update README
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README.md
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README.md
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@ -25,38 +25,261 @@ These templates are intended to be used with VSCode.
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To get started with our project templates install the [copier](https://copier.readthedocs.io/en/stable/) project
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templating utility.
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* [template-service-ros1-catkin](https://git.spirirobotics.com/Spiri/template-service-ros1-catkin)
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- [template-service-ros1-catkin](https://git.spirirobotics.com/Spiri/template-service-ros1-catkin)
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This template uses the last stable release of ROS1 (ros noetic) and supports python and c++ programming
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languages.
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ROS1 is considered end of life. It's recomended to use a ROS2 template instead
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* ROS2 template
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- ROS2 template
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We're working on it...
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## NVIDIA Container Toolkit
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## Single Vehicle Simulation
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[Source](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
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Ensure .env `SIM_DRONE_COUNT` is 1.
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### Installing with Apt
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1. `docker compose --profile uav-sim --profile ui up`
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2. Click `Launch Gazebo` on the menu.
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```bash
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curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
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&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
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sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
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sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
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```
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## Multi vehicle Simulation
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```bash
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sudo apt-get update
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```
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```bash
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sudo apt-get install -y nvidia-container-toolkit
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```
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#### Configuration
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##### Prerequisites
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- You installed a supported container engine (Docker, Containerd, CRI-O, Podman).
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- You installed the NVIDIA Container Toolkit.
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```bash
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sudo nvidia-ctk runtime configure --runtime=docker --cdi.enabled
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```
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```bash
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sudo systemctl restart docker
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```
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##### Rootless Mode
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To configure the container runtime for Docker running in [Rootless mode](https://docs.docker.com/engine/security/rootless/), follow these steps:
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1. Configure the container runtime by using the nvidia-ctk command:
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```bash
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nvidia-ctk runtime configure --runtime=docker --config=$HOME/.config/docker/daemon.json
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```
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2. Restart the Rootless Docker daemon
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```bash
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systemctl --user restart docker
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```
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3. Configure /etc/nvidia-container-runtime/config.toml by using the sudo nvidia-ctk command:
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```bash
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sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
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```
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### Sample Workload
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After you install and configure the toolkit and install an NVIDIA GPU Driver, you can verify your installation by running a sample workload.
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```bash
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sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
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```
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Expected output:
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```console
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 535.86.10 Driver Version: 535.86.10 CUDA Version: 12.2 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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| | | MIG M. |
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|===============================+======================+======================|
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| 0 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
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| N/A 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
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| | | N/A |
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+-------------------------------+----------------------+----------------------+
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+-----------------------------------------------------------------------------+
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| Processes: |
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| GPU GI CI PID Type Process name GPU Memory |
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| ID ID Usage |
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|=============================================================================|
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| No running processes found |
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+-----------------------------------------------------------------------------+
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```
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## Support for Container Device Interface(CDI)
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[Source](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html)
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### Prerequisites
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- You installed either the NVIDIA Container Toolkit or you installed the `nvidia-container-toolkit-base` package.
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The base package includes the container runtime and the `nvidia-ctk` command-line interface, but avoids installing the container runtime hook and transitive dependencies.
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The hook and dependencies are not needed on machines that use CDI exclusively
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- You installed an NVIDIA GPU Driver.
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Two common locations for CDI specifications are `/etc/cdi/` and `/var/run/cdi/`. The contents of the `/var/run/cdi/` directory are cleared on boot.
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1. Generate the CDI specification file:
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```bash
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sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml
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```
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Example output
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```console
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INFO[0000] Auto-detected mode as "nvml"
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INFO[0000] Selecting /dev/nvidia0 as /dev/nvidia0
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INFO[0000] Selecting /dev/dri/card1 as /dev/dri/card1
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INFO[0000] Selecting /dev/dri/renderD128 as /dev/dri/renderD128
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INFO[0000] Using driver version xxx.xxx.xx
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...
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```
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2. (Optional) Check the names of the generated devices:
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```bash
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nvidia-ctk cdi list
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```
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Output
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```console
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INFO[0000] Found 9 CDI devices
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nvidia.com/gpu=all
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nvidia.com/gpu=0
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```
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### Sample Workload
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```bash
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docker run --rm -ti --runtime=nvidia \
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-e NVIDIA_VISIBLE_DEVICES=nvidia.com/gpu=all \
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ubuntu nvidia-smi -L
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```
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Output
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```console
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GPU 0: NVIDIA GeForce RTX 3080 Laptop GPU (UUID: GPU-17c2b9a6-6be2-3857-f8e0-88143e2e621b)
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```
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## Technologies
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* Ubuntu 22.04.1 amd64
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* Docker Compose version v2.29.7
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* Python 3.10.12
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* pip 22.0.2
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* NVIDIA Container Toolkit CLI version 1.16.2
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* NVIDIA Driver 550.120
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* CUDA Version 12.4
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## How to Run
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Ensure variables in the `.env` file are correct.
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Install the required libraries in the python script.
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```bash
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pip install -r requirements.txt
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```
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### First Terminal
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1. `docker compose --profile ui up`
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2. Click `Launch Gazebo` on the menu.
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1. Start the user interface with the following command.
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```bash
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docker compose --profile ui up
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```
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2. Click `Launch Gazebo` on the menu.
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Simulated world and the drone model should be up and running in a Gazebo instance.
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### Second Terminal
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This will launch ardupilot, mavproxy and mavros sessions, and will scale by the `SIM_DRONE_COUNT` env variable.
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This will launch ardupilot, mavproxy and mavros services, and will scale up by the `SIM_DRONE_COUNT` env variable.
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1. `python3 sim_drone.py`
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1. Start the docker services with the following python script.
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```bash
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python3 sim_drone.py
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```
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### QGroundControl
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Simulated vehicle(s) should be connected to QGroundControl. `SIM_DRONE_COUNT` value should match the detected vehicle count on GCS.
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## Simulation Environment Variables
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`DRONE_SYS_ID` and `INSTANCE` environment variables are incremented by 1 for each additional simulated vehicle. `SERIAL0_PORT`, `SITL_PORT`, `MAVROS2_PORT`, `MAVROS1_PORT`,`FDM_PORT_IN` and `GSTREAMER_UDP_PORT` are incremented by 10. Without in-depth knowledge, changing the default value for these ports are not recommended.
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| Variable | Type | Default | Description |
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| :----------------: | :----: | :--------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------- |
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| DRONE_SYS_ID | int | 1 | System-ID for the simulated drone. |
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| INSTANCE | int | 0 | Instance of simulator. |
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| SERIAL0_PORT | int | 5760 | Mavproxy master port the simulation communicating on. |
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| SITL_PORT | int | 5501 | Mavproxy Software in the Loop(SITL) port to send simulated RC input for the simulator. |
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| MAVROS2_PORT | int | 14560 | MAVROS ROS 2 UDP port |
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| MAVROS1_PORT | int | 14561 | MAVROS ROS 1 UDP port |
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| FDM_PORT_IN | int | 9002 | Gazebo Flight Dynamics Model (FDM) UDP port |
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| GSTREAMER_UDP_PORT | int | 5600 | UDP Video Streaming port |
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| ROS_MASTER_URI | string | "http://0.0.0.0:11311" | This tells ROS 1 nodes where they can locate the master |
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| ARDUPILOT_VEHICLE | string | "-v copter -f gazebo-mu --model=JSON -L CitadelHill" | "-v" is vehicle type,"-L" start location, "-f" is vehicle frame type, "--model" overrides simulation model to use |
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| WORLD_FILE_NAME | string | "citadel_hill_world.sdf" | Name of the file that exists in the worlds folder. |
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| WORLD_NAME | string | "citadel_hill" | Name of the world defined in the world file. |
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| DRONE_MODEL | string | "spiri_mu" | Drone model that exists in the models folder. |
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| SIM_DRONE_COUNT | int | 1 | Number of drones to be simulated. |
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| GCS_PORT | int | 14550 | Ground Control Station(GCS) UDP connection port. |
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### Understanding Multi-Vehicle Simulation Parameters
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For instance, if `SIM_DRONE_COUNT` is 2, each additional vehicle's ports are incremented by 10.
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First simulated vehicle would have these following values,
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| Variable | Value |
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| :----------------: | :---: |
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| DRONE_SYS_ID | 1 |
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| INSTANCE | 0 |
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| SERIAL0_PORT | 5760 |
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| SITL_PORT | 5501 |
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| MAVROS2_PORT | 14560 |
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| MAVROS1_PORT | 14561 |
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| FDM_PORT_IN | 9002 |
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| GSTREAMER_UDP_PORT | 5600 |
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Second Vehicle,
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| Variable | Value |
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| :----------------: | :---: |
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| DRONE_SYS_ID | 2 |
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| INSTANCE | 1 |
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| SERIAL0_PORT | 5770 |
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| SITL_PORT | 5511 |
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| MAVROS2_PORT | 14570 |
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| MAVROS1_PORT | 14571 |
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| FDM_PORT_IN | 9012 |
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| GSTREAMER_UDP_PORT | 5610 |
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Video stream would be available on UDP port on 5610 for the second vehicle after enabling streaming.
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