Installation 🛠

Pip

CPUGPU

# 0. Prerequisites: Python3.8+ & Pip
# 1. Pull git repository from github
git clone https://github.com/sdpkjc/abcdrl.git && cd abcdrl
# 2. Install dependencies
pip install -r requirements/requirements.txt
# 3. Run DQN algorithm
python abcdrl/dqn.py

# 0. Prerequisites: Conda & Nvidia Driver
# 1. Pull git repository from github
git clone https://github.com/sdpkjc/abcdrl.git && cd abcdrl
# 2. Create and activate virtual environment
conda create -n abcdrl python=3.9 pip && conda activate abcdrl
# 3. Install cudatoolkit and the corresponding version of Pytorch
conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
# 4. Install dependencies
pip install -r requirements/requirements.txt
# 5. Run DQN algorithm
python abcdrl/dqn.py

Note

There are many ways to install pytorch, refer to Mu Li's video tutorials for details.

Version selection of cudatoolkit is related to Nvidia Driver version, refer to Mu Li's video tutorial and Pytorch installation page.

Docker

CPUGPU

# 0. Prerequisites: Docker
# 1. Pull git repository from github
git clone https://github.com/sdpkjc/abcdrl.git && cd abcdrl
# 2. Build docker image
docker build . -t abcdrl
# 3. Run DQN algorithm
docker run --rm abcdrl python abcdrl/dqn.py

# 0. Prerequisites: Docker & Nvidia Drive & NVIDIA Container Toolkit
# 1. Pull git repository from github
git clone https://github.com/sdpkjc/abcdrl.git && cd abcdrl
# 2. Build docker image
docker build . -t abcdrl
# 3. Run DQN algorithm
docker run --rm --gpus all abcdrl python abcdrl/dqn.py

Note

Docker Container parameters and the detailed installation process of the NVIDIA Container Toolkit can be found here: Nvidia Docker.

Warning

Build docker image using our provided Dockerfile and train on GPU. Nvidia Driver needs to support CUDA11.7.

Using nvidia-smi command, look at the CUDA Version: xx.x in the top right corner. It need to be 11.7 or greater.