CLI Commands

Dora CLI Commands

Complete reference for the dora command-line interface

Installation

# Via pip (recommended)
pip install dora-rs-cli

# Via cargo
cargo install dora-cli

# Via shell installer (macOS/Linux)
curl --proto '=https' --tlsv1.2 -LsSf \
  https://github.com/dora-rs/dora/releases/latest/download/dora-cli-installer.sh | sh

Command Overview

Creating Projects

New Dataflow

# Create new dataflow project
dora new my_project --kind dataflow

# With Python language
dora new my_project --kind dataflow --lang python

# With Rust language
dora new my_project --kind dataflow --lang rust

New Node

# Create new Python node
dora new my_node --kind node --lang python

# Create new Rust node
dora new my_node --kind node --lang rust

New Operator

# Create new operator
dora new my_operator --kind operator --lang rust

Building

# Build all nodes in dataflow
dora build dataflow.yml

# Build with uv (faster Python packages)
dora build dataflow.yml --uv

# Build specific node
dora build dataflow.yml --node camera

Running Dataflows

Standalone Mode (dora run)

# Run dataflow directly (no daemon)
dora run dataflow.yml

# Run with uv for Python packages
dora run dataflow.yml --uv

# Run from URL
dora run https://example.com/dataflow.yml

Daemon Mode

# Step 1: Start coordinator and daemon
dora up

# Step 2: Start dataflow
dora start dataflow.yml

# Step 3: Check status
dora list

# Step 4: Stop dataflow
dora stop <dataflow-id>

# Step 5: Shutdown daemon
dora destroy

Monitoring

List Running Dataflows

# List all running dataflows
dora list
# or
dora ps

View Logs

# View logs for a node
dora logs <dataflow-id> <node-id>

# Follow logs (like tail -f)
dora logs <dataflow-id> <node-id> --follow

Check System Status

# Check dora system status
dora check
# or
dora system status

Visualization

# Generate Mermaid diagram
dora graph dataflow.yml

# Output to file
dora graph dataflow.yml > graph.md

Example output:

flowchart TB
  camera[camera]
  detector[detector]
  plot[plot]

  camera -- image --> detector
  camera -- image --> plot
  detector -- bbox --> plot

Distributed Mode

Coordinator

# Start coordinator on custom port
dora coordinator --port 6012

Daemon

# Connect daemon to remote coordinator
dora daemon --coordinator-addr 192.168.1.100:6012

Remote Dataflow

# dataflow.yml with deployment config
nodes:
  - id: camera
    path: camera_node.py
    _unstable_deploy:
      machine: robot-1

  - id: processor
    path: processor_node.py
    _unstable_deploy:
      machine: server-1

Common Workflows

Development Workflow

# 1. Create project
dora new my_robot --kind dataflow

# 2. Edit dataflow.yml and create nodes

# 3. Build
dora build dataflow.yml --uv

# 4. Run and test
dora run dataflow.yml --uv

# 5. Stop with Ctrl+C

Production Workflow

# 1. Start services
dora up

# 2. Deploy dataflow
dora start dataflow.yml

# 3. Monitor
dora list
dora logs <id> <node>

# 4. Update (stop and restart)
dora stop <id>
dora start dataflow.yml

# 5. Shutdown
dora destroy

Remote Dataflow

# On coordinator machine
dora coordinator

# On each robot/server
dora daemon --coordinator-addr <coordinator-ip>:6012

# Deploy dataflow
dora start dataflow.yml

Environment Variables

Troubleshooting

Common Issues

"Daemon not running"

# Start the daemon
dora up

"Port already in use"

# Kill existing processes
dora destroy
# Then restart
dora up

"Build failed"

# Check build output
dora build dataflow.yml --uv 2>&1 | tee build.log

Debug Mode

# Run with debug output
RUST_LOG=debug dora run dataflow.yml

Related Skills

• dataflow-config - YAML configuration
• node-api-rust - Rust node development
• node-api-python - Python node development