Provides a quick and easy way to get up and running with a DeepRacer training environment using a cloud virtual machine or a local compter, such AWS EC2 Accelerated Computing instances or the Azure N-Series Virtual Machines.
DRfC runs on Ubuntu 20.04, 22.04 and 24.04. GPU acceleration requires a NVIDIA GPU, preferrably with more than 8GB of VRAM.
DeepRacer-For-Cloud (DRfC) started as an extension of the work done by Alex (https://github.com/alexschultz/deepracer-for-dummies), which is again a wrapper around the amazing work done by Chris (https://github.com/crr0004/deepracer). 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 Simapp repository.
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 container, 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
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-training-local 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.