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Choose a workflow

The shortest route depends on the artifact you already have.

GoalWhat you needRecommended route
Check the installationA clean checkoutRun one quickstart smoke test
Understand the methodNothingRead Particle flow and MLPF
Evaluate a published modelA compatible checkpoint and ROOT fileRun the Key4HEP standalone evaluator
Train a modelA prepared TFDS datasetDownload and verify a dataset, then train a model
Reproduce a datasetGenerator configuration and detector softwareFollow dataset generation
Validate CMS reconstructionCMSSW outputUse the CMS validation workflow
Validate CLD or CLIC reconstructionEDM4hep ROOT plus a checkpointUse the Key4HEP validation workflow
Compare deployment formatsA checkpoint, model_kwargs.pkl, and TFDSRun PyTorch-to-ONNX validation

Three levels of use

1. Use published artifacts

This is the recommended starting point. Download a dataset or checkpoint and avoid detector simulation. It is the quickest route to model evaluation and training experiments.

Use the dataset catalog to choose a compatible detector, input representation, version, and model recipe.

2. Run a local smoke workflow

The scripts in scripts/local_test_*.sh download two small input files, build a small dataset, and run a short CPU training. They establish that the software components fit together. Physics performance requires the dedicated validation workflows.

3. Produce data at scale

The Pixi and Snakemake workflow covers simulation, postprocessing, TFDS creation, training, and validation. A full campaign can use many batch jobs and substantial storage. It requires a reviewed site configuration and detector software access.

The generation guide explains the shared stages. The CMS and Key4HEP pages document only the detector-specific differences.

Detector choice

CLD and CLIC support both reconstructed track/cluster inputs and lower-level tracker/calorimeter hit inputs. The hit workflows have much larger events and are best treated as research workflows. See Current capabilities.