Skip to content
Switch branches/tags


Provides a quick and easy way to get up and running with a DeepRacer training environment in Azure or AWS, using either the Azure N-Series Virtual Machines or AWS EC2 Accelerated Computing instances.


DeepRacer-For-Cloud (DRfC) started as an extension of the work done by Alex (, which is again a wrapper around the amazing work done by Chris ( With the introduction of the second generation Deepracer Console the repository has been split up. This repository contains the scripts needed to run the training, but depends on Docker Hub to provide pre-built docker images. All the under-the-hood building capabilities are in the Deepracer Build repository.

Main Features

DRfC supports a wide set of features to ensure that you can focus on creating the best model:

  • User-friendly
    • Based on the continously updated community Robomaker and Sagemaker containers, supporting a wide range of CPU and GPU setups.
    • Wide set of scripts (dr-*) enables effortless training.
    • Detection of your AWS DeepRacer Console models; allows upload of a locally trained model to any of them.
  • Modes
    • Time Trial
    • Object Avoidance
    • Head-to-Bot
  • Training
    • Multiple Robomaker instances per Sagemaker (N:1) to improve training progress.
    • Multiple training sessions in parallel - each being (N:1) if hardware supports it - to test out things in parallel.
    • Connect multiple nodes together (Swarm-mode only) to combine the powers of multiple computers/instances.
  • Evaluation
    • Evaluate independently from training.
    • Save evaluation run to MP4 file in S3.
  • Logging
    • Training metrics and trace files are stored to S3.
    • Optional integration with AWS CloudWatch.
    • Optional exposure of Robomaker internal log-files.
  • Technology
    • Supports both Docker Swarm (used for connecting multiple nodes together) and Docker Compose (used to support OpenGL)


Full documentation can be found on the Deepracer-for-Cloud GitHub Pages.


  • For general support it is suggested to join the AWS DeepRacing Community. The Community Slack has a channel #dr-drfc-setup where the community provides active support.
  • Create a GitHub issue if you find an actual code issue, or where updates to documentation would be required.


Creates an AWS DeepRacing training environment which can be deployed in the cloud, or locally on Ubuntu Linux, Windows or Mac.