roar-cli 0.2.1

Reproducibility and provenance tracker for ML training pipelines

roar

Run Observation & Artifact Registration

A local front-end to TReqs' Graph Lineage-as-a-Service (GLaaS). Roar tracks data artifacts and execution steps in ML pipelines, enabling reproducibility and lineage queries.

Installation

pip install roar-cli
# or with uv
uv pip install roar-cli

Requires Python 3.10+ and Linux (x86_64 or aarch64) for full functionality.

Platform Support

Platform	roar run	Other commands
Linux x86_64	Full support	Full support
Linux aarch64	Full support	Full support
macOS	Not supported	Full support
Windows	Not supported	Full support

The roar run command uses a native tracer binary that requires Linux. Other commands work on all platforms.

Development Installation

# Clone the repository
git clone https://github.com/treqs/roar.git
cd roar

# Install in development mode (automatically builds tracer if Rust is installed)
uv pip install -e ".[dev]"
# or without uv
pip install -e ".[dev]"

Quick Start

# Initialize roar in your project
cd my-ml-project
roar init

# Run commands with provenance tracking
roar run python preprocess.py --input data.csv --output features.parquet
roar run python train.py --data features.parquet --output model.pt
roar run python evaluate.py --model model.pt --output metrics.json

Commands

roar init

Initialize roar in the current directory. Creates a .roar/ directory to store the local database.

roar init

roar run <command>

Run a command with provenance tracking. Roar captures:

• Files read and written
• Git commit and branch
• Execution time and exit code
• Command arguments

roar run python train.py --epochs 10 --lr 0.001
roar run ./scripts/preprocess.sh
roar run torchrun --nproc_per_node=4 train.py

roar reproduce <hash>

Reproduce an artifact by tracing its lineage.

# Show the reproduction plan
roar reproduce abc123de

# Run reproduction immediately
roar reproduce abc123de --run

# Run without prompts
roar reproduce abc123de --run -y

roar build <command>

Run a build step with provenance tracking. Build steps run before pipeline steps during reproduction.

# Compile native extensions
roar build maturin develop --release
roar build make -j4

# Install local packages
roar build pip install -e .

Use for setup that should run before the main pipeline (compiling, installing).

roar auth

Manage GLaaS (Graph Lineage-as-a-Service) authentication.

roar auth register    # Register SSH key with GLaaS server
roar auth test        # Test authentication
roar auth status      # Show authentication status

roar config

View or set configuration options.

roar config list
roar config get <key>
roar config set <key> <value>

Available configuration options:

Key	Default	Description
output.track_repo_files	false	Include repo files in provenance
output.quiet	false	Suppress written files report
filters.ignore_system_reads	true	Ignore /sys, /etc reads
filters.ignore_package_reads	true	Ignore installed package reads
filters.ignore_torch_cache	true	Ignore torch/triton cache
filters.ignore_tmp_files	true	Ignore /tmp files
glaas.url	(none)	GLaaS server URL

Concepts

Artifacts

Data files tracked by their content hash (BLAKE3). The same file content always has the same hash, regardless of filename or location.

Jobs

Recorded executions that consume input artifacts and produce output artifacts. Each roar run creates a job record.

Collections

Named groups of artifacts, used for downloaded datasets or upload bundles.

Workflow Example

# Record your pipeline
roar run python preprocess.py
roar run python train.py --epochs 10
roar run python evaluate.py

# Later, reproduce an artifact
roar reproduce <model-hash> --run

Git Integration

Roar automatically captures git metadata:

• Current commit hash
• Branch name
• Repository path

Data Storage

All data is stored locally in .roar/roar.db (SQLite). The database includes:

• Artifact hashes and metadata
• Job records with inputs/outputs
• Hash cache for performance

Add .roar/ to your .gitignore (roar offers to do this during roar init).

GLaaS Server

Roar can register artifacts and jobs with a GLaaS (Graph Lineage-as-a-Service) server using the roar register command.

Server Setup

# Install with server dependencies
uv pip install -e ".[server]"
# or without uv
pip install -e ".[server]"

# Run the server
glaas-server

# Or with custom host/port
GLAAS_HOST=0.0.0.0 GLAAS_PORT=8080 glaas-server

The server provides:

• REST API for artifact and job registration
• Web UI at / with artifact and job browsers
• Search and filtering by command, GPU, file type, etc.

Client Configuration

# Set the GLaaS server URL
roar config set glaas.url http://localhost:8000

# Register your SSH key
roar auth register

# Test authentication
roar auth test

Development

Prerequisites

• Python 3.10+
• Rust toolchain (for building the tracer) - install from https://rustup.rs/

Setup

# Install dev dependencies (automatically builds tracer if Rust is installed)
uv pip install -e ".[dev]"

Running Quality Checks

# Linting
ruff check .

# Format check
ruff format --check

# Type checking
mypy roar

# Run all checks at once
ruff check . && ruff format --check && mypy roar

Running Tests

# Run all tests (excluding those requiring a live GLaaS server)
pytest tests/ -v -m "not glaas and not live_glaas"

# Run with coverage
pytest tests/ -v --cov=roar --cov-report=term-missing -m "not glaas and not live_glaas"

# Run tests in parallel
pytest tests/ -v -n auto -m "not glaas and not live_glaas"

# Run only unit tests (fast)
pytest tests/ -v -m "not integration and not e2e and not glaas and not live_glaas"

License

Apache 2.0