Remote Use

Instructions for using Robotics Backend

Minimum System Requirements

CPU: A 4-cored processor.
RAM: 2 gb RAM.
Memory: 20 gb of disk space.

Linux Users

Download Docker (minimum version of docker-py: 5.0.3).
Pull the current distribution of Robotics Backend (currently version 4.6.18):

docker pull jderobot/robotics-backend:latest

In order to obtain optimal performance, Docker should be using multiple CPU cores. In case of Docker for Mac or Docker for Windows, the VM should be assigned a greater number of cores.
It is recommended to use the latest image. However, older distributions of Robotics Backend can be found here.

Windows Users

Windows users should choose WSL 2 backend Docker installation if possible, as it has better performance than Hyper-V.

Install WSL2. Here’s a link to the tutorial (minimum version of docker hub: 4.30.0).
Install Docker Desktop. Docker made an app for Windows users to adapt the user experience. You can download it from this link.
Enable Docker Desktop WSL integration: In order to wsl2 to recognise Docker, you need to enable it. For that, go to Docker Desktop -> Settings -> Resources -> WSL integration. Click on the check box and the slider.
Pull the current distribution of Robotics Backend (currently version 4.6.18):

docker pull jderobot/robotics-backend:latest

In order to obtain optimal performance, Docker should be using multiple CPU cores. In case of Docker for Mac or Docker for Windows, the VM should be assigned a greater number of cores.
It is recommended to use the latest image. However, older distributions of Robotics Backend can be found here.

MacOs (NOT TESTED!)

Remember to add minium docker version to run a Bt Studio.

2. How to launch a Robotics Backend container?

Start a new docker container of the image and keep it running in the background:
The priority order is: NVIDIA -> Intel -> Only CPU

docker run --rm -it $(nvidia-smi >/dev/null 2>&1 && echo "--gpus all" || echo "") --device /dev/dri -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-backend:latest

Advanced Instructions on Linux

NVIDIA: For NVIDIA GPUs, acceleration can be achieved by installing the nvidia-container-runtime package, and then running the auto command above.
Integrated GPU ```bash docker run –rm -it –device /dev/dri -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-backend:latest

- **Only CPU:** 
```bash
docker run --rm -it -p 6080:6080 -p 1108:1108 -p 7163:7163 jderobot/robotics-backend:latest

Windows

For Windows machines, acceleration can be achieved for NVIDIA GPUs if a valid CUDA installation is available. Useful docs for proper installation of WSL2 + CUDA + Docker Desktop:

Once these requirements are ready, you should be able to run Bt Studio with GPU acceleration as follows:

docker run --rm -it --gpus all -v /usr/lib/wsl:/usr/lib/wsl -e LD_LIBRARY_PATH=/usr/lib/wsl/lib --device /dev/dri -p 6080:6080 -p 1108:1108 -p 7163:7163 -p 7164:7164 --link academy_db jderobot/robotics-backend::latest

Tips for Docker

Make sure to remove the container if you have problems with the exercise not loading with the following commands.

Locate the docker container used for Robotics Backend using

docker ps -a

Remove that container using

docker rm CONTAINER_ID

JdeRobot