datasynth-cli

Command-line interface for synthetic accounting data generation.

Overview

datasynth-cli provides the datasynth-data binary for command-line usage:

• generate: Generate synthetic data from configuration
• init: Create configuration files with industry presets
• validate: Validate configuration files
• info: Display available presets and options

Installation

cargo build --release
# Binary at: target/release/datasynth-data

Commands

generate

Generate synthetic financial data.

# From configuration file
datasynth-data generate --config config.yaml --output ./output

# Demo mode with defaults
datasynth-data generate --demo --output ./demo-output

# Override seed
datasynth-data generate --config config.yaml --output ./output --seed 12345

# Verbose output
datasynth-data generate --config config.yaml --output ./output -v

init

Create a configuration file from presets.

# Industry preset with complexity
datasynth-data init --industry manufacturing --complexity medium -o config.yaml

Available industries:

• manufacturing
• retail
• financial_services
• healthcare
• technology
• energy
• telecom
• transportation
• hospitality

validate

Validate configuration files.

datasynth-data validate --config config.yaml

info

Display available options.

datasynth-data info

fingerprint

Privacy-preserving fingerprint operations.

# Extract fingerprint
datasynth-data fingerprint extract --input ./data.csv --output ./fp.dsf --privacy-level standard

# Validate fingerprint
datasynth-data fingerprint validate ./fp.dsf

# View fingerprint details
datasynth-data fingerprint info ./fp.dsf --detailed

# Evaluate fidelity
datasynth-data fingerprint evaluate --fingerprint ./fp.dsf --synthetic ./output/ --threshold 0.8

# Federated aggregation (v0.5.0)
datasynth-data fingerprint federated --sources ./a.dsf ./b.dsf --output ./combined.dsf --method weighted_average

diffusion (v0.5.0)

Diffusion model training and evaluation.

# Train diffusion model from fingerprint
datasynth-data diffusion train --fingerprint ./fp.dsf --output ./model.json

# Evaluate model fit
datasynth-data diffusion evaluate --model ./model.json --samples 5000

causal (v0.5.0)

Causal and counterfactual data generation.

# Generate from causal template
datasynth-data causal generate --template fraud_detection --samples 10000 --output ./causal/

# Run intervention
datasynth-data causal intervene --template fraud_detection --variable transaction_amount --value 50000 --samples 5000 --output ./intervention/

# Validate causal structure
datasynth-data causal validate --data ./causal/ --template fraud_detection

Key Types

CLI Arguments

#![allow(unused)]
fn main() {
#[derive(Parser)]
pub struct Cli {
    #[command(subcommand)]
    pub command: Command,

    /// Enable verbose logging
    #[arg(short, long)]
    pub verbose: bool,

    /// Suppress non-error output
    #[arg(short, long)]
    pub quiet: bool,
}

#[derive(Subcommand)]
pub enum Command {
    Generate(GenerateArgs),
    Init(InitArgs),
    Validate(ValidateArgs),
    Info,
    Fingerprint(FingerprintArgs),   // fingerprint subcommands
    Diffusion(DiffusionArgs),       // v0.5.0: diffusion model commands
    Causal(CausalArgs),             // v0.5.0: causal generation commands
}
}

Generate Arguments

#![allow(unused)]
fn main() {
pub struct GenerateArgs {
    /// Configuration file path
    #[arg(short, long)]
    pub config: Option<PathBuf>,

    /// Use demo preset
    #[arg(long)]
    pub demo: bool,

    /// Output directory (required)
    #[arg(short, long)]
    pub output: PathBuf,

    /// Override random seed
    #[arg(long)]
    pub seed: Option<u64>,

    /// Output format
    #[arg(long, default_value = "csv")]
    pub format: String,

    /// Attach a synthetic data certificate (v0.5.0)
    #[arg(long)]
    pub certificate: bool,
}

pub struct InitArgs {
    // ... existing fields ...

    /// Generate config from natural language description (v0.5.0)
    #[arg(long)]
    pub from_description: Option<String>,
}
}

Signal Handling

On Unix systems, pause/resume generation with SIGUSR1:

# Start in background
datasynth-data generate --config config.yaml --output ./output &

# Toggle pause
kill -USR1 $(pgrep datasynth-data)

Progress bar shows pause state:

[████████░░░░░░░░░░░░] 40% - 40000/100000 entries (PAUSED)

Exit Codes

Environment Variables

SYNTH_DATA_LOG=debug datasynth-data generate --demo --output ./output

Progress Display

During generation, a progress bar shows:

Generating synthetic data...
[████████████████████] 100% - 100000/100000 entries
Phase: Transactions | 85,432 entries/sec | ETA: 0:00

Generation complete!
- Journal entries: 100,000
- Document flows: 15,000
- Output: ./output/
- Duration: 1.2s

Usage Examples

Basic Generation

datasynth-data init --industry manufacturing -o config.yaml
datasynth-data generate --config config.yaml --output ./output

Scripting

#!/bin/bash
for industry in manufacturing retail healthcare; do
    datasynth-data init --industry $industry --complexity medium -o ${industry}.yaml
    datasynth-data generate --config ${industry}.yaml --output ./output/${industry}
done

CI/CD

# GitHub Actions
- name: Generate Test Data
  run: |
    cargo build --release
    ./target/release/datasynth-data generate --demo --output ./test-data

Reproducible Generation

# Same seed = same output
datasynth-data generate --config config.yaml --output ./run1 --seed 42
datasynth-data generate --config config.yaml --output ./run2 --seed 42
diff -r run1 run2  # No differences

See Also

• CLI Reference
• Quick Start
• datasynth-runtime