Building a Modern Offline ROS 2 Workshop Infrastructure: ROSCon India 2025

ROS2

Docker

Zenoh

Workshop

ROSCon

NVIDIA

Gazebo

How we built Docker infrastructure with modern Gazebo Sim, GPU acceleration, DDS↔︎Zenoh bridging, complete offline support, and handled last-minute deprecation challenges

Author

Physical AI Lab Team

Published

December 15, 2025

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║                                                                               ║
║                    India 2025 Workshop - Container Launcher                   ║
║                         December 18-20, COEP Pune                             ║
║                                                                               ║
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TL;DR

We built a production-grade Docker infrastructure for ROSCon India 2025 workshops (Dec 18-20, COEP Pune):

7 Docker images for offline ROS 2 workshops
Modern Gazebo Sim (NOT Classic - avoided EOL trap!)
GPU acceleration + Zenoh bridging + complete Nav2 stack
100% offline-capable with bundled models
Multi-role certification process for quality assurance

Key Innovation: Discovered TurtleBot3 uses deprecated Gazebo Classic (EOL Jan 2025) at Phase 7, pivoted to ros-gz-sim-demos before committing to 80GB of offline images. This saved the workshop from teaching deprecated technology.

Why I Built This: As a workshop attendee, I wanted to arrive prepared—not scrambling to install dependencies while the instructor moves on. This setup gives me multiple environments ready to go, so I can focus on learning and experimenting rather than troubleshooting.

The Problem

Context: ROSCon India 2025 marks the first ROSCon in India, happening December 18-20 at COEP Pune. I’m attending two hands-on workshops:

Workshop 3: Zenoh Networking (DDS ↔︎ Zenoh bridging over WiFi)
Workshop 4: IMU Perception with Visual SLAM

What I Wanted to Prepare For:

No Internet Dependency: I didn’t want to be stuck downloading packages while the workshop moved on
GPU-Ready: My RTX 5090 should be ready for VSLAM and NVBlox demos out of the box
Multiple Middleware Options: Both CycloneDDS and Zenoh containers, so I can follow either track
Real Hardware Ready: RealSense D435i camera and IMU working before I arrive
Experiment Freely: Extra environments to try things without breaking the workshop setup
Modern Stack: Discovered TurtleBot3 uses deprecated Gazebo Classic—pivoted to modern Gazebo Sim

Architecture Overview

Three-Tier Docker Image Strategy

TIER 1: Base Images (pulled from registries)
├── nvcr.io/nvidia/isaac/ros:x86_64-ros2_humble (~22GB)
└── osrf/ros:jazzy-desktop-full (~3.5GB)

TIER 2: Custom Base Images (build locally)
├── isaac-ros-base:humble (VSLAM, NVBlox, RealSense, Go2 SDK, Nav2, ros-gz-sim-demos)
└── jazzy-base:latest (MuJoCo, Claude Code, Playwright, Nav2, ros-gz-sim-demos)

TIER 3: Workshop Images (docker-compose)
├── workshop3-humble-dds (CycloneDDS + Zenoh Bridge)
├── workshop3-jazzy-zenoh (rmw_zenoh via apt)
├── workshop3-humble-zenoh (rmw_zenoh source - BROKEN, needs Rust)
├── workshop4-imu (IMU tools, robot_localization)
└── robot-humble (Jetson communication)

Two-Track Rationale

Track A: NVIDIA Isaac ROS (Humble) - GPU-accelerated perception (VSLAM, NVBlox) - CycloneDDS 0.10.2 (required by Unitree Go2 SDK) - Mature ecosystem for robotics

Track B: OSRF Jazzy - Latest ROS 2 LTS - rmw_zenoh available via apt (no source build needed) - Cleaner for Zenoh workshop demos

Why Not One Image? NVIDIA base image is 22GB (overkill for Zenoh demos). Different RMW implementations require separation.

The Docker Images

Image	Base	Size	Purpose	Status
isaac-ros-base	NVIDIA Isaac ROS Humble	24.1GB	GPU perception, Go2 SDK, Nav2, ros-gz-sim-demos	✅ Production
jazzy-base	OSRF Jazzy	2.85GB	Latest ROS 2, rmw_zenoh, ros-gz-sim-demos	✅ Production
workshop3-humble-dds	isaac-ros-base	24.2GB	CycloneDDS + zenoh-bridge v1.7.1	✅ Primary W3
workshop3-jazzy-zenoh	jazzy-base	2.95GB	Native rmw_zenoh v0.2.9	✅ Primary W3 (alt)
workshop3-humble-zenoh	isaac-ros-base	24.2GB	rmw_zenoh source (needs Rust)	❌ Broken (known)
workshop4-imu	isaac-ros-base	24.2GB	imu_tools, robot_localization, rtabmap	✅ Production
robot-humble	isaac-ros-base	24.1GB	Same as base (Jetson comms)	✅ Production

Total Disk: ~150GB uncompressed → ~80-95GB compressed (zstd -19)

Key Technical Details

Choosing Modern Gazebo Over TurtleBot3

While setting up TurtleBot3 (the standard tutorial robot), I noticed this warning:

ros2 launch turtlebot3_gazebo empty_world.launch.py
# [WARN] Gazebo Classic is end-of-life. Please migrate to new Gazebo.

Gazebo Classic EOL is January 31, 2025—just 6 weeks after the workshop. I switched to ros-gz-sim-demos instead:

ros2 launch ros_gz_sim_demos diff_drive.launch.py
# Uses modern Gazebo Sim - actively maintained

Bonus: The models are bundled in the Debian package—no internet needed to download from Gazebo Fuel:

/opt/ros/jazzy/share/ros_gz_sim_demos/models/vehicle/
/opt/ros/jazzy/share/ros_gz_sim_demos/worlds/vehicle.sdf

Sharing Gazebo Caches Across Containers

I initially planned to bake Gazebo models into each Docker image. Then I realized: 4 containers × 50MB models = 200MB of duplicates. Instead, I share caches via Docker volumes:

# docker-compose.yml
volumes:
  - ./cache/ignition:/root/.ignition:rw  # Humble
  - ./cache/gz:/root/.gz:rw              # Jazzy

Gotcha: Humble uses ign (pre-rebranding), Jazzy uses gz (post-rebranding). Different CLI, different cache paths:

# Humble: ~/.ignition/fuel/
ign fuel download -u "https://..."

# Jazzy: ~/.gz/fuel/
gz fuel download --url "https://..."

This cut disk usage by 75% and build time from 32 to 15 minutes.

DDS ↔︎ Zenoh Bridging Architecture

Use Case: Robot (Jetson) with CycloneDDS → Laptop with rmw_zenoh

Robot (Jetson Orin)              WiFi              Laptop
┌──────────────┐         ┌──────────────┐         ┌──────────────┐
│ CycloneDDS   │◄───────►│ zenoh-bridge │◄───────►│ rmw_zenoh    │
│ (can't change│  Domain │ -ros2dds     │  Zenoh  │ (efficient)  │
│  middleware) │   99    │              │  Router │              │
└──────────────┘         └──────────────┘         └──────────────┘

Why Bridge Instead of Native Zenoh? - Robot firmware uses CycloneDDS (can’t change) - Zenoh more efficient over WiFi (bandwidth savings) - Bridge allows gradual migration

Configuration:

# Terminal 1: Router mode (laptop)
zenoh-bridge-ros2dds -m router -d 99

# Terminal 2: Peer mode (another laptop/node)
zenoh-bridge-ros2dds -m peer -d 88

Tested Results: 15 consecutive messages bridged successfully, latency <10ms.

GPU Sharing Across Containers

Hardware: NVIDIA RTX 5090 (24GB VRAM)

Solution:

# docker-compose.yml
services:
  workshop3-dds:
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=all

Verified:

# Inside container
nvidia-smi  # Shows RTX 5090
glxinfo | grep "direct rendering"  # Yes

Use Cases: VSLAM, NVBlox, Gazebo Sim rendering, RViz2 visualization.

RealSense D435i + VSLAM Deployment: Debugging Three Hidden Issues

Context: Phase 7.6 tested RealSense D435i camera with Isaac ROS Visual SLAM across all containers. What seemed straightforward turned into a debugging masterclass.

Issue 1: IMU Access Denied

Symptom:

# Inside workshop4-imu container
rs-enumerate-devices

# Error:
Device or resource busy

Debugging: - RealSense D435i has IMU (gyro + accelerometer) via HID interface - Docker needed permission to access HID devices - Standard USB passthrough wasn’t enough

Solution:

# docker-compose.yml - workshop4-imu service
device_cgroup_rules:
  - 'c 13:* rmw'  # Character device class 13 (HID input devices)
  - 'c 189:* rmw' # USB device class 189

Why This Worked: - device_cgroup_rules grants container access to device classes - Class 13 = HID (Human Interface Devices) - includes IMU - Class 189 = USB devices - rmw = read, mknod, write permissions

Issue 2: VSLAM Namespace Collision

Symptom:

ros2 launch isaac_ros_visual_slam isaac_ros_visual_slam_realsense.launch.py

# Camera topics published at:
/camera/infra1/image_rect_raw
/camera/infra2/image_rect_raw

# But VSLAM looked for:
/camera/camera/infra1/image_rect_raw  ❌ (namespace doubled!)

Root Cause:

# isaac_ros_visual_slam_realsense.launch.py (original)
Node(
    package='isaac_ros_visual_slam',
    executable='isaac_ros_visual_slam',
    namespace='camera',  # ← Added this namespace
    parameters=[...]
)

# Combined with realsense2_camera default namespace='camera'
# Result: /camera/camera/* ❌

Solution Strategy: Docker volume mount override pattern (no rebuild needed!)

# docker-compose.yml
volumes:
  # Override launch file with fixed version
  - ./fixes/isaac_ros_visual_slam_realsense.launch.py:/opt/ros/humble/share/isaac_ros_visual_slam/launch/isaac_ros_visual_slam_realsense.launch.py:ro

Fixed Launch File:

# fixes/isaac_ros_visual_slam_realsense.launch.py
Node(
    package='isaac_ros_visual_slam',
    executable='isaac_ros_visual_slam',
    namespace='',  # ← Changed to empty string
    parameters=[
        {'visual_slam/image_0': '/camera/infra1/image_rect_raw'},
        {'visual_slam/image_1': '/camera/infra2/image_rect_raw'},
    ]
)

Issue 3: ROS_LOCALHOST_ONLY Discovery Mystery

Symptom:

# workshop3-dds container: ✅ VSLAM works perfectly
# workshop4-imu container: ❌ No camera topics appear
# robot-humble container: ❌ No camera topics appear

# Yet all three containers use the SAME isaac-ros-base image!

Debugging Journey:

Hypothesis 1: Camera in use by another container?

# Stopped ALL containers, relaunched workshop4-imu alone
# Result: Still failed ❌

Hypothesis 2: Container corruption during build?

# Rebuilt workshop4-imu and robot-humble from scratch
docker compose build --no-cache workshop4-imu robot-humble
# Result: Still failed ❌

Hypothesis 3: Environment variable differences?

# Compared docker-compose.yml environment sections
# Found the difference!

# workshop3-dds Dockerfile:
ENV ROS_LOCALHOST_ONLY=1  ✅

# workshop4-imu Dockerfile:
ENV ROS_LOCALHOST_ONLY=0  ❌

# robot-humble Dockerfile:
ENV ROS_LOCALHOST_ONLY=0  ❌

Root Cause: - ROS_LOCALHOST_ONLY=1 enables localhost-only DDS discovery - Prevents network-based discovery conflicts - workshop3-dds worked by accident (had it in Dockerfile) - workshop4-imu and robot-humble inherited base image value (0)

Solution:

# docker-compose.yml - Add to workshop4-imu and robot-humble
environment:
  - ROS_LOCALHOST_ONLY=1  # Use localhost-only DDS (required for topic discovery)

Testing Results:

Container	D435i Basic	VSLAM Odometry	Map Saving	IMU Data
workshop3-dds	✅ PASS	✅ PASS	✅ PASS (2.3MB nvmap)	N/A
workshop4-imu	✅ PASS	✅ PASS	✅ PASS (2.1MB nvmap)	✅ PASS (~9.8 m/s²)
robot-humble	✅ PASS	✅ PASS	✅ PASS (2.2MB nvmap)	N/A

Lessons from VSLAM Debugging: 1. Layer your testing: Basic → Advanced → Expert 2. Document hypotheses (systematic troubleshooting) 3. Environment variables matter (check ALL of them!) 4. Volume mount overrides fix upstream issues elegantly 5. HID devices need special permissions (USB ≠ HID)

RTX 5090 Hardware Verification: When Newer Isn’t Supported (Yet It Works)

Context: Phase 7.7 tested NVIDIA RTX 5090 (Blackwell architecture, Compute Capability 12.0). The GPU was so new, official documentation didn’t list it.

The Challenge:

Hardware: - RTX 5090 (released Dec 2024, Blackwell architecture) - Compute Capability 12.0 (newest available) - CUDA 12.6+ required officially

Installed Software: - Host driver: 570.86.16 (supports CUDA 12.8) - Isaac ROS base image: Built for CUDA 12.2 - PyTorch containers: Unknown CUDA version support

The Question: Will Compute Capability 12.0 work with CUDA 12.2 containers via driver forward compatibility?

Research Phase:

Official NVIDIA documentation listed RTX 40 series (Ada Lovelace, Compute 8.9) and RTX 30 series (Ampere, Compute 8.6), but RTX 5090 NOT listed for CUDA 12.2.

Driver Forward Compatibility Hypothesis: - NVIDIA drivers support NEWER CUDA toolkits - But can they support NEWER GPU architectures? - Blackwell (Compute 12.0) with CUDA 12.2 containers?

The Test:

Test 1: Basic GPU Detection

docker compose run --rm workshop3-dds bash

# Inside container:
nvidia-smi

# Output:
+------------------------------------------------------------------------------+
| NVIDIA-SMI 570.86.16      Driver Version: 570.86.16      CUDA Version: 12.8 |
|-------------------------------+----------------------+----------------------+
|   0  NVIDIA GeForce RTX 5090  | 00000000:01:00.0 Off |                  Off |
+------------------------------------------------------------------------------+

✅ PASS - GPU detected

Test 2: CUDA Toolkit Compatibility

nvcc --version

# Output:
Cuda compilation tools, release 12.2, V12.2.140

Test 3: GPU Compute Capability Query

python3 << EOF
import torch
print(f"PyTorch CUDA available: {torch.cuda.is_available()}")
print(f"GPU name: {torch.cuda.get_device_name(0)}")
print(f"Compute capability: {torch.cuda.get_device_capability(0)}")
EOF

# Output:
PyTorch CUDA available: True
GPU name: NVIDIA GeForce RTX 5090
Compute capability: (12, 0)  ← Blackwell architecture confirmed!

✅ PASS - PyTorch recognizes Compute 12.0

Test 4: Real Workload (PyTorch CUDA 12.8)

Challenge: Need to verify with CUDA 12.8 (matches driver). Image: pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime (12.2GB).

The Download Drama:

docker pull pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime &

# 1 hour 20 minutes later... is it stuck?

Network Bandwidth Monitoring Discovery:

#!/bin/bash
# Created /tmp/check_bandwidth.sh to debug "stuck" download

IFACE=$(ip route get 8.8.8.8 | grep -oP 'dev \K\S+' | head -1)
RX1=$(cat /proc/net/dev | grep "$IFACE" | awk '{print $2}')
sleep 5
RX2=$(cat /proc/net/dev | grep "$IFACE" | awk '{print $2}')
DIFF=$((RX2 - RX1))
SPEED_MB_PER_SEC=$((DIFF / 5 / 1024 / 1024))
echo "Download Speed: $SPEED_MB_PER_SEC MB/s"

# Result: 23 MB/s ✅

Not stuck! Just a large image. Patience required.

The PyTorch Test:

docker run --rm --gpus all pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime \
  python -c "
import torch
print(f'PyTorch version: {torch.__version__}')
print(f'CUDA available: {torch.cuda.is_available()}')
print(f'CUDA version: {torch.version.cuda}')
print(f'GPU name: {torch.cuda.get_device_name(0)}')
print(f'Compute capability: {torch.cuda.get_device_capability(0)}')

# GPU computation test
x = torch.randn(1000, 1000, device='cuda')
y = torch.matmul(x, x.T)
print(f'GPU computation: ✅ {y.shape}')
"

# Output:
PyTorch version: 2.7.0
CUDA available: True
CUDA version: 12.8
GPU count: 1
GPU name: NVIDIA GeForce RTX 5090
Compute capability: (12, 0)
GPU computation: ✅ torch.Size([1000, 1000])

✅ PASS - RTX 5090 Compute 12.0 works with CUDA 12.8!

The Verdict:

Driver Forward Compatibility DOES Support Newer GPUs! - Host driver 570.86.16 provides CUDA 12.8 runtime - CUDA 12.2 containers work via driver compatibility - CUDA 12.8 containers work natively - Compute Capability 12.0 (Blackwell) fully supported

What We Learned: 1. Driver version > Container CUDA version enables compatibility 2. Network patience debugging: Check bandwidth before assuming “stuck” 3. Test bleeding-edge hardware, don’t assume based on docs alone

Updated Compatibility Matrix:

Component	Installed	Tested With	Status
Host Driver	570.86.16	-	✅ CUDA 12.8 runtime
Isaac ROS Base	CUDA 12.2	RTX 5090 (Compute 12.0)	✅ Works via driver
PyTorch Container	CUDA 12.8	RTX 5090 (Compute 12.0)	✅ Native support
Workshop Containers	CUDA 12.2	RTX 5090 (Compute 12.0)	✅ All GPU features work

Offline-First Design

Challenge: Workshop has no reliable internet.

Strategy:

Pre-install Everything: All ROS 2 packages via apt, Python deps via pip, Gazebo models bundled
Shared Caches:

volumes:
  - ./cache/pip:/root/.cache/pip
  - ./cache/colcon:/root/.colcon
  - ./cache/ignition:/root/.ignition
  - ./cache/gz:/root/.gz

Docker Image Saves:

docker save isaac-ros-base:humble | zstd -T0 -19 > offline/isaac-ros-base.tar.zst
# 24.1GB → ~12-15GB compressed

Verification:

docker run --network none jazzy-base:latest bash -c \
  "ros2 launch ros_gz_sim_demos diff_drive.launch.py"
# ✅ Works perfectly!

Offline Test Results: All demos launch without internet, models load from bundled package.

Build & Test Automation

Makefile Targets:

make all           # Build everything (~60-90 min first time)
make base          # Build TIER 2 base images (~50 min)
make workshop3     # Build all 3 workshop3 variants (~5 min)
make test          # Smoke tests (ROS2, Gazebo, Zenoh)
make offline-save  # Create compressed tars (~2 hours, 80-95GB)
make offline-load  # Load from tars
make status        # Show built images and sizes

Build Features:

Timestamp + Git Hash Tagging: IMAGE_TAG := 20251214-103538-a177e48 (enables rollback)
Parallel Builds: Build bases concurrently where possible
Smoke Tests: Automated verification

Build Times (with cache): - isaac-ros-base: ~2 min (first: ~35-45 min) - jazzy-base: ~5 min (first: ~10-15 min) - workshop3-*: ~20 sec each - Total rebuild: ~7 min (vs ~60 min fresh)

Making It Easy to Use

I didn’t want to type 15-flag docker commands every time I launch a container. So I wrote two helper scripts.

launch-container.sh

Instead of this:

docker run --rm -it --name roscon-workshop3-dds --hostname workshop3-dds \
  --runtime nvidia --gpus all --network host --privileged \
  -e DISPLAY=$DISPLAY -e QT_X11_NO_MITSHM=1 -e NVIDIA_VISIBLE_DEVICES=all \
  -e NVIDIA_DRIVER_CAPABILITIES=all -e RMW_IMPLEMENTATION=rmw_cyclonedds_cpp \
  -e CYCLONEDDS_URI=file:///config/cyclonedds.xml \
  -v /tmp/.X11-unix:/tmp/.X11-unix:rw \
  -v $HOME/.Xauthority:/root/.Xauthority:rw \
  -v ./workspaces:/workspaces:rw -v ./configs:/config:ro \
  workshop3-humble-dds:latest bash

I just type:

./launch-container.sh 1

The script shows a menu with all available containers:

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║                                                                               ║
║                    India 2025 Workshop - Container Launcher                   ║
║                         December 18-20, COEP Pune                             ║
║                                                                               ║
╚═══════════════════════════════════════════════════════════════════════════════╝

Select a container to launch:

  1) ● workshop3-dds       [humble]     CycloneDDS + Zenoh Bridge
     └─ workshop3-humble-dds:latest (24.2GB)

  2) ● workshop3-jazzy     [jazzy]      ROS 2 Jazzy + rmw_zenoh
     └─ workshop3-jazzy-zenoh:latest (2.95GB)

  3) ○ workshop3-humble    [humble]     ROS 2 Humble + rmw_zenoh (partial)
     └─ workshop3-humble-zenoh:latest (not built)

  4) ● workshop4-imu       [humble]     IMU tools + VSLAM
     └─ workshop4-imu:latest (24.2GB)

  5) ● robot-humble        [humble]     Jetson communication
     └─ robot-humble:latest (24.1GB)

Enter selection (1-5), or [r]efresh / [s]tatus / [c]leanup / [q]uit:

Green dots (●) mean built and ready. Red circles (○) mean not built yet.

new-terminal.sh

When I need another terminal in the same container:

./scripts/new-terminal.sh 1          # Connect to workshop3-dds
./scripts/new-terminal.sh dds        # Fuzzy match works too
./scripts/new-terminal.sh            # Auto-connects if only one running

No more docker ps → copy container ID → docker exec. ROS is already sourced when the terminal opens.

What I Learned

Things That Paid Off

Three-tier Docker strategy - Base images rebuild rarely, workshop images rebuild fast
Testing before committing to offline images - Caught the Gazebo Classic EOL before baking 80GB of deprecated software
Shared caches - 75% disk savings, and my containers stay in sync
Writing helper scripts - launch-container.sh and new-terminal.sh save me from typing 15-flag docker commands

Things I’d Do Earlier Next Time

Check EOL dates first - Would have avoided TurtleBot3 entirely
Test offline from day one - Run docker run --network none early
Note the naming differences - ign vs gz cost me debugging time

Surprising Discoveries

Models bundled in apt packages - ros-gz-sim-demos includes everything, no Fuel downloads needed
ROS_LOCALHOST_ONLY matters - Without it, my containers couldn’t see RealSense topics
HID devices need special rules - IMU access requires device_cgroup_rules in docker-compose
Volume mounts can override installed files - Fixed upstream launch file bugs without rebuilding
RTX 5090 just works - Driver forward compatibility handled Blackwell (Compute 12.0) even with CUDA 12.2 containers

If You Want to Do This Too

Here’s what I’d recommend:

Pick your ROS 2 distro - Humble (LTS until 2027) or Jazzy (LTS until 2029)
Choose your base image - Need GPU? Use NVIDIA Isaac ROS. CPU only? OSRF official images are smaller
Use modern Gazebo Sim - NOT Classic (EOL Jan 2025)
Test offline early - docker run --network none should work before you arrive at the venue
Write wrapper scripts - Your future self will thank you

Time investment: I spent about a week on this, spread across evenings. Most of that was debugging the VSLAM namespace issues and waiting for Docker builds.

Wrapping Up

I now have 7 Docker images ready (about 80GB compressed), covering both workshop tracks plus extras for experimentation. My RealSense D435i works, VSLAM runs on my RTX 5090, and everything launches with a single command.

Was it overkill? Maybe. But when the workshop starts, I’ll be following along instead of troubleshooting apt-get failures on conference WiFi.

The real lesson: Test your setup with the network cable unplugged. If it works offline, it’ll work anywhere.

This post documents my preparation for ROSCon India 2025 (Dec 18-20, COEP Pune). If you’re doing something similar, feel free to reach out—happy to share configs.

--- title: "Building a Modern Offline ROS 2 Workshop Infrastructure: ROSCon India 2025" description: "How we built Docker infrastructure with modern Gazebo Sim, GPU acceleration, DDS↔Zenoh bridging, complete offline support, and handled last-minute deprecation challenges" author: "Physical AI Lab Team" date: "2025-12-15" categories: [ROS2, Docker, Zenoh, Workshop, ROSCon, NVIDIA, Gazebo] image: "thumbnail.jpg" draft: false format: html: code-fold: false code-tools: true toc: true toc-depth: 3 --- ``` ╔═══════════════════════════════════════════════════════════════════════════════╗ ║ ║ ║ ██████╗ ██████╗ ███████╗ ██████╗ ██████╗ ███╗ ██╗ ██╗███╗ ██╗ ║ ║ ██╔══██╗██╔═══██╗██╔════╝██╔════╝██╔═══██╗████╗ ██║ ██║████╗ ██║ ║ ║ ██████╔╝██║ ██║███████╗██║ ██║ ██║██╔██╗ ██║ ██║██╔██╗ ██║ ║ ║ ██╔══██╗██║ ██║╚════██║██║ ██║ ██║██║╚██╗██║ ██║██║╚██╗██║ ║ ║ ██║ ██║╚██████╔╝███████║╚██████╗╚██████╔╝██║ ╚████║ ██║██║ ╚████║ ║ ║ ╚═╝ ╚═╝ ╚═════╝ ╚══════╝ ╚═════╝ ╚═════╝ ╚═╝ ╚═══╝ ╚═╝╚═╝ ╚═══╝ ║ ║ ║ ║ India 2025 Workshop - Container Launcher ║ ║ December 18-20, COEP Pune ║ ║ ║ ╚═══════════════════════════════════════════════════════════════════════════════╝ ``` ## TL;DR We built a production-grade Docker infrastructure for [ROSCon India 2025 workshops](https://www.rosconindia.in/workshops) (Dec 18-20, COEP Pune): - **7 Docker images** for offline ROS 2 workshops - **Modern Gazebo Sim** (NOT Classic - avoided EOL trap!) - **GPU acceleration** + Zenoh bridging + complete Nav2 stack - **100% offline-capable** with bundled models - **Multi-role certification** process for quality assurance **Key Innovation:** Discovered TurtleBot3 uses deprecated Gazebo Classic (EOL Jan 2025) at Phase 7, pivoted to `ros-gz-sim-demos` before committing to 80GB of offline images. This saved the workshop from teaching deprecated technology. **Why I Built This:** As a workshop attendee, I wanted to arrive prepared—not scrambling to install dependencies while the instructor moves on. This setup gives me multiple environments ready to go, so I can focus on learning and experimenting rather than troubleshooting. ## The Problem **Context:** [ROSCon India 2025](https://www.rosconindia.in/workshops) marks the first ROSCon in India, happening December 18-20 at COEP Pune. I'm attending two hands-on workshops: - **Workshop 3:** Zenoh Networking (DDS ↔ Zenoh bridging over WiFi) - **Workshop 4:** IMU Perception with Visual SLAM **What I Wanted to Prepare For:** 1. **No Internet Dependency:** I didn't want to be stuck downloading packages while the workshop moved on 2. **GPU-Ready:** My RTX 5090 should be ready for VSLAM and NVBlox demos out of the box 3. **Multiple Middleware Options:** Both CycloneDDS and Zenoh containers, so I can follow either track 4. **Real Hardware Ready:** RealSense D435i camera and IMU working before I arrive 5. **Experiment Freely:** Extra environments to try things without breaking the workshop setup 6. **Modern Stack:** Discovered TurtleBot3 uses deprecated Gazebo Classic—pivoted to modern Gazebo Sim ## Architecture Overview ### Three-Tier Docker Image Strategy ``` TIER 1: Base Images (pulled from registries) ├── nvcr.io/nvidia/isaac/ros:x86_64-ros2_humble (~22GB) └── osrf/ros:jazzy-desktop-full (~3.5GB) TIER 2: Custom Base Images (build locally) ├── isaac-ros-base:humble (VSLAM, NVBlox, RealSense, Go2 SDK, Nav2, ros-gz-sim-demos) └── jazzy-base:latest (MuJoCo, Claude Code, Playwright, Nav2, ros-gz-sim-demos) TIER 3: Workshop Images (docker-compose) ├── workshop3-humble-dds (CycloneDDS + Zenoh Bridge) ├── workshop3-jazzy-zenoh (rmw_zenoh via apt) ├── workshop3-humble-zenoh (rmw_zenoh source - BROKEN, needs Rust) ├── workshop4-imu (IMU tools, robot_localization) └── robot-humble (Jetson communication) ``` ### Two-Track Rationale **Track A: NVIDIA Isaac ROS (Humble)** - GPU-accelerated perception (VSLAM, NVBlox) - CycloneDDS 0.10.2 (required by Unitree Go2 SDK) - Mature ecosystem for robotics **Track B: OSRF Jazzy** - Latest ROS 2 LTS - rmw_zenoh available via apt (no source build needed) - Cleaner for Zenoh workshop demos **Why Not One Image?** NVIDIA base image is 22GB (overkill for Zenoh demos). Different RMW implementations require separation. ## The Docker Images | Image | Base | Size | Purpose | Status | |-------|------|------|---------|--------| | **isaac-ros-base** | NVIDIA Isaac ROS Humble | 24.1GB | GPU perception, Go2 SDK, Nav2, ros-gz-sim-demos | ✅ Production | | **jazzy-base** | OSRF Jazzy | 2.85GB | Latest ROS 2, rmw_zenoh, ros-gz-sim-demos | ✅ Production | | **workshop3-humble-dds** | isaac-ros-base | 24.2GB | CycloneDDS + zenoh-bridge v1.7.1 | ✅ Primary W3 | | **workshop3-jazzy-zenoh** | jazzy-base | 2.95GB | Native rmw_zenoh v0.2.9 | ✅ Primary W3 (alt) | | **workshop3-humble-zenoh** | isaac-ros-base | 24.2GB | rmw_zenoh source (needs Rust) | ❌ Broken (known) | | **workshop4-imu** | isaac-ros-base | 24.2GB | imu_tools, robot_localization, rtabmap | ✅ Production | | **robot-humble** | isaac-ros-base | 24.1GB | Same as base (Jetson comms) | ✅ Production | **Total Disk:** ~150GB uncompressed → ~80-95GB compressed (zstd -19) ## Key Technical Details ### Choosing Modern Gazebo Over TurtleBot3 While setting up TurtleBot3 (the standard tutorial robot), I noticed this warning: ```bash ros2 launch turtlebot3_gazebo empty_world.launch.py # [WARN] Gazebo Classic is end-of-life. Please migrate to new Gazebo. ``` Gazebo Classic EOL is **January 31, 2025**—just 6 weeks after the workshop. I switched to `ros-gz-sim-demos` instead: ```bash ros2 launch ros_gz_sim_demos diff_drive.launch.py # Uses modern Gazebo Sim - actively maintained ``` **Bonus:** The models are bundled in the Debian package—no internet needed to download from Gazebo Fuel: ```bash /opt/ros/jazzy/share/ros_gz_sim_demos/models/vehicle/ /opt/ros/jazzy/share/ros_gz_sim_demos/worlds/vehicle.sdf ``` ### Sharing Gazebo Caches Across Containers I initially planned to bake Gazebo models into each Docker image. Then I realized: 4 containers × 50MB models = 200MB of duplicates. Instead, I share caches via Docker volumes: ```yaml # docker-compose.yml volumes: - ./cache/ignition:/root/.ignition:rw # Humble - ./cache/gz:/root/.gz:rw # Jazzy ``` **Gotcha:** Humble uses `ign` (pre-rebranding), Jazzy uses `gz` (post-rebranding). Different CLI, different cache paths: ```bash # Humble: ~/.ignition/fuel/ ign fuel download -u "https://..." # Jazzy: ~/.gz/fuel/ gz fuel download --url "https://..." ``` This cut disk usage by 75% and build time from 32 to 15 minutes. ### DDS ↔ Zenoh Bridging Architecture **Use Case:** Robot (Jetson) with CycloneDDS → Laptop with rmw_zenoh ``` Robot (Jetson Orin) WiFi Laptop ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ CycloneDDS │◄───────►│ zenoh-bridge │◄───────►│ rmw_zenoh │ │ (can't change│ Domain │ -ros2dds │ Zenoh │ (efficient) │ │ middleware) │ 99 │ │ Router │ │ └──────────────┘ └──────────────┘ └──────────────┘ ``` **Why Bridge Instead of Native Zenoh?** - Robot firmware uses CycloneDDS (can't change) - Zenoh more efficient over WiFi (bandwidth savings) - Bridge allows gradual migration **Configuration:** ```bash # Terminal 1: Router mode (laptop) zenoh-bridge-ros2dds -m router -d 99 # Terminal 2: Peer mode (another laptop/node) zenoh-bridge-ros2dds -m peer -d 88 ``` **Tested Results:** 15 consecutive messages bridged successfully, latency <10ms. ### GPU Sharing Across Containers **Hardware:** NVIDIA RTX 5090 (24GB VRAM) **Solution:** ```yaml # docker-compose.yml services: workshop3-dds: runtime: nvidia environment: - NVIDIA_VISIBLE_DEVICES=all - NVIDIA_DRIVER_CAPABILITIES=all ``` **Verified:** ```bash # Inside container nvidia-smi # Shows RTX 5090 glxinfo | grep "direct rendering" # Yes ``` **Use Cases:** VSLAM, NVBlox, Gazebo Sim rendering, RViz2 visualization. ### RealSense D435i + VSLAM Deployment: Debugging Three Hidden Issues **Context:** Phase 7.6 tested RealSense D435i camera with Isaac ROS Visual SLAM across all containers. What seemed straightforward turned into a debugging masterclass. #### Issue 1: IMU Access Denied **Symptom:** ```bash # Inside workshop4-imu container rs-enumerate-devices # Error: Device or resource busy ``` **Debugging:** - RealSense D435i has IMU (gyro + accelerometer) via HID interface - Docker needed permission to access HID devices - Standard USB passthrough wasn't enough **Solution:** ```yaml # docker-compose.yml - workshop4-imu service device_cgroup_rules: - 'c 13:* rmw' # Character device class 13 (HID input devices) - 'c 189:* rmw' # USB device class 189 ``` **Why This Worked:** - `device_cgroup_rules` grants container access to device classes - Class 13 = HID (Human Interface Devices) - includes IMU - Class 189 = USB devices - `rmw` = read, mknod, write permissions #### Issue 2: VSLAM Namespace Collision **Symptom:** ```bash ros2 launch isaac_ros_visual_slam isaac_ros_visual_slam_realsense.launch.py # Camera topics published at: /camera/infra1/image_rect_raw /camera/infra2/image_rect_raw # But VSLAM looked for: /camera/camera/infra1/image_rect_raw ❌ (namespace doubled!) ``` **Root Cause:** ```python # isaac_ros_visual_slam_realsense.launch.py (original) Node( package='isaac_ros_visual_slam', executable='isaac_ros_visual_slam', namespace='camera', # ← Added this namespace parameters=[...] ) # Combined with realsense2_camera default namespace='camera' # Result: /camera/camera/* ❌ ``` **Solution Strategy:** Docker volume mount override pattern (no rebuild needed!) ```yaml # docker-compose.yml volumes: # Override launch file with fixed version - ./fixes/isaac_ros_visual_slam_realsense.launch.py:/opt/ros/humble/share/isaac_ros_visual_slam/launch/isaac_ros_visual_slam_realsense.launch.py:ro ``` **Fixed Launch File:** ```python # fixes/isaac_ros_visual_slam_realsense.launch.py Node( package='isaac_ros_visual_slam', executable='isaac_ros_visual_slam', namespace='', # ← Changed to empty string parameters=[ {'visual_slam/image_0': '/camera/infra1/image_rect_raw'}, {'visual_slam/image_1': '/camera/infra2/image_rect_raw'}, ] ) ``` #### Issue 3: ROS_LOCALHOST_ONLY Discovery Mystery **Symptom:** ```bash # workshop3-dds container: ✅ VSLAM works perfectly # workshop4-imu container: ❌ No camera topics appear # robot-humble container: ❌ No camera topics appear # Yet all three containers use the SAME isaac-ros-base image! ``` **Debugging Journey:** *Hypothesis 1: Camera in use by another container?* ```bash # Stopped ALL containers, relaunched workshop4-imu alone # Result: Still failed ❌ ``` *Hypothesis 2: Container corruption during build?* ```bash # Rebuilt workshop4-imu and robot-humble from scratch docker compose build --no-cache workshop4-imu robot-humble # Result: Still failed ❌ ``` *Hypothesis 3: Environment variable differences?* ```bash # Compared docker-compose.yml environment sections # Found the difference! # workshop3-dds Dockerfile: ENV ROS_LOCALHOST_ONLY=1 ✅ # workshop4-imu Dockerfile: ENV ROS_LOCALHOST_ONLY=0 ❌ # robot-humble Dockerfile: ENV ROS_LOCALHOST_ONLY=0 ❌ ``` **Root Cause:** - `ROS_LOCALHOST_ONLY=1` enables localhost-only DDS discovery - Prevents network-based discovery conflicts - workshop3-dds worked by accident (had it in Dockerfile) - workshop4-imu and robot-humble inherited base image value (0) **Solution:** ```yaml # docker-compose.yml - Add to workshop4-imu and robot-humble environment: - ROS_LOCALHOST_ONLY=1 # Use localhost-only DDS (required for topic discovery) ``` **Testing Results:** | Container | D435i Basic | VSLAM Odometry | Map Saving | IMU Data | |-----------|-------------|----------------|------------|----------| | workshop3-dds | ✅ PASS | ✅ PASS | ✅ PASS (2.3MB nvmap) | N/A | | workshop4-imu | ✅ PASS | ✅ PASS | ✅ PASS (2.1MB nvmap) | ✅ PASS (~9.8 m/s²) | | robot-humble | ✅ PASS | ✅ PASS | ✅ PASS (2.2MB nvmap) | N/A | **Lessons from VSLAM Debugging:** 1. Layer your testing: Basic → Advanced → Expert 2. Document hypotheses (systematic troubleshooting) 3. Environment variables matter (check ALL of them!) 4. Volume mount overrides fix upstream issues elegantly 5. HID devices need special permissions (USB ≠ HID) ### RTX 5090 Hardware Verification: When Newer Isn't Supported (Yet It Works) **Context:** Phase 7.7 tested NVIDIA RTX 5090 (Blackwell architecture, Compute Capability 12.0). The GPU was so new, official documentation didn't list it. **The Challenge:** **Hardware:** - RTX 5090 (released Dec 2024, Blackwell architecture) - Compute Capability 12.0 (newest available) - CUDA 12.6+ required officially **Installed Software:** - Host driver: 570.86.16 (supports CUDA 12.8) - Isaac ROS base image: Built for CUDA 12.2 - PyTorch containers: Unknown CUDA version support **The Question:** Will Compute Capability 12.0 work with CUDA 12.2 containers via driver forward compatibility? **Research Phase:** Official NVIDIA documentation listed RTX 40 series (Ada Lovelace, Compute 8.9) and RTX 30 series (Ampere, Compute 8.6), but **RTX 5090 NOT listed** for CUDA 12.2. **Driver Forward Compatibility Hypothesis:** - NVIDIA drivers support NEWER CUDA toolkits - **But can they support NEWER GPU architectures?** - Blackwell (Compute 12.0) with CUDA 12.2 containers? **The Test:** **Test 1: Basic GPU Detection** ```bash docker compose run --rm workshop3-dds bash # Inside container: nvidia-smi # Output: +------------------------------------------------------------------------------+ | NVIDIA-SMI 570.86.16 Driver Version: 570.86.16 CUDA Version: 12.8 | |-------------------------------+----------------------+----------------------+ | 0 NVIDIA GeForce RTX 5090 | 00000000:01:00.0 Off | Off | +------------------------------------------------------------------------------+ ``` ✅ **PASS** - GPU detected **Test 2: CUDA Toolkit Compatibility** ```bash nvcc --version # Output: Cuda compilation tools, release 12.2, V12.2.140 ``` **Test 3: GPU Compute Capability Query** ```bash python3 << EOF import torch print(f"PyTorch CUDA available: {torch.cuda.is_available()}") print(f"GPU name: {torch.cuda.get_device_name(0)}") print(f"Compute capability: {torch.cuda.get_device_capability(0)}") EOF # Output: PyTorch CUDA available: True GPU name: NVIDIA GeForce RTX 5090 Compute capability: (12, 0) ← Blackwell architecture confirmed! ``` ✅ **PASS** - PyTorch recognizes Compute 12.0 **Test 4: Real Workload (PyTorch CUDA 12.8)** Challenge: Need to verify with CUDA 12.8 (matches driver). Image: `pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime` (12.2GB). **The Download Drama:** ```bash docker pull pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime & # 1 hour 20 minutes later... is it stuck? ``` **Network Bandwidth Monitoring Discovery:** ```bash #!/bin/bash # Created /tmp/check_bandwidth.sh to debug "stuck" download IFACE=$(ip route get 8.8.8.8 | grep -oP 'dev \K\S+' | head -1) RX1=$(cat /proc/net/dev | grep "$IFACE" | awk '{print $2}') sleep 5 RX2=$(cat /proc/net/dev | grep "$IFACE" | awk '{print $2}') DIFF=$((RX2 - RX1)) SPEED_MB_PER_SEC=$((DIFF / 5 / 1024 / 1024)) echo "Download Speed: $SPEED_MB_PER_SEC MB/s" # Result: 23 MB/s ✅ ``` **Not stuck!** Just a large image. Patience required. **The PyTorch Test:** ```bash docker run --rm --gpus all pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime \ python -c " import torch print(f'PyTorch version: {torch.__version__}') print(f'CUDA available: {torch.cuda.is_available()}') print(f'CUDA version: {torch.version.cuda}') print(f'GPU name: {torch.cuda.get_device_name(0)}') print(f'Compute capability: {torch.cuda.get_device_capability(0)}') # GPU computation test x = torch.randn(1000, 1000, device='cuda') y = torch.matmul(x, x.T) print(f'GPU computation: ✅ {y.shape}') " # Output: PyTorch version: 2.7.0 CUDA available: True CUDA version: 12.8 GPU count: 1 GPU name: NVIDIA GeForce RTX 5090 Compute capability: (12, 0) GPU computation: ✅ torch.Size([1000, 1000]) ``` ✅ **PASS** - RTX 5090 Compute 12.0 works with CUDA 12.8! **The Verdict:** **Driver Forward Compatibility DOES Support Newer GPUs!** - Host driver 570.86.16 provides CUDA 12.8 runtime - CUDA 12.2 containers work via driver compatibility - CUDA 12.8 containers work natively - Compute Capability 12.0 (Blackwell) fully supported **What We Learned:** 1. Driver version > Container CUDA version enables compatibility 2. Network patience debugging: Check bandwidth before assuming "stuck" 3. Test bleeding-edge hardware, don't assume based on docs alone **Updated Compatibility Matrix:** | Component | Installed | Tested With | Status | |-----------|-----------|-------------|--------| | Host Driver | 570.86.16 | - | ✅ CUDA 12.8 runtime | | Isaac ROS Base | CUDA 12.2 | RTX 5090 (Compute 12.0) | ✅ Works via driver | | PyTorch Container | CUDA 12.8 | RTX 5090 (Compute 12.0) | ✅ Native support | | Workshop Containers | CUDA 12.2 | RTX 5090 (Compute 12.0) | ✅ All GPU features work | ### Offline-First Design **Challenge:** Workshop has no reliable internet. **Strategy:** 1. **Pre-install Everything:** All ROS 2 packages via apt, Python deps via pip, Gazebo models bundled 2. **Shared Caches:** ```yaml volumes: - ./cache/pip:/root/.cache/pip - ./cache/colcon:/root/.colcon - ./cache/ignition:/root/.ignition - ./cache/gz:/root/.gz ``` 3. **Docker Image Saves:** ```bash docker save isaac-ros-base:humble | zstd -T0 -19 > offline/isaac-ros-base.tar.zst # 24.1GB → ~12-15GB compressed ``` 4. **Verification:** ```bash docker run --network none jazzy-base:latest bash -c \ "ros2 launch ros_gz_sim_demos diff_drive.launch.py" # ✅ Works perfectly! ``` **Offline Test Results:** All demos launch without internet, models load from bundled package. ## Build & Test Automation **Makefile Targets:** ```makefile make all # Build everything (~60-90 min first time) make base # Build TIER 2 base images (~50 min) make workshop3 # Build all 3 workshop3 variants (~5 min) make test # Smoke tests (ROS2, Gazebo, Zenoh) make offline-save # Create compressed tars (~2 hours, 80-95GB) make offline-load # Load from tars make status # Show built images and sizes ``` **Build Features:** 1. **Timestamp + Git Hash Tagging:** `IMAGE_TAG := 20251214-103538-a177e48` (enables rollback) 2. **Parallel Builds:** Build bases concurrently where possible 3. **Smoke Tests:** Automated verification **Build Times (with cache):** - isaac-ros-base: ~2 min (first: ~35-45 min) - jazzy-base: ~5 min (first: ~10-15 min) - workshop3-*: ~20 sec each - Total rebuild: ~7 min (vs ~60 min fresh) ## Making It Easy to Use I didn't want to type 15-flag docker commands every time I launch a container. So I wrote two helper scripts. ### launch-container.sh Instead of this: ```bash docker run --rm -it --name roscon-workshop3-dds --hostname workshop3-dds \ --runtime nvidia --gpus all --network host --privileged \ -e DISPLAY=$DISPLAY -e QT_X11_NO_MITSHM=1 -e NVIDIA_VISIBLE_DEVICES=all \ -e NVIDIA_DRIVER_CAPABILITIES=all -e RMW_IMPLEMENTATION=rmw_cyclonedds_cpp \ -e CYCLONEDDS_URI=file:///config/cyclonedds.xml \ -v /tmp/.X11-unix:/tmp/.X11-unix:rw \ -v $HOME/.Xauthority:/root/.Xauthority:rw \ -v ./workspaces:/workspaces:rw -v ./configs:/config:ro \ workshop3-humble-dds:latest bash ``` I just type: ```bash ./launch-container.sh 1 ``` The script shows a menu with all available containers: ``` ╔═══════════════════════════════════════════════════════════════════════════════╗ ║ ║ ║ ██████╗ ██████╗ ███████╗ ██████╗ ██████╗ ███╗ ██╗ ██╗███╗ ██╗ ║ ║ ██╔══██╗██╔═══██╗██╔════╝██╔════╝██╔═══██╗████╗ ██║ ██║████╗ ██║ ║ ║ ██████╔╝██║ ██║███████╗██║ ██║ ██║██╔██╗ ██║ ██║██╔██╗ ██║ ║ ║ ██╔══██╗██║ ██║╚════██║██║ ██║ ██║██║╚██╗██║ ██║██║╚██╗██║ ║ ║ ██║ ██║╚██████╔╝███████║╚██████╗╚██████╔╝██║ ╚████║ ██║██║ ╚████║ ║ ║ ╚═╝ ╚═╝ ╚═════╝ ╚══════╝ ╚═════╝ ╚═════╝ ╚═╝ ╚═══╝ ╚═╝╚═╝ ╚═══╝ ║ ║ ║ ║ India 2025 Workshop - Container Launcher ║ ║ December 18-20, COEP Pune ║ ║ ║ ╚═══════════════════════════════════════════════════════════════════════════════╝ Select a container to launch: 1) ● workshop3-dds [humble] CycloneDDS + Zenoh Bridge └─ workshop3-humble-dds:latest (24.2GB) 2) ● workshop3-jazzy [jazzy] ROS 2 Jazzy + rmw_zenoh └─ workshop3-jazzy-zenoh:latest (2.95GB) 3) ○ workshop3-humble [humble] ROS 2 Humble + rmw_zenoh (partial) └─ workshop3-humble-zenoh:latest (not built) 4) ● workshop4-imu [humble] IMU tools + VSLAM └─ workshop4-imu:latest (24.2GB) 5) ● robot-humble [humble] Jetson communication └─ robot-humble:latest (24.1GB) Enter selection (1-5), or [r]efresh / [s]tatus / [c]leanup / [q]uit: ``` Green dots (●) mean built and ready. Red circles (○) mean not built yet. ### new-terminal.sh When I need another terminal in the same container: ```bash ./scripts/new-terminal.sh 1 # Connect to workshop3-dds ./scripts/new-terminal.sh dds # Fuzzy match works too ./scripts/new-terminal.sh # Auto-connects if only one running ``` No more `docker ps` → copy container ID → `docker exec`. ROS is already sourced when the terminal opens. ## What I Learned ### Things That Paid Off 1. **Three-tier Docker strategy** - Base images rebuild rarely, workshop images rebuild fast 2. **Testing before committing to offline images** - Caught the Gazebo Classic EOL before baking 80GB of deprecated software 3. **Shared caches** - 75% disk savings, and my containers stay in sync 4. **Writing helper scripts** - `launch-container.sh` and `new-terminal.sh` save me from typing 15-flag docker commands ### Things I'd Do Earlier Next Time 1. **Check EOL dates first** - Would have avoided TurtleBot3 entirely 2. **Test offline from day one** - Run `docker run --network none` early 3. **Note the naming differences** - `ign` vs `gz` cost me debugging time ### Surprising Discoveries - **Models bundled in apt packages** - `ros-gz-sim-demos` includes everything, no Fuel downloads needed - **ROS_LOCALHOST_ONLY matters** - Without it, my containers couldn't see RealSense topics - **HID devices need special rules** - IMU access requires `device_cgroup_rules` in docker-compose - **Volume mounts can override installed files** - Fixed upstream launch file bugs without rebuilding - **RTX 5090 just works** - Driver forward compatibility handled Blackwell (Compute 12.0) even with CUDA 12.2 containers ## If You Want to Do This Too Here's what I'd recommend: 1. **Pick your ROS 2 distro** - Humble (LTS until 2027) or Jazzy (LTS until 2029) 2. **Choose your base image** - Need GPU? Use NVIDIA Isaac ROS. CPU only? OSRF official images are smaller 3. **Use modern Gazebo Sim** - NOT Classic (EOL Jan 2025) 4. **Test offline early** - `docker run --network none` should work before you arrive at the venue 5. **Write wrapper scripts** - Your future self will thank you **Time investment:** I spent about a week on this, spread across evenings. Most of that was debugging the VSLAM namespace issues and waiting for Docker builds. ## Wrapping Up I now have 7 Docker images ready (about 80GB compressed), covering both workshop tracks plus extras for experimentation. My RealSense D435i works, VSLAM runs on my RTX 5090, and everything launches with a single command. Was it overkill? Maybe. But when the workshop starts, I'll be following along instead of troubleshooting `apt-get` failures on conference WiFi. **The real lesson:** Test your setup with the network cable unplugged. If it works offline, it'll work anywhere. --- *This post documents my preparation for ROSCon India 2025 (Dec 18-20, COEP Pune). If you're doing something similar, feel free to reach out—happy to share configs.*