Prune the uv cache at the end of every CI job

The uv cache lives on a persistent volume shared by every job on the
runner (/srv/act-runner-cache/uv), so nothing trimmed it and it grew
without bound. `uv cache prune --ci` drops the entries that are not worth
keeping between runs (pre-built wheels for local sources) while leaving
the downloaded-wheel cache that makes `uv sync` fast.

In bump-version the step carries the same is_merge guard as the rest of
that job, since uv is only set up on a merge push.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.3.9"
+current_version = "0.3.10"
 parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
 serialize = ["{major}.{minor}.{patch}"]
 search = "{current_version}"

.gitea/workflows/ci.yml

@@ -28,6 +28,9 @@ jobs:
           echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV"
       - run: uv sync --extra cpu --extra dev
       - run: uv run ruff check .
+      # The uv cache is a persistent volume shared by every job on this
+      # runner, so each job trims what it no longer needs before exiting.
+      - run: uv cache prune --ci
 
   ruff-format:
     name: Format (ruff format)
@@ -46,6 +49,7 @@ jobs:
           echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV"
       - run: uv sync --extra cpu --extra dev
       - run: uv run ruff format --check .
+      - run: uv cache prune --ci
 
   type-check:
     name: Type check (ty)
@@ -64,6 +68,7 @@ jobs:
           echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV"
       - run: uv sync --extra cpu --extra dev
       - run: uv run ty check .
+      - run: uv cache prune --ci
 
   test:
     name: Tests
@@ -87,6 +92,7 @@ jobs:
         with:
           name: coverage-report
           path: coverage.xml
+      - run: uv cache prune --ci
 
   bump-version:
     name: Bump version, tag, and update changelog on merge to master
@@ -173,6 +179,10 @@ jobs:
             git tag -a "$TAG" -m "$TAG"
             git push origin "refs/tags/$TAG"
           fi
+      # Same guard as every other step here: on a non-merge push uv was never
+      # set up, so there is nothing to prune.
+      - run: uv cache prune --ci
+        if: steps.merge_check.outputs.is_merge == 'true'
 
   sync-version-on-tag:
     name: Sync project version with tag
@@ -201,3 +211,4 @@ jobs:
           else
             echo "Tag version matches project version ($CURRENT_VERSION)"
           fi
+      - run: uv cache prune --ci

CHANGELOG.md

@@ -1,5 +1,13 @@
 # Changelog
 
+## [0.3.10] - 2026-08-26
+
+### Changed
+
+- Backfill CHANGELOG.md for v0.2.0-v0.3.2
+
+- Docs: bring README and CLAUDE.md in line with v0.3.9
+
 ## [0.3.9] - 2026-08-24
 
 ### Added
@@ -65,4 +73,479 @@
 
 - Document CI_TOKEN's write:repository scope requirement [gitea #50](https://git.larsbogner.de/lars/giant/issues/50)
 
-# Changelog
+## [0.3.2] - 2026-08-17
+
+### Added
+
+- Add configs/baseline.toml as the kept reference model
+
+
+### Fixed
+
+- Clamp analysis histogram bins before the i32 cast, not after [gitea #61](https://git.larsbogner.de/lars/giant/issues/61)
+
+- Clip raw predicted log_mass in decode_secondaries [gitea #54](https://git.larsbogner.de/lars/giant/issues/54)
+
+
+### Changed
+
+- Let dwarf warm-cache take --config so it can't under-warm a config's cache keys [gitea #59](https://git.larsbogner.de/lars/giant/issues/59)
+
+- Implement n_sec.mode = "stop_token" for the AR secondary decoder [gitea #40](https://git.larsbogner.de/lars/giant/issues/40)
+
+- Bump patch version to 0.3.2
+
+## [0.3.1] - 2026-08-14
+
+### Added
+
+- Add an Objective registry for the flow/ddpm/wgan generator choice [gitea #32](https://git.larsbogner.de/lars/giant/issues/32)
+
+
+### Changed
+
+- Make trunk architecture selectable via a registry [gitea #33](https://git.larsbogner.de/lars/giant/issues/33)
+
+- Make ResBlock's conditioning-injection mechanism selectable [gitea #34](https://git.larsbogner.de/lars/giant/issues/34)
+
+- Make HistoryEncoder a pluggable registry, like Router/Objective [gitea #35](https://git.larsbogner.de/lars/giant/issues/35)
+
+- Deduplicate n_sec_head/type_head MLPs into build_mlp_head [gitea #36](https://git.larsbogner.de/lars/giant/issues/36)
+
+- Give the cond_cat/cond_cont column layout one owner [gitea #37](https://git.larsbogner.de/lars/giant/issues/37)
+
+- Give Stage1Model/Stage2OneShot/Stage2Autoregressive a shared StageModel base [gitea #39](https://git.larsbogner.de/lars/giant/issues/39)
+
+- Pass ConditioningAxisConfig/ParticleTypeConfig themselves instead of raw dicts [gitea #38](https://git.larsbogner.de/lars/giant/issues/38)
+
+- Bump patch version to 0.3.1
+
+## [0.3.0] - 2026-08-13
+
+### Added
+
+- Add v0.3.0 design doc: Stage-2 autoregressive redesign
+
+- Add pytest-cov to dev deps and run coverage in CI
+
+- Add coverage for router-center seeding, geometry batch reader, material topN cache, and setup-cache corruption paths
+
+- Add render.py coverage: figure params, router diagnostics plots, gallery/condor glue
+
+- Add unknown-key validation to config.toml merge (issues.md Issue 2)
+
+- Add consumed-keys audit test (issues.md Issue 5)
+
+
+### Fixed
+
+- Fix test_render_all_run_gallery_invokes_subprocess clobbering LaTeX's own subprocess.run
+
+
+### Removed
+
+- Remove issues.md
+
+
+### Changed
+
+- Refine v0.3.0 design: defaults, deferred scope, open questions
+
+- Document the differentiability position and its validation obligation
+
+- V0.3.0 step 1: new nested config schema, v0.2 migration shim
+
+- V0.3.0 step 2: network.py refactor to composable stage models
+
+- V0.3.0 step 3: per-stage train.py trainers + pipeline.py/cli.py rewrite
+
+- V0.3.0 step 4: type map + particle_type.target = "onehot"/"embedding"
+
+- V0.3.0 step 5: Stage2Autoregressive (history=markov) + §11.4 grad instrumentation
+
+- V0.3.0 step 6: sample.py/rollout.py AR generation + class->PDG decode
+
+- V0.3.0 step 7: AttentionHistory (KV-cached) + scheduled/never teacher forcing
+
+- V0.3.0 post-implementation audit: resolve all 9 tracked discrepancies
+
+- Refactor train.py into giant/training/ around a metrics collector
+
+- Silence the fork-safety warning from num_workers>0 pipeline tests
+
+- Deduplicate giant/training/trainers.py shared per-stage logic
+
+- Rewrite README for v0.3.0 architecture, quick start, and data columns
+
+- Bump version to 0.3.0
+
+- Delete docs/v0.3.0-design.md and strip all references to it
+
+- Apply ruff format
+
+- Downgrade coverage-report upload to actions/upload-artifact@v3
+
+- Bump ruff line-length to 120 and reformat
+
+- Make config dataclasses the single source of truth for DEFAULT_CONFIG
+
+- Extract giant train/new-run's CLI override mapping into a table-driven function (issues.md Issues 3 & 4)
+
+- Mark issues.md Issues 3 & 4 as fixed
+
+- Extract predict/rollout's duplicated inference bootstrap into giant.checkpoint_io (issues.md Issue 5)
+
+- Mark issues.md Issue 5 as fixed
+
+- Unify the two v0.2->v0.3 migration surfaces (issues.md Issue 6)
+
+- Type the data/model/training batch contracts with NamedTuples (issues.md Issue 7)
+
+- Split giant/model/network.py into giant/model/ (issues.md Issue 8)
+
+- Move scripts/ to giant/tools/ (issues.md Issue 9)
+
+- Reject stage2_model.stage1_context = 'sampled' as unimplemented (issues.md Issue 1)
+
+- Honour wgan.critic_hidden_dim/critic_n_res_blocks in build_critics [gitea #28](https://git.larsbogner.de/lars/giant/issues/28)
+
+- Validate stage2_model.autoregressive.order in validate_config [gitea #30](https://git.larsbogner.de/lars/giant/issues/30)
+
+- Decouple secondary-species vocabulary from conditioning.particle.emb_dim [gitea #29](https://git.larsbogner.de/lars/giant/issues/29)
+
+- Skip router auxiliary loss compute when their lambda is 0 [gitea #31](https://git.larsbogner.de/lars/giant/issues/31)
+
+## [0.2.0] - 2026-08-04
+
+### Added
+
+- Add CLAUDE.md with architecture overview and dev commands
+
+- Add streaming data pipeline and giant CLI entry point
+
+- Add giant predict command
+
+- Add ROOT-to-parquet conversion script with convert dependency group
+
+- Add post_pos as a model target via travel_dir decomposition
+
+- Add --coord local mode to predict for raw-space prediction debugging
+
+- Add KL divergence to marginal validation and hook it into the training loop
+
+- Add graceful shutdown on SIGINT/SIGTERM
+
+- Add configurable dropout to ResBlocks
+
+- Add giant.analysis module for notebook-based model quality diagnostics
+
+- Add lazy polars I/O and duplicate KL/constraint checks for giant.analysis
+
+- Add ruff and ty as dev dependencies, fix lint/type findings
+
+- Add linear warmup before cosine LR decay
+
+- Add --batch-size auto to estimate batch size from free GPU memory
+
+- Add hyperparameter scan
+
+- Add --batch-size auto to predict, matching train
+
+- Add tqdm progress bar to predict
+
+- Add KL bar plots and sample_frac to load_predicted_local; ignore root parquet scratch files
+
+- Add event-level shower observables to giant.analysis
+
+- Add total length traveled per event to event observables
+
+- Add pdg energy/length contribution pie plots
+
+- Add export script for Tier 4 event-level/pdg-share plots
+
+- Add mean/median deposited energy and step length plots per event
+
+- Add export script for ETP group-update presentation plots
+
+- Add photon edep export scripts and per-step presentation plots
+
+- Add tooling for a versioned geant_steps dataset layout
+
+- Add --copy mode to migrate_geant_steps.py
+
+- Add update-manifest and create-manifest subcommands to bump_dataset_version
+
+- Add --to flag for bump-gen/bump-schema and --gen flag for update-manifest
+
+- Add disk usage summary to dwarf status
+
+- Add file counts and reference tracking to dwarf status
+
+- Add --comment option to predict, recorded in YAML sidecar
+
+- Add energy-conservation PoC ODE-step comparison scripts
+
+- Add autoregressive shower rollout driver
+
+- Add fast slab lookup for the GeometryOracle, replacing knn as the default
+
+- Add load_rollout_vs_truth to compare rollouts against held-out truth data
+
+- Add mixture-of-experts routing prototype for Stage 1 and Stage 2
+
+- Add ProcessRouter for physics-process-based expert gating
+
+- Add PdgRouter for particle-type-based expert gating
+
+- Add ComposedRouter for multi-axis MoE gating
+
+- Add EMA weights, weight decay, step-based LR schedule, and grad-norm logging to training
+
+- Add WGAN-GP mode as a throwaway fast-eval experiment
+
+- Add router gating diagnostic for MoE checkpoints
+
+- Add Gitea Actions CI pipeline
+
+- Add configs for router energy (embedding/physical) and WGAN baseline runs
+
+- Add opt-in Weights & Biases logging for the training loop
+
+- Add test coverage for resolve_expert_dims
+
+- Add regression coverage for vocab/process index-map builders
+
+- Add dwarf warm-cache to precompute the setup-stage sidecar
+
+- Add giant new-run to scaffold a config.toml + run dir ahead of training
+
+- Add learnable per-expert width and shared temperature to EnergyRouter
+
+- Add opt-in straight-through Gumbel-softmax combine weights to MoE router
+
+- Add gumbel router configs sweeping learnable-knob combinations
+
+- Add gumbel/learn_centers/learn_width/learn_temperature to out-dir naming
+
+- Add bigger WGAN config (hidden_dim=512, n_blocks=6)
+
+- Add data-integrity guards against silent NaN/Inf propagation and races
+
+
+### Fixed
+
+- Fix column names to match actual parquet schema
+
+- Fix installed torch version to be compatible with cuda drivers
+
+- Fix miniCaloSim link in README
+
+- Fix giant.analysis import after Phase 2 dataset API changes
+
+- Fix silent failure modes surfaced by extensive code review
+
+- Fix ruff, ty, and pytest failures; apply ruff format
+
+- Clamp n_sec classification label to K_MAX
+
+- Fix rollout edep mismatch and add truth overlay to Tier 4 observables
+
+- Fix crashes in physical-property conditioning edge cases
+
+- Fix router experts silently ignoring --hidden-dim/--n-blocks
+
+- Fix conditioning="physical" so it can actually generalize past training vocab
+
+- Fix training-loop checkpoint/resume and WGAN bugs
+
+- Fix stale-partial reuse and n_chunks mismatch in analysis condor pipeline
+
+- Fix CLI/tooling robustness gaps and dedupe the Conditioning enum
+
+- Fix test_write_submit_requires_synced_venv for active-venv resolution
+
+
+### Removed
+
+- Remove scripts/train.py in favor of the giant train CLI
+
+- Drop orphaned child tracks instead of nulling secondary targets
+
+
+### Changed
+
+- Initial commit: giant surrogate model with two-phase roadmap in README
+
+- Implement Phase 1: full data pipeline, model, training, and config support
+
+- Handle material column as string type
+
+- Rename pre_energy/post_energy columns to pre_E/post_E
+
+- Rename direction columns from pre_dir_x/y/z to pre_dx/dy/dz
+
+- Batch StreamingStepsDataset internally instead of per-row collate
+
+- Dedup training pipeline, add seeding/resume and per-epoch metrics logging
+
+- Split torch into cpu/cuda extras and pin dependency version bounds
+
+- Apply ruff format and document lint/type tooling in CLAUDE.md
+
+- Update README to match current architecture and tooling
+
+- Make sampler step count configurable for validation
+
+- Calibrate auto batch size separately for inference vs training
+
+- Skip rows with unknown PDG codes during predict
+
+- Buffer predict rows across row-group boundaries before inference
+
+- Export plots for knowledge base
+
+- Rework validation notebook with markdown sections and Tier 4 plots
+
+- Allow steps_to_parquet.py to accept multiple ROOT input files
+
+- Encode edep/secondary/post energy as a conservation-constrained simplex
+
+- Expose dataset/conversion scripts as uv entry points
+
+- Restrict holdout overlap check to holdout vs dev/full only
+
+- Route predict output to UUID-named parquet with YAML reference sidecar
+
+- Implement Phase 2: secondary particle prediction
+
+- Unify dataset/tooling scripts into a single `dwarf` Typer CLI
+
+- Fold --to/--gen dataset-versioning flags into the dwarf CLI
+
+- Prefix default train output dir with current date
+
+- Color-code dwarf status output by tree level
+
+- Show VERSIONS.md reason extracts in dwarf status
+
+- Wire up predict CLI to load and run the Stage-2 sec_decoder
+
+- Wire up n_sec/species/energy-fraction validation for Stage 2
+
+- Detach Stage-2 type-embedding target to stop self-referential collapse
+
+- Weight Stage-2 secondary loss equally between direction and type-embedding dims
+
+- Recalibrate batch-size estimate for the post-Phase-2 model size
+
+- Update CLAUDE.md and README for the implemented Phase 2 model
+
+- Error on missing secondary lists instead of silently zeroing Stage-2 targets
+
+- Derive a unique per-job seed for minicalosim shard generation
+
+- Rescale secondary energies to exactly consume the e_sec budget
+
+- Support --energy-gev in dwarf make-root for the new minicalosim energy arg
+
+- Stream giant rollout output instead of buffering the whole run
+
+- Scale auto batch-size estimate by MoE expert count during training
+
+- Rewrite analysis module as a lean, fully-streaming pipeline
+
+- Reimplement rollout-vs-truth comparison on the streaming analysis module
+
+- Condition on material/particle physical properties instead of learned embeddings
+
+- Ignore the scratchpad working directory
+
+- Quote the on: key in the CI workflow
+
+- Split CI lint stage into parallel jobs
+
+- Rewrite analysis as streaming rollout-vs-reference plotting pipeline
+
+- Analyze: drive prep/submit from the rollout YAML sidecar
+
+- Analyze: show model/training params on rendered figures
+
+- Deps: install plotstyle from git.larsbogner.de package index
+
+- Analyze: drop stale ty:ignore on plotstyle import
+
+- Test: replace prep(**_CTX) splat with a typed _prep helper
+
+- Analyze: add MoE router gating/share diagnostic plots
+
+- Chore: remove stray CUDA sanity script and stale Phase 2 planning doc
+
+- Docs: document compute environment, WGAN/MoE status, and condor-gpu-train-rollout
+
+- Analyze: normalize pdg dtype in open_side to fix rollout/reference concat
+
+- Analyze: chunk per-plot aggregation across HTCondor jobs
+
+- Analyze: expose bin/pdg options on `analyze submit`
+
+- Analyze: estimate per-job HTCondor walltime from chunk row count
+
+- Analyze: run condor compute jobs via .venv/bin/giant, not uv run
+
+- Analyze: default condor docker image to alma9-gridjob
+
+- Analyze: raise default condor job memory request to 8192 MB
+
+- Analyze: recalibrate condor walltime model from real cluster timings
+
+- Transforms: pad legacy cond normalizers for pre-physical-conditioning checkpoints
+
+- Analyze: default run directory to <repo>/analysis_runs, gitignored
+
+- Docs: record first MoE router rollout benchmark result in the roadmap
+
+- Router: seed EnergyRouter centers from data quantiles instead of a fixed linspace
+
+- Docs: note the EnergyRouter centers_init fix in the roadmap
+
+- Analyze: thread full model/training/rollout/dataset params to plots
+
+- Ci: share one uv sync across jobs, gate tests on lint+type-check, sync tag/version on release tags
+
+- Ci: replace unsupported artifact sharing with a bind-mounted uv cache
+
+- Ci: stop setup-uv from overriding UV_CACHE_DIR
+
+- Ci: re-pin UV_CACHE_DIR after setup-uv, which exports its own value regardless of enable-cache
+
+- Ci: set UV_LINK_MODE=copy to silence the cross-filesystem hardlink warning
+
+- Log batch-level metrics to W&B, not just per-epoch summaries
+
+- Log router health, WGAN grad-norm split, n_sec accuracy, GPU/throughput to W&B
+
+- Persist global_step across --resume so W&B step stays monotonic
+
+- Timestamp default checkpoint dir to avoid W&B run-id collisions
+
+- Skip empty-slice mean/std in sec phys validation print
+
+- Speed up giant train's setup stage
+
+- Speed up _WelfordAccumulator's per-chunk update
+
+- Make default checkpoint out_dir name reflect only non-default hyperparams
+
+- Cache giant train's setup stage in a sidecar file
+
+- Pass --seed through to the train/val event split
+
+- Offset event_id per file to avoid cross-file collisions
+
+- Store a quantile grid instead of a raw reservoir sample in the setup cache
+
+- Scope wandb run config to only-active hyperparameters
+
+- Resolve giant condor wrapper from the active venv, not a hardcoded path
+
+- Bump version to 0.2.0

CLAUDE.md

@@ -7,18 +7,22 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 ```bash
 uv sync --extra cpu                # install dependencies with CPU-only torch (standard/default)
 uv sync --extra cuda               # install dependencies with CUDA 11.8 torch
-uv sync --extra cpu --extra dev    # add dev extras (pytest, etc.)
+uv sync --extra cpu --extra dev    # add dev extras (pytest, ruff, ty, bump-my-version, git-cliff, + all runtime extras)
 uv sync --extra cpu --extra geometry  # add scikit-learn for the geometry oracle (giant rollout)
+uv sync --extra cpu --extra workflow  # add b2luigi for `giant workflow` pipeline orchestration
 pytest               # run tests
 giant new-run --hidden-dim 512 --lr 3e-4        # scaffold a config.toml + run dir ahead of training
-giant train path/to/steps.parquet --mode flow   # train (flow matching)
+giant train path/to/steps.parquet               # train (defaults: stage 1 flow, stage 2 wgan + autoregressive)
-giant train path/to/steps.parquet --mode ddpm   # train (DDPM baseline)
+giant train path/to/steps.parquet --mode flow   # set both stages' generative objective at once
-giant train path/to/steps.parquet --mode wgan   # train (WGAN-GP, single-pass eval; implemented, not yet tested)
+giant train path/to/steps.parquet --stage1-generator flow --stage2-generator wgan   # per-stage override
-giant train path/to/steps.parquet --router --router-type energy  # MoE routing trunk (implemented; first rollout benchmark failed with lambda_balance=0, retrain needed — see Roadmap)
+giant train path/to/steps.parquet --router --router-type energy  # MoE routing trunk (see Roadmap for status)
+giant model summary --config config.toml         # build-only: parameter counts + which config keys actually bite
 giant predict path/to/steps.parquet --checkpoint ckpt/best.pt   # per-step predictions
 giant rollout path/to/steps.parquet --checkpoint ckpt/best.pt --geometry oracle.pkl  # full showers
-giant analyze submit rollout.yaml --accounting-group cms   # parallel rollout-vs-reference analysis on HTCondor
+giant workflow run spec.toml --batch --workers 20          # whole pipeline (cache-warm -> train -> rollout -> analysis)
-giant analyze render <run_dir> --gallery                   # render PDFs + HTML gallery (run_dir from prep/submit)
+giant analyze prep rollout.yaml --chunks 32                # lay out an analysis run dir (compute jobs come from the workflow)
+giant analyze render <run_dir> --gallery                   # render PDFs + HTML gallery (run_dir from prep)
+giant analyze metrics <train_run_dir>                      # training-progress plots from metrics.csv
 dwarf --help                                     # dataset/tooling CLI: convert, migrate, bump-gen,
                                                   # bump-schema, status, update-manifest, create-manifest,
                                                   # make-root, build-geometry-oracle, warm-cache, hparam-scan
@@ -27,6 +31,8 @@ dwarf --help                                     # dataset/tooling CLI: convert,
 
 `cpu` and `cuda` are mutually exclusive — pick one to select the torch build (pinned to 2.3.x; newer torch requires newer NVIDIA drivers). Plain `uv sync` with no extra will not install torch at all; uv has no concept of a "default extra", so `--extra cpu` should always be included unless you need GPU support.
 
+`configs/` holds kept reference configs (`baseline.toml`, `default.toml`, the router/WGAN scan configs) — pass them with `--config`.
+
 ### Lint and type checking
 
 ```bash
@@ -37,60 +43,86 @@ uv run ty check .       # type check
 
 Part of the `dev` extra. Run these periodically (not just at commit time) to catch drift early.
 
+### Release tooling
+
+Merges to `master` auto-bump the patch version, tag, and update `CHANGELOG.md` via the Gitea workflow in `.gitea/workflows/ci.yml` (bump-my-version + git-cliff). Don't hand-edit the version in `pyproject.toml` or write changelog entries by hand.
+
 ## Compute environment
 
 Work on this repo happens across three kinds of machine:
 
 - **Local dev machines** (laptop + desktop, identical): repo at `~/Programming/giant`, no access to `/ceph` — datasets, training results, and models aren't reachable here.
 - **Portal machines** (`portal1`, `deepthought`, `deepthought2`, `bms1`, `bms2`, `bms3`): repo lives under `/work`, and `/ceph` holds ROOT/parquet files and trained models. **These are shared with other users** — stay strictly within `/work/lbogner` and `/ceph/lbogner`, and keep resource usage to roughly a quarter of CPU/RAM and a single GPU so as not to disturb other users' jobs.
-- **HTCondor worker nodes**: never run or SSH onto these directly — the only sanctioned path is submitting jobs through condor (`giant analyze submit`, and the in-progress remote-GPU train/rollout submission on `condor-gpu-train-rollout`). `/ceph` is available there; `/work` is only sometimes mounted, depending on the node.
+- **HTCondor worker nodes**: never run or SSH onto these directly — the only sanctioned path is `giant workflow run <spec.toml> --batch` (b2luigi, see the Workflow section), which submits and polls every job. `/ceph` is available there; `/work` is only sometimes mounted, depending on the node.
 
 ## Architecture
 
-GIANT is a conditional generative surrogate for the Geant4 step function. It replaces the stochastic physics engine: given a pre-step particle state (conditioning), it samples a post-step outcome — now including the variable-length list of secondary particles the step produces (Phase 2, see Roadmap).
+GIANT is a conditional generative surrogate for the Geant4 step function. It replaces the stochastic physics engine: given a pre-step particle state (conditioning), it samples a post-step outcome — including the variable-length list of secondary particles the step produces.
 
-**Data pipeline** (`giant/data/`): parquet files from miniCaloSim are loaded into numpy arrays (`loader.py`), then log-transformed and rotated into a local coordinate frame where `pre_dir = ẑ` (`transforms.py`), before being wrapped in a PyTorch `Dataset` (`dataset.py`). Train/val split is by `event_id` to avoid leaking correlated steps from the same shower.
+**Data pipeline** (`giant/data/`): parquet files from miniCaloSim are loaded into numpy arrays (`loader.py`), then log-transformed and rotated into a local coordinate frame where `pre_dir = ẑ` (`transforms.py`), before being wrapped in a PyTorch `Dataset` (`dataset.py`, streaming variant included). Train/val split is by `event_id` (`--seed`-controlled) to avoid leaking correlated steps from the same shower. Loading a directory or `.manifest` of several parquet files offsets each file's `event_id`s by a per-file stride so ids stay globally unique. `setup_cache.py` persists the pre-epoch setup scan (vocab maps, event split, process maps, normalizer stats) as a sidecar so repeated runs over the same `data` path don't rescan (`--cache-setup`/`--rebuild-setup-cache`, precomputable with `dwarf warm-cache --config ...`).
 
 **Stage-1 output space (9D, `giant/constants.py:LOCAL_TARGET_NAMES`):** `log_step_length`, two additive-log-ratio (ALR) coordinates `edep_logit`/`sec_logit` of a **deposit / secondary / post-energy simplex**, `post_dir` (post-scattering momentum direction, unit vector in the local frame), and `travel_dir` (direction of `post_pos - pre_pos`, unit vector in the local frame). The energy simplex decodes via softmax over `[edep_logit, sec_logit, 0]` × `pre_E` so `edep + e_sec + post_E == pre_E` holds by construction — energy conservation is architectural, not learned (see `energy_simplex_decode`). `post_pos` is not a raw target — it's reconstructed at inference as `pre_pos + step_length * world_frame(travel_dir)`, since `step_length` already encodes that displacement's magnitude and duplicating it would let the two become inconsistent.
 
-**Conditioning vector (15D continuous, `COND_DIM`):** pre-step position, log(pre-energy), pre-step direction, layer ID (`COND_DIM_BASE=8`) — plus, since particle/material physical-property conditioning (`model.conditioning`, see below), 7 more columns: particle `log(mass)`/`charge` (`PARTICLE_PHYS_DIM=2`, `giant/particles.py`) and material `Z_eff`/`A_eff`/`log(density)`/`log(X0)`/`log(λ_int)` (`MATERIAL_PHYS_DIM=5`, `giant/materials.py`). `n_sec` and `e_sec` are **not conditioning inputs** (that was Phase 1 / the energy-conservation PoC); the model predicts them.
+**Conditioning vector (15D continuous, `COND_DIM`):** pre-step position, log(pre-energy), pre-step direction, layer ID (`COND_DIM_BASE=8`) — plus 7 physical-property columns: particle `log(mass)`/`charge` (`PARTICLE_PHYS_DIM=2`, `giant/particles.py`) and material `Z_eff`/`A_eff`/`log(density)`/`log(X0)`/`log(λ_int)` (`MATERIAL_PHYS_DIM=5`, `giant/materials.py`). `n_sec` and `e_sec` are **not conditioning inputs** — the model predicts them. `giant/cond_layout.py` is the single source of truth for the `cond_cont`/`cond_cat` column layout shared by `giant.data.transforms`, `giant.model.encoders`, and `giant.model.routers`.
 
-`ConditionEncoder`/`SecondaryConditionEncoder` (`giant/model/network.py`) support two mutually exclusive `conditioning` modes, selected per-checkpoint (`model_config["conditioning"]`, defaulting to `"embedding"` for old checkpoints without the key, `"physical"` for new `giant train` runs — see `--conditioning`):
+`ConditionEncoder` (`giant/model/encoders.py`) configures the particle and material identity axes **independently** (`conditioning.particle` / `conditioning.material`, each a `ConditioningAxisConfig` with `type`/`emb_dim`/`n_layers`), so they may mix freely. Three per-axis modes:
-- **`"embedding"`** (original Phase 2 design): a learned `nn.Embedding` per PDG code / material name, indexed by a dataset-scoped dense vocab (`pdg_map`/`mat_map`). Memorizes the training menu.
+- **`"physical"`** (default): the axis's raw physical properties routed through a small MLP — computable for any PDG code / material name, which is what lets the surrogate generalize beyond the training menu. `giant/particles.py` decodes nuclear/ion PDG codes (the `10LZZZAAAI` scheme) via the scikit-HEP `particle` package with a Z/A-digit-decode fallback for isomer codes the package's ground-state-only table misses. `giant/materials.py` ships real Geant4-11.4.1-derived values for every material the detector geometry actually produces; the sole exception is `G4_LYSO` (not a stock Geant4 NIST material, never actually constructed by the geometry — see the module docstring), which stays `MaterialProperties(None, ...)` and raises loudly (`MaterialPropertiesNotFilledError`) rather than silently defaulting.
-- **`"physical"`** (default): the 7 physical-property columns above are each routed through a small MLP (`particle_mlp`/`material_mlp`) to the same `emb_dim` width the embedding tables would have produced — a drop-in replacement computable for any PDG code / material name, not just ones seen in training, which is what lets the surrogate generalize to a held-out material or species. `giant/particles.py` decodes nuclear/ion PDG codes (the `10LZZZAAAI` scheme) via the scikit-HEP `particle` package with a Z/A-digit-decode fallback for isomer codes the package's ground-state-only table misses. `giant/materials.py` ships real Geant4-11.4.1-derived `z_eff`/`a_eff`/`density`/`x0`/`lambda_int` values for every material the detector geometry actually produces; the sole exception is `G4_LYSO` (not a stock Geant4 NIST material, never actually constructed by the geometry — see the module docstring), which stays `MaterialProperties(None, ...)` and raises loudly (`MaterialPropertiesNotFilledError`) rather than silently defaulting if it's ever requested.
+- **`"embedding"`**: a learned `nn.Embedding` per PDG code / material name, indexed by a dataset-scoped dense vocab. Memorizes the training menu; the generalization-comparison baseline, and the only mode compatible with `stage2_model.particle_type.target = "embedding"`.
+- **`"onehot"`**: a fixed, unlearned vector over the top `emb_dim - 1` codes by training-set count plus one "other" bin. Not a reparameterization of `"embedding"` — the vocabulary cap is the real difference.
 
-**Model** (`giant/model/network.py`): a two-stage model, both checkpointed together.
+`conditioning.share_stages` decides whether the two stages get one shared encoder instance or two identically-configured independent ones.
-- **Stage 1 — `DenoisingMLP`:** `ResBlock` stack with a `SinusoidalEmbedding` for the flow/diffusion time variable and a `ConditionEncoder` fusing the conditioning. Predicts the 9D primary vector field, plus an `n_sec_head` classifier over `{0..K_MAX}` (`K_MAX=15`) that runs on the condition encoding alone (no diffusion noise), callable via `predict_n_sec`.
-- **Stage 2 — `SecondaryDecoder`:** a second flow-matching net (`SecondaryConditionEncoder` fuses the pre-step conditioning with the Stage-1 outcome) that generates all `K_MAX` secondary slots at once. Each slot is `(stick-breaking energy logit, local-frame direction 3D, log-mass, charge)` = `SEC_SLOT_DIM=6`, ordered by descending energy; slots beyond the predicted `n_sec` are masked. Secondary energies are a **stick-breaking partition of the `e_sec` budget** from Stage 1 (they sum to it), so the whole chain conserves energy. A secondary's mass/charge are regressed directly against a fixed physics-derived target (its ground-truth PDG code's `giant.particles.particle_mass_charge`) — not a learned/moving embedding target, so nothing needs detaching. **No snapping at inference**: the predicted (mass, charge) are used as-is as the secondary's physical identity, including for its own future conditioning if it goes on to take further steps in a rollout. A separate, reporting-only nearest-known-PDG lookup (`giant.particles.nearest_known_pdg`) is used purely to populate a nominal `pdg` label for output rows / `"embedding"`-mode fallback conditioning — it never feeds back into the model.
 
-`schedule.py` provides both a `CosineSchedule` for DDPM and the flow matching loss utilities (Lipman et al. 2022 conditional flow matching).
+**Model** (`giant/model/`, both stages checkpointed together). `network.py` is only a re-export shim now; the real code is split by concern:
+- `layers.py` — `ResBlock`/`AdaLNResBlock` + `BLOCK_REGISTRY` (conditioning-injection mechanism is selectable), `SinusoidalEmbedding`, `ContextAdapter`, `build_mlp_head`.
+- `encoders.py` — `ConditionEncoder` (above).
+- `trunks.py` — `TRUNK_REGISTRY`/`build_trunk`: everything downstream of the fused conditioning vector, as a registrable expert *body* (`resmlp` default, plus a `none` variant). `RoutedTrunk` builds `router.n_experts` instances of whichever body is named, so mixing is orthogonal to which body is mixed.
+- `routers.py` — `Router` base + `ROUTER_REGISTRY`: `energy`/`pdg`/`process`/`composed`/`none`. Soft-mixed at train time, **top-1 dispatched at eval time** (each row runs exactly one small expert), which is the actual inference-speed win. `EnergyRouter`/`PdgRouter` gate on a quantity known at inference; `ProcessRouter` runs its own small classifier (process isn't known upfront); `ComposedRouter` gates jointly over outer-product expert cells via repeated `--router-axis "type:key=val,..."`. The `--router*`/`--n-experts` CLI flags target `stage1_model.router` only; stage 2's router is config-file-only (`stage2_model.router`). `EnergyRouter` accepts `centers_init`, which `giant/pipeline.py` auto-populates from real data quantiles via a reservoir sample collected during the normalizer-fitting pass.
+- `history.py` — `HISTORY_REGISTRY`/`build_history`: `markov` (previous token only), `attention` (causal self-attention, KV-cached at inference via `init_cache`/`step`), `none`. Stage-2 autoregressive only.
+- `objectives.py` — `Objective` base + registry for `flow`/`ddpm`/`wgan`: answers in one place whether a stage needs a time embedding, is adversarial, folds the secondary type slice into its trunk output, what its trunk input is, and which loss it trains against.
+- `models.py` — the composed stage models: `Stage1Model`, `Stage2OneShot`, `Stage2Autoregressive`, `CriticModel`, all on a shared `StageModel` base.
+- `builders.py` — `build_models`/`build_critics`, assembling the above from a config dict.
+- `schedule.py` (`CosineSchedule` for DDPM + conditional-flow-matching losses), `wgan.py` (gradient penalty / critic / generator losses, Gulrajani et al. 2017), `summary.py` (`giant model summary`), `_legacy.py` (v0.2 checkpoint migration).
 
-**Samplers** (`giant/sample.py`): DDPM, DDIM, and flow matching (ODE integration, ~10 steps). Flow matching is the primary mode.
+**Stage 1 — primary step.** Trunk (routed or not) over the fused conditioning, plus a `SinusoidalEmbedding` of the flow/diffusion time for non-adversarial objectives, predicting the 9D vector field. An `n_sec` classifier head over `{0..k_max}` runs on the condition encoding alone; `stage2_model.n_sec.owner` decides whether it lives on stage 1 (v0.2 checkpoints) or stage 2 (default).
 
-**WGAN-GP mode (`--mode wgan`, implemented, not yet tested):** a throwaway fast-eval alternative to the flow/DDPM samplers above — single forward pass instead of ~10 ODE steps. Dedicated noise-conditioned generators (`WGANGenerator`/`WGANSecondaryGenerator`, `giant/model/network.py`) stand in for `DenoisingMLP`/`SecondaryDecoder`, trained against `Critic`/`SecondaryCritic` discriminators with the gradient-penalty loss in `giant/model/wgan.py` (Gulrajani et al. 2017); `sample_wgan` (`giant/sample.py`) does the single-pass draw at inference. Not yet validated against the flow-matching baseline.
+**Stage 2 — secondaries.** Conditioned on the pre-step state plus a projected stage-1 outcome (`stage2_model.context_dim`; `stage1_context` selects ground-truth vs sampled context, annealable via `ctx_p_start`/`ctx_p_end`). Two decoders (`stage2_model.decoder`):
+- **`autoregressive`** (default): one secondary at a time in descending-energy order, each token conditioned on a `HistoryEncoder` summary of prior tokens, with teacher forcing (`always`/`scheduled`/`never`, `tf_p_start`/`tf_p_end`). `n_sec.mode = "stop_token"` lets the length be emitted by the sequence itself instead of the classifier head.
+- **`one_shot`**: all `k_max` slots in one pass, masked past the predicted `n_sec` (the v0.2 behaviour).
 
-**MoE routing trunk (`--router`, implemented; first rollout benchmark shows the experts don't specialize — see Roadmap):** an alternative to `DenoisingMLP`'s monolithic `ResBlock` trunk — a `Router` (`giant/model/network.py`, `ROUTER_REGISTRY`/`build_router`) gates between small per-expert `ResBlock` stacks (`Expert`), soft-mixed over all experts at train time but **top-1 dispatched at eval time** (each row runs exactly one small expert), which is the actual inference-speed win. Router types gate on different conditioning axes: `EnergyRouter`/`PdgRouter` read a quantity already known at inference time, `ProcessRouter` runs its own small classifier over pre-step conditioning (since process isn't known upfront); `ComposedRouter` gates jointly over multiple axes (outer-product expert cells) via repeated `--router-axis "type:key=val,..."` flags. Config lives under `model.router` (`giant/config.py`), deep-merged one level so `router.enabled` alone doesn't drop the rest of the defaults.
+Secondary energies are a **stick-breaking partition of the `e_sec` budget** from Stage 1 (they sum to it), so the whole chain conserves energy. Particle identity is set by `stage2_model.particle_type.target`: `"onehot"` (default — categorical over the top `n_classes - 1` PDG codes by training count plus "other", with configurable `other_policy` and `class_weighting`), `"physical"` (continuous `(log-mass, charge)` regressed against `giant.particles.particle_mass_charge`), or `"embedding"` (nearest-row snap into the conditioning embedding table; requires `conditioning.particle.type = "embedding"`).
 
-**Validation** (`giant/validate.py`): step-level marginal comparisons.
+**Samplers** (`giant/sample.py`): DDPM, DDIM, flow matching (ODE integration, ~10 steps), and single-pass WGAN, plus the stage-2 secondary sampling loop (one-shot and autoregressive).
 
-**Analysis** (`giant/analysis/`, `giant analyze` CLI): a lean, streaming rollout-vs-reference plotting pipeline that compares one or more autoregressive `giant rollout` runs against a single held-out miniCaloSim reference steps file shared by all of them, and produces publication-styled PDFs assembled into an HTML gallery — one distinctly colored series per rollout, one reference line/panel. It exploits the fact that rollout output and a raw reference file share a world-frame physical column subset under identical names (`pre_*`/`post_*`/`edep`/`step_length`/`pdg`/`material`/`event_id`), so no ALR/local-frame decode is needed — everything is world-frame mm/MeV. Structure: `sources.py` (canonical LazyFrames + `RolloutSpec`/`RolloutSide` — a rollout's opened frames + per-checkpoint diagnostic inputs — + synthetic-termination-row filtering + the secondary view, which is `generation>0 & step_no==0` rollout tracks vs exploded `sec_*_list` reference columns), `reduce.py` (the streaming primitives — a single `hist1d` `group_by([group,bin]).len()` pass, per-event scalars, edep-weighted depth/transverse profiles, species share, leakage), `grouping.py`/`context.py` (fixed bin edges + energy-quantile/pdg/material group sets resolved once by `prep` into `shared.json` over the union of the reference and every rollout, so every compute job is one pass with no range scan), `catalog.py` (the declarative `PlotSpec` registry — marginals × {overall,energy,pdg,material}, per-event totals, shower profiles, species/leakage, secondaries; `Bundle.rollouts` is a name-keyed dict of `RolloutSide`, and every `compute_partial`/`finalize` builds a `Reduced.payload["series"]` dict keyed the same way, with `payload["reference"]` as the one distinguished non-rollout entry), and `render.py` (the only module importing ETPlot's `plotstyle`/LaTeX; dispatches on `Reduced.kind`, writes PDFs + `metadata.yaml`; each rollout gets a stable `ps.get_color(i)` slot by its position in `series`, the reference always draws in one fixed dashed-ink style). The two heatmap-shaped specs (`marginal_distance_summary`, `n_sec_confusion`) and the router/type-embedding diagnostics (`router_gating.py`, `type_embedding_distance.py`) are inherently one-matrix/one-checkpoint per rollout, so they render as one panel per rollout instead of one line/bar per rollout. **Input is one or more `giant rollout` YAML sidecars** (`condor.py:load_rollout_yamls`, wrapping the single-YAML `load_rollout_yaml`): each YAML's `output`/`dataset` keys name its rollout parquet and seed file (= the reference truth); every supplied YAML must resolve to the same `dataset`, checked up front with a clear error otherwise (the premise is "N candidates vs one ground truth"). Each rollout's series name comes from a repeated `--label` CLI flag, else the YAML stem (N>1), else `"rollout"` (a single YAML — matching pre-multi-rollout output exactly). `prep` derives its own **run directory** next to the *first* rollout's parquet (`<...>/analysis_<tag(s)>/`) holding `shared.json`, `run_meta.json` (`RunMeta.rollouts: list[{name,path,plot_meta}]`, insertion order = CLI order = every plot's series order), `reduced_partial/`, `reduced/`, `plots/`. **Compute/merge/render split:** `giant analyze submit a.yaml [b.yaml ...] --chunks N` runs `prep` (recording the run's chunk count `N` in `run_meta.json`) then submits one HTCondor job per (plot, chunk) pair (`compute-one --id --chunk --run-dir`, polars/numpy only — no LaTeX on workers), each streaming over an `event_id`-disjoint slice (`event_id % N == chunk`) of the reference **and every rollout** and writing a small `reduced_partial/<id>__<chunk>.json`; every `PlotSpec` (`catalog.py`) splits into a `compute_partial`/`finalize` pair so a plot's chunks can be summed/concatenated back together correctly per rollout (`chunkable=False` specs — the router diagnostics, already bounded/subsampled — always run as a single chunk regardless of `N`). The local `giant analyze render <run_dir>` first joins every plot's chunk partials into `reduced/<id>.json` (`merge_all`, a no-op join when `N=1`), then turns those into the styled PDF/gallery tree. See `giant/analysis/__init__.py`.
+**Training** (`giant/training/`): `loop.py` (epoch loop, graceful shutdown, best-checkpoint selection), `trainers.py` (`StageSpec` + per-stage flow/ddpm and WGAN-GP trainers, and the `MetricSpec` declarations that define `metrics.csv`'s columns), `stage2_inputs.py` (ground-truth stage-2 targets + teacher-forcing inputs), `metrics.py` (`MetricsCollector`: `metrics.csv`, W&B logging, progress/summary), `checkpoint.py`, `amp.py` (`train.precision = fp32|bf16` autocast), `plots.py` (`giant analyze metrics`). Per-stage `init_from`/`freeze` lets one stage be retrained against a fixed, known-good other stage while still producing a complete rollout-capable checkpoint.
 
-**Shower rollout** (`giant/rollout.py`, `giant rollout` CLI): autoregressively steps the two-stage model into a full shower — each primary post-step becomes the next pre-step, secondaries are pushed as new tracks, and per-step `material`/`layer_id` come from a `GeometryOracle` (`giant/geometry.py`, built via `dwarf build-geometry-oracle`) that learns position → (material, layer_id) from data and flags detector escape by nearest-neighbour distance. Tracks terminate on energy cutoff, per-track max steps, escape, or natural end; energy is deposited locally on every stop except escape (leakage), so showers conserve energy by construction.
+**Config** (`giant/config.py`): frozen dataclasses are the single source of truth; `DEFAULT_CONFIG` is *generated* from `GiantConfig().to_dict()` rather than hand-maintained. Blocks: `[conditioning]`, `[stage1_model]`, `[stage2_model]`, `[train]`, `[meta]`. Unknown keys are rejected on merge (with a did-you-mean suggestion), and `tests/test_config_consumed_keys.py` audits that every key is actually read somewhere.
+
+**Validation** (`giant/validate.py`): step-level marginal + KL-divergence comparisons during training (`--validate-every`).
+
+**Analysis** (`giant/analysis/`, `giant analyze` CLI): a lean, streaming rollout-vs-reference plotting pipeline that compares one or more autoregressive `giant rollout` runs against a single held-out miniCaloSim reference steps file shared by all of them, and produces publication-styled PDFs assembled into an HTML gallery — one distinctly colored series per rollout, one reference line/panel. It exploits the fact that rollout output and a raw reference file share a world-frame physical column subset under identical names (`pre_*`/`post_*`/`edep`/`step_length`/`pdg`/`material`/`event_id`), so no ALR/local-frame decode is needed — everything is world-frame mm/MeV. Structure: `sources.py` (canonical LazyFrames + `RolloutSpec`/`Side` — a rollout's opened frames + per-checkpoint diagnostic inputs — + synthetic-termination-row filtering + the secondary view, which is `generation>0 & step_no==0` rollout tracks vs exploded `sec_*_list` reference columns), `variables.py` (the per-step value expressions shared by range sizing and the plot registry), `reduce.py` (the streaming primitives — a single `hist1d` `group_by([group,bin]).len()` pass, per-event scalars, edep-weighted depth/transverse profiles, species share, leakage), `grouping.py`/`context.py` (fixed bin edges + energy-quantile/pdg/material group sets resolved once by `prep` into `shared.json` over the union of the reference and every rollout, so every compute job is one pass with no range scan), `reduced.py` (`Partial`/`Reduced` — the compact self-describing JSON a compute job emits), `catalog.py` (the declarative `PlotSpec` registry — marginals × {overall,energy,pdg,material}, per-event totals, shower profiles/containment, species/leakage, secondaries, distance/confusion summaries, router and type-embedding diagnostics; `giant analyze list` prints every id), `runtime_estimate.py` (per-(plot, chunk) walltime estimates for the submit description), and `render.py` (the only module importing ETPlot's `plotstyle`/LaTeX; dispatches on `Reduced.kind`, writes PDFs + `metadata.yaml`; each rollout gets a stable `ps.get_color(i)` slot by its position in `series`, the reference always draws in one fixed dashed-ink style). `Bundle.rollouts` is a name-keyed dict of `Side`, and every `compute_partial`/`finalize` builds a `Reduced.payload["series"]` dict keyed the same way, with `payload["reference"]` as the one distinguished non-rollout entry. The heatmap-shaped specs (`marginal_distance_summary`, `n_sec_confusion`) and the checkpoint-bound diagnostics (`router_gating.py`, `type_embedding_distance.py`) are inherently one-matrix/one-checkpoint per rollout, so they render as one panel per rollout instead of one line/bar per rollout.
+
+**Input is one or more `giant rollout` YAML sidecars** (`run.py:load_rollout_yamls`): each YAML's `output`/`dataset` keys name its rollout parquet and seed file (= the reference truth); every supplied YAML must resolve to the same `dataset`, checked up front with a clear error otherwise (the premise is "N candidates vs one ground truth"). Each rollout's series name comes from a repeated `--label` CLI flag, else the YAML stem (N>1), else `"rollout"` (a single YAML). `prep` creates a **run directory** (`<cwd>/analysis_runs/analysis_<id>/` by default, `--run-dir` to override) holding `shared.json`, `run_meta.json` (`RunMeta.rollouts: list[{name,path,plot_meta}]`, insertion order = CLI order = every plot's series order), `reduced_partial/`, `reduced/`, `plots/`. **Compute/merge/render split:** `giant analyze prep a.yaml [b.yaml ...] --chunks N` records `N` in `run_meta.json`, and the workflow's `AnalysisComputeTask` runs one HTCondor job per (plot, chunk) pair (`compute-one --id --chunk --run-dir`, polars/numpy only — no LaTeX on workers), each streaming over an `event_id`-disjoint slice (`event_id % N == chunk`) of the reference **and every rollout** and writing a small `reduced_partial/<id>__<chunk>.json`; every `PlotSpec` splits into a `compute_partial`/`finalize` pair so chunks can be summed/concatenated back per rollout (`chunkable=False` specs — the checkpoint-bound diagnostics, already bounded/subsampled — always run as a single chunk). The local `giant analyze render <run_dir>` first joins every plot's chunk partials into `reduced/<id>.json` (`merge_all`, a no-op join when `N=1`; `merge-one` does a single plot for debugging), then turns those into the styled PDF/gallery tree. `giant analyze metrics <train_run_dir>` is a separate, unrelated entry point: training-progress plots straight from a run's `metrics.csv`.
+
+**Workflow orchestration** (`giant/workflow/`, `giant workflow run` CLI): b2luigi is the **only sanctioned way to run a multi-step pipeline**; `giant`/`dwarf` are single-step primitives the tasks invoke. One workflow TOML (`configs/workflow_example.toml`) parameterises a whole experiment — `[workflow]`/`[condor]`/`[dataset]`/`[geometry]` plus repeated `[[train]]`/`[[rollout]]`/`[[analysis]]` tables, each cross-referenced by name — and `spec.py` parses it into frozen dataclasses, rejecting unknown keys and dangling references. Every task's output directory is `<result_dir>/<kind>/name=<name>/spec_hash=<hash>/…`, where the 8-hex `spec_hash` covers that task's resolved sub-spec **and its transitive parents**, so an edited spec re-runs exactly the affected subtree instead of silently reusing stale outputs. The DAG (`tasks.py`): `DatasetTask` (external, fails fast if `/ceph` isn't mounted) → `WarmCacheTask` / `GeometryOracleTask` → `TrainEpochTask(name, milestone)` → `TrainTask` → `RolloutTask` → `AnalysisPrepTask` → `AnalysisComputeTask(name, plot_id, chunk)` → `AnalysisRenderTask` → `WorkflowTask`. Training is fanned out into **one short GPU job per epoch** (`epochs_per_job` trades queue waits back), chained by `--resume` on the previous job's `last.pt` — the loop already handles that unchanged — and `TrainTask` republishes `best.pt`/`last.pt`/a concatenated `metrics.csv` so nothing downstream sees the fan-out. `StreamingStepsDataset.set_epoch` and `config.epoch_seed` (both applied per epoch by `training/loop.py`) derive the batch order and the global RNG state from `(seed, epoch)`, so epoch *k* is bit-identical either way — verified by diffing a chained run's `metrics.csv` against a single 3-epoch `giant train`. `AnalysisRenderTask` is always local (the only step importing plotstyle/LaTeX); `htcondor.py` holds the CPU/GPU submit settings, with the GPU requirement strings (`TARGET.ProvidesEtpCeph` + device/memory pins) ported from the `condor-gpu-train-rollout` branch. `run.py` is the script b2luigi re-executes on workers (`--spec` forwarded via `task_cmd_additional_args`, so a worker resolves the identical graph); `giant workflow run` is a thin exec of it. Needs `uv sync --extra cpu --extra workflow`.
+
+**Shower rollout** (`giant/rollout.py`, `giant rollout` CLI): autoregressively steps the two-stage model into a full shower, advancing tracks breadth-first (every sweep steps all active tracks once, in `batch_size` chunks, so many tracks share each forward pass). Each primary post-step becomes the next pre-step, secondaries are pushed as new tracks, and per-step `material`/`layer_id` come from a `GeometryOracle` (`giant/geometry.py`, built via `dwarf build-geometry-oracle`) that learns position → (material, layer_id) from data and flags detector escape by nearest-neighbour distance. Tracks terminate on one of the `TERM_*` reasons in `constants.py` (energy cutoff, max steps, escape, natural end, unknown pdg, max tracks); energy is deposited locally on every stop except escape (leakage), so showers conserve energy by construction. `giant/checkpoint_io.py` is the shared checkpoint → ready-to-run-models path used by both `predict` and `rollout`.
 
 ## Roadmap
 
 **Phase 1 (done):** number of secondaries and their total energy were conditioning inputs; the model predicted only the 9D primary post-step (energy-conservation PoC).
 
-**Phase 2 (implemented — baseline):** the two-stage model above jointly predicts `n_sec`, the energy simplex (`e_sec` falls out of it), and each secondary's energy/direction/species, so a rollout is self-contained (no ground-truth secondary counts injected). This is the "get a baseline out" track agreed with Jan & Tobias (2026-07-07).
+**Phase 2 (done):** the two-stage model jointly predicts `n_sec`, the energy simplex (`e_sec` falls out of it), and each secondary's energy/direction/species, so a rollout is self-contained (no ground-truth secondary counts injected).
 
-**Physical-property conditioning (implemented):** `model.conditioning = "physical" | "embedding"` (see above) replaces the learned PDG/material embeddings with a small MLP over particle mass/charge and material Z_eff/A_eff/density/X0/λ_int, and Stage 2 predicts a secondary's mass/charge directly instead of a snapped species embedding. `"embedding"` stays available as the generalization-comparison baseline. `giant/materials.py`'s table is already filled with real values for every material the geometry produces. **Not yet done:** the actual held-out-material/species generalization comparison against the `"embedding"` baseline is unrun — the 34GB multi-material dataset at the repo root (6 materials, 237 PDG codes including nuclear/ion codes) is the natural dataset for that experiment.
+**Physical-property conditioning (implemented, default):** `conditioning.particle.type` / `conditioning.material.type` = `physical | embedding | onehot`. **Not yet done:** the actual held-out-material/species generalization comparison against the `"embedding"` baseline is unrun — the 34GB multi-material dataset (6 materials, 237 PDG codes including nuclear/ion codes) is the natural dataset for that experiment.
 
-**Faster-eval architectures (implemented, validation in progress):** both tracks below target a ~10× native-Geant4 eval budget and are now wired into `giant train`/`giant/model/network.py`, but neither has a validated result yet — treat both as unproven until the corresponding analysis run says otherwise:
+**v0.3.0 — Stage-2 autoregressive redesign (implemented, released; on `master` since 2026-08-13):** motivated by the 2026-08-03 WGAN rollout benchmark, which failed specifically at the secondary-species level (zero photon secondaries, ~4M hallucinated `-14` muon antineutrinos). Stage 2 became autoregressive in descending-energy order with teacher forcing, and the particle-type representation went back to **categorical** (`particle_type.target = "onehot"`), reversing the 2026-07-17 continuous `(log-mass, charge)` target. The config break (`[conditioning]`/`[stage1_model]`/`[stage2_model]`/`[train]` replacing the flat `train.mode` + `[model]`) makes per-stage generators, stage-2-only training, and one-shot-vs-autoregressive comparison all expressible, and the `network.py` refactor into composable parts (encoder × trunk × objective) also makes routed WGAN work for the first time.
-- **WGAN-GP** (`--mode wgan`, see Architecture above): implemented, **not yet tested** — no rollout-vs-reference analysis run against it yet.
-- **MoE routing trunk** (`--router`, see Architecture above): implemented, **first rollout benchmark done (2026-07-22), result: needs retraining with a different router config, not abandoned.** A 10-expert `EnergyRouter` run (`n_experts=10`, `temperature=0.5`, `learn_centers=true`, **`lambda_balance=0.0`**, only 20 fine-tuning epochs resumed from a non-routed checkpoint) diverged badly from Geant4 on step granularity, secondary species, and shower shape, despite roughly matching bulk total deposited energy. The `router_gating` diagnostic plot points at the likely cause: the ten experts overlap heavily across ~5 decades of pre-step energy instead of partitioning it — even the top-energy expert only reaches ~60–65% gate weight at the highest energies plotted — so eval-time top-1 (Voronoi) dispatch is choosing among near-ties rather than real specialists. Two contributors were identified: the missing load-balancing loss (`lambda_balance=0.0`), and `EnergyRouter`'s center init (`torch.linspace(-2, 2, n_experts)`) assuming a roughly uniform z-normalized energy distribution, which real energy spectra don't match. **Fixed (2026-07-27):** `EnergyRouter` now accepts an optional `centers_init` (backward compatible — omitting it keeps the old linspace), and `giant train` auto-populates it from real data quantiles via a reservoir sample collected during the existing normalizer-fitting pass in `giant/pipeline.py` (no extra file scan), for `--router-type energy` only. The routing *strategy* itself may still be sound, but the specific benchmarked config wasn't. **Next step before further evaluation: retrain with `lambda_balance > 0` and the new quantile-seeded centers (and consider more epochs / a from-scratch run rather than a short fine-tune), then re-check whether `router_gating` sharpens up.** Full writeup: `/home/lars/knowledge-base/experiments/giant-router-energy-rollout-validation.md`.
 
-A sampling-calorimeter (multi-material) dataset is still a planned future direction, not yet built. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`).
+v0.2 configs and checkpoints are auto-migrated (`config.migrate_config`, `model._legacy._migrate_legacy_model_config`, both drawing on shared facts in `giant/_migration.py`). **v0.2 checkpoint-loading support has no expiry decided yet**: `/ceph` still holds pre-v0.3.0 checkpoints and analysis runs referencing them, so don't delete or substantially alter either migration function or `tests/legacy/network_v02_snapshot.py` (the frozen v0.2 snapshot they're tested against) without an explicit decision to do so first.
 
-**v0.3.0 — Stage-2 autoregressive redesign (designed, not implemented; branch `v0.3.0-stage2-autoregressive`):** the 2026-08-03 WGAN rollout benchmark failed specifically at the secondary-species level (zero photon secondaries, ~4M hallucinated `-14` muon antineutrinos). The agreed response pivots Stage 2 to **autoregressive generation** in descending-energy order with teacher forcing, and switches the particle-type representation back to **categorical** (top N−1 by training-set count + an "other" bucket), reversing the 2026-07-17 continuous `(log-mass, charge)` target. This requires a config break: `[conditioning]` / `[stage1_model]` / `[stage2_model]` / `[train]` blocks replace the single global `train.mode` + `[model]`, so per-stage generators (`stage1 = flow` + `stage2 = wgan`), stage-2-only training, and one-shot-vs-autoregressive comparison are all expressible. `network.py` is refactored from ten permutation classes into composable parts (encoder × trunk × objective), which also makes routed WGAN work for the first time. This config break is why v0.2-shaped configs/checkpoints need migrating at all (`config.migrate_config`, `model.network._migrate_legacy_model_config`, both drawing on shared facts in `giant/_migration.py`) — v0.2 checkpoint-loading support has **no expiry decided yet**: `/ceph` still holds pre-v0.3.0 checkpoints and analysis runs referencing them, so don't delete or substantially alter either migration function or `tests/legacy/network_v02_snapshot.py` (the frozen v0.2 snapshot they're tested against) without an explicit decision to do so first.
+**Faster-eval architectures — both implemented, neither validated.** Target is a ~10× native-Geant4 eval budget; no eval-latency number exists for any configuration yet, so that budget is unverified across the board.
+- **WGAN-GP** (`--stage2-generator wgan`, now the stage-2 default): first rollout benchmark 2026-08-03 failed with secondary-species mode collapse — the failure v0.3.0 was designed to address. **No post-v0.3.0 benchmark has been run.** Writeup: `/home/lars/knowledge-base/experiments/giant-wgan-physical-rollout-validation.md`.
+- **MoE routing trunk** (`--router`): first rollout benchmark 2026-07-22 diverged badly from Geant4 on step granularity, secondary species, and shower shape, despite roughly matching bulk total deposited energy. Cause identified as a bad config, not a bad idea: `lambda_balance=0.0` (no load-balancing loss) plus `EnergyRouter`'s `torch.linspace(-2, 2, n_experts)` center init assuming a roughly uniform z-normalized energy distribution — so the ten experts overlapped across ~5 decades of energy instead of partitioning it, and eval-time top-1 dispatch chose among near-ties rather than real specialists. Both prerequisites are fixed in code (quantile-seeded `centers_init` from `pipeline.py`, `lambda_balance` exposed). **Next step: retrain with `lambda_balance > 0` and quantile-seeded centers (consider a from-scratch run rather than a short fine-tune), then re-check whether `router_gating` sharpens up.** Writeup: `/home/lars/knowledge-base/experiments/giant-router-energy-rollout-validation.md`.
 
-**Condor-submitted GPU training/rollout (in progress, `condor-gpu-train-rollout` branch, not yet merged):** moves `giant train`/`giant rollout` off the shared portal GPU dev machines (see Compute environment) onto remote-GPU HTCondor submission on TOpAS/NEMO2 (`giant/condor.py`). Partway between "needs major features" and feature-complete — not ready to merge yet.
+A sampling-calorimeter (multi-material) dataset track is still open and unblocked, not yet started. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`).
+
+**Condor-submitted GPU training/rollout (`condor-gpu-train-rollout` branch, superseded):** its goal — moving `giant train`/`giant rollout` off the shared portal GPU dev machines onto remote-GPU HTCondor submission — is now met by the b2luigi workflow above. Its `train-submit`/`rollout-submit` commands are deliberately **not** ported and must not be revived when that branch is eventually merged; the only part that survived is `_gpu_requirements`, which moved into `giant/workflow/htcondor.py`.

README.md

@@ -10,6 +10,7 @@ A conditional generative model that replaces the Geant4 step function: given a p
 uv sync --extra cpu               # install deps (CPU torch; use --extra cuda for GPU)
 
 giant new-run --hidden-dim 512 --lr 3e-4          # scaffold config.toml + run dir
+giant model summary --config config.toml          # parameter counts + which config keys actually bite
 giant train path/to/steps.parquet                 # train (flow + wgan by default)
 giant predict path/to/steps.parquet --checkpoint checkpoints/.../best.pt
 
@@ -42,9 +43,9 @@ A **two-stage model**, checkpointed together. Either stage's outcome can be prod
 
 Either way, secondary energies stick-break the `e_sec` budget handed down from Stage 1, so the full chain conserves energy. A secondary's particle identity is represented as `onehot` (categorical, top-N PDG codes + "other"), `physical` (continuous log-mass/charge), or `embedding` (nearest-neighbour lookup).
 
-**Conditioning.** Pre-step position/energy/direction/layer, plus particle mass/charge and material Z_eff/A_eff/density/X0/λ_int, encoded the same three ways as particle identity above (`--conditioning`) — the `physical` representation generalizes to species/materials outside the training menu since it's computed rather than looked up. `n_sec`/`e_sec` are always model outputs, never conditioning inputs.
+**Conditioning.** Pre-step position/energy/direction/layer, plus particle mass/charge and material Z_eff/A_eff/density/X0/λ_int, encoded the same three ways as particle identity above. The particle and material axes are configured independently (`conditioning.particle.type` / `conditioning.material.type`; `--conditioning` sets both at once) and may mix — the `physical` representation generalizes to species/materials outside the training menu since it's computed rather than looked up. `n_sec`/`e_sec` are always model outputs, never conditioning inputs.
 
-**MoE routing** (`--router`, either stage): a pluggable `Router` (`energy`/`pdg`/`process`/`composed` axes) top-1-dispatches each row to one of several small expert trunks at eval time, instead of running one monolithic trunk.
+**MoE routing** (`--router`): a pluggable `Router` (`energy`/`pdg`/`process`/`composed` axes) top-1-dispatches each row to one of several small expert trunks at eval time, instead of running one monolithic trunk. The CLI flags configure Stage 1's router; Stage 2 has its own `stage2_model.router` block, config-file only.
 
 ## Data
 
@@ -64,12 +65,24 @@ giant/
 │   ├── data/
 │   │   ├── loader.py       # parquet → numpy arrays (incl. streaming/chunked reads)
 │   │   ├── transforms.py   # log transforms, local-frame rotation, energy simplex, secondary encode/decode
-│   │   └── dataset.py      # StepsDataset / StreamingStepsDataset (PyTorch)
+│   │   ├── dataset.py      # StepsDataset / StreamingStepsDataset (PyTorch)
+│   │   └── setup_cache.py  # sidecar cache for the pre-epoch setup scan (vocab/split/normalizers)
 │   ├── model/
-│   │   ├── network.py      # ConditionEncoder, Stage1Model, Stage2OneShot/Stage2Autoregressive, Router/MoE, CriticModel
+│   │   ├── models.py       # Stage1Model, Stage2OneShot, Stage2Autoregressive, CriticModel
+│   │   ├── builders.py     # build_models / build_critics — config dict → assembled stage models
+│   │   ├── encoders.py     # ConditionEncoder (physical / embedding / onehot, per axis)
+│   │   ├── layers.py       # ResBlock/AdaLNResBlock registry, SinusoidalEmbedding, MLP heads
+│   │   ├── trunks.py       # trunk registry (resmlp, none) + RoutedTrunk (MoE expert bodies)
+│   │   ├── routers.py      # Router registry: energy / pdg / process / composed / none
+│   │   ├── history.py      # stage-2 AR history encoders: markov / attention (KV-cached) / none
+│   │   ├── objectives.py   # flow / ddpm / wgan objective registry
 │   │   ├── schedule.py     # CosineSchedule (DDPM) and flow matching utilities
-│   │   └── wgan.py         # WGAN-GP gradient penalty / critic / generator losses
+│   │   ├── wgan.py         # WGAN-GP gradient penalty / critic / generator losses
+│   │   ├── summary.py      # build-only introspection behind `giant model summary`
+│   │   ├── _legacy.py      # v0.2 checkpoint model_config/state-dict migration
+│   │   └── network.py      # re-export shim over all of the above
 │   ├── constants.py        # output/conditioning dims, K_MAX, secondary slot layout, schema keys
+│   ├── cond_layout.py      # single source of truth for the cond_cont/cond_cat column layout
 │   ├── particles.py        # PDG → (mass, charge) decode, incl. nuclear/ion codes; onehot/embedding secondary-identity decode
 │   ├── materials.py        # material name → (Z_eff, A_eff, density, X0, λ_int)
 │   ├── config.py           # default hyperparameters, TOML config merging, device autodetect
@@ -79,20 +92,33 @@ giant/
 │   │   ├── trainers.py     #   StageSpec + flow/ddpm and WGAN-GP per-stage trainers
 │   │   ├── stage2_inputs.py#   ground-truth stage-2 targets + autoregressive/teacher-forcing inputs
 │   │   ├── metrics.py      #   MetricsCollector: metrics.csv columns, W&B logging, progress/summary
+│   │   ├── amp.py          #   bf16 autocast (`train.precision`)
+│   │   ├── plots.py        #   training-progress plots (`giant analyze metrics`)
 │   │   └── checkpoint.py   #   checkpoint assembly/restore (format unchanged since v0.2)
 │   ├── sample.py           # DDPM / DDIM / flow matching / WGAN samplers + secondary sampling
+│   ├── checkpoint_io.py    # checkpoint → ready-to-run models/normalizers (predict + rollout)
 │   ├── geometry.py         # GeometryOracle: position → (material, layer_id, escaped) for rollout
 │   ├── rollout.py          # autoregressive shower rollout driver
 │   ├── validate.py         # step-level marginal + KL-divergence validation
+│   ├── _migration.py       # shared v0.2 → v0.3 facts used by both migration surfaces
 │   ├── analysis/           # rollout-vs-reference analysis pipeline (see `giant analyze` below)
 │   │   ├── sources.py      #   canonical LazyFrames + secondary view
+│   │   ├── variables.py    #   per-step value expressions shared by range sizing and the catalog
 │   │   ├── reduce.py       #   streaming reduction primitives (hist1d, per-event scalars, profiles, ...)
 │   │   ├── grouping.py     #   fixed bin edges + energy/pdg/material group sets
 │   │   ├── context.py      #   resolves grouping into `shared.json` once per run
-│   │   ├── catalog.py      #   declarative PlotSpec registry
+│   │   ├── reduced.py      #   Partial/Reduced — the compact JSON a compute job emits
-│   │   ├── condor.py       #   prep / compute-one / submit-description plumbing
+│   │   ├── catalog.py      #   declarative PlotSpec registry (`giant analyze list`)
+│   │   ├── router_gating.py / type_embedding_distance.py   # checkpoint-bound diagnostics
+│   │   ├── runtime_estimate.py  #   per-(plot, chunk) walltime estimates for the job requests
+│   │   ├── run.py          #   prep / compute-one / merge plumbing
 │   │   └── render.py       #   PDFs + HTML gallery (only module importing plotstyle/LaTeX)
-│   └── cli.py              # `giant train` / `new-run` / `predict` / `rollout` / `analyze` Typer app
+│   ├── workflow/           # b2luigi pipeline orchestration (`giant workflow run spec.toml`)
+│   │   ├── spec.py         #   workflow TOML -> frozen dataclasses, validation, per-task spec hashes
+│   │   ├── htcondor.py     #   CPU/GPU submit settings (docker image, +RemoteJob, GPU requirements)
+│   │   ├── tasks.py        #   the task graph: cache-warm -> train (one job/epoch) -> rollout -> analysis
+│   │   └── run.py          #   the script b2luigi re-executes on every worker
+│   └── cli.py              # `giant train` / `new-run` / `model summary` / `predict` / `rollout` / `analyze` / `workflow`
 ├── giant/tools/                        # dataset/tooling logic, unified under the `dwarf` CLI (`dwarf --help`)
 │   ├── dwarf.py                        # Typer app: convert, migrate, bump-gen, bump-schema, status,
 │   │                                    #   update-manifest, create-manifest, make-root,
@@ -116,8 +142,14 @@ giant/
 uv sync --extra cpu                   # CPU-only torch (use --extra cuda for CUDA 11.8 instead)
 uv sync --extra cpu --extra dev       # add dev tools (pytest, ruff, ty)
 uv sync --extra cpu --extra geometry  # add scikit-learn, for `dwarf build-geometry-oracle` / rollout
+uv sync --extra cpu --extra analysis  # matplotlib/polars/plotstyle, for `giant analyze render`
+uv sync --extra cpu --extra convert   # uproot/awkward/polars, for `dwarf convert`
+uv sync --extra cpu --extra wandb     # W&B logging (`giant train --wandb`)
+uv sync --extra cpu --extra workflow  # b2luigi, for `giant workflow run`
 ```
 
+The `dev` extra pulls in `convert`, `analysis`, `geometry`, `wandb` and `workflow` as well.
+
 `cpu` and `cuda` are mutually exclusive — pick one to select the torch build (pinned to 2.3.x). Plain `uv sync` installs no torch at all. See `CLAUDE.md` for details.
 
 ## Training, prediction, rollout
@@ -137,11 +169,13 @@ Useful flags on `giant train`:
 - `--stage2-decoder {autoregressive,one_shot}` — Stage 2 decoding strategy (see Architecture)
 - `--conditioning {physical,embedding,onehot}` — conditioning representation
 - `--router` / `--router-type` / `--n-experts` / `--router-axis` — MoE routing
+- `--stage2-stage1-context {truth,sampled}` — feed Stage 2 the ground-truth or the model's own sampled Stage-1 outcome (annealable via `stage2_model.ctx_p_start`/`ctx_p_end`)
+- `--precision {fp32,bf16}` — bf16 autocast in the training loop
 - `--wandb` — log per-epoch metrics to Weights & Biases (needs `uv sync --extra wandb`); metric names are `<stage>/<split>/<metric>` plus an unprefixed run-level tail, all derived from `giant/training/trainers.py` `MetricSpec`s
 - `--no-cache-setup` / `--rebuild-setup-cache` — control the setup-stage sidecar cache (vocab maps, event split, normalizer stats); `dwarf warm-cache` precomputes it
 - `--stage1-init-from`/`--stage2-init-from` (checkpoint `.pt`) + `--stage1-freeze`/`--stage2-freeze` — load a stage's weights from another checkpoint and never update them, so the other stage can be retrained alone against a fixed, known-good one while still producing a complete, rollout-capable checkpoint
 
-Config-file-only knobs (no CLI flag — use `--config config.toml`): `stage2_model.autoregressive.teacher_forcing`/`.history`, `stage2_model.particle_type.target`. v0.2 flat-schema configs and checkpoints load fine (auto-migrated).
+Config-file-only knobs (no CLI flag — use `--config config.toml`): `stage2_model.autoregressive.teacher_forcing`/`.history`, `stage2_model.particle_type.target`/`.class_weighting`, `stage2_model.n_sec.mode`/`.owner`, `conditioning.share_stages`, `stage*_model.trunk.*` and the finer `router` knobs (`lambda_balance`, `gumbel`, `learn_width`, …). `configs/` holds kept reference configs. v0.2 flat-schema configs and checkpoints load fine (auto-migrated).
 
 `giant rollout` seeds showers from each event's highest-energy entry step, then autoregressively steps the model to completion, pushing secondaries as new tracks and looking up `material`/`layer_id` from the geometry oracle each step. Tracks terminate on energy cutoff, max steps, detector escape, or natural end; energy is deposited locally on every stop except escape, so showers conserve energy by construction.
 
@@ -151,12 +185,31 @@ Config-file-only knobs (no CLI flag — use `--config config.toml`): `stage2_mod
 - `giant analyze` — deeper rollout-vs-reference diagnostics (marginals by energy/pdg/material, per-event totals, shower profiles, species share, leakage, secondaries):
 
 ```bash
-giant analyze submit rollout.yaml --accounting-group cms   # prep + one HTCondor job per plot (compute only)
+giant analyze prep rollout.yaml --chunks 8                 # lay out the run directory
-giant analyze submit a.yaml b.yaml --accounting-group cms --label flow --label wgan  # N rollouts vs one shared reference
+giant analyze prep a.yaml b.yaml --label flow --label wgan  # N rollouts vs one shared reference
-giant analyze render <run_dir> --gallery                   # local: styled PDFs + HTML gallery (needs LaTeX)
+giant analyze render <run_dir> --gallery                   # local: merge chunks, then styled PDFs + HTML gallery (needs LaTeX)
+
+giant analyze list                                         # every catalog plot id
+giant analyze compute-one --id marginal_edep --run-dir <run_dir> --chunk 0   # what a condor job runs
+giant analyze merge-one --id marginal_edep --run-dir <run_dir>               # merge one plot's chunks (debugging)
 ```
 
-`<run_dir>` is derived next to the first rollout's parquet (`analyze prep`/`submit` print it). Multiple rollout YAMLs must all name the same reference (`dataset`) file; each renders as its own colored series against one reference line/panel. Compute jobs are polars/numpy only; only `render` needs LaTeX, so it always runs locally.
+The per-(plot, chunk) compute jobs themselves are submitted by the workflow (below), not by `giant analyze` — these commands are the single-step primitives it calls. `<run_dir>` defaults to `<cwd>/analysis_runs/analysis_<id>` (`--run-dir` overrides it; `prep` prints it). Multiple rollout YAMLs must all name the same reference (`dataset`) file; each renders as its own colored series against one reference line/panel. Compute jobs are polars/numpy only; only `render` needs LaTeX, so it always runs locally.
+
+## Workflow orchestration
+
+Multi-step pipelines run through [b2luigi](https://github.com/belle2/b2luigi) — one spec file describes a whole experiment, and every step's outputs are files on `/ceph` that are only recomputed when their spec (or an upstream one) changes:
+
+```bash
+uv sync --extra cpu --extra workflow
+giant workflow run configs/workflow_example.toml --mode dry-run   # what would run
+giant workflow run configs/workflow_example.toml --mode show-output  # where every output goes
+giant workflow run configs/workflow_example.toml --batch --workers 20  # submit to HTCondor and wait
+```
+
+The spec holds `[workflow]`/`[condor]`/`[dataset]`/`[geometry]` plus repeated `[[train]]`, `[[rollout]]` and `[[analysis]]` tables cross-referenced by name (see `configs/workflow_example.toml`). The task graph is `DatasetTask → WarmCacheTask/GeometryOracleTask → TrainEpochTask… → TrainTask → RolloutTask → AnalysisPrepTask → AnalysisComputeTask(plot, chunk) → AnalysisRenderTask`. Training is split into one short GPU job per epoch (chained by `--resume`), which schedules better on a busy farm and survives preemption; `TrainTask` then publishes one `best.pt`/`last.pt`/`metrics.csv` for everything downstream. Rendering always runs locally, since it is the only step that needs LaTeX.
+
+Separately, `giant analyze metrics <train_run_dir>` renders training-progress plots (loss/lr/accuracy/grad-norm/router/wgan/throughput) straight from a training run's `metrics.csv`.
 
 ## Development
 

configs/workflow_example.toml

@@ -0,0 +1,68 @@
+# Example GIANT workflow spec — `giant workflow run configs/workflow_example.toml`.
+#
+# One file parameterises a whole experiment: the datasets, the geometry oracle,
+# N trainings, N rollouts, and the analyses comparing them. Every task's output
+# directory carries a hash of its resolved sub-spec (plus its parents), so
+# editing anything here re-runs exactly the affected subtree and nothing else.
+#
+# result_dir/log_dir must be visible from both the submit host and the workers
+# (i.e. on /ceph) — there is deliberately no HTCondor file transfer.
+
+[workflow]
+name       = "baseline-vs-router"
+result_dir = "/ceph/lbogner/workflows/baseline-vs-router"
+log_dir    = "/ceph/lbogner/workflows/baseline-vs-router/logs"
+
+[condor]
+accounting_group = "cms"
+repo_dir         = "/work/lbogner/giant"   # also b2luigi's working_dir
+env_script       = "/work/lbogner/giant/condor_env.sh"
+docker_image_cpu = "cverstege/alma9-gridjob"
+docker_image_gpu = "mschnepf/slc7-condocker"
+remote           = true
+
+[dataset]
+steps     = "/ceph/lbogner/geant_steps/train/"     # training data
+reference = "/ceph/lbogner/geant_steps/holdout/"   # rollout seeds + analysis truth
+
+[geometry]
+method    = "slab"
+subsample = 500_000
+
+[[train]]
+name           = "baseline"
+config         = "configs/baseline.toml"
+epochs         = 200
+epochs_per_job = 1            # one short GPU job per epoch, chained
+request_gpus   = 1
+gpu_memory_mb  = 20000
+overrides      = { lr = 3e-4 }   # `giant train` flag names
+
+[[train]]
+name           = "router-balanced"
+config         = "configs/router.toml"
+epochs         = 200
+epochs_per_job = 1
+request_gpus   = 1
+gpu_memory_mb  = 20000
+
+[[rollout]]
+name          = "baseline"
+train         = "baseline"     # -> [[train]].name
+n_events      = 2000
+energy_cutoff = 0.1
+
+[[rollout]]
+name          = "router-balanced"
+train         = "router-balanced"
+n_events      = 2000
+energy_cutoff = 0.1
+
+[[analysis]]
+name        = "baseline-vs-router"
+rollouts    = ["baseline", "router-balanced"]
+chunks      = 32
+energy_bins = 4
+bins        = 50
+top_pdg     = 6
+gallery     = true

giant/analysis/__init__.py

@@ -4,7 +4,7 @@ Compares one or more autoregressive ``giant rollout`` runs against a single
 held-out miniCaloSim reference file shared by all of them, producing
 publication-styled comparison plots (one colored series per rollout, one
 reference line) generated in parallel on HTCondor (one job per plot x data
-chunk, compute/merge/render split).
+chunk, compute/merge/render split) — orchestrated by ``giant/workflow``.
 
 Only ``render`` (and the ``render`` CLI path) imports plotstyle/LaTeX; everything
 re-exported here is plotstyle-free so it runs on a compute worker. Import
@@ -12,10 +12,9 @@ re-exported here is plotstyle-free so it runs on a compute worker. Import
 """
 
 from giant.analysis.catalog import build_catalog, catalog_ids, get_spec
-from giant.analysis.condor import (
+from giant.analysis.run import (
     LoadedRollout,
     RunMeta,
-    SubmitConfig,
     compute_one,
     compute_reduced,
     derive_run_dir,
@@ -24,7 +23,6 @@ from giant.analysis.condor import (
     merge_all,
     merge_one,
     prep,
-    write_submit,
 )
 from giant.analysis.context import Context, build_context
 from giant.analysis.reduced import Partial, Reduced
@@ -37,7 +35,6 @@ __all__ = [
     "get_spec",
     "LoadedRollout",
     "RunMeta",
-    "SubmitConfig",
     "compute_one",
     "compute_reduced",
     "derive_run_dir",
@@ -46,7 +43,6 @@ __all__ = [
     "merge_all",
     "merge_one",
     "prep",
-    "write_submit",
     "Context",
     "build_context",
     "Partial",

giant/analysis/render.py

@@ -535,7 +535,7 @@ def render_run(run_dir: str | Path, *, run_gallery: bool = False) -> list[Path]:
     (checkpoint, paths, cutoffs) from ``run_meta.json`` into every plot's
     gallery metadata and renders.
     """
-    from giant.analysis.condor import RunMeta, merge_all
+    from giant.analysis.run import RunMeta, merge_all
 
     run_dir = Path(run_dir)
     merge_all(run_dir)

giant/analysis/run.py

@@ -1,4 +1,6 @@
-"""HTCondor orchestration driven by one or more ``giant rollout`` YAML sidecars.
+"""Analysis run directories: prep, per-(plot, chunk) compute, and merge.
+
+Driven by one or more ``giant rollout`` YAML sidecars.
 
 A rollout writes a YAML sidecar (``giant/cli.py:_write_prediction_ref`` +
 rollout extras) that already names both files we need and carries the run's
@@ -22,13 +24,16 @@ everything out under it:
     <run_dir>/reduced/<id>.json   merged, per plot
     <run_dir>/plots/<family>/<id>.pdf   rendered locally
 
-Job model (one condor job per (plot, chunk), compute/merge/render split):
+Job model (one job per (plot, chunk), compute/merge/render split). Job
+submission itself is b2luigi's (``giant/workflow/tasks.py`` — ``AnalysisPrepTask``
+/ ``AnalysisComputeTask`` / ``AnalysisRenderTask``); this module only provides
+the three steps they call:
 
-1. ``prep`` runs once on the submit node — reads the YAML, resolves the shared
+1. ``prep`` runs once locally — reads the YAML, resolves the shared
    context from a subsample, writes ``shared.json`` + ``run_meta.json``
    (including the run's configured ``n_chunks``).
 2. one job per catalog id x chunk index runs ``giant analyze compute-one
-   --run-dir`` on a worker — a single streaming pass over that
+   --run-dir`` (or ``compute_one`` in-process) on a worker — a single streaming pass over that
    ``event_id``-disjoint chunk, writing ``reduced_partial/<id>__<chunk>.json``
    (polars/numpy only, no LaTeX). Specs marked ``chunkable=False``
    (``PlotSpec``, ``catalog.py``) always run as a single chunk.
@@ -38,15 +43,15 @@ Job model (one condor job per (plot, chunk), compute/merge/render split):
    ``reduced/<id>.json``, then renders those into the styled PDF + gallery tree
    (that step imports plotstyle/LaTeX).
 
-Files on ``/ceph`` or ``/work`` are reached via ``ProvidesETPResources``; no
+Files on ``/ceph`` or ``/work`` are reached directly (see
-HTCondor file transfer of the multi-GB inputs.
+``giant/workflow/htcondor.py``); no HTCondor file transfer of the multi-GB
+inputs.
 """
 
 from __future__ import annotations
 
 import json
 import shutil
-import sys
 from collections.abc import Sequence
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -57,7 +62,6 @@ import yaml
 from giant.analysis.catalog import Bundle, catalog_ids, get_spec
 from giant.analysis.context import Context, build_context
 from giant.analysis.reduced import Partial
-from giant.analysis.runtime_estimate import estimate_runtime_s
 from giant.analysis.sources import RolloutSpec, Side, open_side
 
 # Keys copied verbatim from a rollout YAML into each plot's gallery metadata.
@@ -429,136 +433,3 @@ def merge_one(spec_id: str, run_dir: str | Path) -> Path:
 def merge_all(run_dir: str | Path) -> list[Path]:
     """Merge every catalog plot's chunk partials into ``reduced/<id>.json``."""
     return [merge_one(spec_id, run_dir) for spec_id in catalog_ids()]
-
-
-# ---------------------------------------------------------------------------
-# submit description
-# ---------------------------------------------------------------------------
-
-
-@dataclass
-class SubmitConfig:
-    run_dir: Path
-    accounting_group: str
-    repo_dir: Path
-    docker_image: str = "cverstege/alma9-gridjob"
-    request_memory_mb: int = 8192
-    request_cpus: int = 1
-    remote: bool = False  # +RemoteJob (grid I/O) vs ProvidesETPResources (local files)
-    n_chunks: int = 1  # per-plot data chunks; ignored for chunkable=False specs
-
-
-_WRAPPER = """#!/bin/bash
-set -euo pipefail
-cd {repo_dir}
-exec {giant_exe} analyze compute-one --id "$1" --chunk "$2" --run-dir {run_dir}
-"""
-
-
-def _submit_description(cfg: SubmitConfig, wrapper: Path, jobs_file: Path) -> str:
-    reqs_attrs = "+RemoteJob = True\n" if cfg.remote else "requirements = TARGET.ProvidesETPResources\n"
-    return (
-        "universe = docker\n"
-        f"docker_image = {cfg.docker_image}\n"
-        f"executable = {wrapper}\n"
-        "arguments = $(plotid) $(chunk)\n"
-        "should_transfer_files = YES\n"
-        "when_to_transfer_output = ON_EXIT\n"
-        f"request_memory = {cfg.request_memory_mb}\n"
-        f"request_cpus = {cfg.request_cpus}\n"
-        "+RequestWalltime = $(walltime)\n"
-        f"accounting_group = {cfg.accounting_group}\n"
-        f"{reqs_attrs}"
-        f"output = {cfg.run_dir}/logs/$(plotid)__$(chunk).out\n"
-        f"error = {cfg.run_dir}/logs/$(plotid)__$(chunk).err\n"
-        f"log = {cfg.run_dir}/logs/condor.log\n"
-        f"queue plotid,chunk,walltime from {jobs_file}\n"
-    )
-
-
-def _job_walltimes(run_dir: Path, ids: list[str], n_chunks: int) -> list[tuple[str, int, int]]:
-    """``(spec_id, chunk, walltime_s)`` for every job, sized from ``run_meta.json``.
-
-    Row counts come from ``prep``'s ``RunMeta.rows_per_chunk``/``total_rows``;
-    ``chunkable=False`` specs (router diagnostics) always use the dataset
-    total since they run as a single job regardless of ``n_chunks``.
-    """
-    meta = RunMeta.load(run_dir / "run_meta.json")
-    jobs: list[tuple[str, int, int]] = []
-    for spec_id in ids:
-        chunkable = get_spec(spec_id).chunkable
-        chunks = range(n_chunks) if chunkable else [0]
-        for chunk in chunks:
-            n_rows = meta.rows_per_chunk[chunk] if chunkable else meta.total_rows
-            jobs.append((spec_id, chunk, estimate_runtime_s(spec_id, n_rows)))
-    return jobs
-
-
-def _resolve_giant_executable(repo_dir: Path) -> Path:
-    """Path to the ``giant`` entry point to bake into the condor wrapper script.
-
-    Prefers the venv currently running this process (``sys.executable``'s
-    sibling ``giant``) so a submit from a non-default venv (e.g. ``--extra
-    cuda`` on a dev box) doesn't silently pick up a different one; falls back
-    to ``repo_dir/.venv/bin/giant`` for the case this is invoked from outside
-    any venv (e.g. a system Python).
-    """
-    active = Path(sys.executable).parent / "giant"
-    if active.exists():
-        return active
-    venv_giant = repo_dir / ".venv" / "bin" / "giant"
-    if not venv_giant.exists():
-        raise FileNotFoundError(
-            f"no `giant` executable found next to {sys.executable} or at "
-            f"{venv_giant} — condor jobs run it directly (no `uv` on the "
-            f"worker image), so run `uv sync --extra cpu` in {repo_dir} "
-            "before submitting."
-        )
-    return venv_giant
-
-
-def write_submit(cfg: SubmitConfig, ids: list[str] | None = None) -> Path:
-    """Write the wrapper script, (plot, chunk) job list, and HTCondor submit
-    description.
-
-    Each catalog id gets ``cfg.n_chunks`` jobs, except ``chunkable=False``
-    specs (the router diagnostics), which always get exactly one regardless of
-    ``cfg.n_chunks``. Every job's ``+RequestWalltime`` is estimated from its
-    chunk's row count (``runtime_estimate.estimate_runtime_s``, requires
-    ``run_meta.json`` from ``prep`` to already carry ``rows_per_chunk``).
-    Returns the submit description path (``<run_dir>/analyze.sub``). Does not
-    submit — call ``condor_submit`` on the returned file.
-
-    ``cfg.n_chunks`` and the run directory's own ``RunMeta.n_chunks`` (fixed by
-    ``prep``, and what ``RunMeta.rows_per_chunk`` was sized against) are two
-    independent values — checked equal up front so a mismatch is a clear error
-    here rather than an ``IndexError`` out of ``_job_walltimes``.
-    """
-    giant_exe = _resolve_giant_executable(cfg.repo_dir)
-
-    ids = ids or catalog_ids()
-    run_dir = cfg.run_dir
-    meta = RunMeta.load(run_dir / "run_meta.json")
-    if cfg.n_chunks != meta.n_chunks:
-        raise ValueError(
-            f"SubmitConfig.n_chunks={cfg.n_chunks} does not match the "
-            f"n_chunks this run directory was prepped with "
-            f"(RunMeta.n_chunks={meta.n_chunks} in {run_dir}/run_meta.json) — "
-            "re-run `prep` with the desired n_chunks, or fix cfg.n_chunks to "
-            "match it."
-        )
-    (run_dir / "logs").mkdir(parents=True, exist_ok=True)
-    (run_dir / "reduced").mkdir(parents=True, exist_ok=True)
-    (run_dir / "reduced_partial").mkdir(parents=True, exist_ok=True)
-
-    wrapper = run_dir / "run_compute.sh"
-    wrapper.write_text(_WRAPPER.format(repo_dir=cfg.repo_dir, giant_exe=giant_exe, run_dir=run_dir))
-    wrapper.chmod(0o755)
-
-    jobs = _job_walltimes(run_dir, ids, cfg.n_chunks)
-    jobs_file = run_dir / "jobs.txt"
-    jobs_file.write_text("\n".join(f"{i},{k},{w}" for i, k, w in jobs) + "\n")
-
-    sub = run_dir / "analyze.sub"
-    sub.write_text(_submit_description(cfg, wrapper, jobs_file))
-    return sub

giant/analysis/runtime_estimate.py

@@ -1,4 +1,4 @@
-"""Per-(plot, chunk) HTCondor walltime estimates for `giant analyze submit`.
+"""Per-(plot, chunk) HTCondor walltime estimates for the analysis compute jobs.
 
 Each catalog spec's compute cost is close to linear in the number of input
 rows a `compute-one` job streams over — every spec is one (or a couple of)

giant/cli.py

@@ -4,6 +4,7 @@ from enum import Enum
 import math
 from pathlib import Path
 import re
+import sys
 from typing import Optional
 import uuid as uuid_mod
 
@@ -191,8 +192,17 @@ def _write_prediction_ref(
     out: Path,
     dataset_path: Path,
     comment: str | None = None,
+    explicit_out: bool = False,
 ) -> Path:
-    """Write a YAML sidecar in the checkpoint directory and return its path."""
+    """Write the YAML sidecar and return its path.
+
+    With an explicit ``--out`` the sidecar sits next to the output file as
+    ``out.with_suffix(".yaml")`` — a *deterministic* path, which is what lets
+    a workflow task (``giant/workflow/tasks.py``) declare it as a target.
+    Without one, the historic uuid-named file under the checkpoint directory
+    is kept, so ad-hoc runs and the ``/ceph`` predictions convention are
+    unaffected.
+    """
     ref = {
         "prediction_id": pred_uuid,
         "output": str(out),
@@ -202,7 +212,7 @@ def _write_prediction_ref(
     }
     if comment is not None:
         ref["comment"] = comment
-    ref_path = checkpoint.parent / f"{pred_uuid}.yaml"
+    ref_path = out.with_suffix(".yaml") if explicit_out else checkpoint.parent / f"{pred_uuid}.yaml"
     ref_path.write_text(yaml.dump(ref, default_flow_style=False, sort_keys=False))
     return ref_path
 
@@ -1076,6 +1086,7 @@ def predict(
     bs = batch_size_value
 
     # --- Output path ---
+    explicit_out = out is not None
     out, dataset_path, pred_uuid = _resolve_prediction_output(data, out)
     out.parent.mkdir(parents=True, exist_ok=True)
     typer.echo(f"output: {out}")
@@ -1295,7 +1306,7 @@ def predict(
     if writer is not None:
         writer.close()
 
-    ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset_path, comment)
+    ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset_path, comment, explicit_out=explicit_out)
     typer.echo(f"reference: {ref_path}")
 
     if skipped:
@@ -1443,6 +1454,7 @@ def rollout(
     seeds = _seed_from_data(files, n_events)
     typer.echo(f"seeded {len(seeds['event_id']):,} shower(s)")
 
+    explicit_out = out is not None
     out, dataset_path, pred_uuid = _resolve_prediction_output(data, out)
     out.parent.mkdir(parents=True, exist_ok=True)
 
@@ -1503,7 +1515,7 @@ def rollout(
 
     l1_summary = l1_dist_collector.summary()
 
-    ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset_path)
+    ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset_path, explicit_out=explicit_out)
     ref = yaml.safe_load(ref_path.read_text())
     ref.update(
         {
@@ -1547,6 +1559,49 @@ def rollout(
     typer.echo(f"reference: {ref_path}")
 
 
+workflow_app = typer.Typer(
+    no_args_is_help=True,
+    help="b2luigi pipeline orchestration: one spec file -> cache-warm, train, rollout, analysis.",
+)
+app.add_typer(workflow_app, name="workflow")
+
+
+@workflow_app.command("run")
+def workflow_run(
+    spec: Annotated[Path, typer.Argument(help="Workflow TOML (see configs/workflow_example.toml)")],
+    batch: Annotated[
+        bool,
+        typer.Option("--batch/--local", help="Submit batch-system tasks to HTCondor, or run everything locally"),
+    ] = False,
+    workers: Annotated[int, typer.Option("--workers", help="Concurrent luigi workers")] = 1,
+    mode: Annotated[
+        str,
+        typer.Option(
+            "--mode",
+            help="run | dry-run (print pending tasks) | show-output (print every target) | remove (delete outputs)",
+        ),
+    ] = "run",
+    scheduler_host: Annotated[Optional[str], typer.Option("--scheduler-host", help="luigid host")] = None,
+    scheduler_port: Annotated[Optional[int], typer.Option("--scheduler-port", help="luigid port")] = None,
+) -> None:
+    """Run a workflow spec end to end (the only sanctioned multi-step entry point).
+
+    A thin exec of `giant/workflow/run.py`, which b2luigi also re-executes on
+    every worker — so there is one documented entry point and one code path.
+    """
+    import subprocess
+
+    script = Path(__file__).resolve().parent / "workflow" / "run.py"
+    cmd = [sys.executable, str(script), "--spec", str(spec), "--workers", str(workers), "--mode", mode]
+    if batch:
+        cmd.append("--batch")
+    if scheduler_host:
+        cmd += ["--scheduler-host", scheduler_host]
+    if scheduler_port:
+        cmd += ["--scheduler-port", str(scheduler_port)]
+    raise typer.Exit(subprocess.run(cmd).returncode)
+
+
 analyze_app = typer.Typer(
     no_args_is_help=True,
     help="Rollout-vs-reference analysis: parallel compute on HTCondor + local render.",
@@ -1677,83 +1732,5 @@ def analyze_metrics(
     typer.echo(f"rendered {len(paths)} plots -> {paths[0].parent if paths else '(nothing to render)'}")
 
 
-@analyze_app.command("submit")
-def analyze_submit(
-    rollout_yamls: Annotated[
-        list[Path],
-        typer.Argument(
-            help="giant rollout YAML sidecar(s). Multiple compare N rollouts against one "
-            "shared reference — every YAML must name the same `dataset`."
-        ),
-    ],
-    accounting_group: Annotated[str, typer.Option("--accounting-group")],
-    label: Annotated[
-        Optional[list[str]],
-        typer.Option(
-            "--label",
-            help="Series name for a rollout YAML, positionally matched to it — give none, "
-            'or exactly one per YAML. Defaults to the YAML stem (or "rollout" for a '
-            "single YAML).",
-        ),
-    ] = None,
-    run_dir: Annotated[
-        Optional[Path],
-        typer.Option(
-            "--run-dir",
-            "-o",
-            help="Override the run directory (default: <cwd>/analysis_runs/analysis_<id>)",
-        ),
-    ] = None,
-    docker_image: Annotated[str, typer.Option("--docker-image")] = "cverstege/alma9-gridjob",
-    request_memory: Annotated[int, typer.Option("--request-memory", help="MB")] = 8192,
-    remote: Annotated[
-        bool,
-        typer.Option("--remote/--local", help="+RemoteJob vs ProvidesETPResources"),
-    ] = False,
-    chunks: Annotated[
-        int,
-        typer.Option(
-            "--chunks",
-            help="Split each plot's data into this many event_id chunks/jobs",
-        ),
-    ] = 1,
-    n_energy_bins: Annotated[int, typer.Option("--energy-bins")] = 4,
-    n_marginal_bins: Annotated[int, typer.Option("--bins")] = 50,
-    top_k_pdg: Annotated[int, typer.Option("--top-pdg")] = 6,
-    dry_run: Annotated[bool, typer.Option("--dry-run", help="Write files but don't condor_submit")] = False,
-) -> None:
-    """prep + write the HTCondor submit description (one job per plot x chunk), then submit."""
-    import subprocess
-
-    from giant.analysis import SubmitConfig, prep, write_submit
-
-    path = prep(
-        rollout_yamls,
-        run_dir,
-        n_chunks=chunks,
-        default_base=Path.cwd() / "analysis_runs",
-        labels=label,
-        n_energy_bins=n_energy_bins,
-        n_marginal_bins=n_marginal_bins,
-        top_k_pdg=top_k_pdg,
-    )
-    cfg = SubmitConfig(
-        run_dir=path,
-        accounting_group=accounting_group,
-        repo_dir=Path.cwd(),
-        docker_image=docker_image,
-        request_memory_mb=request_memory,
-        remote=remote,
-        n_chunks=chunks,
-    )
-    sub = write_submit(cfg)
-    typer.echo(f"run directory: {path}")
-    typer.echo(f"wrote submit description: {sub}")
-    if dry_run:
-        typer.echo("dry-run: not submitting")
-        return
-    subprocess.run(["condor_submit", str(sub)], check=True)
-
-
 if __name__ == "__main__":
     app()

giant/config.py

@@ -1769,6 +1769,19 @@ def resolve_default_out_dir(cfg: dict, base: Path = Path("checkpoints")) -> Path
     return out_dir
 
 
+def epoch_seed(seed: int, epoch: int) -> int:
+    """Per-epoch derivative of the run seed.
+
+    Reseeding the global RNGs from this at the top of every epoch makes epoch
+    *k* draw the same noise whether it runs inside one long `giant train` or
+    as its own resumed job in a per-epoch workflow chain
+    (`giant/workflow/tasks.py:TrainEpochTask`) — without it, a fresh process
+    would restart the stream at epoch 1's state. Mirrors what
+    `StreamingStepsDataset.set_epoch` does for the batch order.
+    """
+    return (int(seed) * 1_000_003 + int(epoch)) % (2**32)
+
+
 def seed_everything(seed: int) -> None:
     random.seed(seed)
     np.random.seed(seed)

giant/data/dataset.py

@@ -97,6 +97,7 @@ class StreamingStepsDataset(IterableDataset):
         mat_topn_map: dict[str, int] | None = None,
         sec_type_class_map: dict | None = None,
         k_max: int = K_MAX,
+        seed: int = 0,
     ) -> None:
         self.files = list(files)
         self._offsets = {path: event_id_offset(i) for i, path in enumerate(self.files)}
@@ -117,16 +118,35 @@ class StreamingStepsDataset(IterableDataset):
         self.mat_topn_map = mat_topn_map
         self.sec_type_class_map = sec_type_class_map
         self.k_max = k_max
+        self.seed = seed
+        self.epoch = 0
+        self._rng = np.random.default_rng()
+
+    def set_epoch(self, epoch: int) -> None:
+        """Select the shuffle stream for `epoch` (the DistributedSampler convention).
+
+        The training loop calls this at the top of every epoch. Shuffling is
+        seeded from `(seed, epoch, worker_id)` rather than the global numpy
+        state so epoch *k*'s batch order is the same whether it runs as epoch
+        *k* of one long `giant train`, or as its own resumed job in a
+        per-epoch workflow chain (`giant/workflow/tasks.py:TrainEpochTask`).
+        Workers are re-forked from this object each epoch (no
+        `persistent_workers`), so setting it here reaches them.
+        """
+        self.epoch = int(epoch)
 
     def __iter__(self):
         worker_info = torch.utils.data.get_worker_info()
         files = self.files
+        worker_id = worker_info.id if worker_info is not None else 0
         if worker_info is not None:
-            files = files[worker_info.id :: worker_info.num_workers]
+            files = files[worker_id :: worker_info.num_workers]
+
+        self._rng = np.random.default_rng([self.seed, self.epoch, worker_id])
 
         if self.shuffle:
             files = list(files)
-            np.random.default_rng().shuffle(files)
+            self._rng.shuffle(files)
 
         buf_cont: list[np.ndarray] = []
         buf_cat: list[np.ndarray] = []
@@ -222,7 +242,7 @@ class StreamingStepsDataset(IterableDataset):
         styp = np.concatenate(buf_type)
 
         if self.shuffle:
-            idx = np.random.permutation(len(cont))
+            idx = self._rng.permutation(len(cont))
             cont, cat, tgt = cont[idx], cat[idx], tgt[idx]
             nsec, sec, proc, styp = nsec[idx], sec[idx], proc[idx], styp[idx]
 

giant/pipeline.py

@@ -435,6 +435,7 @@ def run_train_job(
         mat_topn_map=cond_mat_topn,
         sec_type_class_map=sec_type_class_map,
         k_max=k_max,
+        seed=t["seed"],
     )
     val_ds = StreamingStepsDataset(
         files=files,

giant/tools/profile_analysis_costs.py

@@ -26,7 +26,7 @@ import numpy as np
 import polars as pl
 
 from giant.analysis.catalog import catalog_ids, get_spec
-from giant.analysis.condor import compute_reduced
+from giant.analysis.run import compute_reduced
 from giant.analysis.context import build_context
 from giant.analysis.sources import RolloutSpec
 

giant/training/loop.py

@@ -18,6 +18,7 @@ import torch
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
+from giant import config
 from giant.data.loader import TopNMap
 from giant.data.setup_cache import topnmap_to_json
 from giant.training.checkpoint import build_checkpoint, init_stages_from_checkpoints, load_checkpoint
@@ -184,6 +185,17 @@ def train(
             if device.type == "cuda":
                 torch.cuda.reset_peak_memory_stats(device)
             collector.start_epoch(epoch)
+            # Epoch-aware RNG: same noise (and, below, same batch order) for
+            # epoch k whether the run is one process or a chain of per-epoch
+            # jobs. See giant.config.epoch_seed.
+            config.seed_everything(config.epoch_seed(t["seed"], epoch))
+            # Epoch-aware shuffle stream (see StreamingStepsDataset.set_epoch):
+            # keeps epoch k's batch order identical whether it runs here or as
+            # its own resumed per-epoch job in a b2luigi workflow.
+            # (tests hand `train` a plain list of batches, which has neither)
+            set_epoch = getattr(getattr(train_loader, "dataset", None), "set_epoch", None)
+            if callable(set_epoch):
+                set_epoch(epoch)
             for trainer in trainers.values():
                 trainer.train_mode()
 

giant/training/plots.py

@@ -115,7 +115,7 @@ def derive_metrics_dir(
 
     Precedence: an explicit `out_dir` always wins. Otherwise
     `default_base / f"metrics_{run_dir.name}"` (the CLI passes the repo's
-    gitignored `analysis_runs/`, matching `giant.analysis.condor.derive_run_dir`'s
+    gitignored `analysis_runs/`, matching `giant.analysis.run.derive_run_dir`'s
     convention) — training-progress plots live alongside rollout-vs-reference
     analysis runs, not inside the training run directory itself.
     """

giant/workflow/__init__.py

@@ -0,0 +1,40 @@
+"""b2luigi orchestration of the full GIANT pipeline.
+
+One workflow TOML (``spec.py``) parameterises an entire experiment — dataset,
+geometry oracle, N trainings, N rollouts, N analyses — and ``giant workflow
+run <spec.toml>`` turns it into a b2luigi task graph (``tasks.py``) whose
+targets are files on ``/ceph``: nothing is recomputed that already exists,
+every step waits for its inputs, and HTCondor submission/polling is b2luigi's
+job rather than a hand-rolled submit-file generator.
+
+This is the only sanctioned way to run a multi-step pipeline; ``giant`` and
+``dwarf`` stay single-step primitives that these tasks invoke.
+
+``tasks``/``run`` import b2luigi, so they are *not* imported here — a plain
+``import giant.workflow`` (or ``giant.workflow.spec``) works without the
+``workflow`` extra installed.
+"""
+
+from giant.workflow.spec import (
+    AnalysisSpec,
+    CondorSpec,
+    DatasetSpec,
+    GeometrySpec,
+    RolloutSpec,
+    TrainSpec,
+    WorkflowSpec,
+    load_spec,
+    spec_hash,
+)
+
+__all__ = [
+    "AnalysisSpec",
+    "CondorSpec",
+    "DatasetSpec",
+    "GeometrySpec",
+    "RolloutSpec",
+    "TrainSpec",
+    "WorkflowSpec",
+    "load_spec",
+    "spec_hash",
+]

giant/workflow/htcondor.py

@@ -0,0 +1,82 @@
+"""HTCondor job descriptions for the workflow tasks.
+
+b2luigi writes every key of a task's ``htcondor_settings`` dict straight into
+that job's submit description, so these helpers are just the ETP-specific
+resource/requirement conventions in one place:
+
+* **CPU jobs** (setup cache, geometry oracle, analysis compute) keep what
+  the deleted ``giant analyze submit`` used: ``+RemoteJob`` for grid I/O, or
+  ``TARGET.ProvidesETPResources`` when the files are local to the cluster.
+* **GPU jobs** (training epochs, rollout) are remote-only, so they always
+  carry ``+RemoteJob`` and reach ``/ceph`` through
+  ``TARGET.ProvidesEtpCeph`` — the requirement strings are ported from the
+  ``condor-gpu-train-rollout`` branch's ``giant/condor.py`` rather than
+  rewritten, since they encode what the ETP HTCondor wiki documents for
+  TOpAS/NEMO2 GPU workers.
+"""
+
+from __future__ import annotations
+
+from giant.workflow.spec import CondorSpec
+
+__all__ = ["cpu_settings", "gpu_settings", "gpu_requirements"]
+
+
+def cpu_settings(
+    condor: CondorSpec,
+    *,
+    request_memory_mb: int | None = None,
+    request_cpus: int | None = None,
+    walltime_s: int | None = None,
+) -> dict:
+    settings: dict = {
+        "universe": "docker",
+        "docker_image": condor.docker_image_cpu,
+        "request_memory": request_memory_mb if request_memory_mb is not None else condor.request_memory_mb,
+        "request_cpus": request_cpus if request_cpus is not None else condor.request_cpus,
+        "accounting_group": condor.accounting_group,
+        "should_transfer_files": "YES",
+        "when_to_transfer_output": "ON_EXIT",
+    }
+    if condor.remote:
+        settings["+RemoteJob"] = "True"
+    else:
+        settings["requirements"] = "TARGET.ProvidesETPResources"
+    if walltime_s is not None:
+        settings["+RequestWalltime"] = int(walltime_s)
+    return settings
+
+
+def gpu_requirements(gpu_type: str | None = None, gpu_memory_mb: int | None = None) -> str:
+    """``TARGET.ProvidesEtpCeph`` (remote /ceph access) ANDed with any GPU pin."""
+    clauses = ["TARGET.ProvidesEtpCeph =?= True"]
+    if gpu_type is not None:
+        clauses.append(f'TARGET.GPUs_DeviceName =?= "{gpu_type}"')
+    if gpu_memory_mb is not None:
+        clauses.append(f"TARGET.GPUs_GlobalMemoryMb >= {gpu_memory_mb}")
+    return " && ".join(clauses)
+
+
+def gpu_settings(
+    condor: CondorSpec,
+    *,
+    request_gpus: int = 1,
+    gpu_type: str | None = None,
+    gpu_memory_mb: int | None = None,
+    request_memory_mb: int = 16384,
+    request_cpus: int = 4,
+    walltime_s: int = 86400,
+) -> dict:
+    return {
+        "universe": "docker",
+        "docker_image": condor.docker_image_gpu,
+        "request_memory": request_memory_mb,
+        "request_cpus": request_cpus,
+        "RequestGPUs": request_gpus,
+        "+RequestWalltime": int(walltime_s),
+        "accounting_group": condor.accounting_group,
+        "should_transfer_files": "YES",
+        "when_to_transfer_output": "ON_EXIT",
+        "+RemoteJob": "True",
+        "requirements": f"({gpu_requirements(gpu_type, gpu_memory_mb)})",
+    }

giant/workflow/run.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python
+"""Entry point b2luigi re-executes on every worker.
+
+Locally this is what ``giant workflow run <spec.toml>`` execs; on a batch
+worker it is what the generated wrapper script runs (after ``cd repo_dir`` and
+sourcing ``env_script``), with ``--spec`` forwarded via the
+``task_cmd_additional_args`` setting so the worker resolves exactly the same
+spec — and therefore the same task graph and output paths — as the submitter.
+
+b2luigi needs a real script path for that re-execution, which is why this is a
+script rather than a ``python -m`` module.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+# Allow `python giant/workflow/run.py` from a checkout that isn't installed.
+sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
+
+import b2luigi  # noqa: E402
+
+from giant.workflow.spec import WorkflowSpec, load_spec  # noqa: E402
+from giant.workflow.tasks import WorkflowTask, set_spec  # noqa: E402
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="Run a GIANT workflow spec with b2luigi.")
+    parser.add_argument("--spec", required=True, help="Workflow TOML (see configs/workflow_example.toml)")
+    parser.add_argument("--workers", type=int, default=1, help="Concurrent luigi workers")
+    parser.add_argument(
+        "--batch",
+        action="store_true",
+        help="Submit batch-system tasks to HTCondor (otherwise everything runs locally)",
+    )
+    parser.add_argument(
+        "--mode",
+        choices=("run", "dry-run", "show-output", "remove"),
+        default="run",
+        help="run (default), dry-run (print pending tasks), show-output (print every target), remove (delete outputs)",
+    )
+    parser.add_argument("--scheduler-host", default=None, help="luigid host (default: local scheduler)")
+    parser.add_argument("--scheduler-port", type=int, default=None, help="luigid port")
+    return parser
+
+
+def configure(spec: WorkflowSpec, spec_path: Path, batch: bool) -> None:
+    """Wire b2luigi's settings from the spec.
+
+    ``/ceph`` is shared between submit host and workers, so there is
+    deliberately no ``transfer_files``: ``result_dir``/``log_dir`` must live
+    somewhere both sides can see.
+    """
+    set_spec(spec)
+
+    b2luigi.set_setting("result_dir", spec.result_dir)
+    b2luigi.set_setting("log_dir", spec.log_dir)
+    b2luigi.set_setting("task_file_dir", str(Path(spec.result_dir) / "task_files"))
+    b2luigi.set_setting("use_parameter_name_in_output", True)
+    b2luigi.set_setting("batch_system", "htcondor" if batch else "local")
+    b2luigi.set_setting("working_dir", spec.condor.repo_dir)
+    b2luigi.set_setting("job_name", spec.name)
+    if spec.condor.env_script:
+        b2luigi.set_setting("env_script", spec.condor.env_script)
+    # The worker command is `<executable> [<basename of this file>] --batch-runner
+    # --task-id ...`, run after `cd working_dir`. Only the *basename* would be
+    # used, so the filename is dropped and the repo-relative script path is
+    # made part of the executable instead.
+    b2luigi.set_setting("add_filename_to_cmd", False)
+    b2luigi.set_setting("executable", [".venv/bin/python", "giant/workflow/run.py"])
+    b2luigi.set_setting("task_cmd_additional_args", ["--spec", str(spec_path)])
+
+
+def main(argv: list[str] | None = None) -> None:
+    args, _ = build_parser().parse_known_args(argv)
+    spec_path = Path(args.spec).resolve()
+    spec = load_spec(spec_path)
+    configure(spec, spec_path, batch=args.batch)
+
+    kwargs: dict = {}
+    if args.scheduler_host:
+        kwargs["scheduler_host"] = args.scheduler_host
+    if args.scheduler_port:
+        kwargs["scheduler_port"] = args.scheduler_port
+
+    b2luigi.process(
+        WorkflowTask(workflow_name=spec.name),
+        workers=args.workers,
+        batch=args.batch,
+        dry_run=args.mode == "dry-run",
+        show_output=args.mode == "show-output",
+        remove=args.mode == "remove",
+        auto_confirm=args.mode == "remove",
+        # run.py owns --spec/--mode/...; b2luigi must not choke on them.
+        ignore_additional_command_line_args=True,
+        **kwargs,
+    )
+
+
+if __name__ == "__main__":
+    main()

giant/workflow/spec.py

@@ -0,0 +1,383 @@
+"""Workflow TOML -> frozen dataclasses, validation, and per-task spec hashes.
+
+One spec file is the only place a pipeline is parameterised (see
+``configs/workflow_example.toml``):
+
+    [workflow]  name / result_dir / log_dir
+    [condor]    accounting group, repo dir, env script, docker images
+    [dataset]   steps (training) + reference (rollout seeds & analysis truth)
+    [geometry]  geometry-oracle build options
+    [[train]]   one per training run      (name, config, epochs, overrides, ...)
+    [[rollout]] one per rollout           (name, train = <a [[train]].name>, ...)
+    [[analysis]] one per comparison       (name, rollouts = [<[[rollout]].name>, ...])
+
+Every task carries its ``name`` plus a short ``spec_hash`` — 8 hex of the
+canonical JSON of its own resolved sub-spec **including its transitive
+parents**. That is what makes an edited spec produce a fresh result directory
+instead of silently reusing outputs computed under different settings: change
+the dataset and every hash downstream of it changes too.
+
+Unknown keys are rejected (with the valid ones listed), in the same spirit as
+``giant.config.validate_config_keys`` — a typo in a workflow spec would
+otherwise be a silently ignored setting on a multi-day pipeline.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import tomllib
+from dataclasses import MISSING, dataclass, field, fields, is_dataclass
+from pathlib import Path
+from typing import Any
+
+__all__ = [
+    "AnalysisSpec",
+    "CondorSpec",
+    "DatasetSpec",
+    "GeometrySpec",
+    "RolloutSpec",
+    "TrainSpec",
+    "WorkflowSpec",
+    "load_spec",
+    "spec_hash",
+]
+
+
+class WorkflowSpecError(ValueError):
+    """Raised for any malformed workflow spec (unknown key, bad reference, ...)."""
+
+
+# ---------------------------------------------------------------------------
+# sub-specs
+# ---------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class CondorSpec:
+    """Where and how jobs run — the batch-system half of the spec.
+
+    ``repo_dir`` doubles as b2luigi's ``working_dir`` (jobs ``cd`` there before
+    running ``giant/workflow/run.py``), and ``env_script`` is sourced first,
+    since submit and worker machines don't share an environment.
+    """
+
+    accounting_group: str
+    repo_dir: str
+    env_script: str = ""
+    docker_image_cpu: str = "cverstege/alma9-gridjob"
+    docker_image_gpu: str = "mschnepf/slc7-condocker"
+    remote: bool = True
+    request_cpus: int = 1
+    request_memory_mb: int = 8192
+
+
+@dataclass(frozen=True)
+class DatasetSpec:
+    """The two datasets every pipeline needs.
+
+    ``steps`` is what training reads; ``reference`` is the held-out file
+    rollouts are seeded from and the analysis compares against (the "one
+    ground truth" premise of ``giant.analysis``).
+    """
+
+    steps: str
+    reference: str
+
+
+@dataclass(frozen=True)
+class GeometrySpec:
+    """``dwarf build-geometry-oracle`` options (see giant/tools/geometry_oracle.py)."""
+
+    method: str = "slab"
+    k: int = 1
+    subsample: int = 500_000
+    escape_factor: float = 5.0
+    seed: int = 0
+    depth_axis: int = 2
+    n_bins: int = 2000
+
+
+@dataclass(frozen=True)
+class TrainSpec:
+    """One training run, fanned out into ``ceil(epochs / epochs_per_job)`` jobs.
+
+    ``overrides`` are ``[train]``/model config keys merged on top of ``config``
+    exactly as ``giant train``'s flags are (``giant.config.merge_cli_overrides``),
+    so anything expressible on the CLI is expressible here.
+    """
+
+    name: str
+    config: str | None = None
+    epochs: int = 1
+    epochs_per_job: int = 1
+    overrides: dict[str, Any] = field(default_factory=dict)
+    request_gpus: int = 1
+    gpu_type: str | None = None
+    gpu_memory_mb: int | None = None
+    request_memory_mb: int = 16384
+    request_cpus: int = 4
+    walltime_s: int = 86400
+    num_workers: int = 4
+    shuffle_buffer: int = 65536
+    device: str | None = None
+
+
+@dataclass(frozen=True)
+class RolloutSpec:
+    """One ``giant rollout`` run against the checkpoint of ``train``."""
+
+    name: str
+    train: str
+    n_events: int | None = None
+    energy_cutoff: float = 0.1
+    max_steps: int = 1000
+    steps: int = 10
+    batch_size: int = 4096
+    max_tracks_per_event: int | None = None
+    escape_threshold: float | None = None
+    weights: str = "raw"
+    seed: int | None = None
+    request_gpus: int = 1
+    gpu_type: str | None = None
+    gpu_memory_mb: int | None = None
+    request_memory_mb: int = 16384
+    request_cpus: int = 2
+    walltime_s: int = 86400
+    device: str | None = None
+
+
+@dataclass(frozen=True)
+class AnalysisSpec:
+    """One rollout-vs-reference comparison (N rollout series, one reference)."""
+
+    name: str
+    rollouts: tuple[str, ...]
+    chunks: int = 1
+    energy_bins: int = 4
+    bins: int = 50
+    top_pdg: int = 6
+    gallery: bool = False
+    request_memory_mb: int = 8192
+    request_cpus: int = 1
+
+
+@dataclass(frozen=True)
+class WorkflowSpec:
+    """A whole pipeline: the parsed spec file plus name-keyed lookups."""
+
+    name: str
+    result_dir: str
+    log_dir: str
+    condor: CondorSpec
+    dataset: DatasetSpec
+    geometry: GeometrySpec
+    trains: tuple[TrainSpec, ...]
+    rollouts: tuple[RolloutSpec, ...]
+    analyses: tuple[AnalysisSpec, ...]
+    path: str = ""
+
+    # -- lookups ----------------------------------------------------------
+    def train(self, name: str) -> TrainSpec:
+        return _lookup(self.trains, name, "train")
+
+    def rollout(self, name: str) -> RolloutSpec:
+        return _lookup(self.rollouts, name, "rollout")
+
+    def analysis(self, name: str) -> AnalysisSpec:
+        return _lookup(self.analyses, name, "analysis")
+
+    # -- hashes -----------------------------------------------------------
+    # Each one folds in everything upstream of it, so a change anywhere in a
+    # task's ancestry moves its result directory (and only the affected
+    # subtree's).
+    def dataset_hash(self) -> str:
+        return spec_hash(self.dataset)
+
+    def warm_cache_hash(self, train_name: str) -> str:
+        # The setup cache depends on the dataset and on what this training's
+        # config asks of it (val split, conditioning, router) — not on how
+        # many epochs it runs for, so epochs/resources are deliberately left
+        # out and two trainings sharing a config share one warm-cache job.
+        t = self.train(train_name)
+        return spec_hash(self.dataset, t.config, t.overrides)
+
+    def geometry_hash(self) -> str:
+        return spec_hash(self.dataset, self.geometry)
+
+    def train_hash(self, name: str) -> str:
+        return spec_hash(self.dataset, self.train(name))
+
+    def rollout_hash(self, name: str) -> str:
+        ro = self.rollout(name)
+        return spec_hash(self.dataset, self.geometry, self.train(ro.train), ro)
+
+    def analysis_hash(self, name: str) -> str:
+        an = self.analysis(name)
+        parents = [self.rollout(r) for r in an.rollouts]
+        train_parents = [self.train(r.train) for r in parents]
+        return spec_hash(self.dataset, self.geometry, train_parents, parents, an)
+
+
+def _lookup(items, name: str, kind: str):
+    for item in items:
+        if item.name == name:
+            return item
+    known = ", ".join(sorted(i.name for i in items)) or "(none defined)"
+    raise WorkflowSpecError(f"no [[{kind}]] named {name!r} in this workflow — defined: {known}")
+
+
+# ---------------------------------------------------------------------------
+# hashing
+# ---------------------------------------------------------------------------
+
+
+def spec_hash(*parts: Any, length: int = 8) -> str:
+    """Short stable hash of one or more (sub-)specs.
+
+    Canonical JSON (sorted keys, dataclasses expanded) so the value depends
+    only on the resolved settings — not on key order in the TOML, nor on
+    which defaults were written out explicitly.
+    """
+    payload = json.dumps([_canonical(p) for p in parts], sort_keys=True, separators=(",", ":"))
+    return hashlib.sha256(payload.encode()).hexdigest()[:length]
+
+
+def _canonical(value: Any) -> Any:
+    if is_dataclass(value) and not isinstance(value, type):
+        return {f.name: _canonical(getattr(value, f.name)) for f in fields(value)}
+    if isinstance(value, dict):
+        return {str(k): _canonical(v) for k, v in value.items()}
+    if isinstance(value, (list, tuple)):
+        return [_canonical(v) for v in value]
+    if isinstance(value, Path):
+        return str(value)
+    return value
+
+
+# ---------------------------------------------------------------------------
+# parsing
+# ---------------------------------------------------------------------------
+
+
+def _build(cls, data: dict, where: str):
+    """Instantiate a frozen sub-spec, rejecting unknown/missing keys loudly."""
+    valid = {f.name for f in fields(cls)}
+    unknown = sorted(set(data) - valid)
+    if unknown:
+        raise WorkflowSpecError(f"{where}: unknown key(s) {unknown} — valid keys: {sorted(valid)}")
+    required = {f.name for f in fields(cls) if f.default is MISSING and f.default_factory is MISSING}
+    missing = sorted(required - set(data))
+    if missing:
+        raise WorkflowSpecError(f"{where}: missing required key(s) {missing}")
+    return cls(**data)
+
+
+def load_spec(path: str | Path) -> WorkflowSpec:
+    """Parse and validate a workflow TOML file."""
+    path = Path(path)
+    try:
+        raw = tomllib.loads(path.read_text())
+    except tomllib.TOMLDecodeError as exc:
+        raise WorkflowSpecError(f"{path}: not valid TOML — {exc}") from exc
+    return parse_spec(raw, path=path)
+
+
+def parse_spec(raw: dict, path: str | Path = "") -> WorkflowSpec:
+    """Validate an already-parsed workflow spec mapping."""
+    top_valid = {"workflow", "condor", "dataset", "geometry", "train", "rollout", "analysis"}
+    unknown = sorted(set(raw) - top_valid)
+    if unknown:
+        raise WorkflowSpecError(
+            f"{path or '<spec>'}: unknown top-level table(s) {unknown} — valid: {sorted(top_valid)}"
+        )
+
+    for required in ("workflow", "condor", "dataset"):
+        if required not in raw:
+            raise WorkflowSpecError(f"{path or '<spec>'}: missing required [{required}] table")
+
+    wf = dict(raw["workflow"])
+    wf_valid = {"name", "result_dir", "log_dir"}
+    wf_unknown = sorted(set(wf) - wf_valid)
+    if wf_unknown:
+        raise WorkflowSpecError(f"[workflow]: unknown key(s) {wf_unknown} — valid keys: {sorted(wf_valid)}")
+    if "name" not in wf or "result_dir" not in wf:
+        raise WorkflowSpecError("[workflow]: 'name' and 'result_dir' are required")
+    result_dir = str(Path(wf["result_dir"]).expanduser())
+    log_dir = str(Path(wf.get("log_dir", Path(result_dir) / "logs")).expanduser())
+
+    condor = _build(CondorSpec, dict(raw["condor"]), "[condor]")
+    dataset = _build(DatasetSpec, dict(raw["dataset"]), "[dataset]")
+    geometry = _build(GeometrySpec, dict(raw.get("geometry", {})), "[geometry]")
+
+    trains = tuple(_build(TrainSpec, dict(t), f"[[train]] #{i}") for i, t in enumerate(raw.get("train", [])))
+    rollouts = tuple(_build(RolloutSpec, dict(r), f"[[rollout]] #{i}") for i, r in enumerate(raw.get("rollout", [])))
+    analyses = tuple(
+        _build(AnalysisSpec, {**a, "rollouts": tuple(a.get("rollouts", ()))}, f"[[analysis]] #{i}")
+        for i, a in enumerate(raw.get("analysis", []))
+    )
+
+    _check_unique(trains, "train")
+    _check_unique(rollouts, "rollout")
+    _check_unique(analyses, "analysis")
+
+    train_names = {t.name for t in trains}
+    for ro in rollouts:
+        if ro.train not in train_names:
+            raise WorkflowSpecError(
+                f"[[rollout]] {ro.name!r}: train={ro.train!r} names no [[train]] — defined: {sorted(train_names)}"
+            )
+    rollout_names = {r.name for r in rollouts}
+    for an in analyses:
+        if not an.rollouts:
+            raise WorkflowSpecError(f"[[analysis]] {an.name!r}: 'rollouts' must name at least one [[rollout]]")
+        for r in an.rollouts:
+            if r not in rollout_names:
+                raise WorkflowSpecError(
+                    f"[[analysis]] {an.name!r}: rollout {r!r} is not defined — "
+                    f"defined: {sorted(rollout_names) or '(none)'}"
+                )
+        if len(set(an.rollouts)) != len(an.rollouts):
+            raise WorkflowSpecError(f"[[analysis]] {an.name!r}: repeated rollout name(s) in 'rollouts'")
+        if an.chunks < 1:
+            raise WorkflowSpecError(f"[[analysis]] {an.name!r}: chunks must be >= 1, got {an.chunks}")
+
+    for t in trains:
+        if t.epochs < 1:
+            raise WorkflowSpecError(f"[[train]] {t.name!r}: epochs must be >= 1, got {t.epochs}")
+        if t.epochs_per_job < 1:
+            raise WorkflowSpecError(f"[[train]] {t.name!r}: epochs_per_job must be >= 1, got {t.epochs_per_job}")
+
+    return WorkflowSpec(
+        name=wf["name"],
+        result_dir=result_dir,
+        log_dir=log_dir,
+        condor=condor,
+        dataset=dataset,
+        geometry=geometry,
+        trains=trains,
+        rollouts=rollouts,
+        analyses=analyses,
+        path=str(path),
+    )
+
+
+def _check_unique(items, kind: str) -> None:
+    names = [i.name for i in items]
+    dupes = sorted({n for n in names if names.count(n) > 1})
+    if dupes:
+        raise WorkflowSpecError(f"[[{kind}]] names must be unique — repeated: {dupes}")
+
+
+def epoch_milestones(train: TrainSpec) -> list[int]:
+    """Cumulative epoch counts, one per chained ``TrainEpochTask``.
+
+    ``epochs_per_job`` trades queue waits against job length: with
+    ``epochs=10, epochs_per_job=3`` this is ``[3, 6, 9, 10]``, i.e. job *k*
+    resumes job *k-1*'s ``last.pt`` and trains up to its own milestone.
+    """
+    step = train.epochs_per_job
+    milestones = list(range(step, train.epochs + 1, step))
+    if not milestones or milestones[-1] != train.epochs:
+        milestones.append(train.epochs)
+    return milestones

giant/workflow/tasks.py

@@ -0,0 +1,647 @@
+"""The b2luigi task graph: cache-warm -> train -> rollout -> analysis.
+
+    DatasetTask (external)  ─┬─> WarmCacheTask(train) ─> TrainEpochTask(train, 1..N) ─> TrainTask(train) ─┐
+                             └─> GeometryOracleTask ──┐                                                   │
+                                                      └──> RolloutTask(rollout) <────────────────────────┘
+                                                                       │
+    AnalysisPrepTask(analysis) ─> AnalysisComputeTask(analysis, plot, chunk) ─> AnalysisRenderTask(analysis)
+                                                                                        ^
+    WorkflowTask (wrapper) ─────────────────────────────────────────────────────────────┘
+
+Every task's output directory is ``<result_dir>/<kind>/name=<name>/spec_hash=
+<hash>/…`` — the hash covers the task's resolved sub-spec *and its transitive
+parents* (``giant/workflow/spec.py``), so editing the spec produces a fresh
+directory for exactly the affected subtree instead of silently reusing stale
+outputs.
+
+Task bodies never reimplement anything: they call the same entry points the
+CLIs do (``run_warm_setup_cache``, ``run_build_geometry_oracle``,
+``run_train_job``, ``giant.analysis.prep``/``compute_one``/``merge_all``,
+``render_run``), or shell out to ``giant rollout``, which has no library-level
+entry point of its own.
+
+Training is fanned out into **one short GPU job per epoch** (or per
+``epochs_per_job`` epochs): job *k* runs ``run_train_job`` with ``epochs = k``
+and ``resume = <job k-1>/last.pt``, which the training loop already handles
+(``giant/training/loop.py`` sets ``start_epoch = ckpt["epoch"] + 1`` and
+returns early when the checkpoint already covers ``epochs``). A 200-epoch run
+then becomes 200 schedulable jobs that survive preemption and give luigi a
+real progress signal, at the cost of one (cache-warmed) setup scan and one
+queue wait per job.
+"""
+
+from __future__ import annotations
+
+import json
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+import b2luigi
+
+from giant.workflow.htcondor import cpu_settings, gpu_settings
+from giant.workflow.spec import WorkflowSpec, epoch_milestones
+
+__all__ = [
+    "AnalysisComputeTask",
+    "AnalysisPrepTask",
+    "AnalysisRenderTask",
+    "DatasetTask",
+    "GeometryOracleTask",
+    "RolloutTask",
+    "TrainEpochTask",
+    "TrainTask",
+    "WarmCacheTask",
+    "WorkflowTask",
+    "analysis_dir",
+    "analysis_jobs",
+    "get_spec",
+    "set_spec",
+]
+
+
+# ---------------------------------------------------------------------------
+# the active spec
+# ---------------------------------------------------------------------------
+# luigi parameters must be simple scalars, so tasks carry only `name` +
+# `spec_hash` and read the rest out of the one spec this process was started
+# with. Batch workers re-execute `run.py --spec <same file>` (see
+# `task_cmd_additional_args` there), so they resolve the identical spec.
+
+_SPEC: WorkflowSpec | None = None
+
+
+def set_spec(spec: WorkflowSpec) -> None:
+    global _SPEC
+    _SPEC = spec
+
+
+def get_spec() -> WorkflowSpec:
+    if _SPEC is None:
+        raise RuntimeError("no workflow spec loaded — call giant.workflow.tasks.set_spec() first")
+    return _SPEC
+
+
+def _result_dir(*parts: str) -> Path:
+    return Path(get_spec().result_dir).joinpath(*parts)
+
+
+def _task_dir(kind: str, name: str, spec_hash: str) -> Path:
+    """``<result_dir>/<kind>/name=<name>/spec_hash=<hash>``."""
+    return _result_dir(kind, f"name={name}", f"spec_hash={spec_hash}")
+
+
+def analysis_dir(spec: WorkflowSpec, name: str) -> Path:
+    """The analysis run directory — what ``prep`` lays out and every later step reads."""
+    return Path(spec.result_dir) / "analysis" / f"name={name}" / f"spec_hash={spec.analysis_hash(name)}"
+
+
+def analysis_jobs(spec: WorkflowSpec, name: str) -> list[tuple[str, int]]:
+    """Every ``(plot_id, chunk)`` compute job of one analysis.
+
+    ``chunkable=False`` specs (the checkpoint-bound diagnostics, already
+    bounded/subsampled) always run as a single chunk — the same rule the
+    deleted ``_job_walltimes`` applied.
+    """
+    from giant.analysis.catalog import catalog_ids, get_spec as get_plot_spec
+
+    chunks = spec.analysis(name).chunks
+    jobs: list[tuple[str, int]] = []
+    for plot_id in catalog_ids():
+        n = chunks if get_plot_spec(plot_id).chunkable else 1
+        jobs.extend((plot_id, chunk) for chunk in range(n))
+    return jobs
+
+
+def _giant_cmd() -> list[str]:
+    """How to invoke the ``giant`` CLI from inside a task (worker or locally)."""
+    return [sys.executable, "-m", "giant.cli"]
+
+
+# ---------------------------------------------------------------------------
+# inputs
+# ---------------------------------------------------------------------------
+
+
+class DatasetTask(b2luigi.ExternalTask):
+    """A steps parquet file or directory that must already exist.
+
+    Nothing produces it, so a missing path is a hard, immediate error rather
+    than a job that fails hours later — the usual cause being ``/ceph`` not
+    mounted on the machine the workflow was started from.
+    """
+
+    path = b2luigi.Parameter()
+
+    def output(self):
+        return b2luigi.LocalTarget(str(self.path))
+
+    def complete(self):
+        if not Path(str(self.path)).exists():
+            raise FileNotFoundError(
+                f"dataset {self.path!r} does not exist — is /ceph mounted on this machine? "
+                "(see CLAUDE.md's Compute environment section)"
+            )
+        return True
+
+
+# ---------------------------------------------------------------------------
+# setup stage
+# ---------------------------------------------------------------------------
+
+
+class WarmCacheTask(b2luigi.Task):
+    """Precompute one training's setup-stage sidecar (vocab maps, event split,
+    normalizer stats) so every per-epoch job is a cache hit instead of a
+    full rescan.
+
+    The real product (``<data>.giant_train_cache.json``) lives next to the
+    dataset, not under ``result_dir``, so the target here is a small stamp
+    recording that sidecar's path/mtime/size.
+    """
+
+    name = b2luigi.Parameter()
+    spec_hash = b2luigi.Parameter()
+
+    @property
+    def htcondor_settings(self):
+        spec = get_spec()
+        return cpu_settings(spec.condor, request_memory_mb=32768, request_cpus=4, walltime_s=21600)
+
+    def requires(self):
+        yield DatasetTask(path=get_spec().dataset.steps)
+
+    def output(self):
+        return b2luigi.LocalTarget(str(_task_dir("warm_cache", str(self.name), str(self.spec_hash)) / "stamp.json"))
+
+    def run(self):
+        from giant.data.setup_cache import sidecar_path
+        from giant.tools.warm_setup_cache import run_warm_setup_cache
+
+        spec = get_spec()
+        train = spec.train(str(self.name))
+        run_warm_setup_cache(
+            data=spec.dataset.steps,
+            config_path=Path(train.config) if train.config else None,
+        )
+        sidecar = Path(sidecar_path(spec.dataset.steps))
+        stamp = {
+            "sidecar": str(sidecar),
+            "mtime": sidecar.stat().st_mtime if sidecar.exists() else None,
+            "size": sidecar.stat().st_size if sidecar.exists() else None,
+        }
+        out = Path(self.output().path)
+        out.parent.mkdir(parents=True, exist_ok=True)
+        out.write_text(json.dumps(stamp, indent=2))
+
+
+class GeometryOracleTask(b2luigi.Task):
+    """Build the position -> (material, layer_id) oracle every rollout needs."""
+
+    spec_hash = b2luigi.Parameter()
+
+    @property
+    def htcondor_settings(self):
+        spec = get_spec()
+        return cpu_settings(spec.condor, request_memory_mb=32768, request_cpus=4, walltime_s=21600)
+
+    def requires(self):
+        yield DatasetTask(path=get_spec().dataset.steps)
+
+    def output(self):
+        return b2luigi.LocalTarget(
+            str(_result_dir("geometry", f"spec_hash={self.spec_hash}") / "oracle.pkl"),
+        )
+
+    def run(self):
+        from giant.tools.geometry_oracle import run_build_geometry_oracle
+
+        spec = get_spec()
+        g = spec.geometry
+        out = Path(self.output().path)
+        out.parent.mkdir(parents=True, exist_ok=True)
+        run_build_geometry_oracle(
+            data=Path(spec.dataset.steps),
+            out=out,
+            method=g.method,
+            k=g.k,
+            subsample=g.subsample,
+            escape_factor=g.escape_factor,
+            seed=g.seed,
+            depth_axis=g.depth_axis,
+            n_bins=g.n_bins,
+        )
+
+
+# ---------------------------------------------------------------------------
+# training
+# ---------------------------------------------------------------------------
+
+
+def _train_cfg(spec: WorkflowSpec, name: str, epochs: int) -> dict:
+    """The merged config one training job runs, resolved exactly as `giant train` does."""
+    from giant import config as gconfig
+
+    train = spec.train(name)
+    flags = {**train.overrides, "epochs": epochs}
+    overrides = gconfig.overrides_from_flags(flags)
+    cfg = gconfig.merge_cli_overrides(
+        gconfig.DEFAULT_CONFIG,
+        Path(train.config) if train.config else None,
+        overrides,
+    )
+    gconfig.validate_config(cfg, resume=True)
+    return cfg
+
+
+class TrainEpochTask(b2luigi.Task):
+    """Epochs up to ``milestone`` of one training, resuming the previous job.
+
+    Target is ``last.pt``. ``best.pt`` is written by the loop *only when that
+    epoch improved*, and ``best_val_loss`` travels inside the checkpoint, so
+    the global best comparison stays correct across jobs: "``best.pt`` exists
+    in milestone dir *k*" means exactly "one of that job's epochs was the best
+    so far".
+    """
+
+    name = b2luigi.Parameter()
+    spec_hash = b2luigi.Parameter()
+    milestone = b2luigi.IntParameter()
+
+    @property
+    def htcondor_settings(self):
+        spec = get_spec()
+        train = spec.train(str(self.name))
+        return gpu_settings(
+            spec.condor,
+            request_gpus=train.request_gpus,
+            gpu_type=train.gpu_type,
+            gpu_memory_mb=train.gpu_memory_mb,
+            request_memory_mb=train.request_memory_mb,
+            request_cpus=train.request_cpus,
+            walltime_s=train.walltime_s,
+        )
+
+    @property
+    def _dir(self) -> Path:
+        return _task_dir("train_epoch", str(self.name), str(self.spec_hash)) / f"epochs={int(self.milestone)}"
+
+    def _previous_milestone(self) -> int | None:
+        spec = get_spec()
+        milestones = epoch_milestones(spec.train(str(self.name)))
+        index = milestones.index(int(self.milestone))
+        return milestones[index - 1] if index > 0 else None
+
+    def requires(self):
+        previous = self._previous_milestone()
+        if previous is None:
+            yield WarmCacheTask(name=self.name, spec_hash=get_spec().warm_cache_hash(str(self.name)))
+        else:
+            yield TrainEpochTask(name=self.name, spec_hash=self.spec_hash, milestone=previous)
+
+    def output(self):
+        return b2luigi.LocalTarget(str(self._dir / "last.pt"))
+
+    def run(self):
+        import torch
+
+        from giant import config as gconfig
+        from giant.pipeline import run_train_job
+
+        spec = get_spec()
+        train = spec.train(str(self.name))
+        cfg = _train_cfg(spec, str(self.name), int(self.milestone))
+
+        previous = self._previous_milestone()
+        resume = None
+        if previous is not None:
+            resume = _task_dir("train_epoch", str(self.name), str(self.spec_hash)) / f"epochs={previous}" / "last.pt"
+
+        device = torch.device(train.device) if train.device else gconfig.auto_device()
+        out_dir = self._dir
+        out_dir.mkdir(parents=True, exist_ok=True)
+        run_train_job(
+            data=Path(spec.dataset.steps),
+            cfg=cfg,
+            out_dir=out_dir,
+            device=device,
+            shuffle_buffer=train.shuffle_buffer,
+            num_workers=train.num_workers,
+            resume=resume,
+            cache_setup=True,
+        )
+
+
+class TrainTask(b2luigi.Task):
+    """Publish one training's canonical outputs, hiding the epoch fan-out.
+
+    Everything downstream (``RolloutTask``, humans, ``giant analyze metrics``)
+    points here and never has to know which milestone directory happened to
+    hold the best checkpoint.
+    """
+
+    name = b2luigi.Parameter()
+    spec_hash = b2luigi.Parameter()
+    batch_system = "local"
+
+    @property
+    def _milestones(self) -> list[int]:
+        return epoch_milestones(get_spec().train(str(self.name)))
+
+    def requires(self):
+        yield TrainEpochTask(name=self.name, spec_hash=self.spec_hash, milestone=self._milestones[-1])
+
+    @property
+    def _dir(self) -> Path:
+        return _task_dir("train", str(self.name), str(self.spec_hash))
+
+    def output(self):
+        d = self._dir
+        return {
+            "best.pt": b2luigi.LocalTarget(str(d / "best.pt")),
+            "last.pt": b2luigi.LocalTarget(str(d / "last.pt")),
+            "metrics.csv": b2luigi.LocalTarget(str(d / "metrics.csv")),
+        }
+
+    def run(self):
+        epoch_base = _task_dir("train_epoch", str(self.name), str(self.spec_hash))
+        milestone_dirs = [epoch_base / f"epochs={m}" for m in self._milestones]
+
+        best_dirs = [d for d in milestone_dirs if (d / "best.pt").exists()]
+        if not best_dirs:
+            raise FileNotFoundError(
+                f"no best.pt in any milestone directory under {epoch_base} — "
+                "did every epoch job run with a validation split?"
+            )
+        out = self._dir
+        out.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(best_dirs[-1] / "best.pt", out / "best.pt")
+        shutil.copy2(milestone_dirs[-1] / "last.pt", out / "last.pt")
+        for extra in ("config.toml", "run_meta.json"):
+            src = milestone_dirs[-1] / extra
+            if src.exists():
+                shutil.copy2(src, out / extra)
+
+        # One metrics.csv for the whole run: the first job's header, then
+        # every job's rows in epoch order, so `giant analyze metrics` sees a
+        # single continuous training curve.
+        lines: list[str] = []
+        header: str | None = None
+        for d in milestone_dirs:
+            csv = d / "metrics.csv"
+            if not csv.exists():
+                continue
+            rows = csv.read_text().splitlines()
+            if not rows:
+                continue
+            if header is None:
+                header = rows[0]
+            lines.extend(rows[1:])
+        (out / "metrics.csv").write_text("\n".join([header or ""] + lines) + "\n")
+
+
+# ---------------------------------------------------------------------------
+# rollout
+# ---------------------------------------------------------------------------
+
+
+class RolloutTask(b2luigi.Task):
+    """Roll one trained checkpoint forward into full showers.
+
+    ``giant rollout`` has no library-level entry point, so this shells out to
+    the CLI — with an explicit ``--out``, which puts the YAML sidecar at the
+    deterministic ``rollout.yaml`` next to the parquet (see
+    ``giant/cli.py:_write_prediction_ref``).
+    """
+
+    name = b2luigi.Parameter()
+    spec_hash = b2luigi.Parameter()
+
+    @property
+    def htcondor_settings(self):
+        spec = get_spec()
+        ro = spec.rollout(str(self.name))
+        return gpu_settings(
+            spec.condor,
+            request_gpus=ro.request_gpus,
+            gpu_type=ro.gpu_type,
+            gpu_memory_mb=ro.gpu_memory_mb,
+            request_memory_mb=ro.request_memory_mb,
+            request_cpus=ro.request_cpus,
+            walltime_s=ro.walltime_s,
+        )
+
+    @property
+    def _dir(self) -> Path:
+        return _task_dir("rollout", str(self.name), str(self.spec_hash))
+
+    def requires(self):
+        spec = get_spec()
+        ro = spec.rollout(str(self.name))
+        yield TrainTask(name=ro.train, spec_hash=spec.train_hash(ro.train))
+        yield GeometryOracleTask(spec_hash=spec.geometry_hash())
+        yield DatasetTask(path=spec.dataset.reference)
+
+    def output(self):
+        d = self._dir
+        return {
+            "rollout.parquet": b2luigi.LocalTarget(str(d / "rollout.parquet")),
+            "rollout.yaml": b2luigi.LocalTarget(str(d / "rollout.yaml")),
+        }
+
+    def run(self):
+        spec = get_spec()
+        ro = spec.rollout(str(self.name))
+        out = self._dir / "rollout.parquet"
+        out.parent.mkdir(parents=True, exist_ok=True)
+
+        checkpoint = _task_dir("train", ro.train, spec.train_hash(ro.train)) / "best.pt"
+        oracle = _result_dir("geometry", f"spec_hash={spec.geometry_hash()}") / "oracle.pkl"
+
+        cmd = [
+            *_giant_cmd(),
+            "rollout",
+            spec.dataset.reference,
+            "--checkpoint",
+            str(checkpoint),
+            "--geometry",
+            str(oracle),
+            "--out",
+            str(out),
+            "--energy-cutoff",
+            str(ro.energy_cutoff),
+            "--max-steps",
+            str(ro.max_steps),
+            "--steps",
+            str(ro.steps),
+            "--batch-size",
+            str(ro.batch_size),
+            "--weights",
+            ro.weights,
+        ]
+        for flag, value in (
+            ("--n-events", ro.n_events),
+            ("--max-tracks-per-event", ro.max_tracks_per_event),
+            ("--escape-threshold", ro.escape_threshold),
+            ("--seed", ro.seed),
+            ("--device", ro.device),
+        ):
+            if value is not None:
+                cmd += [flag, str(value)]
+        subprocess.run(cmd, check=True)
+
+
+# ---------------------------------------------------------------------------
+# analysis
+# ---------------------------------------------------------------------------
+
+
+class AnalysisPrepTask(b2luigi.Task):
+    """Resolve the shared bin edges/group sets once, for every compute job.
+
+    Cheap and streaming, so it runs locally: everything after it needs
+    ``shared.json``/``run_meta.json`` to already exist.
+    """
+
+    name = b2luigi.Parameter()
+    spec_hash = b2luigi.Parameter()
+    batch_system = "local"
+
+    def requires(self):
+        spec = get_spec()
+        for rollout_name in spec.analysis(str(self.name)).rollouts:
+            yield RolloutTask(name=rollout_name, spec_hash=spec.rollout_hash(rollout_name))
+
+    @property
+    def _dir(self) -> Path:
+        return analysis_dir(get_spec(), str(self.name))
+
+    def output(self):
+        d = self._dir
+        return {
+            "shared.json": b2luigi.LocalTarget(str(d / "shared.json")),
+            "run_meta.json": b2luigi.LocalTarget(str(d / "run_meta.json")),
+        }
+
+    def run(self):
+        from giant.analysis import prep
+
+        spec = get_spec()
+        an = spec.analysis(str(self.name))
+        yamls = [_task_dir("rollout", r, spec.rollout_hash(r)) / "rollout.yaml" for r in an.rollouts]
+        prep(
+            yamls,
+            run_dir=self._dir,
+            n_chunks=an.chunks,
+            labels=list(an.rollouts),
+            n_energy_bins=an.energy_bins,
+            n_marginal_bins=an.bins,
+            top_k_pdg=an.top_pdg,
+        )
+
+
+class AnalysisComputeTask(b2luigi.Task):
+    """One (plot, chunk) streaming reduction — the replaced ``jobs.txt`` row.
+
+    The output path is the on-disk contract ``compute-one``/``merge_one``
+    already share (``reduced_partial/<id>__<chunk>.json``), declared
+    explicitly rather than through b2luigi's own output naming so that
+    contract is untouched.
+    """
+
+    name = b2luigi.Parameter()
+    spec_hash = b2luigi.Parameter()
+    plot_id = b2luigi.Parameter()
+    chunk = b2luigi.IntParameter()
+
+    @property
+    def htcondor_settings(self):
+        # A property, so it is evaluated at submit time — i.e. after prep has
+        # written run_meta.json, whose row counts size the walltime request.
+        from giant.analysis import RunMeta
+        from giant.analysis.runtime_estimate import estimate_runtime_s
+
+        spec = get_spec()
+        an = spec.analysis(str(self.name))
+        walltime = None
+        meta_path = analysis_dir(spec, str(self.name)) / "run_meta.json"
+        if meta_path.exists():
+            from giant.analysis.catalog import get_spec as get_plot_spec
+
+            meta = RunMeta.load(meta_path)
+            chunkable = get_plot_spec(str(self.plot_id)).chunkable
+            n_rows = meta.rows_per_chunk[int(self.chunk)] if chunkable and meta.rows_per_chunk else meta.total_rows
+            walltime = estimate_runtime_s(str(self.plot_id), n_rows)
+        return cpu_settings(
+            spec.condor,
+            request_memory_mb=an.request_memory_mb,
+            request_cpus=an.request_cpus,
+            walltime_s=walltime,
+        )
+
+    def requires(self):
+        yield AnalysisPrepTask(name=self.name, spec_hash=self.spec_hash)
+
+    def output(self):
+        run_dir = analysis_dir(get_spec(), str(self.name))
+        return b2luigi.LocalTarget(str(run_dir / "reduced_partial" / f"{self.plot_id}__{int(self.chunk)}.json"))
+
+    def run(self):
+        from giant.analysis import compute_one
+
+        compute_one(str(self.plot_id), analysis_dir(get_spec(), str(self.name)), chunk_index=int(self.chunk))
+
+
+class AnalysisRenderTask(b2luigi.Task):
+    """Merge every plot's chunk partials, then render the PDFs + gallery.
+
+    Always local — this is the only step that imports plotstyle/LaTeX, which
+    the compute worker images don't have.
+    """
+
+    name = b2luigi.Parameter()
+    spec_hash = b2luigi.Parameter()
+    batch_system = "local"
+
+    def requires(self):
+        spec = get_spec()
+        for plot_id, chunk in analysis_jobs(spec, str(self.name)):
+            yield AnalysisComputeTask(
+                name=self.name,
+                spec_hash=self.spec_hash,
+                plot_id=plot_id,
+                chunk=chunk,
+            )
+
+    def output(self):
+        run_dir = analysis_dir(get_spec(), str(self.name))
+        return b2luigi.LocalTarget(str(run_dir / "plots" / "metadata.yaml"))
+
+    def run(self):
+        # render_run joins every plot's chunk partials (merge_all) before
+        # rendering, so this one call is the whole merge+render step.
+        from giant.analysis.render import render_run
+
+        spec = get_spec()
+        render_run(analysis_dir(spec, str(self.name)), run_gallery=spec.analysis(str(self.name)).gallery)
+
+
+class WorkflowTask(b2luigi.WrapperTask):
+    """The whole pipeline: every analysis in the spec, rendered."""
+
+    workflow_name = b2luigi.Parameter()
+
+    def requires(self):
+        spec = get_spec()
+        if not spec.analyses:
+            # A spec with no [[analysis]] still has work to do — fall back to
+            # the deepest tasks it does define.
+            for ro in spec.rollouts:
+                yield RolloutTask(name=ro.name, spec_hash=spec.rollout_hash(ro.name))
+            if not spec.rollouts:
+                for tr in spec.trains:
+                    yield TrainTask(name=tr.name, spec_hash=spec.train_hash(tr.name))
+            return
+        for an in spec.analyses:
+            yield AnalysisRenderTask(name=an.name, spec_hash=spec.analysis_hash(an.name))

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "giant"
-version = "0.3.9"
+version = "0.3.10"
 description = "Geant4 step-function surrogate via conditional flow matching"
 readme = "README.md"
 requires-python = ">=3.12"
@@ -28,7 +28,7 @@ dev = [
     "ty>=0.0.50,<0.1",
     "bump-my-version>=1.2,<2",
     "git-cliff>=2,<3",
-    "giant[convert,analysis,geometry,wandb]",
+    "giant[convert,analysis,geometry,wandb,workflow]",
 ]
 geometry = [
     "scikit-learn>=1.4,<2",
@@ -49,6 +49,11 @@ analysis = [
     # `giant analyze render` step imports it; compute workers never do.
     "plotstyle>=1.0.0",
 ]
+# b2luigi pulls luigi + tenacity; the only sanctioned way to chain a
+# multi-step pipeline (see giant/workflow/).
+workflow = [
+    "b2luigi>=1.0,<2",
+]
 
 [project.scripts]
 giant = "giant.cli:app"
@@ -103,3 +108,12 @@ explicit = true
 name = "larsbogner"
 url = "https://git.larsbogner.de/api/packages/lars/pypi/simple/"
 explicit = true
+
+# luigi builds task constructors from class-level Parameter descriptors, so a
+# static checker sees no keyword parameters at all on `Task(name=..., ...)`.
+# The workflow code is written against that API; nothing else in the repo is.
+[[tool.ty.overrides]]
+include = ["giant/workflow/**", "tests/test_workflow_tasks.py"]
+
+[tool.ty.overrides.rules]
+unknown-argument = "ignore"

tests/test_analysis_run.py

@@ -1,8 +1,7 @@
-"""Tests for the rollout-YAML(s) → run-directory flow, compute, and submit."""
+"""Tests for the rollout-YAML(s) → run-directory flow, compute, and merge."""
 
 from __future__ import annotations
 
-import sys
 from pathlib import Path
 
 import pyarrow.parquet as pq
@@ -11,8 +10,6 @@ import yaml
 
 from giant.analysis import (
     RunMeta,
-    SubmitConfig,
-    catalog_ids,
     compute_one,
     compute_reduced,
     derive_run_dir,
@@ -20,10 +17,8 @@ from giant.analysis import (
     load_rollout_yamls,
     merge_one,
     prep,
-    write_submit,
 )
-from giant.analysis.catalog import get_spec
+from giant.analysis.run import Context
-from giant.analysis.condor import Context
 from giant.analysis.reduced import Partial, Reduced
 from giant.constants import PREDICT_COORD_METADATA_KEY, ROLLOUT_COORD_VALUE
 from tests.test_analysis_reduce import _reference_frame, _rollout_frame
@@ -86,14 +81,6 @@ def _write_two_inputs(tmp_path: Path) -> tuple[Path, Path]:
     return paths[0], paths[1]
 
 
-def _fake_venv(repo_dir: Path) -> None:
-    """Stand in for a `uv sync`'d venv: write_submit checks `.venv/bin/giant` exists."""
-    giant = repo_dir / ".venv" / "bin" / "giant"
-    giant.parent.mkdir(parents=True, exist_ok=True)
-    giant.write_text("#!/bin/bash\n")
-    giant.chmod(0o755)
-
-
 def _prep(rollout_yamls, run_dir: str | Path | None = None, chunks: int = 1, labels=None) -> Path:
     """``prep`` with small test-sized context bins/sampling."""
     return prep(
@@ -313,73 +300,6 @@ def test_compute_reduced_rejects_out_of_range_chunk(tmp_path: Path):
         compute_one("marginal_edep", run_dir, chunk_index=1)
 
 
-def test_write_submit_description(tmp_path: Path):
-    run_dir = _prep([_write_inputs(tmp_path)])
-    _fake_venv(tmp_path)
-    cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path)
-    txt = write_submit(cfg).read_text()
-    assert "universe = docker" in txt
-    assert "docker_image = cverstege/alma9-gridjob" in txt
-    assert "requirements = TARGET.ProvidesETPResources" in txt
-    assert "accounting_group = cms" in txt
-    assert "+RequestWalltime = $(walltime)" in txt
-    assert "queue plotid,chunk,walltime from" in txt
-    jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()]
-    assert [i for i, _, _ in jobs] == catalog_ids()
-    assert all(k == "0" for _, k, _ in jobs)  # n_chunks=1 default
-    assert all(int(w) > 0 for _, _, w in jobs)
-    wrapper = run_dir / "run_compute.sh"
-    assert wrapper.exists() and (wrapper.stat().st_mode & 0o111)
-    body = wrapper.read_text()
-    assert "giant analyze compute-one --id" in body
-    assert "--chunk" in body and "--run-dir" in body
-
-
-def test_write_submit_requires_synced_venv(tmp_path: Path, monkeypatch: pytest.MonkeyPatch):
-    run_dir = _prep([_write_inputs(tmp_path)])
-    cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path)
-    # No `giant` next to the (fake) active interpreter, so this falls through
-    # to repo_dir/.venv/bin/giant, which _write_inputs/_prep also didn't create.
-    monkeypatch.setattr(sys, "executable", str(tmp_path / "not-a-venv" / "bin" / "python"))
-    with pytest.raises(FileNotFoundError, match="uv sync"):
-        write_submit(cfg)
-
-
-def test_write_submit_remote_flag(tmp_path: Path):
-    run_dir = _prep([_write_inputs(tmp_path)])
-    _fake_venv(tmp_path)
-    cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, remote=True)
-    txt = write_submit(cfg).read_text()
-    assert "+RemoteJob = True" in txt
-    assert "ProvidesETPResources" not in txt
-
-
-def test_write_submit_chunks_respect_chunkable(tmp_path: Path):
-    assert get_spec("router_gating").chunkable is False
-    run_dir = _prep([_write_inputs(tmp_path)], chunks=4)
-    _fake_venv(tmp_path)
-    cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4)
-    write_submit(cfg)
-    jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()]
-    counts: dict[str, int] = {}
-    for spec_id, _, _ in jobs:
-        counts[spec_id] = counts.get(spec_id, 0) + 1
-    assert counts["marginal_edep"] == 4
-    assert counts["router_gating"] == 1  # chunkable=False, ignores n_chunks
-
-
-def test_write_submit_rejects_n_chunks_mismatch_with_run_meta(tmp_path: Path):
-    """cfg.n_chunks must match the n_chunks the run_dir was actually prepped
-    with — RunMeta.rows_per_chunk is sized to the prepped value, so a
-    mismatch would otherwise surface as a confusing IndexError deep inside
-    _job_walltimes instead of a clear error here."""
-    run_dir = _prep([_write_inputs(tmp_path)], chunks=2)
-    _fake_venv(tmp_path)
-    cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4)
-    with pytest.raises(ValueError, match="n_chunks"):
-        write_submit(cfg)
-
-
 def test_estimate_runtime_s_scales_with_rows_and_margin():
     from giant.analysis import RUNTIME_SAFETY_MARGIN, estimate_runtime_s
     from giant.analysis.runtime_estimate import _FIXED_OVERHEAD_S
@@ -389,18 +309,3 @@ def test_estimate_runtime_s_scales_with_rows_and_margin():
     large = estimate_runtime_s("marginal_edep", 100_000_000)
     assert small >= (1 + RUNTIME_SAFETY_MARGIN) * _FIXED_OVERHEAD_S
     assert large > small  # bigger chunk -> longer estimate
-
-
-def test_write_submit_walltime_grows_with_chunk_rows(tmp_path: Path):
-    """A chunked run's later job walltimes track that chunk's row count."""
-    from giant.analysis.runtime_estimate import estimate_runtime_s
-
-    run_dir = _prep([_write_inputs(tmp_path)], chunks=2)
-    meta = RunMeta.load(run_dir / "run_meta.json")
-    _fake_venv(tmp_path)
-    cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=2)
-    write_submit(cfg)
-    jobs = {(i, int(k)): int(w) for i, k, w in (line.split(",") for line in (run_dir / "jobs.txt").read_text().split())}
-    for chunk in range(2):
-        expected = estimate_runtime_s("marginal_edep", meta.rows_per_chunk[chunk])
-        assert jobs[("marginal_edep", chunk)] == expected

tests/test_render.py

@@ -206,21 +206,21 @@ def test_render_all_run_gallery_invokes_subprocess(tmp_path: Path, monkeypatch):
     assert kwargs == {"check": True}
 
 
-def test_render_run_glues_condor_run_meta_into_render_all(tmp_path: Path, monkeypatch):
+def test_render_run_glues_run_meta_into_render_all(tmp_path: Path, monkeypatch):
-    from giant.analysis import condor as condor_mod
+    from giant.analysis import run as run_mod
 
     run_dir = tmp_path / "run"
     (run_dir / "reduced").mkdir(parents=True)
 
     merge_calls = []
-    monkeypatch.setattr(condor_mod, "merge_all", lambda rd: merge_calls.append(Path(rd)))
+    monkeypatch.setattr(run_mod, "merge_all", lambda rd: merge_calls.append(Path(rd)))
-    meta = condor_mod.RunMeta(
+    meta = run_mod.RunMeta(
         rollouts=[{"name": "rollout", "path": "rollout.parquet", "plot_meta": {"checkpoint": "ckpt/best.pt"}}],
         reference="reference.parquet",
         run_dir=str(run_dir),
         title="my-run",
     )
-    monkeypatch.setattr(condor_mod.RunMeta, "load", classmethod(lambda cls, p: meta))
+    monkeypatch.setattr(run_mod.RunMeta, "load", classmethod(lambda cls, p: meta))
 
     Reduced("s", "species", "single_hist", "Single", "x", {"edges": [0, 1], "series": {"rollout": [1]}}).save(
         run_dir / "reduced" / "s.json"

tests/test_workflow_spec.py

@@ -0,0 +1,148 @@
+"""Workflow spec parsing, validation, and spec hashes (gitea #83)."""
+
+import pytest
+
+from giant.workflow.spec import (
+    WorkflowSpecError,
+    epoch_milestones,
+    load_spec,
+    parse_spec,
+    spec_hash,
+)
+
+MINIMAL = {
+    "workflow": {"name": "wf", "result_dir": "/tmp/wf"},
+    "condor": {"accounting_group": "cms", "repo_dir": "/work/lbogner/giant"},
+    "dataset": {"steps": "/data/train", "reference": "/data/holdout"},
+    "train": [{"name": "a", "epochs": 3}],
+    "rollout": [{"name": "a", "train": "a"}],
+    "analysis": [{"name": "cmp", "rollouts": ["a"], "chunks": 4}],
+}
+
+
+def _spec(**patch):
+    raw = {k: (v.copy() if isinstance(v, dict) else list(v)) for k, v in MINIMAL.items()}
+    raw.update(patch)
+    return parse_spec(raw)
+
+
+def test_parses_minimal_spec():
+    spec = _spec()
+    assert spec.name == "wf"
+    assert spec.log_dir == "/tmp/wf/logs"  # derived from result_dir
+    assert spec.train("a").epochs == 3
+    assert spec.rollout("a").train == "a"
+    assert spec.analysis("cmp").rollouts == ("a",)
+    # defaults come from the dataclasses, not the file
+    assert spec.geometry.method == "slab"
+    assert spec.condor.docker_image_gpu == "mschnepf/slc7-condocker"
+
+
+def test_example_config_is_valid():
+    spec = load_spec("configs/workflow_example.toml")
+    assert {t.name for t in spec.trains} == {"baseline", "router-balanced"}
+    assert spec.analysis("baseline-vs-router").rollouts == ("baseline", "router-balanced")
+
+
+@pytest.mark.parametrize(
+    "patch, message",
+    [
+        ({"train": [{"name": "a"}, {"name": "a"}]}, "unique"),
+        ({"rollout": [{"name": "r", "train": "nope"}]}, "names no"),
+        ({"analysis": [{"name": "c", "rollouts": ["nope"]}]}, "not defined"),
+        ({"analysis": [{"name": "c", "rollouts": []}]}, "at least one"),
+        ({"analysis": [{"name": "c", "rollouts": ["a"], "chunks": 0}]}, "chunks must be"),
+        ({"train": [{"name": "a", "epochs": 0}]}, "epochs must be"),
+        ({"train": [{"name": "a", "epchs": 3}]}, "unknown key"),
+        ({"geometry": {"methd": "slab"}}, "unknown key"),
+    ],
+)
+def test_validation_errors(patch, message):
+    with pytest.raises(WorkflowSpecError, match=message):
+        _spec(**patch)
+
+
+def test_unknown_top_level_table_rejected():
+    with pytest.raises(WorkflowSpecError, match="unknown top-level"):
+        _spec(nonsense={})
+
+
+def test_missing_required_table_rejected():
+    raw = {k: v for k, v in MINIMAL.items() if k != "dataset"}
+    with pytest.raises(WorkflowSpecError, match=r"missing required \[dataset\]"):
+        parse_spec(raw)
+
+
+def test_unknown_lookup_names_are_explicit():
+    spec = _spec()
+    with pytest.raises(WorkflowSpecError, match="no \\[\\[train\\]\\] named 'zzz'"):
+        spec.train("zzz")
+
+
+def test_hash_is_stable_and_order_independent():
+    a = _spec()
+    b = parse_spec(
+        {
+            "dataset": MINIMAL["dataset"],
+            "condor": MINIMAL["condor"],
+            "workflow": MINIMAL["workflow"],
+            "train": MINIMAL["train"],
+            "rollout": MINIMAL["rollout"],
+            "analysis": MINIMAL["analysis"],
+        }
+    )
+    assert a.train_hash("a") == b.train_hash("a")
+    assert a.analysis_hash("cmp") == b.analysis_hash("cmp")
+    assert len(a.train_hash("a")) == 8
+
+
+def test_hash_changes_with_own_settings():
+    base = _spec()
+    changed = _spec(train=[{"name": "a", "epochs": 4}])
+    assert base.train_hash("a") != changed.train_hash("a")
+
+
+def test_hash_propagates_from_parents():
+    """A dataset change must move every downstream task's directory."""
+    base = _spec()
+    changed = _spec(dataset={"steps": "/data/other", "reference": "/data/holdout"})
+    assert base.train_hash("a") != changed.train_hash("a")
+    assert base.rollout_hash("a") != changed.rollout_hash("a")
+    assert base.analysis_hash("cmp") != changed.analysis_hash("cmp")
+
+    # ... and so must a change to a training the analysis transitively uses.
+    retrained = _spec(train=[{"name": "a", "epochs": 9}])
+    assert retrained.analysis_hash("cmp") != base.analysis_hash("cmp")
+    # while an unrelated knob on the analysis leaves the training alone
+    rebinned = _spec(analysis=[{"name": "cmp", "rollouts": ["a"], "chunks": 4, "bins": 99}])
+    assert rebinned.train_hash("a") == base.train_hash("a")
+    assert rebinned.analysis_hash("cmp") != base.analysis_hash("cmp")
+
+
+def test_warm_cache_hash_ignores_epochs():
+    """Epoch count doesn't change the setup cache, so it must not re-warm it."""
+    base = _spec()
+    longer = _spec(train=[{"name": "a", "epochs": 50}])
+    assert base.warm_cache_hash("a") == longer.warm_cache_hash("a")
+    other_cfg = _spec(train=[{"name": "a", "epochs": 3, "config": "configs/router.toml"}])
+    assert base.warm_cache_hash("a") != other_cfg.warm_cache_hash("a")
+
+
+def test_spec_hash_expands_dataclasses():
+    spec = _spec()
+    assert spec_hash(spec.dataset) == spec_hash(spec.dataset)
+    assert spec_hash(spec.dataset) != spec_hash(spec.geometry)
+
+
+@pytest.mark.parametrize(
+    "epochs, per_job, expected",
+    [
+        (3, 1, [1, 2, 3]),
+        (10, 3, [3, 6, 9, 10]),
+        (9, 3, [3, 6, 9]),
+        (1, 5, [1]),
+    ],
+)
+def test_epoch_milestones(epochs, per_job, expected):
+    spec = _spec(train=[{"name": "a", "epochs": epochs, "epochs_per_job": per_job}])
+    assert epoch_milestones(spec.train("a")) == expected

tests/test_workflow_tasks.py

@@ -0,0 +1,172 @@
+"""Workflow task graph: dependencies, output paths, condor settings (gitea #83)."""
+
+import pytest
+
+from giant.analysis.catalog import catalog_ids, get_spec as get_plot_spec
+from giant.workflow import tasks
+from giant.workflow.spec import parse_spec
+
+CONDOR = {
+    "accounting_group": "cms",
+    "repo_dir": "/work/lbogner/giant",
+    "env_script": "/work/lbogner/giant/condor_env.sh",
+}
+
+RAW = {
+    "workflow": {"name": "wf", "result_dir": "/results/wf"},
+    "condor": CONDOR,
+    "dataset": {"steps": "/data/train", "reference": "/data/holdout"},
+    "train": [
+        {"name": "base", "epochs": 3, "gpu_memory_mb": 20000},
+        {"name": "router", "epochs": 2},
+    ],
+    "rollout": [
+        {"name": "base", "train": "base"},
+        {"name": "router", "train": "router"},
+    ],
+    "analysis": [{"name": "cmp", "rollouts": ["base", "router"], "chunks": 4}],
+}
+
+
+@pytest.fixture
+def spec():
+    s = parse_spec(RAW)
+    tasks.set_spec(s)
+    return s
+
+
+def _requires(task):
+    return list(task.requires() or [])
+
+
+def test_epoch_chain_is_linear_and_rooted_at_warm_cache(spec):
+    h = spec.train_hash("base")
+    third = tasks.TrainEpochTask(name="base", spec_hash=h, milestone=3)
+    second = _requires(third)
+    assert [type(t) for t in second] == [tasks.TrainEpochTask]
+    assert second[0].milestone == 2
+    first = _requires(second[0])[0]
+    assert first.milestone == 1
+    root = _requires(first)
+    assert [type(t) for t in root] == [tasks.WarmCacheTask]
+    # the warm cache is keyed by its own hash, not the training's
+    assert root[0].spec_hash == spec.warm_cache_hash("base")
+
+
+def test_epoch_task_outputs_last_pt_per_milestone(spec):
+    h = spec.train_hash("base")
+    path = tasks.TrainEpochTask(name="base", spec_hash=h, milestone=2).output().path
+    assert path == f"/results/wf/train_epoch/name=base/spec_hash={h}/epochs=2/last.pt"
+
+
+def test_train_task_requires_final_epoch_and_publishes_canonical_outputs(spec):
+    h = spec.train_hash("base")
+    train = tasks.TrainTask(name="base", spec_hash=h)
+    (dep,) = _requires(train)
+    assert isinstance(dep, tasks.TrainEpochTask) and dep.milestone == 3
+    out = train.output()
+    assert set(out) == {"best.pt", "last.pt", "metrics.csv"}
+    assert out["best.pt"].path == f"/results/wf/train/name=base/spec_hash={h}/best.pt"
+    # local: it only copies files around, no reason to queue a job for it
+    assert train.batch_system == "local"
+
+
+def test_rollout_requires_training_geometry_and_reference(spec):
+    ro = tasks.RolloutTask(name="base", spec_hash=spec.rollout_hash("base"))
+    deps = _requires(ro)
+    assert [type(d) for d in deps] == [tasks.TrainTask, tasks.GeometryOracleTask, tasks.DatasetTask]
+    assert deps[0].name == "base"
+    assert deps[2].path == "/data/holdout"
+    out = ro.output()
+    assert out["rollout.yaml"].path.endswith("rollout.yaml")
+    # the sidecar sits next to the parquet — the deterministic path
+    # `giant rollout --out` now produces
+    assert out["rollout.yaml"].path[: -len(".yaml")] == out["rollout.parquet"].path[: -len(".parquet")]
+
+
+def test_analysis_prep_requires_every_named_rollout(spec):
+    prep = tasks.AnalysisPrepTask(name="cmp", spec_hash=spec.analysis_hash("cmp"))
+    deps = _requires(prep)
+    assert [d.name for d in deps] == ["base", "router"]
+    assert all(isinstance(d, tasks.RolloutTask) for d in deps)
+    assert prep.batch_system == "local"
+
+
+def test_compute_job_enumeration_collapses_non_chunkable_specs(spec):
+    jobs = tasks.analysis_jobs(spec, "cmp")
+    non_chunkable = [i for i in catalog_ids() if not get_plot_spec(i).chunkable]
+    expected = (len(catalog_ids()) - len(non_chunkable)) * 4 + len(non_chunkable)
+    assert len(jobs) == expected
+    assert non_chunkable, "expected some chunkable=False specs in the catalog"
+    for spec_id in non_chunkable:
+        assert [c for i, c in jobs if i == spec_id] == [0]
+
+
+def test_compute_output_matches_the_on_disk_contract(spec):
+    h = spec.analysis_hash("cmp")
+    task = tasks.AnalysisComputeTask(name="cmp", spec_hash=h, plot_id="event_mean_length", chunk=2)
+    assert task.output().path == (
+        f"/results/wf/analysis/name=cmp/spec_hash={h}/reduced_partial/event_mean_length__2.json"
+    )
+    (dep,) = _requires(task)
+    assert isinstance(dep, tasks.AnalysisPrepTask)
+
+
+def test_render_requires_every_compute_job_and_runs_locally(spec):
+    render = tasks.AnalysisRenderTask(name="cmp", spec_hash=spec.analysis_hash("cmp"))
+    deps = _requires(render)
+    assert len(deps) == len(tasks.analysis_jobs(spec, "cmp"))
+    assert render.batch_system == "local"  # the only step importing plotstyle/LaTeX
+    assert render.output().path.endswith("/plots/metadata.yaml")
+
+
+def test_workflow_task_wraps_every_analysis(spec):
+    deps = _requires(tasks.WorkflowTask(workflow_name="wf"))
+    assert [(type(d), d.name) for d in deps] == [(tasks.AnalysisRenderTask, "cmp")]
+
+
+def test_workflow_without_analysis_falls_back_to_rollouts():
+    raw = {k: v for k, v in RAW.items() if k != "analysis"}
+    tasks.set_spec(parse_spec(raw))
+    deps = _requires(tasks.WorkflowTask(workflow_name="wf"))
+    assert [type(d) for d in deps] == [tasks.RolloutTask, tasks.RolloutTask]
+
+
+def test_gpu_settings_carry_remote_ceph_and_pins(spec):
+    settings = tasks.TrainEpochTask(name="base", spec_hash=spec.train_hash("base"), milestone=1).htcondor_settings
+    assert settings["+RemoteJob"] == "True"
+    assert settings["RequestGPUs"] == 1
+    assert "TARGET.ProvidesEtpCeph =?= True" in settings["requirements"]
+    assert "TARGET.GPUs_GlobalMemoryMb >= 20000" in settings["requirements"]
+    assert settings["accounting_group"] == "cms"
+    assert settings["docker_image"] == "mschnepf/slc7-condocker"
+
+
+def test_cpu_settings_used_for_analysis_compute(spec):
+    task = tasks.AnalysisComputeTask(
+        name="cmp", spec_hash=spec.analysis_hash("cmp"), plot_id="event_mean_length", chunk=0
+    )
+    settings = task.htcondor_settings
+    assert settings["docker_image"] == "cverstege/alma9-gridjob"
+    assert "RequestGPUs" not in settings
+    # no run_meta.json yet (prep hasn't run), so no walltime is claimed
+    assert "+RequestWalltime" not in settings
+
+
+def test_cpu_settings_local_files_use_provides_etp_resources():
+    raw = {**RAW, "condor": {**CONDOR, "remote": False}}
+    spec = parse_spec(raw)
+    tasks.set_spec(spec)
+    settings = tasks.GeometryOracleTask(spec_hash=spec.geometry_hash()).htcondor_settings
+    assert settings["requirements"] == "TARGET.ProvidesETPResources"
+    assert "+RemoteJob" not in settings
+
+
+def test_missing_dataset_fails_immediately(spec):
+    with pytest.raises(FileNotFoundError, match="/ceph"):
+        tasks.DatasetTask(path="/data/train").complete()
+
+
+def test_dataset_that_exists_is_complete(tmp_path, spec):
+    (tmp_path / "steps.parquet").write_text("")
+    assert tasks.DatasetTask(path=str(tmp_path / "steps.parquet")).complete()

uv.lock

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 ]
 
+[[package]]
+name = "b2luigi"
+version = "1.2.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cachetools" },
+    { name = "colorama" },
+    { name = "gitpython" },
+    { name = "jinja2" },
+    { name = "luigi" },
+    { name = "parse" },
+    { name = "setuptools" },
+    { name = "tenacity" },
+    { name = "webdavclient3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e5/5d/0c3e0602b6cf80a2cfebbe54c330f3623b4de227c2b0f8cbff437dc8d62c/b2luigi-1.2.9.tar.gz", hash = "sha256:3f6734b06970cd5bf6bb62c45c095e9c02b6e283a45b76a29ffabdeaa8fce0c0", size = 786252, upload-time = "2026-04-17T16:38:23.316Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/82/1a8d3c20bc235e665300c42783eb8bf9f2aac49c7eae644273191e5e01e8/b2luigi-1.2.9-py3-none-any.whl", hash = "sha256:9ca28b4203f5946394b609da432e53b3c9e35540c840bdc6497cce6ab4c82e2e", size = 115103, upload-time = "2026-04-17T16:38:21.044Z" },
+]
+
 [[package]]
 name = "bracex"
 version = "3.0.1"
@@ -176,6 +196,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b3/0b/5885530f79d4400368b9d4dcb9b39274c0d52e633f7871e7fc6feceea1e3/bump_my_version-1.5.1-py3-none-any.whl", hash = "sha256:df3e2989d0d7fe704718feb24a5880f089b6b6369e427a4445b89c3adebfcff1", size = 65090, upload-time = "2026-08-06T14:26:37.083Z" },
 ]
 
+[[package]]
+name = "cachetools"
+version = "7.1.7"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/70/d2/47e8bc06fe2a06d3f5bdf20f1126ab66c4e99dc48d940e7ba873f7ac7131/cachetools-7.1.7.tar.gz", hash = "sha256:a3e2a00b14d8f8a6b70c1dae7b4685e7ad3bc965c5b42124a2d6ce895da6cf50", size = 40680, upload-time = "2026-08-01T21:20:40.434Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e4/d8/767faeda872075724b95dd675466a645f1b92aadcdcf2d1429dcfd76c176/cachetools-7.1.7-py3-none-any.whl", hash = "sha256:ef98ef375ad188819ef2f9b3645e3987f4b8c5b7550e436ad998c2de78296df0", size = 16830, upload-time = "2026-08-01T21:20:38.977Z" },
+]
+
 [[package]]
 name = "certifi"
 version = "2026.7.22"
@@ -605,6 +634,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/05/7f/798705f5296a58ca505d600456748d1be48078eac8a7050d8a98bc9edb89/decorator-5.3.1-py3-none-any.whl", hash = "sha256:f47fe6fdbd2edd623ecfe36875d37aba411624e2670dd395dddae1358689bb3c", size = 10365, upload-time = "2026-05-18T06:03:26.517Z" },
 ]
 
+[[package]]
+name = "docutils"
+version = "0.23"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/39/a4/5180d9afc57e8fca05601dd652bdff19604c218814037fe90ffc7625a50a/docutils-0.23.tar.gz", hash = "sha256:746f5060322511280a1e50eb76846ed6bf2342984b2ac04dc42caa1a8d78799e", size = 2303823, upload-time = "2026-05-27T17:41:06.934Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/32/91/30151a39f7570f448ed84529390628a651d7f27c87d73c9b887f8189695e/docutils-0.23-py3-none-any.whl", hash = "sha256:25d013af9bf23bc1c7b2b093dff4208166c53a94786c9e447808335ef1185fea", size = 634701, upload-time = "2026-05-27T17:40:58.442Z" },
+]
+
 [[package]]
 name = "executing"
 version = "2.2.1"
@@ -675,7 +713,7 @@ wheels = [
 
 [[package]]
 name = "giant"
-version = "0.3.9"
+version = "0.3.10"
 source = { editable = "." }
 dependencies = [
     { name = "numpy" },
@@ -728,10 +766,14 @@ geometry = [
 wandb = [
     { name = "wandb" },
 ]
+workflow = [
+    { name = "b2luigi" },
+]
 
 [package.metadata]
 requires-dist = [
     { name = "awkward", marker = "extra == 'convert'", specifier = ">=2.6,<3" },
+    { name = "b2luigi", marker = "extra == 'workflow'", specifier = ">=1.0,<2" },
     { name = "bump-my-version", marker = "extra == 'dev'", specifier = ">=1.2,<2" },
     { name = "giant", extras = ["convert", "analysis", "geometry", "wandb"], marker = "extra == 'dev'" },
     { name = "git-cliff", marker = "extra == 'dev'", specifier = ">=2,<3" },
@@ -757,7 +799,7 @@ requires-dist = [
     { name = "uproot", marker = "extra == 'convert'", specifier = ">=5.3,<6" },
     { name = "wandb", marker = "extra == 'wandb'", specifier = ">=0.16,<1" },
 ]
-provides-extras = ["cpu", "cuda", "dev", "geometry", "wandb", "convert", "analysis"]
+provides-extras = ["cpu", "cuda", "dev", "geometry", "wandb", "convert", "analysis", "workflow"]
 
 [[package]]
 name = "git-cliff"
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 ]
 
+[[package]]
+name = "gitdb"
+version = "4.0.12"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "smmap" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/72/94/63b0fc47eb32792c7ba1fe1b694daec9a63620db1e313033d18140c2320a/gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571", size = 394684, upload-time = "2025-01-02T07:20:46.413Z" }
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+]
+
+[[package]]
+name = "gitpython"
+version = "3.1.60"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "gitdb" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/84/14/e6b1a48d831755a53c2029351fcef82e70db4a08f338daefe29d8d0cf31c/gitpython-3.1.60.tar.gz", hash = "sha256:e936431879fa85581b4311fa63492ea52251909e2d655b6529c704c904ddcc24", size = 230793, upload-time = "2026-08-25T18:33:46.102Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/71/63/ba28697918b7c190af9f3f21940d03e8814e25dd4ddd39d6929f3a553995/gitpython-3.1.60-py3-none-any.whl", hash = "sha256:39548bffb8fa0f3a548133348868bb4838e79d73283052207dc97781a569b6b4", size = 221893, upload-time = "2026-08-25T18:33:44.75Z" },
+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
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+]
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+version = "3.7.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "python-daemon" },
+    { name = "python-dateutil" },
+    { name = "tenacity" },
+    { name = "tornado" },
+    { name = "typing-extensions" },
+]
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