FedML-AI/FedML

Features

• Distributed Training Frameworks - Provides a distributed training framework for executing large-scale model training and fine-tuning across multi-cloud and decentralized GPU clusters.
• AI Workload Orchestration - Pairs AI jobs with economical GPU resources and auto-provisions environments to eliminate manual setup.
• Distributed GPU Training - Provides a system for distributing the computational load of large-scale model training across multiple GPUs and multi-cloud environments.
• Distributed Training Orchestration - Coordinates synchronization and parallelization across compute clusters for large-scale model training.
• LLM Fine-Tuning - Adapts large language models using serverless workflows and pre-built training templates.
• Large-Scale Model Training - Executes distributed machine learning workloads across clusters to train foundational models exceeding single-device capacity.
• Distributed Training Platforms - Implements a platform designed for scaling model training across distributed hardware clusters, including on-premise and cloud environments.
• Decentralized Machine Learning - Executes machine learning tasks across distributed users or edge nodes while keeping raw data localized.
• Distributed Training - Enables the scaling of machine learning model training and fine-tuning across multiple compute nodes to accelerate workflows.
• Edge AI Model Deployment - Installs toolkits on smartphones and IoT devices to enable local model training and execution.
• Model Inference and Serving - Deploys trained models to production environments with a focus on low-latency inference and high-scalability.
• Federated Learning Frameworks - Offers a unified framework for collaborative machine learning and training across decentralized edge devices and organizational silos.
• Model Serving & Deployment - Implements infrastructure for hosting and serving machine learning models across various compute planes via network endpoints.
• Privacy-Preserving Model Training - Trains models across decentralized edge nodes and smartphones while sharing only model updates to maintain data privacy.
• Foundational Model Adaptation - Adapts open-source foundational models using specific datasets for deployment on GPU clusters.
• Federated Learning - Trains shared machine learning models across decentralized edge devices and organizational silos without centralizing private data.
• AI Workload Schedulers - Schedules machine learning workloads across on-premises clusters and multiple GPU cloud environments.
• GPU Cluster Management Platforms - Manages resource allocation and concurrent job execution via queues for private or on-premises GPU hardware.
• GPU Resource Orchestrators - Dynamically manages and allocates GPU hardware across shared AI workloads across different cloud providers.
• GPU Workload Orchestration - Schedules and provisions AI workloads across different cloud providers and private hardware to optimize cost and resource utilization.
• GPU Cluster Job Schedulers - Provides orchestration for resource allocation and task execution across heterogeneous GPU clusters.
• End-to-End Training Pipelines - Orchestrates end-to-end machine learning operation pipelines across multi-cloud and edge environments.
• Distributed Training - Distributes the training of foundational models across decentralized GPUs, multi-cloud environments, and edge servers.
• Memory-Constrained Inference - Implements specialized memory management and optimization to run large models on hardware with limited VRAM.
• Experiment Tracking - Records metrics, logs, and hardware performance to compare and evaluate different training runs.
• Model Serving Engines - Provides an optimized engine for low-latency and scalable inference of large language models on diverse compute planes.
• Low-Latency Serving Techniques - Provides a framework for deploying models using techniques that minimize inference latency and maximize throughput.
• ML Pipeline Automation - Orchestrates sequences of interdependent training jobs and data management tasks into automated machine learning pipelines.
• GPU Environment Provisioning - Automates the setup of execution environments and GPU resource allocation via configuration files.
• AI Model Production Deployment - Transitions trained models to cloud or on-premise servers for industrial-level scaling.
• GPU Training Deployments - Provisions and executes machine learning tasks across multi-cloud or on-premises GPU resources.
• GPU Resource Allocators - Controls the allocation and placement of workloads across available GPU resources to optimize hardware utilization.
• AI Job Launchers - Runs machine learning jobs on public, decentralized, or on-premise GPU clusters via a command-line interface.
• Compute Resource Monetization - Allows users to earn payment by contributing unused GPU capacity to a shared compute marketplace.
• GPU Resource Provisioning - Automates the allocation of GPU compute resources and the setup of the required execution environment.
• GPU Resource Pooling - Aggregates GPU hardware from multiple contributors into a shared pool for distributed machine learning execution.
• GPU Compute Marketplaces - Aggregates idle GPU capacity from contributors into a shared pool for on-demand AI task execution.
• Training Workflow Orchestrators - Orchestrates sequences of interdependent training jobs into a single automated pipeline.
• Serverless Training Pipelines - Orchestrates interdependent machine learning jobs into automated serverless pipelines.
• Job Performance Profiling - Tracks real-time billing, system metrics, and logs to diagnose performance bottlenecks in training jobs.
• Federated Learning - Library for secure and collaborative decentralized machine learning.
• Federated Learning Frameworks - A research-oriented library for distributed and federated machine learning.
• Privacy and Safety - Platform for distributed and federated machine learning.

Star-Verlauf