FedML
★ 4.0k
vs
on-policy
★ 2.0k
FedML vs on-policy
FedML: FedML is a unified and scalable open-source machine learning library powered by TensorOpera AI, enabling training and deployment of AI jobs anywhere at any scale. It offers holistic support for MLOps, scheduling, and high-performance ML libraries, including federated learning, distributed training, and generative AI functionalities.; on-policy: This repository implements MAPPO, a multi-agent variant of PPO, widely used in cooperative multi-agent games and research. It provides robust implementations for various multi-agent environments like StarCraft II, Hanabi, and Google Research Football, along with detailed training scripts and hyperparameter guidance.
01
TL;DR
Choose FedML if…Distributed training and fine-tuning of large models (including LLMs)
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
02
Side-by-Side Comparison
03
Features
FedML01Unified and scalable ML library
02Support for Generative AI and LLMs (fine-tuning, deployment)
03Federated Learning platform (on-device, cross-cloud)
04Distributed Training for large and foundational models
05Model serving platform for high scalability and low latency
on-policy01Implementation of MAPPO (Multi-Agent PPO)
02Support for diverse multi-agent environments (e.g., StarCraft II, Hanabi)
03Ready-to-use training scripts for various scenarios
04Detailed hyperparameter guidance and updated results
05Default support for shared policy among agents
04
Use Cases
FedML↳Distributed training and fine-tuning of large models (including LLMs)
↳Scalable deployment and serving of AI models
↳Federated learning across various decentralized environments
on-policy↳Research and experimentation in cooperative multi-agent reinforcement learning
↳Benchmarking and evaluating PPO's effectiveness in MARL scenarios
↳Training AI agents for popular multi-agent games like StarCraft II and Hanabi
05
Best For
FedMLTrendingEssential
on-policyTrendingReinforcement LearningMulti-Agent AI
FAQ
FAQ
What is the difference between FedML and on-policy?
Both FedML and on-policy are in the LLM Infra category. FedML has 4.0k stars, while on-policy has 2.0k stars.
Which is better, FedML or on-policy?
The best choice depends on your use case. Choose FedML if Distributed training and fine-tuning of large models (including LLMs), and on-policy if Research and experimentation in cooperative multi-agent reinforcement learning.
Is FedML free or open source?
Yes, FedML is open source on GitHub (Apache-2.0).
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
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