|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Ray||25,773||80||199||5 hours ago||76||June 09, 2022||2,838||apache-2.0||Python|
|Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.|
|Annotated_deep_learning_paper_implementations||22,464||1||17 days ago||76||June 27, 2022||17||mit||Jupyter Notebook|
|🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠|
|D2l En||17,942||3 days ago||100||other||Python|
|Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.|
|Pytorch Tutorial||7,372||2 months ago||28||mit||Jupyter Notebook|
|Build your neural network easy and fast, 莫烦Python中文教学|
|Wandb||6,105||39||232||14 hours ago||237||July 05, 2022||840||mit||Python|
|🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.|
|Stable Baselines3||5,783||34||2 days ago||49||June 14, 2022||70||mit||Python|
|PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.|
|Practical_rl||5,347||3 days ago||40||unlicense||Jupyter Notebook|
|A course in reinforcement learning in the wild|
|Deep Reinforcement Learning||4,419||3 months ago||2||mit||Jupyter Notebook|
|Repo for the Deep Reinforcement Learning Nanodegree program|
|Trlx||3,387||2 days ago||100||mit||Python|
|A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)|
|Polyaxon||3,317||4||11||2 days ago||334||June 05, 2022||122||apache-2.0|
|MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle|
This is pytorch implementation of distributed deep reinforcement learning.
In our system, there are two processes, Actor and Learner. In Learner process, thread of the replay memory runs at the same time, and these processes communicate using Redis.
git clone https://github.com/neka-nat/distributed_rl.git cd distributed_rl poetry install
sudo apt-get install redis-server
Setting Atari. openai/atari-py
The following command is running all actors and learner in localhost. The number of actor's processes is given as an argument.
poetry shell ./run.sh 4
Run r2d2 mode.
./run.sh 4 config/all_r2d2.conf
cd distributed_rl docker-compose up -d
Create EKS resource.
cd terraform terraform init terraform plan terraform apply