FedML VS HandyRL

Compare FedML vs HandyRL and see what are their differences.

FedML

FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://fedml.ai) is your generative AI platform at scale. (by FedML-AI)
federated-learning Deep Learning distributed-training Blockchain edge-ai Machine Learning on-device-training inference-engine Marketplace Mlops Privacy Security
Source Code
fedml.ai
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HandyRL

HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments. (by DeNA)
reinforcement-learning Pytorch Games policy-gradient Deep Learning Machine Learning distributed-training
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FedML HandyRL
6 1
4,060 282
1.9% 0.7%
9.9 4.3
2 days ago about 21 hours ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

FedML

Posts with mentions or reviews of FedML. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-22.

HandyRL

Posts with mentions or reviews of HandyRL. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing FedML and HandyRL you can also consider the following projects:

federated-xgboost - Federated gradient boosted decision tree learning

adaptdl - Resource-adaptive cluster scheduler for deep learning training.

alpa - Training and serving large-scale neural networks with auto parallelization.

PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

experta - Expert Systems for Python

nes-torch - Minimal PyTorch Library for Natural Evolution Strategies

determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

MetisFL - The first open Federated Learning framework implemented in C++ and Python.

pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.

hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

FedML vs federated-xgboost HandyRL vs adaptdl FedML vs alpa HandyRL vs PPO-PyTorch FedML vs experta HandyRL vs nes-torch FedML vs adaptdl HandyRL vs determined FedML vs MetisFL HandyRL vs pytorch-learn-reinforcement-learning FedML vs hivemind HandyRL vs wandb