🦬 MultiBeast

A research codebase for asynchronous RL.

Quickstart

pip install multibeast provides:

🐄 Asynchronous, distributed RL algorithms based on the IMPALA implementation in moolib, the successor to torchbeast.

🐂 Training results tracked and available on wandb, with hydra config files, for reproducibility.

🐃 An avoidance of inheritance & complexity, to facilitate fast research iteration. Core implementation details are kept as simple and self-contained as possible, in one folder.

See examples/ on how to use multibeast in your code.

Setup from source

[optional] Install moolib manually

# Setup conda env
conda create --name mb python=3.7
conda activate mb
pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

# install moolib
conda install -c conda-forge cmake==3.14.5  # need cmake>=3.14 for moolib
cd third_party/moolib/
USE_CUDA=1 python setup.py build --debug install  # or pip install -e .

Compiling in Docker

If you are having issues building moolib, then try compiling inside a Docker container.

export CONDA_PATH=~/miniconda3
export REPO_PATH=$PWD

docker pull nvidia/cuda:11.1.1-cudnn8-devel-ubuntu18.04
docker run -it \
  -v $REPO_PATH:$REPO_PATH \
  -v $CONDA_PATH:$CONDA_PATH \
  -e REPO_PATH=${REPO_PATH} \
  -e CONDA_PATH=${CONDA_PATH} \
  --gpus all \
  --name mb \
  nvidia/cuda:11.1.1-cudnn8-devel-ubuntu18.04 bash

# inside container
export PATH=${CONDA_PATH}/bin:$PATH
cd $REPO_PATH

# then go back and install moolib

If you run into the following error, then try cloning moolib/third_party/ submodule repos first by running git submodule update --init --recursive:

CMake Error at CMakeLists.txt:51 (pybind11_add_module):
  Unknown CMake command "pybind11_add_module".

If cudnn is missing then just try reinstalling cuda:

apt-get update \
  && apt-get install -y -qq --no-install-recommends \
    git \
    vim \
    wget \
    pkg-config \
    software-properties-common \
  && rm -rf /var/lib/apt/lists/*

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin \
  && mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600 \
  && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub \
  && add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
apt-get update \
  && apt-get -y install cuda-toolkit-11-4 \
  && apt-get clean && rm -rf /var/lib/apt/lists/* 
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

Verify moolib:

pip install autorom[accept-rom-license]
pip install gym[atari]

python -m moolib.broker  # Note that a **single broker is enough** for all your experiments.
export BROKER_PORT=4431  # Should match `Broker listening at 0.0.0.0:4431`
export BROKER_IP=$(echo $SSH_CONNECTION | cut -d' ' -f3)  # Should give your machine's IP.
export BROKER_IP=0.0.0.0  # can also use local IP address if broker if on same machine

python -m examples.vtrace.experiment connect=$BROKER_IP:$BROKER_PORT \
    wandb=0 \
    savedir=outputs/moolib-atari/savedir \
    project=moolib-atari \
    group=Zaxxon-Breakout \
    env.name=ALE/Breakout-v5

# To add more peers to this experiment, start more processes with the
# same `project` and `group` settings, using a different setting for
# `device` (default: `'cuda:0'`).

Try pip uninstall gym atari-py ale-py && pip install gym[atari] if you run into the following error:

Error in env: ModuleNotFoundError: No module named 'gym.envs.atari'

[link]

If you run into the following error, then try using the local IP address (0.0.0.0 or 127.0.0.1):

terminate called after throwing an instance of 'std::runtime_error'
  what():  In connectFromLoop at .../moolib/src/tensorpipe/tensorpipe/transport/uv/uv.h:313 "rv < 0: network is unreachable"
Aborted (core dumped)

[link]

Install multibeast

git clone https://github.com/etaoxing/multibeast.git
git submodule update --init --recursive  # can skip if moolib is installed manually
pip install -e .

etaoxing / multibeast