willitrun 0.1.0

Will your ML model run on your device? Find out in one command.

willitrun

CLI to tell you if an ML model will fit and run on your device, using real benchmarks + lightweight estimation.

• Real data first: SQLite DB of measured benchmarks (NVIDIA, Ultralytics, MLPerf, community).
• Edge to cloud: Jetson, Apple Silicon, desktop GPUs, SBCs.
• Vision + LLMs: Looks up exact matches, falls back to FLOPs/TFLOPS scaling with memory fit checks.
• CLI-first: Fast, scriptable, offline-capable once the DB is installed.

Quick Start (users)

The interactive UI is the primary entrypoint:

pip install willitrun
willitrun

Pick a model (name, HF ID, or local file), then pick a device. You’ll get a verdict and context instantly.

Power users (non-interactive):

willitrun <model> --device <device> [--json]

Install extras as needed:

# PyTorch/ONNX profiling (local .pt / .onnx parsing)
pip install willitrun[profiling,onnx]

How it works

Data pipeline (contributors)

• Raw (immutable cache): ingest scripts fetch and store HTTP responses in data/raw/{source}/{timestamp}.ext with TTL (configurable via tool.willitrun.raw_ttl_days).
• Normalized: the same scripts parse raw into JSONL at data/normalized/{source}.jsonl using the single BenchmarkRecord schema (willitrun/pipeline/schema.py).
• Serving: make build_db (or python scripts/run_pipeline.py build) validates, deduplicates by source priority, and writes data/benchmarks.db + data/benchmarks.meta.json. The packaged copy in willitrun/data/ is refreshed by make wheel.

Estimation

• Tier 1 (lookup): exact model+device benchmarks from SQLite.
• Tier 2 (estimate): FLOPs/TFLOPS scaling plus memory fit with 20% overhead; emits ranges and a confidence marker.

Supported inputs

• Model name from DB (yolov8n, resnet50, …)
• Local PyTorch / ONNX file
• HuggingFace model ID

Commands

User CLI:

willitrun --list-devices
willitrun --list-models
willitrun <model> --device <device> [--json]

Pipeline (for data contributors/maintainers):

make fetch       # run all ingests (respects cache TTL)
make build_db    # normalized -> SQLite + metadata
make status      # coverage summary + gaps
make wheel       # rebuild DB, sync into package, build wheel

Contributing data

1. Add benchmarks: append JSON lines to data/normalized/<source>.jsonl following BenchmarkRecord.
2. Add devices/models: edit data/devices.yaml / data/models.yaml.
3. Rebuild DB: make build_db (regenerates DB + meta).
4. Run tests: pytest.
5. Open a PR with the updated normalized file(s), DB, and metadata.

Development

pip install -e ".[dev]"
make build_db
pytest

Release flow

make build_db          # refresh DB + metadata
make wheel             # sync into package and build

CI (GitHub Actions) runs make build_db + pytest on push/PR to guard against stale DBs and failing tests.

Limitations

• Ingest scripts need network access; cached raw files avoid re-fetching within TTL.
• Tier 2 estimates rely on available reference benchmarks; uncommon models/devices may show wider ranges.

Roadmap

• More benchmark data (community PRs welcome!)
• Web frontend for shareability
• Training memory estimation
• Multi-GPU / model parallelism awareness
• Auto-detect local hardware

License

MIT