https://github.com/redleader962/dockerized-snow

Containerized development workflow for the NorLab_MPPI and SNOW_AutoRally projects leveraging nvidia-docker technology. Believe it or not, it's configured for developing with ROS melodic in python 3.6.
https://github.com/redleader962/dockerized-snow

deep-reinforcement-learning model-predictive-control nvidia-docker python-3-6 robotics ros-melodic

Last synced: 3 months ago
JSON representation

Host: GitHub
URL: https://github.com/redleader962/dockerized-snow
Owner: RedLeader962
Created: 2021-06-16T02:39:57.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2022-03-22T22:39:58.000Z (about 4 years ago)
Last Synced: 2025-01-19T07:42:21.818Z (over 1 year ago)
Topics: deep-reinforcement-learning, model-predictive-control, nvidia-docker, python-3-6, robotics, ros-melodic
Language: Shell
Homepage:
Size: 12.4 MB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# _Dockerized-SNOW_

^{Project related link:

NLSAR

(Youtrack) •

norlabsnow

(Docker Hub) •

NorLab_MPPI

(GitHub) •

SNOW_AutoRally

(GitHub)}

[comment]: <> (
)

Containerized development workflow for the _NorLab_MPPI_ and _SNOW_AutoRally_ projects leveraging [_nvidia-docker_](https://github.com/NVIDIA/nvidia-docker) technology.
Believe it or not, it's configured for developing with **ROS melodic** in **python 3.6**.

**Key benefit:** custom dependency management, development environment consistency, easy deployment to robots compute box and results reproducibility.

Author: [Luc Coupal](https://redleader962.github.io)

![](visual/splash_and_promt_screenshot_6.png)

## Container prompt legend

[comment]: <> (![](drawio/prompt_legend.png))

## Usage

Quick start for the NorLab_MPPI project on x86 workstation

```

To open an terminal inside _MyCoolNmae_, use the following convenient script
```shell
ds_attach MyCoolNmae
```
or use `sudo docker exec -it MyCoolName bash`

Quick start for the NorLab_MPPI project on Apple M1 (arm64) workstation

- It's the same image as the `arm64-l4t` but with PyTorch and Numba compiled specifically for `arm64-Darwin`
- **Tips:** You can use the `--help` flag for usage instruction on most `ds_*` command
- Be advise, cuda is not supported on Apple computer so PyTorch and Numba will work on cpu

```shell
# Create a directory for your development source code if you dont already have one
mkdir -p ~/Repositories && cd ~/Repositories

# Clone both repositories
sudo git clone https://github.com/norlab-ulaval/NorLab_MPPI.git
sudo git clone https://github.com/RedLeader962/Dockerized-SNOW.git

# Install aliases
cd ~/Repositories/Dockerized-SNOW
source ds_setup.bash

# Pull the norlab-mppi-develop image from norlabsnow Dockerhub with the arm64-Darwin-ubuntu18.04 tag
sudo docker pull norlabsnow/norlab-mppi-develop:arm64-Darwin-ubuntu18.04

# Create a new docker image instance for development on your machine and start working on the
# NorLab_MPPI project using ROS melodic, Python 3 and Pytorch right away.
ds_instantiate_develop --runTag=arm64-Darwin-ubuntu18.04 --osx --name=MyCoolName --src="$HOME/Repositories/NorLab_MPPI"

```

To open an terminal inside _MyCoolName_, use the following convenient script
```shell
ds_attach MyCoolName
```
or use `sudo docker exec -it MyCoolName bash`

Quick start for the NorLab_MPPI project on Apple (x86) workstation

- **Tips:** You can use the `--help` flag for usage instruction on most `ds_*` command
- Be advise, cuda is not supported on Apple computer so PyTorch and Numba will work on cpu only

Use a x86 docker image and let docker use roseta to emulate the x86 architecture
```shell
# Install roseta
softwareupdate --install-rosetta
```

```shell
# Create a directory for your development source code if you dont already have one
mkdir -p ~/Repositories && cd ~/Repositories

# Clone both repositories
sudo git clone https://github.com/norlab-ulaval/NorLab_MPPI.git
sudo git clone https://github.com/RedLeader962/Dockerized-SNOW.git

# Install aliases
cd ~/Repositories/Dockerized-SNOW
source ds_setup.bash

# Pull the norlab-mppi-develop image from norlabsnow Dockerhub with the arm64-Darwin-ubuntu18.04 tag
sudo docker pull norlabsnow/norlab-mppi-develop:x86-ubuntu18.04

# Create a new docker image instance for development on your machine and start working on the
# NorLab_MPPI project using ROS melodic, Python 3 and Pytorch right away.
ds_instantiate_develop --platform='linux/amd64' --runTag=x86-ubuntu18.04 --osx --name=MyCoolName --src="$HOME/Repositories/NorLab_MPPI"
# Runnning `uname -m` inside the container will confirm the type of architecture

```

To open an terminal inside _MyCoolname_, use the following convenient script
```shell
ds_attach MyCoolname
```
or use `sudo docker exec -it MyCoolName bash`

Quick start for the SNOW_AutoRally project on x86 workstation

- **Requirement:** _docker_ and _nvidia container toolkit_ must be installed ([follow install step 1 and 2](how_to_readme/README_x86_architecture.md))
- **Tips:** You can use the `--help` flag for usage instruction on any dockerized-snow bash script

```shell
# Create a directory for your development source code if you dont already have one
mkdir -p ~/Repositories && cd ~/Repositories

# Clone both repositories
sudo git clone https://github.com/RedLeader962/SNOW_AutoRally.git
sudo git clone https://github.com/RedLeader962/Dockerized-SNOW.git
cd ~/Repositories/Dockerized-SNOW

# Pull the norlab-mppi-develop image from norlabsnow Dockerhub with the x86-ubuntu18.04 tag
bash ds_build_dependencies.bash --x86 --GT-AR
bash ds_build_develop.bash --x86 --GT-AR

# Create a new docker image instance for development on your machine and start working on the
# NorLab_MPPI project using ROS melodic, Python 3 and Pytorch right away.
bash ds_instantiate_develop.bash --runTag=x86-ubuntu18.04 --name=THEgtar --src="$HOME/Repositories/SNOW_AutoRally"

.|'''.|
||.. '
····································· Dockerized-SNOW ••········································
''|||.
. '||
|'....|'

https://norlab.ulaval.ca
https://redleader962.github.io

root@norlab-og:/#
```
Then follow the step at [SNOW_AutoRally: Autonomous Driving in Simulation using MPPI](https://github.com/RedLeader962/SNOW_AutoRally#autonomous-driving-in-simulation-using-mppi)
```docker
roslaunch autorally_gazebo autoRallyTrackGazeboSim.launch

...
```

## High level architecture

_{( View diagram

fullscreen

•

edit

diagram )}

---
### Instruction:
- [**★ | Setup PyCharm for local development using a Docker Python interpreter**](how_to_readme/README_setup_pycharm_python_docker_interpreter.md)
- [**Using the _nvidia-docker_ image on a _Jetson_ device (`arm64-l4t`)**](how_to_readme/README_Jetson_builded.md)
- [**Using the _nvidia-docker_ image on a `x86` host**](how_to_readme/README_x86_architecture.md)
- [**Building the `arm64-l4t` nvidia-docker image on a `x86` host using _qemu_ virtualization**](how_to_readme/README_cross_compiler.md)

### How-to
- [**How-to _nvidia-docker_ manualy (a quick start)**](how_to_readme/README_docker_manualy_quickstart.md)
- [**Test AutoRally Configuration (**Revised instruction**)**](https://github.com/RedLeader962/SNOW_AutoRally#test-autorally-configuration-in-gazebo-revised-instruction)
- [How-to push image localy builded image to docker hub from command line](how_to_readme/README_push_to_dockerhub.md)

### Images:
To pull the latest image from _docker hub_, execute the following in terminal:
```shell
sudo docker pull :
```
with ``= _theImageName_ and ``= _theHostArchitecture_

---
### References:

nvidia-docker Documentation:

- [nvidia-docker: Build and run Docker containers leveraging NVIDIA GPUs](https://github.com/NVIDIA/nvidia-docker)
- [NVIDIA Container Runtime on _Jetson_](https://github.com/NVIDIA/nvidia-docker/wiki/NVIDIA-Container-Runtime-on-Jetson)
- [Driver containers](https://github.com/NVIDIA/nvidia-docker/wiki/Driver-containers)
- [NVIDIA Cloud Native Technologies](https://docs.nvidia.com/datacenter/cloud-native/#)
- Base image for _jetson_:
- https://ngc.nvidia.com/catalog/containers/nvidia:l4t-base
- https://developer.nvidia.com/embedded/jetson-cloud-native
- Base image with _CUDA_ and _OpenGL_ support:
- https://hub.docker.com/r/nvidia/cudagl/
- https://github.com/NVIDIA/nvidia-docker/wiki/CUDA
- https://ngc.nvidia.com/catalog/containers/nvidia:cudagl

Docker Documentation:

- [Use the Docker command line | Docker Documentation](https://docs.docker.com/engine/reference/commandline/cli/)
- [Dockerfile reference | Docker Documentation](https://docs.docker.com/engine/reference/builder/)

---

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