DEV Community

Darren Broderick (DBro)
Darren Broderick (DBro)

Posted on

AWS DeepRacer Local Training DRFC

Training Locally on DRFC -> Troubleshooting

Image description

So you're training locally on DRFC for AWS DeepRacer, great!
But it's not always straightforward, sometimes it's easy to forget how to run or update your stack after the inital setup or you get new errors when starting training again, especially after a season break.

This article an be used as a supplement to the main DRFC guide.
It's a list of commands/ steps I follow and troubleshooting problems / solutions I've faced when training locally.
Hopefully it can help you too, BUT it is tailored to how I run things FYI.


  1. Handy monthly items
  2. General Training Starting Steps
  3. Virtual DRFC Upload
  4. Physical DRFC Upload
  5. Container Update Links
  6. Open GL Robomaker
  7. New Sagemaker -> M40 Tagging
  8. Log Analysis
  9. Run Second DRFC Instance
  10. Steps for fresh DRFC
  11. Troubleshooting DRFC
  12. Miscellaneous

Handy monthly items

Latest Track File(For Log Analysis)

Latest Robomaker Container (For Training) deep racercommunity/deepracer-robomaker/tags?page=1&ordering=last_updated

General Training Starting Steps

These are commands I run if starting from a reboot
source bin/
sudo liquidctl set fan1 speed 30 
(This is my own fan setting)
dr-increment-training -f
dr-update OR dr-update-env (I tend to favour -env)
dr-start-training OR dr-start-training -w
dr-start-viewer OR dr-update-viewer OR http://localhost:8100
dr-logs-robomaker (dr-logs-robomaker -n2) for worker 2 etc
nvidia-smi (check temperatures)
htop to check threads and memory usage 
(Try to maximise my worker count, but keep to <75%)
dr-start-evaluation -c & dr-stop-evaluation

Virtual DRFC Upload

aws configure
dr-upload-model -b -f
Uploads best checkpoint to s3

Physical DRFC Upload

dr-upload-car-zip -f
Sagemaker must be running for this to work
Only uses last checkpoint, not best

Container Update Links

Check your version with command "docker images"


For new Sagemaker images follow this guide:

Robomaker deep racercommunity/deepracer-robomaker/tags?page=1&ordering=last_updated

RL Coach
Linux terminal startup script is called ".bashrc"

Open GL Robomaker
example -> docker pull awsdeepracercommunity/deepracer-robomaker:4.0.12-gpu-gl
system.env: (Below bullet points)

DR_HOST_X=True; uses the local X server rather than starting one within the docker container.

DR_ROBOMAKER_IMAGE; choose the tag for an OpenGL enabled image - e.g. cpu-gl-avx for an image where Tensorflow will use CPU orgpu-glor an image where also Tensorflow will use the GPU.
Do echo $DISPLAY and see what that is, should be :0 but might be :1
Make system.env dr_display value same as echo value

source utils/
source utils/
you should see the xorg stuff in nvidia-smi once you run the script
sudo pkill x11vnc
sudo pkill Xorg

New Sagemaker - M40 Tagging

run -> docker tag 2b4e84b8c10a awsdeepracercommunity/deepracer-sagemaker:gpu-m40

Log Analysis

run -> dr-start-loganalysis
Only change needed is for model_logs_root 
e.g. 'minio/bucket/model-name/0'
Might have to upload the new track to tracks folder
Repo for all racer data

Run Second DRFC Instance

Create 2 different run.env or use 2 folders
The DR_RUN_ID keeps things separate
Only 1 minio should be running
Use a unique model name
Run source bin/ run-1.env to activate a separate environment

Steps for fresh DRFC

./bin/ && sudo reboot
docker start
Run LARS script -> source bin/
docker swarm init (If issues run step 7 and grab IP, run step 8, check bottom for example)
ifconfig -a
docker swarm init
docker swarm init - advertise-addr
sudo ./bin/ -a gpu -c local
docker images
docker tag xxxxxxx awsdeepracercommunity/deepracer-sagemaker:gpu-m40
source bin/
vim run.env
vim system.env
aws configure - profile minio
aws configure 
(use real AWS IAM details below to allow upload of models)
docker ps -a
Setup multiple GPU
cd custom-files
vim on the 3 files

Different editor option to vim

Troubleshooting DRFC

General Tip
It's always worth checking if you are missing anything new that might have been added to the default files that DRFC would then be expecting.
In particular, the system.env or template-run.env files and compare them with your own.

Troubleshooting Docker Start

Docker failed to start
docker ps -a
docker service ls
sudo service docker status
sudo service - status-all
sudo systemctl status docker.service

Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?
sudo systemctl stop docker
sudo systemctl start docker
sudo systemctl enable docker
sudo systemctl restart docker
sudo service docker restart
snap list
sudo su THEN apt-get install
Re-run Installing Docker (From Lars)
cat /etc/docker/daemon.json
apt-cache policy docker-ce
sudo tail /var/log/syslog
sudo cat /var/log/syslog | grep dockerd | tail

"For me it was a missing file"
udo gedit /etc/docker/daemon.json
Make /etc/docker/daemon.json look like below:

 "runtimes": {
 "nvidia": {
 "path": "nvidia-container-runtime",
 "runtimeArgs": []
 "default-runtime": "nvidia"
Make /etc/docker/daemon.json look like below:
sudo systemctl stop docker then sudo systemctl start docker
test with -> docker images

Troubleshooting Docker Swarm

Could not connect to the endpoint URL: "http://localhost:9000/bucket
Error response from daemon: This node is not a swarm manager. Use "docker swarm init" or "docker swarm join" to connect this node to swarm and try again.
You might have to disable ipv6 to stop docker pulling from multiple addresses
Here's how to disable IPv6 on Linux if you're running a Red Hat-based system:
Open the terminal window.
Change to the root user.
Type these commands:
sysctl -w net.ipv6.conf.all.disable_ipv6=1
sysctl -w net.ipv6.conf.default.disable_ipv6=1
sysctl -w net.ipv6.conf.tun0.disable_ipv6=1
To re-enable IPv6, type these commands:
sysctl -w net.ipv6.conf.all.disable_ipv6=0
sysctl -w net.ipv6.conf.default.disable_ipv6=0
sysctl -w net.ipv6.conf.tun0.disable_ipv6=0
sysctl -p

run -> ./bin/
run -> docker pull minio/minio:RELEASE.2022–10–24T18–35–07Z
DR_MINIO_IMAGE in system.env, make sure it's set to:

Other Fixes That Might Work for minio

run -> docker swarm init
Error response from daemon: This node is already part of a swarm. Use "docker swarm leave" to leave this swarm and join another one.
run -> docker swarm leave
run -> docker swarm init
Error response from daemon: could not choose an IP address to advertise since this system has multiple addresses on interface
run -> docker network ls
sagemaker-local should appear in the network
There's a new fix script for this called "" in the bin folder.
run-> docker swarm leave - force
run -> source bin/

Script might need address, error message will say, This node is not a swarm manager. Use "docker swarm init"
run -> docker swarm init (and grab the first addr, example below)
docker swarm init - advertise-addr 2a00:23c8::d6c3:4a71:9adb:87ad

Swarm initialized: current node (wv3eqpslrstc6hm7n65744z) is now a manager.
ifconfig -a
You don't need to join the token

Swarm is a docker concept, you can theoretically connect multiple machines together and run DRFC over multiple machines, sagemaker on one PC, robomakers spread out, but once you have cloned DRFC you can now do bin/ -a gpu -c local

Issue - Minio kept making new containers every 10 seconds

docker service rm s3_minio
source bin/

Issue - Minio containers kept exiting within 7 seconds
docker ps -a
docker service rm s3_minio
docker-compose -f $DR_DIR/docker/docker-compose-local.yml -p s3 up
docker ps
ls -l data
ls -l
Issue was I ran the init script as root
Fix -> chown -R dbro:dbro .
docker-compose -f $DR_DIR/docker/docker-compose-local.yml -p s3 up
docker ps -a showed there were now 2 minio's running
docker-compose -f $DR_DIR/docker/docker-compose-local.yml -p s3 down
docker stack rm s3
docker ps

- - - - - General Notes - - - - -

The m40 runs sagemaker docker
System ram runs robomaker
You can offload some of the robomaker to gpu by using the opengl image, but generally yes
Basically model is living inside the GPU memory
training checkpoints are in - cd data/minio/bucket

Wouldn't go any higher than what "htop" shows below because you're at 80% on all threads
- - - - - Additional Scripts - - -

Create script ""

if [[ "${ARCH}" == "gpu" ]];
    distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
    curl -s -L | sudo apt-key add -
    curl -s -L$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
    sudo apt-get update && sudo apt-get install -y --no-install-recommends nvidia-docker2 nvidia-container-toolkit nvidia-container-runtime
    cat /etc/docker/daemon.json | jq 'del(."default-runtime") + {"default-runtime": "nvidia"}' | sudo tee /etc/docker/daemon.json
Enter fullscreen mode Exit fullscreen mode




Check temperature commands

nvidia-smi -l 60
watch -n900 nvidia-smi (Every 15 minutes auto calls)

Set fan speed commands

sudo liquidctl set fan1 speed 30
sudo liquidctl set fan1 speed 0

Check specs / stats commands

nvidia-smi -L
GeForce GTX 1650 -> nvidia-smi -a -i 0
M40 Specs -> nvidia-smi -a -i 1
lspci -k | grep -EA3 'VGA|3D|Display'
top (checks processors to help see worker limits)
free -m
docker stats
docker run - rm - gpus all nvidia/cuda:11.6.0-base-ubuntu20.04 nvidia-smi

Useful Links

Full Guide -
Sudo -
Training on multiple GPU -
nvidia monitor -
Tesla M40 24GB specs -
Complex shutdown -
Sudo shutdown -
Video trimmer -
Flatpak -

Installation commands

sudo snap install jupyter
sudo apt install git
sudo apt install nvidia-cuda-toolkit
sudo apt install curl
sudo apt install jq
sudo pip install liquidctl (to install fan controller globally)
sudo apt install net-tools
sudo apt install vim
sudo apt-get install htop
sudo apt install hddtemp
sudo apt install lm-sensors
pip install - user pipenv
sudo apt install pipenv
pipenv install jupyterlab

Installing Docker

sudo su (run from root)
curl -fsSL | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] $(lsb_release -cs) stable"
sudo apt-get update && sudo apt-get install -y - no-install-recommends docker-ce docker-ce-cli
sudo apt-get install -y - no-install-recommends nvidia-docker2 nvidia-container-toolkit nvidia-container-runtime
sudo apt-get upgrade

Steps for Cuda upgrade
First removed existing:
sudo dpkg -P $(dpkg -l | grep nvidia-driver | awk '{print $2}')
sudo apt autoremove

then added new:
sudo mv /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv - fetch-keys
sudo add-apt-repository "deb /"
sudo apt update
sudo apt -y install cuda

then rebooted and do nvidia-smi

NVIDIA-SMI 510.47.03

Driver Version: 510.47.03

CUDA Version: 11.6

Thank you!

Top comments (0)