Tools tagged with "Distributed execution"

QuantDinger is a self-hosted AI trading platform for independent traders, Python strategy authors, and small teams. It brings research, strategy development, testing, and trade execution into one workspace while keeping strategy code and broker credentials under the operator's control.

FastDeploy is a self-hosted inference toolkit for teams deploying language and vision-language models on Linux servers. Built on PaddlePaddle, it supports offline inference and online serving.

QuickDesk is a remote desktop app that lets AI agents operate other computers through a built-in MCP server. Developers and administrators can automate desktop tasks while watching the agent's actions and intervening when needed.

Ludwig is a framework for developers and researchers training custom AI models without writing their own training loops. It handles preprocessing, training and evaluation from a model definition, covering LLMs as well as tabular and multimodal tasks.

SpecForge is an open-source framework for teams training draft models to speed up LLM inference in SGLang. It suits developers who want to train on their own hardware and use the resulting drafts directly with SGLang serving.

Hyperopt is a Python library for developers and researchers who need to tune model hyperparameters across complex search spaces. It can handle continuous values, discrete choices and conditional parameters, such as settings that apply only when a particular model or approach is selected. It runs within your Python project, with support for both serial and parallel optimization.

Accelerate is a Python library for developers and researchers who write their own PyTorch training loops. It handles hardware and distributed execution so they can keep control of how their models train.

NVIDIA FLARE is a Python SDK for researchers and data scientists who want to train models across participating sites while keeping training data local. Platform developers can also use it to build systems for collaboration between organizations. It supports on-premise and cloud deployments and is open source under the Apache 2.0 license.

FastChat is a self-hosted LLM platform for developers and researchers who want to serve chat models on their own hardware and compare their responses. It powers Chatbot Arena and includes a locally hosted side-by-side comparison interface. The Python code is open source under Apache 2.0; model licenses apply separately, including Llama's license for Vicuna.

AgentsMesh coordinates AI coding agents across local machines and servers for developers managing concurrent work. A shared console lets teams assign tasks, watch terminals and follow progress across agents.

GPT-NeoX is EleutherAI's framework for researchers and teams training language models with billions of parameters on their own GPU infrastructure. It focuses on training from scratch, with support for fine-tuning existing checkpoints.

Colossal-AI is a Python framework for developers and researchers training or serving large AI models on their own GPUs. It provides tools to spread workloads across GPUs and work within available memory.

AgileRL is an open-source Python framework for teams training AI agents on their own tasks, including LLM agents and agents for robotics, finance or logistics. Its evolutionary hyperparameter tuning adjusts training settings during a run, reducing the need for separate tuning trials. The framework uses the Apache 2.0 license.

Step1X Edit edits existing images from written requests. It's for developers and researchers who want to run image editing on their own hardware, with control over the model and specialized training.

LightGBM is a decision-tree gradient boosting framework for classification, regression and ranking. It trains models on your own machine or across multiple machines, with GPU support for compatible configurations. The code is open source under the MIT license.

Distributed Llama runs language models across your own networked computers. It's for people who want to share inference work and model memory across several devices instead of fitting the whole workload on one machine.

NVIDIA PhysicsNeMo is an open-source Python framework for engineers and researchers building AI models of physical systems. It combines physics constraints with simulation and observed data to train surrogate models that can predict behavior in near real time. The framework uses PyTorch and carries the Apache 2.0 license.

PEFT is a library for developers adapting pretrained AI models on their own hardware. It trains a small portion of a model's parameters to reduce the compute and storage costs of full fine-tuning.

FederatedScope is an open-source federated learning platform for researchers and teams building training systems across separate data holders. It can simulate clients and servers on one device or run them as separate distributed participants. Each participant loads its own data, while models can pass between participants through messages.

Dingo combines rule checks, LLM judges and agent-based evaluation to assess AI training data, fine-tuning datasets and production systems. It's an open-source Python tool under Apache 2.0 for data engineers, ML practitioners and researchers who need to find data problems and compare model or application quality. Evaluation can run locally or across a Spark cluster.

WhipCode is an open-source coding agent for developers tackling jobs that exceed a single model context window. It lets agents divide the work while you follow their progress and send further instructions.

PaddleDetection is a computer vision toolkit for developers building image and video analysis systems. It covers training, adapting and deploying models for uses such as inspection and traffic analysis.

Substra is open-source federated learning software for teams that need to train and validate machine learning models across distributed datasets. It supports production use across a network, as well as simulations on a single machine for testing and debugging federated learning code.

SkyPilot manages AI training, inference and development workloads across infrastructure you control. It's for AI teams sharing compute and infrastructure teams managing jobs across clusters and cloud providers.

SimpleTuner is a diffusion model training toolkit for creators and researchers adapting image, video or audio models to their own datasets. Its web dashboard also supports teams sharing GPU workers.

KanDev is a self-hosted workspace for developers managing AI coding tasks and reviewing the resulting changes. It organizes work on kanban boards and lets you define workflows with your own agent choices and review gates.

Step-Audio 2 is an audio AI model family for developers building voice assistants and applications that interpret recordings. It can reason about what people say and how they say it, as well as sounds beyond speech.

Keras is an open-source Python deep learning framework for developers and researchers who want to build and train models without tying their code to one backend. It runs locally on Linux and macOS, with WSL2 recommended for Windows. Its Apache 2.0 license permits commercial use and modification.

SeedVR is an AI video restoration project for developers and researchers processing real-world footage or AI-generated video on their own GPUs. Its SeedVR2 model restores footage in a single diffusion step, with substantial hardware demands in the supplied implementation.

Data-Juicer is a Python framework for researchers and teams preparing datasets for AI training, evaluation, agents or document retrieval. It processes data on your own machine or across a Ray cluster.

DeepGEMM is an open-source CUDA kernel library for developers building LLM training and inference systems on their own NVIDIA GPUs. It supplies matrix operations and specialized computations for mixture-of-experts (MoE) models.

diffusion-pipe trains image and video diffusion models on your own GPU hardware. It's for people fine-tuning models that are too large to fit on a single GPU or who want to distribute training across several GPUs.

HAT Super Resolution is a local AI image upscaling project for developers, researchers, and people working with low-resolution images. It provides pretrained Hybrid Attention Transformer models and code for training your own.

MedSAM is an open-source model for medical image segmentation that runs locally, aimed at researchers and developers working with medical scans. It builds on Meta's Segment Anything model and uses bounding boxes to identify the region you want to segment. The project uses the Apache 2.0 license and supports macOS.