPU hardware, including training agents that interact with an environment.

Labkit is a local image segmentation plugin for Fiji, aimed at researchers working with microscopy datasets that can be too large for conventional desktop workflows. It supports manual labeling and automated pixel classification, with a design that lets consumer laptops work with images spanning multiple terabytes.