DeepRacer notebook using Amazon SageMaker RL and AWS RoboMaker services
This folder contains examples of how to use RL to train an autonomous deepracer. This is a jailbreaker for the AWS DeepRacer. This gives a glimse of architecture used to get the DeepRacer working.
deepracer_rl.ipynb: notebook for training autonomous race car.
Dockerfile: Custom docker instead of using SageMaker default docker
training_worker.py: Main entrypoint for starting distributed training job
markov/: Helper files for S3 upload/download
presets/default.py: Preset (configuration) for DeepRacer
rewards/default.py: Custom reward function
environments/deepracer_racetrack_env.py: Gym environment file for DeepRacer
actions/model_metadata_10_state.json: JSON file to customize your action space & the speed
lib/: redis configuration file and ppo_head.py customized tensorflow file copied to sagemaker container.
common/: helper function to build docker files.
How to use the notebook
- Login to your AWS account - SageMaker service (SageMaker Link)
- On the left tab select
Create notebook instance
- Fill up the notebook instance name. In the Additional configuration select atleast 25GB. This is because docker gets installed and takes up space.
- Create a new IAM role. Give root permission
- Select the git repository and clone this repository.
- Then click create notebook instance button at the button
- This takes like 2 min to create your notebook instance. Then click on the newly created instance and click on the juypter notebook.
- You will see all the github files and now run
- Run clean robomaker & sagemaker commands in the script only when you are done with training.