Merge remote-tracking branch 'origin/master' into condor-gpu-train-rollout

# Conflicts:
#	CLAUDE.md

CLAUDE.md

@@ -10,12 +10,15 @@ 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 geometry  # add scikit-learn for the geometry oracle (giant rollout)
 pytest               # run tests
+giant new-run --hidden-dim 512 --lr 3e-4 --comment "..."   # scaffold a config.toml + run dir for a new training run
 giant train path/to/steps.parquet --mode flow   # train (flow matching)
 giant train path/to/steps.parquet --mode ddpm   # train (DDPM baseline)
 giant train path/to/steps.parquet --mode wgan   # train (WGAN-GP, single-pass eval; implemented, not yet tested)
 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-submit path/to/steps.parquet --config run/config.toml --accounting-group cms   # train as a remote-GPU HTCondor job (TOpAS/NEMO2)
 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 rollout-submit path/to/steps.parquet --checkpoint ckpt/best.pt --geometry oracle.pkl --accounting-group cms  # rollout as a remote-GPU HTCondor job
 giant analyze submit rollout.yaml --accounting-group cms   # parallel rollout-vs-reference analysis on HTCondor
 giant analyze render <run_dir> --gallery                   # render PDFs + HTML gallery (run_dir from prep/submit)
 dwarf --help                                     # dataset/tooling CLI: convert, migrate, bump-gen,
@@ -76,6 +79,8 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep
 
 **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.
 
+**GPU HTCondor submission** (`giant/condor.py`, `giant train-submit`/`giant rollout-submit`): submits a single `giant train`/`giant rollout` invocation as a remote-GPU HTCondor job (ETP's TOpAS V100/A100 or NEMO2 L40S workers — see the ETP HTCondor wiki's "GPU Jobs" section). GPU workers are remote-only, so submit descriptions always carry `+RemoteJob = True`/`RequestGPUs`, and reach `/ceph` via `requirements = TARGET.ProvidesEtpCeph =?= True` rather than the local-only `TARGET.ProvidesETPResources` `giant analyze submit`'s CPU jobs use — this means the `giant` checkout submitting these jobs (and its `uv sync --extra cuda` venv) needs to live under `/ceph`, not `/work`/`/home`. Each submission writes a self-contained `condor/` (train, nested in `--out`) or `condor_<tag>/` (rollout, sibling to the output parquet) directory: `run.sh` (the wrapper actually executed — for training this re-checks `out_dir/last.pt` on every invocation and adds `--resume`, so a preempted/retried job resumes rather than restarting), `job.sub`, and `submission.json` (a `CondorJobMeta`: accounting group, GPU request, docker image, the exact command, and the assigned cluster id once known) — enough on its own to `condor_history <cluster_id>` a run later. `train-submit` requires `--config` (a TOML) rather than mirroring `train`'s hyperparameter flags, since the config file is already the reproducible source of truth (`giant/config.py:save_config`/`merge_cli_overrides`) and passing the same one on every retry is what keeps a resumed run's architecture from drifting. `giant new-run` scaffolds that TOML: resolves a base `--config` (or built-in defaults) plus a handful of override flags into a fresh run dir via `gconfig.default_out_dir` (hyperparam-named, uuid-tagged so repeat/same-day runs never collide), and prints the matching `giant train`/`giant train-submit` invocations — `giant train` overwrites this scaffolded `config.toml` in place once it actually runs, filling in the real dataset-derived meta section.
+
 ## 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).

README.md

@@ -2,15 +2,19 @@
 
 **G**eant4 **I**nference via **A**utoregressive **N**eural s**T**ep surrogate — a play on *Geant4* and the step function being the computationally heaviest part of the simulation.
 
-Proof-of-concept surrogate model for the Geant4 step function. Given a pre-step particle state, the model samples a physically plausible post-step outcome — the primary's continuation plus the secondary particles it produces — replacing the stochastic Geant4 physics engine with a trained conditional generative model.
+Conditional generative surrogate for the Geant4 step function. Given a pre-step particle state, the model samples a physically plausible post-step outcome — the primary's continuation plus the variable-length list of secondary particles it produces — replacing the stochastic Geant4 physics engine with a trained generative model. A trained checkpoint autoregressively rolls out full showers, stepping each primary and pushing secondaries as new tracks.
 
 Training is driven entirely from parquet files of the miniCaloSim steps tree. No Geant4 runtime dependency.
 
 ## Architecture
 
-A **two-stage conditional flow matching** model (Lipman et al. 2022): a small MLP learns a vector field mapping noise → step outcomes in ~10 ODE steps per sample. Falls back to DDPM for comparison.
+A **two-stage model**, both stages checkpointed together, with a choice of generative mode per stage (`--mode`):
 
-**Stage 1 — primary (9D, diffused):**
+- **`flow`** (default) — conditional flow matching (Lipman et al. 2022): an MLP learns a vector field mapping noise → step outcomes, sampled via ODE integration in ~10 steps.
+- **`ddpm`** — a standard denoising diffusion baseline for comparison (`giant/model/schedule.py:CosineSchedule`).
+- **`wgan`** — a single-pass Wasserstein-GAN-GP generator/critic (`giant/model/wgan.py`), trading iterative sampling for one forward pass, evaluated against a ~10× native-Geant4 latency budget.
+
+**Stage 1 — primary (9D, `giant/constants.py:LOCAL_TARGET_NAMES`):**
 
 | Index | Variable | Encoding |
 |-------|----------|----------|
@@ -19,13 +23,20 @@ A **two-stage conditional flow matching** model (Lipman et al. 2022): a small ML
 | 3–5 | `post_dir` in local frame | unit vector |
 | 6–8 | `travel_dir` (`post_pos − pre_pos`) in local frame | unit vector |
 
-The two energy logits decode via softmax over `[edep_logit, sec_logit, 0]` × `pre_E`, so `edep + e_sec + post_E == pre_E` exactly — **energy conservation is built into the parametrization**, not left to the loss. Stage 1 also has a classifier head predicting the number of secondaries `n_sec ∈ {0..15}` from the conditioning alone.
+The two energy logits decode via softmax over `[edep_logit, sec_logit, 0]` × `pre_E`, so `edep + e_sec + post_E == pre_E` exactly — **energy conservation is built into the parametrization**, not left to the loss (`energy_simplex_decode`). Stage 1 also has a classifier head (`predict_n_sec`) predicting the number of secondaries `n_sec ∈ {0..K_MAX}` (`K_MAX = 15`) from the conditioning alone, no diffusion noise involved.
 
 Both `post_dir` and `travel_dir` are expressed in the coordinate frame where `pre_dir = ẑ`, making the scattering distribution nearly azimuthally symmetric. `post_pos` itself is not a raw target — it's reconstructed at inference as `pre_pos + step_length * world_frame(travel_dir)`, so the two stay consistent by construction instead of being learned (and potentially diverging) independently.
 
-**Stage 2 — secondaries (`SecondaryDecoder`):** conditioned on the pre-step state *and* the Stage-1 outcome, a second flow net generates all `K_MAX = 15` secondary slots at once. Each slot carries a stick-breaking energy fraction, a local-frame direction, and a continuous particle-type embedding (snapped to the nearest PDG at inference), ordered by descending energy; slots beyond the predicted `n_sec` are masked. The secondary energies are a stick-breaking partition of the `e_sec` budget from Stage 1, so the full chain conserves energy. Each secondary's momentum is reconstructed afterward from `(energy, direction, species)` rather than predicted.
+**Stage 2 — secondaries (`SecondaryDecoder`):** conditioned on the pre-step state *and* the Stage-1 outcome, a second net generates all `K_MAX` secondary slots at once — `(stick-breaking energy logit, local-frame direction, log-mass, charge)` per slot, 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, so the whole chain conserves energy. A secondary's mass/charge are regressed directly against its ground-truth PDG code's physical values (`giant.particles.particle_mass_charge`) and used as-is at inference — including for its own conditioning if it takes further steps in a rollout. No snapping to a known PDG code happens in the model path; `giant.particles.nearest_known_pdg` is a reporting-only lookup used to populate a nominal `pdg` label on output rows.
 
-**Conditioning:** PDG code (embedding), pre-step position, log(pre-energy), pre-step direction, material (embedding), layer ID. (`n_sec` / `e_sec` are outputs now, not inputs.)
+**Conditioning (`--conditioning`, per-checkpoint):** pre-step position, log(pre-energy), pre-step direction, layer ID, plus particle/material physical properties — mass/charge (`giant/particles.py`) and Z_eff/A_eff/density/X0/λ_int (`giant/materials.py`). Two mutually exclusive modes:
+
+- **`physical`** (default) — the physical-property columns are routed through small MLPs, computable for any PDG code / material, letting the surrogate generalize to species/materials outside the training menu.
+- **`embedding`** — the original design: a learned `nn.Embedding` per PDG code / material, kept as a generalization-comparison baseline (memorizes the training menu).
+
+`n_sec` and `e_sec` are model outputs, not conditioning inputs — a rollout is self-contained and never injects ground truth.
+
+**Mixture-of-experts routing (`--router`, opt-in):** `giant/model/network.py` also implements a pluggable `Router` contract (`ROUTER_REGISTRY`: `energy`, `pdg`, `process`, plus a `composed` router combining several axes) that splits `DenoisingMLP`/`SecondaryDecoder` into per-expert trunks (`RoutedDenoisingMLP`/`RoutedSecondaryDecoder`), soft-gated in training and hard-dispatched at eval. See `--router-type`/`--n-experts`/`--router-axis` on `giant train`/`giant new-run`.
 
 ## Roadmap
 
@@ -33,11 +44,13 @@ Both `post_dir` and `travel_dir` are expressed in the coordinate frame where `pr
 
 **Phase 2 (implemented — baseline):** the two-stage model above predicts `n_sec` and each secondary's energy, direction, and species jointly with the primary, so a shower rollout is fully self-contained.
 
-**Next directions:** faster-eval architectures against a ~10× native-Geant4 budget (Wasserstein-GAN, mixture-of-experts routing tree), a multi-material sampling-calorimeter dataset, and physical-property conditioning over learned embeddings.
+**Physical-property conditioning (implemented):** replaces learned PDG/material embeddings with physical-property MLPs (see above); Stage 2 predicts a secondary's mass/charge directly instead of a snapped species embedding. Not yet done: the held-out-material/species generalization comparison against the `embedding` baseline.
+
+**In progress:** a WGAN-GP single-pass mode (`--mode wgan`) and mixture-of-experts routing (`--router`) are implemented and under evaluation as faster-eval alternatives to iterative flow/DDPM sampling, against a ~10× native-Geant4 latency budget. A multi-material sampling-calorimeter dataset is the target for both the routing/WGAN evaluation and the physical-property generalization comparison.
 
 ## Data
 
-Input: parquet files produced by [miniCaloSim](https://gitlab.etp.kit.edu/lbogner/minicalosim), or converted from a ROOT file via `uv run dwarf convert`. Each row is one Geant4 step. Train/val split is by `event_id` (not row shuffle) to avoid leaking correlated steps from the same shower.
+Input: parquet files produced by [miniCaloSim](https://gitlab.etp.kit.edu/lbogner/minicalosim), or converted from a ROOT file via `dwarf convert`. Each row is one Geant4 step. Train/val split is by `event_id` (not row shuffle) to avoid leaking correlated steps from the same shower.
 
 ## Project structure
 
@@ -49,21 +62,33 @@ giant/
 │   │   ├── transforms.py   # log transforms, local-frame rotation, energy simplex, secondary encode/decode
 │   │   └── dataset.py      # StepsDataset / StreamingStepsDataset (PyTorch)
 │   ├── model/
-│   │   ├── network.py      # SinusoidalEmbedding, ConditionEncoder, DenoisingMLP, SecondaryDecoder
+│   │   ├── network.py      # ConditionEncoder, DenoisingMLP, SecondaryDecoder, Router/MoE, WGAN generator/critic
-│   │   └── schedule.py     # CosineSchedule (DDPM) and flow matching utilities
+│   │   ├── schedule.py     # CosineSchedule (DDPM) and flow matching utilities
+│   │   └── wgan.py         # WGAN-GP gradient penalty / critic / generator losses
 │   ├── constants.py        # output/conditioning dims, K_MAX, secondary slot layout, schema keys
+│   ├── particles.py        # PDG → (mass, charge) decode, incl. nuclear/ion codes; nearest-known-PDG lookup
+│   ├── materials.py        # material name → (Z_eff, A_eff, density, X0, λ_int)
 │   ├── config.py           # default hyperparameters, TOML config merging, device autodetect
 │   ├── pipeline.py         # builds datasets/normalizers and kicks off a training run
 │   ├── train.py            # two-stage training loop, checkpointing, graceful shutdown
-│   ├── sample.py           # DDPM / DDIM / flow matching samplers + secondary sampling
+│   ├── sample.py           # DDPM / DDIM / flow matching / WGAN samplers + secondary sampling
 │   ├── geometry.py         # GeometryOracle: position → (material, layer_id, escaped) for rollout
 │   ├── rollout.py          # autoregressive shower rollout driver
 │   ├── validate.py         # step-level marginal + KL-divergence validation
-│   ├── analysis.py         # step- and shower-level diagnostics: marginals, correlations, rollout observables
+│   ├── condor.py           # HTCondor submission for `train-submit`/`rollout-submit` (remote GPU workers)
-│   └── cli.py              # `giant train` / `predict` / `rollout` Typer app
+│   ├── analysis/           # rollout-vs-reference analysis pipeline (see `giant analyze` below)
-├── scripts/                            # dataset/tooling logic, unified under the `dwarf` CLI (`uv run dwarf --help`)
+│   │   ├── sources.py      #   canonical LazyFrames + secondary view
+│   │   ├── 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
+│   │   ├── condor.py       #   prep / compute-one / submit-description plumbing
+│   │   └── render.py       #   PDFs + HTML gallery (only module importing plotstyle/LaTeX)
+│   └── cli.py               # `giant train` / `predict` / `rollout` / `new-run` / `*-submit` / `analyze` Typer app
+├── scripts/                            # 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, hparam-scan
+│   │                                    #   update-manifest, create-manifest, make-root, build-geometry-oracle,
+│   │                                    #   hparam-scan
 │   ├── steps_to_parquet.py             # ROOT → parquet conversion (uproot/awkward/polars) — `dwarf convert`
 │   ├── steps_to_parquet_parallel.py    # fan out conversion over several ROOT files — `dwarf convert --jobs N`
 │   ├── migrate_geant_steps.py          # one-time move into the raw/processed/pools/derived layout — `dwarf migrate`
@@ -80,27 +105,48 @@ giant/
 ```bash
 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
 ```
 
-`cpu` and `cuda` are mutually exclusive extras selecting the torch build; plain `uv sync` installs no torch at all. See `CLAUDE.md` for details.
+`cpu` and `cuda` are mutually exclusive extras selecting the torch build (pinned to 2.3.x); plain `uv sync` installs no torch at all. See `CLAUDE.md` for details.
 
 ## Training, prediction, and rollout
 
 ```bash
-giant train path/to/steps.parquet --mode flow
+giant new-run --hidden-dim 512 --lr 3e-4 --comment "..."   # scaffold a config.toml + run dir for a new run
+giant train path/to/steps.parquet --mode flow               # train (flow matching; also --mode ddpm / wgan)
 giant predict path/to/steps.parquet --checkpoint checkpoints/.../best.pt
 
 # Full-shower rollout needs a geometry oracle (position → material/layer_id):
-uv sync --extra cpu --extra geometry
 dwarf build-geometry-oracle path/to/steps.parquet --out oracle.pkl
 giant rollout path/to/steps.parquet --checkpoint checkpoints/.../best.pt --geometry oracle.pkl
 ```
 
 `train`/`predict` accept a TOML config file (`--config`) and CLI overrides for hyperparameters; see `--help` on any command for the full option list. `giant rollout` seeds showers from the highest-energy entry step per event, then autoregressively steps the two-stage model to completion — pushing secondaries as new tracks and looking up `material`/`layer_id` from the oracle at each step. Tracks terminate on energy cutoff, per-track max steps, detector escape, or natural end; energy is deposited locally on every stop except escape (leakage), so showers conserve energy by construction.
 
-## Validation
+### Remote-GPU training and rollout (HTCondor)
 
-`giant.validate.validate_marginals` runs step-level marginal and KL-divergence checks during training (`--validate-every`). For deeper diagnostics on a trained checkpoint — stratified marginals, correlation structure, physical-constraint violations, and shower-level rollout observables (longitudinal/transverse profiles, PDG energy shares) — see `giant.analysis`.
+`giant train-submit`/`giant rollout-submit` run a `giant train`/`giant rollout` invocation as a remote-GPU HTCondor job (ETP's TOpAS V100/A100 or NEMO2 L40S workers). GPU workers are remote-only and reach `/ceph` (not `/work`/`/home`) — the checkout submitting these jobs, and its venv, must live under `/ceph`.
+
+```bash
+giant train-submit path/to/steps.parquet --config run/config.toml --accounting-group cms
+giant rollout-submit path/to/steps.parquet --checkpoint ckpt/best.pt --geometry oracle.pkl --accounting-group cms
+```
+
+Each submission writes a self-contained `condor/`/`condor_<tag>/` directory (`run.sh`, `job.sub`, `submission.json`). Training jobs re-check `out_dir/last.pt` on every invocation and resume automatically if preempted. `train-submit` requires `--config` (rather than mirroring `train`'s hyperparameter flags) since the TOML is the reproducible source of truth, and passing the same one on every retry is what keeps a resumed run's architecture from drifting.
+
+## Validation and analysis
+
+`giant.validate.validate_marginals` runs step-level marginal and KL-divergence checks during training (`--validate-every`).
+
+For deeper rollout-vs-reference diagnostics — marginals stratified by energy/pdg/material, per-event totals, shower profiles, species share, leakage, and secondaries — `giant analyze` runs a streaming compute/render pipeline against a `giant rollout` YAML sidecar:
+
+```bash
+giant analyze submit rollout.yaml --accounting-group cms   # prep + one HTCondor job per plot (compute only)
+giant analyze render <run_dir> --gallery                   # local: styled PDFs + HTML gallery (needs LaTeX)
+```
+
+`<run_dir>` is derived next to the rollout parquet (`analyze prep`/`submit` print it). Compute jobs are polars/numpy only; only `render` imports plotstyle/LaTeX, so it always runs locally.
 
 ## Development
 

giant/cli.py

@@ -1,9 +1,10 @@
 from collections import Counter
-from datetime import date, datetime, timezone
+from datetime import datetime, timezone
 from enum import Enum
 import math
 from pathlib import Path
 import re
+import shlex
 from typing import Optional
 import uuid as uuid_mod
 
@@ -95,6 +96,30 @@ def _batch_size_estimate_dims(model_cfg: dict, training: bool) -> tuple[int, int
     return model_cfg["hidden_dim"], model_cfg["n_blocks"]
 
 
+def _router_cli_overrides(
+    router: bool | None,
+    router_type: str | None,
+    n_experts: int | None,
+    router_axis: list[str] | None,
+) -> dict[str, object]:
+    """Build the `model.router` override dict from `--router`/`--router-type`/
+    `--n-experts`/`--router-axis` flags (empty if none were given). Shared by
+    `train` and `new-run` so both resolve router overrides identically.
+    """
+    cli_router: dict[str, object] = {
+        k: v
+        for k, v in {
+            "enabled": router,
+            "type": router_type,
+            "n_experts": n_experts,
+        }.items()
+        if v is not None
+    }
+    if router_axis:
+        cli_router.update(_parse_router_axis_flags(router_axis))
+    return cli_router
+
+
 def _coerce_scalar(value: str) -> object:
     """Best-effort str -> bool/int/float, else leave as str.
 
@@ -135,15 +160,21 @@ def _parse_router_axis_flags(specs: list[str]) -> dict[str, object]:
 _CEPH_PREDICTIONS = Path("/ceph/lbogner/geant_steps/predictions")
 
 
-def _resolve_prediction_output(data: Path, out: Path | None) -> tuple[Path, Path, str]:
+def _resolve_prediction_output(
+    data: Path, out: Path | None, pred_uuid: str | None = None
+) -> tuple[Path, Path, str]:
     """Return (out_path, resolved_dataset_path, pred_uuid).
 
     When *out* is None the output path is derived from *data*:
     - under /ceph/ → fixed central store with a UUID filename
     - elsewhere   → sibling of *data* with a UUID filename
+
+    *pred_uuid* can be pinned by the caller (e.g. `rollout-submit`, which
+    needs the same id for its condor run-dir name, the output filename, and
+    the YAML sidecar) — default is a freshly generated one, as before.
     """
     dataset_path = data.resolve()
-    pred_uuid = str(uuid_mod.uuid4())
+    pred_uuid = pred_uuid or str(uuid_mod.uuid4())
     if out is None:
         if str(dataset_path).startswith("/ceph/"):
             out = _CEPH_PREDICTIONS / f"{pred_uuid}.parquet"
@@ -451,17 +482,7 @@ def train(
         }.items()
         if v is not None
     }
-    cli_router: dict[str, object] = {
+    cli_router = _router_cli_overrides(router, router_type, n_experts, router_axis)
-        k: v
-        for k, v in {
-            "enabled": router,
-            "type": router_type,
-            "n_experts": n_experts,
-        }.items()
-        if v is not None
-    }
-    if router_axis:
-        cli_router.update(_parse_router_axis_flags(router_axis))
     if cli_router:
         cli_model["router"] = cli_router
     cfg = gconfig.merge_cli_overrides(
@@ -484,16 +505,7 @@ def train(
             f"batch_size: {t['batch_size']} (auto-estimated from free GPU memory)"
         )
 
-    out_dir = out or Path(
+    out_dir = out or gconfig.default_out_dir(cfg)
-        f"checkpoints/{date.today().strftime('%Y%m%d')}"
-        f"_{t['mode']}"
-        f"_h{m['hidden_dim']}"
-        f"_b{m['n_blocks']}"
-        f"_e{m['emb_dim']}"
-        f"_c{m['conditioning']}"
-        f"_lr{t['lr']}"
-        f"_bs{t['batch_size']}"
-    )
 
     typer.echo(f"device:  {_device}")
     typer.echo(f"out_dir: {out_dir}")
@@ -510,6 +522,149 @@ def train(
     )
 
 
+@app.command("new-run")
+def new_run(
+    config: Annotated[
+        Optional[Path],
+        typer.Option(
+            "--config",
+            "-c",
+            help="Base TOML to start from (default: built-in defaults)",
+        ),
+    ] = None,
+    mode: Annotated[Optional[Mode], typer.Option("--mode", "-m")] = None,
+    epochs: Annotated[Optional[int], typer.Option("--epochs", "-e")] = None,
+    batch_size: Annotated[Optional[int], typer.Option("--batch-size", "-b")] = None,
+    lr: Annotated[Optional[float], typer.Option("--lr", "-l")] = None,
+    hidden_dim: Annotated[Optional[int], typer.Option("--hidden-dim", "-H")] = None,
+    n_blocks: Annotated[Optional[int], typer.Option("--n-blocks", "-n")] = None,
+    emb_dim: Annotated[Optional[int], typer.Option("--emb-dim", "-E")] = None,
+    dropout: Annotated[Optional[float], typer.Option("--dropout", "-d")] = None,
+    conditioning: Annotated[
+        Optional[Conditioning], typer.Option("--conditioning")
+    ] = None,
+    router: Annotated[Optional[bool], typer.Option("--router/--no-router")] = None,
+    router_type: Annotated[Optional[str], typer.Option("--router-type")] = None,
+    n_experts: Annotated[Optional[int], typer.Option("--n-experts")] = None,
+    router_axis: Annotated[Optional[list[str]], typer.Option("--router-axis")] = None,
+    out: Annotated[
+        Optional[Path],
+        typer.Option("--out", "-o", help="Run dir (default: auto from hyperparams)"),
+    ] = None,
+    comment: Annotated[
+        Optional[str],
+        typer.Option(
+            "--comment", help="Free-text note recorded in config.toml's meta section"
+        ),
+    ] = None,
+    data: Annotated[
+        Optional[Path],
+        typer.Option(
+            "--data",
+            help="Dataset path to fill in the printed next-step commands "
+            "(not stored in the config)",
+        ),
+    ] = None,
+    force: Annotated[
+        bool,
+        typer.Option(
+            "--force",
+            help="Overwrite config.toml even if --out already has checkpoints",
+        ),
+    ] = False,
+    dry_run: Annotated[
+        bool,
+        typer.Option(
+            "--dry-run", help="Print the resolved config without writing anything"
+        ),
+    ] = False,
+) -> None:
+    """Scaffold a new training run: resolve hyperparams to a config.toml and lay out its run dir.
+
+    This is the config-file-first counterpart to hand-editing a TOML: start
+    from a base --config (or built-in defaults), override a few hyperparams
+    inline, and this resolves+writes the full `config.toml` into a fresh (or
+    explicit --out) run dir — the same file `giant train --config ...` reads
+    and `giant train-submit --config ...` requires. `giant train` itself
+    overwrites this file in place once it actually runs (with the full
+    dataset-derived meta section), so this scaffold's meta section is just a
+    placeholder recording what was asked for and when.
+    """
+    cli_train = {
+        k: v
+        for k, v in {
+            "mode": mode.value if mode is not None else None,
+            "epochs": epochs,
+            "batch_size": batch_size,
+            "lr": lr,
+        }.items()
+        if v is not None
+    }
+    cli_model: dict[str, object] = {
+        k: v
+        for k, v in {
+            "hidden_dim": hidden_dim,
+            "n_blocks": n_blocks,
+            "emb_dim": emb_dim,
+            "dropout": dropout,
+            "conditioning": conditioning.value if conditioning is not None else None,
+        }.items()
+        if v is not None
+    }
+    cli_router = _router_cli_overrides(router, router_type, n_experts, router_axis)
+    if cli_router:
+        cli_model["router"] = cli_router
+
+    cfg = gconfig.merge_cli_overrides(
+        gconfig.DEFAULT_CONFIG, config, cli_train, cli_model
+    )
+    run_dir = (out or gconfig.default_out_dir(cfg)).resolve()
+
+    if not force:
+        existing = [n for n in ("last.pt", "best.pt") if (run_dir / n).exists()]
+        if existing:
+            typer.echo(
+                f"error: {run_dir} already has {', '.join(existing)} — pass "
+                "--force to overwrite its config.toml anyway",
+                err=True,
+            )
+            raise typer.Exit(1)
+
+    typer.echo(f"run dir: {run_dir}")
+
+    if dry_run:
+        typer.echo("dry-run: not writing anything. Resolved config:")
+        for section in ("train", "model"):
+            typer.echo(f"[{section}]")
+            for k, v in cfg[section].items():
+                if k == "router":
+                    continue
+                typer.echo(f"  {k} = {v}")
+        return
+
+    meta = {
+        "git_hash": gconfig.git_hash(),
+        "created_at": datetime.now(timezone.utc).isoformat(timespec="seconds"),
+        "created_by": "giant new-run",
+    }
+    if comment:
+        meta["comment"] = comment
+
+    run_dir.mkdir(parents=True, exist_ok=True)
+    gconfig.save_config(cfg, run_dir, meta)
+    config_path = run_dir / "config.toml"
+    typer.echo(f"wrote {config_path}")
+
+    data_arg = str(data) if data is not None else "<data.parquet>"
+    typer.echo("")
+    typer.echo("next:")
+    typer.echo(f"  giant train {data_arg} --config {config_path} --out {run_dir}")
+    typer.echo(
+        f"  giant train-submit {data_arg} --config {config_path} --out {run_dir} "
+        "--accounting-group <group>"
+    )
+
+
 @app.command()
 def predict(
     data: Annotated[
@@ -998,6 +1153,15 @@ def rollout(
         Optional[int],
         typer.Option("--seed", help="Torch/numpy seed for reproducibility"),
     ] = None,
+    prediction_id: Annotated[
+        Optional[str],
+        typer.Option(
+            "--prediction-id",
+            help="Pin the prediction uuid (output filename, YAML sidecar name) "
+            "instead of generating a fresh one — used by `rollout-submit` so "
+            "its condor run-dir shares an id with the run it submitted",
+        ),
+    ] = None,
 ) -> None:
     """Roll the surrogate forward into full showers (autoregressive)."""
     if seed is not None:
@@ -1051,7 +1215,9 @@ def rollout(
     seeds = _seed_from_data(files, n_events)
     typer.echo(f"seeded {len(seeds['event_id']):,} shower(s)")
 
-    out, dataset_path, pred_uuid = _resolve_prediction_output(data, out)
+    out, dataset_path, pred_uuid = _resolve_prediction_output(
+        data, out, pred_uuid=prediction_id
+    )
     out.parent.mkdir(parents=True, exist_ok=True)
 
     # Written incrementally as each batch of steps is produced, rather than
@@ -1317,5 +1483,352 @@ def analyze_submit(
     subprocess.run(["condor_submit", str(sub)], check=True)
 
 
+def _warn_if_not_ceph(path: Path) -> None:
+    """Remote GPU condor workers only reliably reach /ceph (see giant/condor.py)."""
+    if not str(path).startswith("/ceph/"):
+        typer.echo(
+            f"warning: {path} is not under /ceph/ — remote GPU condor workers "
+            "may not be able to reach it (see TARGET.ProvidesEtpCeph)",
+            err=True,
+        )
+
+
+@app.command("train-submit")
+def train_submit(
+    data: Annotated[
+        Path,
+        typer.Argument(
+            help="Parquet file or directory (must resolve under /ceph so a "
+            "remote GPU worker can reach it)"
+        ),
+    ],
+    config: Annotated[
+        Path,
+        typer.Option(
+            "--config",
+            "-c",
+            help="TOML config — the full, reproducible spec for this run. "
+            "Hyperparams aren't exposed as flags here; edit the TOML instead "
+            "(this is also what makes resume-after-preemption safe: the same "
+            "file is passed on every condor retry, so the architecture never "
+            "drifts from the checkpoint being resumed).",
+        ),
+    ],
+    accounting_group: Annotated[str, typer.Option("--accounting-group")],
+    out: Annotated[
+        Optional[Path],
+        typer.Option(
+            "--out", "-o", help="Checkpoint dir (default: auto from hyperparams)"
+        ),
+    ] = None,
+    request_gpus: Annotated[int, typer.Option("--request-gpus")] = 1,
+    gpu_type: Annotated[
+        Optional[str],
+        typer.Option("--gpu-type", help='Pin GPU model, e.g. "Tesla V100-PCIE-32GB"'),
+    ] = None,
+    gpu_memory_mb: Annotated[Optional[int], typer.Option("--gpu-memory-mb")] = None,
+    request_memory: Annotated[int, typer.Option("--request-memory", help="MB")] = 16384,
+    request_walltime: Annotated[
+        int, typer.Option("--request-walltime", help="Seconds (default: 2 days)")
+    ] = 172800,
+    docker_image: Annotated[
+        str, typer.Option("--docker-image")
+    ] = "mschnepf/slc7-condocker",
+    repo_dir: Annotated[
+        Optional[Path],
+        typer.Option(
+            "--repo-dir",
+            help="The /ceph checkout `uv run` executes from (default: cwd)",
+        ),
+    ] = None,
+    dry_run: Annotated[
+        bool, typer.Option("--dry-run", help="Write files but don't condor_submit")
+    ] = False,
+) -> None:
+    """Submit `giant train` as a single remote-GPU HTCondor job.
+
+    GPU workers (TOpAS/NEMO2) are remote-only, so this always sets
+    `+RemoteJob = True` / `RequestGPUs` and reaches data via
+    `TARGET.ProvidesEtpCeph` rather than the local-only
+    `TARGET.ProvidesETPResources` `giant analyze submit` uses — see
+    giant/condor.py and the ETP HTCondor wiki's "GPU Jobs" section.
+    """
+    from giant.condor import (
+        CondorJobMeta,
+        GpuSubmitConfig,
+        parse_cluster_id,
+        write_gpu_submit,
+    )
+
+    data_path = data.resolve()
+    config_path = config.resolve()
+    _warn_if_not_ceph(data_path)
+
+    cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, config_path, {}, {})
+    out_dir = (out or gconfig.default_out_dir(cfg)).resolve()
+    out_dir.mkdir(parents=True, exist_ok=True)
+    _warn_if_not_ceph(out_dir)
+
+    repo_path = (repo_dir or Path.cwd()).resolve()
+    run_dir = out_dir / "condor"
+
+    q = shlex.quote
+    last_ckpt = out_dir / "last.pt"
+    command = (
+        f"uv run giant train {q(str(data_path))} --config {q(str(config_path))} "
+        f"--out {q(str(out_dir))} --device cuda"
+    )
+    wrapper_body = (
+        "#!/bin/bash\n"
+        "set -euo pipefail\n"
+        f"cd {q(str(repo_path))}\n"
+        'RESUME=""\n'
+        f'[ -f {q(str(last_ckpt))} ] && RESUME="--resume {last_ckpt}"\n'
+        f"exec {command} $RESUME\n"
+    )
+
+    submit_cfg = GpuSubmitConfig(
+        run_dir=run_dir,
+        accounting_group=accounting_group,
+        repo_dir=repo_path,
+        command=command,
+        docker_image=docker_image,
+        request_memory_mb=request_memory,
+        request_gpus=request_gpus,
+        gpu_type=gpu_type,
+        gpu_memory_mb=gpu_memory_mb,
+        request_walltime_s=request_walltime,
+    )
+    sub = write_gpu_submit(submit_cfg, wrapper_body)
+
+    meta = CondorJobMeta(
+        accounting_group=accounting_group,
+        request_gpus=request_gpus,
+        gpu_type=gpu_type,
+        gpu_memory_mb=gpu_memory_mb,
+        request_memory_mb=request_memory,
+        request_walltime_s=request_walltime,
+        docker_image=docker_image,
+        repo_dir=str(repo_path),
+        command=command,
+        submitted_at=datetime.now(timezone.utc).isoformat(),
+    )
+    meta_path = run_dir / "submission.json"
+    meta.save(meta_path)
+
+    typer.echo(f"out dir: {out_dir}")
+    typer.echo(f"wrote submit description: {sub}")
+    if dry_run:
+        typer.echo("dry-run: not submitting")
+        return
+
+    import subprocess
+
+    result = subprocess.run(
+        ["condor_submit", str(sub)], check=True, capture_output=True, text=True
+    )
+    typer.echo(result.stdout)
+    meta.cluster_id = parse_cluster_id(result.stdout)
+    meta.save(meta_path)
+    if meta.cluster_id is not None:
+        typer.echo(f"cluster id: {meta.cluster_id}")
+
+
+@app.command("rollout-submit")
+def rollout_submit(
+    data: Annotated[
+        Path,
+        typer.Argument(
+            help="Parquet file/dir to seed showers from (must resolve under "
+            "/ceph so a remote GPU worker can reach it)"
+        ),
+    ],
+    checkpoint: Annotated[
+        Path,
+        typer.Option("--checkpoint", "-c", help="Checkpoint .pt (best.pt/last.pt)"),
+    ],
+    geometry: Annotated[
+        Path,
+        typer.Option(
+            "--geometry",
+            "-g",
+            help="Geometry oracle .pkl (dwarf build-geometry-oracle)",
+        ),
+    ],
+    accounting_group: Annotated[str, typer.Option("--accounting-group")],
+    energy_cutoff: Annotated[
+        float,
+        typer.Option(
+            "--energy-cutoff",
+            help="Stop a track when its energy drops below this [MeV]",
+        ),
+    ] = 0.1,
+    max_steps: Annotated[
+        int, typer.Option("--max-steps", help="Max steps per individual track")
+    ] = 1000,
+    steps: Annotated[
+        int,
+        typer.Option(
+            "--steps",
+            "-s",
+            help="Flow matching ODE steps per model call (ignored for a wgan checkpoint)",
+        ),
+    ] = 10,
+    weights: Annotated[
+        Weights,
+        typer.Option("--weights", help="raw or ema (see `giant rollout --help`)"),
+    ] = Weights.raw,
+    batch_size: Annotated[
+        int, typer.Option("--batch-size", "-b", help="Tracks stepped per model forward")
+    ] = 4096,
+    max_tracks_per_event: Annotated[
+        Optional[int],
+        typer.Option(
+            "--max-tracks-per-event",
+            help="Safety cap on tracks per shower (sub-cap secondaries deposit in place)",
+        ),
+    ] = None,
+    escape_threshold: Annotated[
+        Optional[float],
+        typer.Option(
+            "--escape-threshold",
+            help="Override the oracle's NN-distance escape threshold [mm]",
+        ),
+    ] = None,
+    n_events: Annotated[
+        Optional[int], typer.Option("--n-events", help="Cap number of seed events")
+    ] = None,
+    seed: Annotated[
+        Optional[int],
+        typer.Option("--seed", help="Torch/numpy seed for reproducibility"),
+    ] = None,
+    out: Annotated[
+        Optional[Path], typer.Option("--out", "-o", help="Output steps parquet")
+    ] = None,
+    request_gpus: Annotated[int, typer.Option("--request-gpus")] = 1,
+    gpu_type: Annotated[
+        Optional[str],
+        typer.Option("--gpu-type", help='Pin GPU model, e.g. "Tesla V100-PCIE-32GB"'),
+    ] = None,
+    gpu_memory_mb: Annotated[Optional[int], typer.Option("--gpu-memory-mb")] = None,
+    request_memory: Annotated[int, typer.Option("--request-memory", help="MB")] = 16384,
+    request_walltime: Annotated[
+        int, typer.Option("--request-walltime", help="Seconds (default: 2 days)")
+    ] = 172800,
+    docker_image: Annotated[
+        str, typer.Option("--docker-image")
+    ] = "mschnepf/slc7-condocker",
+    repo_dir: Annotated[
+        Optional[Path],
+        typer.Option(
+            "--repo-dir",
+            help="The /ceph checkout `uv run` executes from (default: cwd)",
+        ),
+    ] = None,
+    dry_run: Annotated[
+        bool, typer.Option("--dry-run", help="Write files but don't condor_submit")
+    ] = False,
+) -> None:
+    """Submit `giant rollout` as a single remote-GPU HTCondor job.
+
+    No resume logic (unlike `train-submit`) — a retried rollout just starts
+    over, which is fine since it's deterministic given `--seed` and has no
+    epoch-loop state to preserve.
+    """
+    from giant.condor import (
+        CondorJobMeta,
+        GpuSubmitConfig,
+        parse_cluster_id,
+        write_gpu_submit,
+    )
+
+    data_path = data.resolve()
+    checkpoint_path = checkpoint.resolve()
+    geometry_path = geometry.resolve()
+    _warn_if_not_ceph(data_path)
+    _warn_if_not_ceph(checkpoint_path)
+
+    out_path, _dataset_path, pred_uuid = _resolve_prediction_output(data_path, out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    _warn_if_not_ceph(out_path)
+
+    repo_path = (repo_dir or Path.cwd()).resolve()
+    run_dir = out_path.parent / f"condor_{pred_uuid[:8]}"
+
+    q = shlex.quote
+    flags = [
+        f"--checkpoint {q(str(checkpoint_path))}",
+        f"--geometry {q(str(geometry_path))}",
+        f"--energy-cutoff {energy_cutoff}",
+        f"--max-steps {max_steps}",
+        f"--steps {steps}",
+        f"--weights {weights.value}",
+        f"--batch-size {batch_size}",
+        f"--out {q(str(out_path))}",
+        f"--prediction-id {pred_uuid}",
+        "--device cuda",
+    ]
+    if max_tracks_per_event is not None:
+        flags.append(f"--max-tracks-per-event {max_tracks_per_event}")
+    if escape_threshold is not None:
+        flags.append(f"--escape-threshold {escape_threshold}")
+    if n_events is not None:
+        flags.append(f"--n-events {n_events}")
+    if seed is not None:
+        flags.append(f"--seed {seed}")
+
+    command = f"uv run giant rollout {q(str(data_path))} " + " ".join(flags)
+    wrapper_body = (
+        f"#!/bin/bash\nset -euo pipefail\ncd {q(str(repo_path))}\nexec {command}\n"
+    )
+
+    submit_cfg = GpuSubmitConfig(
+        run_dir=run_dir,
+        accounting_group=accounting_group,
+        repo_dir=repo_path,
+        command=command,
+        docker_image=docker_image,
+        request_memory_mb=request_memory,
+        request_gpus=request_gpus,
+        gpu_type=gpu_type,
+        gpu_memory_mb=gpu_memory_mb,
+        request_walltime_s=request_walltime,
+    )
+    sub = write_gpu_submit(submit_cfg, wrapper_body)
+
+    meta = CondorJobMeta(
+        accounting_group=accounting_group,
+        request_gpus=request_gpus,
+        gpu_type=gpu_type,
+        gpu_memory_mb=gpu_memory_mb,
+        request_memory_mb=request_memory,
+        request_walltime_s=request_walltime,
+        docker_image=docker_image,
+        repo_dir=str(repo_path),
+        command=command,
+        submitted_at=datetime.now(timezone.utc).isoformat(),
+    )
+    meta_path = run_dir / "submission.json"
+    meta.save(meta_path)
+
+    typer.echo(f"prediction id: {pred_uuid}")
+    typer.echo(f"output: {out_path}")
+    typer.echo(f"wrote submit description: {sub}")
+    if dry_run:
+        typer.echo("dry-run: not submitting")
+        return
+
+    import subprocess
+
+    result = subprocess.run(
+        ["condor_submit", str(sub)], check=True, capture_output=True, text=True
+    )
+    typer.echo(result.stdout)
+    meta.cluster_id = parse_cluster_id(result.stdout)
+    meta.save(meta_path)
+    if meta.cluster_id is not None:
+        typer.echo(f"cluster id: {meta.cluster_id}")
+
+
 if __name__ == "__main__":
     app()

giant/condor.py

@@ -0,0 +1,129 @@
+"""HTCondor GPU-job submission for `giant train` / `giant rollout`.
+
+Trains and rollouts run as single condor jobs (no plot-style fan-out) on the
+ETP cluster's remote GPU resources (TOpAS V100/A100, NEMO2 L40S — see the ETP
+HTCondor wiki's "GPU Jobs" section). GPU workers are remote-only, so these
+jobs always carry ``+RemoteJob = True`` and ``RequestGPUs``; the local-only
+``TARGET.ProvidesETPResources`` requirement ``giant.analysis.condor``'s CPU
+jobs use is replaced by ``TARGET.ProvidesEtpCeph =?= True``, the
+wiki-documented way for a remote job to reach ``/ceph`` without HTCondor
+file-transferring multi-GB checkpoints/datasets.
+
+`giant/cli.py`'s `train-submit`/`rollout-submit` commands build a
+`GpuSubmitConfig`, write the wrapper + submit description via
+`write_gpu_submit`, run `condor_submit`, and record what was submitted (and,
+once known, the assigned cluster id) in a `CondorJobMeta` JSON sidecar next
+to the job files — so the run directory alone is enough to understand what
+ran, on what resources, and how to look it up later
+(``condor_history <cluster_id>``).
+"""
+
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import asdict, dataclass
+from pathlib import Path
+
+
+@dataclass
+class GpuSubmitConfig:
+    """Everything needed to write one single-job GPU submit description."""
+
+    run_dir: Path
+    accounting_group: str
+    repo_dir: Path
+    command: str
+    docker_image: str = "mschnepf/slc7-condocker"
+    request_memory_mb: int = 16384
+    request_gpus: int = 1
+    gpu_type: str | None = None  # -> TARGET.GPUs_DeviceName =?= "..."
+    gpu_memory_mb: int | None = None  # -> TARGET.GPUs_GlobalMemoryMb >= N
+    request_walltime_s: int = 172800  # 2 days
+
+
+def _gpu_requirements(cfg: GpuSubmitConfig) -> str:
+    """`TARGET.ProvidesEtpCeph` (remote /ceph access) ANDed with any GPU pin."""
+    clauses = ["TARGET.ProvidesEtpCeph =?= True"]
+    if cfg.gpu_type is not None:
+        clauses.append(f'TARGET.GPUs_DeviceName =?= "{cfg.gpu_type}"')
+    if cfg.gpu_memory_mb is not None:
+        clauses.append(f"TARGET.GPUs_GlobalMemoryMb >= {cfg.gpu_memory_mb}")
+    return " && ".join(clauses)
+
+
+def _gpu_submit_description(cfg: GpuSubmitConfig, wrapper: Path) -> str:
+    return (
+        "universe = docker\n"
+        f"docker_image = {cfg.docker_image}\n"
+        f"executable = {wrapper}\n"
+        "should_transfer_files = YES\n"
+        "when_to_transfer_output = ON_EXIT\n"
+        f"request_memory = {cfg.request_memory_mb}\n"
+        f"RequestGPUs = {cfg.request_gpus}\n"
+        f"+RequestWalltime = {cfg.request_walltime_s}\n"
+        f"accounting_group = {cfg.accounting_group}\n"
+        "+RemoteJob = True\n"
+        f"requirements = ({_gpu_requirements(cfg)})\n"
+        f"output = {cfg.run_dir}/logs/job.out\n"
+        f"error = {cfg.run_dir}/logs/job.err\n"
+        f"log = {cfg.run_dir}/logs/job.log\n"
+        "queue 1\n"
+    )
+
+
+def write_gpu_submit(cfg: GpuSubmitConfig, wrapper_body: str) -> Path:
+    """Write the wrapper script + submit description under ``cfg.run_dir``.
+
+    Returns the submit description path (``<run_dir>/job.sub``). Does not
+    call ``condor_submit`` — that's the caller's job (``giant/cli.py``), same
+    contract as ``giant.analysis.condor.write_submit``.
+    """
+    run_dir = cfg.run_dir
+    (run_dir / "logs").mkdir(parents=True, exist_ok=True)
+
+    wrapper = run_dir / "run.sh"
+    wrapper.write_text(wrapper_body)
+    wrapper.chmod(0o755)
+
+    sub = run_dir / "job.sub"
+    sub.write_text(_gpu_submit_description(cfg, wrapper))
+    return sub
+
+
+_CLUSTER_ID_RE = re.compile(r"submitted to cluster (\d+)")
+
+
+def parse_cluster_id(condor_submit_stdout: str) -> int | None:
+    """Extract the assigned cluster id from `condor_submit`'s stdout, if present."""
+    m = _CLUSTER_ID_RE.search(condor_submit_stdout)
+    return int(m.group(1)) if m else None
+
+
+@dataclass
+class CondorJobMeta:
+    """What was submitted — written to ``<run_dir>/submission.json``.
+
+    ``cluster_id`` starts unset and is filled in by the caller once
+    ``condor_submit``'s stdout has been parsed, so the run directory alone is
+    enough to ``condor_history <cluster_id>`` this job later.
+    """
+
+    accounting_group: str
+    request_gpus: int
+    gpu_type: str | None
+    gpu_memory_mb: int | None
+    request_memory_mb: int
+    request_walltime_s: int
+    docker_image: str
+    repo_dir: str
+    command: str
+    submitted_at: str
+    cluster_id: int | None = None
+
+    def save(self, path: str | Path) -> None:
+        Path(path).write_text(json.dumps(asdict(self), indent=2))
+
+    @classmethod
+    def load(cls, path: str | Path) -> "CondorJobMeta":
+        return cls(**json.loads(Path(path).read_text()))

giant/config.py

@@ -2,7 +2,8 @@ import random
 import subprocess
 import sys
 import tomllib
-from datetime import datetime, timezone
+import uuid
+from datetime import date, datetime, timezone
 from pathlib import Path
 
 import numpy as np
@@ -294,6 +295,26 @@ def save_config(cfg: dict, out_dir: Path, meta: dict) -> None:
     (out_dir / "config.toml").write_text("\n".join(lines))
 
 
+def default_out_dir(cfg: dict, base: Path = Path("checkpoints")) -> Path:
+    """Auto-derived checkpoint dir: hyperparams for readability + a short
+    uuid tag so same-day/same-hyperparam runs (including a repeat condor
+    submission) never collide on an existing directory.
+    """
+    t, m = cfg["train"], cfg["model"]
+    tag = uuid.uuid4().hex[:8]
+    return base / (
+        f"{date.today().strftime('%Y%m%d')}"
+        f"_{t['mode']}"
+        f"_h{m['hidden_dim']}"
+        f"_b{m['n_blocks']}"
+        f"_e{m['emb_dim']}"
+        f"_c{m['conditioning']}"
+        f"_lr{t['lr']}"
+        f"_bs{t['batch_size']}"
+        f"_{tag}"
+    )
+
+
 def build_run_meta(
     data: Path,
     seed: int,

tests/test_cli_new_run.py

@@ -0,0 +1,118 @@
+"""Tests for `giant new-run` (config.toml + run-dir scaffolding)."""
+
+from __future__ import annotations
+
+import tomllib
+from pathlib import Path
+
+from typer.testing import CliRunner
+
+from giant.cli import app
+
+runner = CliRunner()
+
+
+def test_writes_config_with_overrides_applied(tmp_path: Path):
+    out_dir = tmp_path / "run1"
+    result = runner.invoke(
+        app,
+        [
+            "new-run",
+            "--out",
+            str(out_dir),
+            "--mode",
+            "ddpm",
+            "--hidden-dim",
+            "128",
+            "--n-blocks",
+            "4",
+            "--lr",
+            "0.0005",
+        ],
+    )
+    assert result.exit_code == 0, result.output
+
+    config_path = out_dir / "config.toml"
+    assert config_path.exists()
+    with open(config_path, "rb") as f:
+        cfg = tomllib.load(f)
+
+    assert cfg["train"]["mode"] == "ddpm"
+    assert cfg["train"]["lr"] == 0.0005
+    assert cfg["model"]["hidden_dim"] == 128
+    assert cfg["model"]["n_blocks"] == 4
+    # untouched defaults still present
+    assert cfg["train"]["epochs"] == 100
+    assert "router" in cfg["model"]
+
+    assert str(out_dir) in result.output
+    assert "<data.parquet>" in result.output
+    assert "giant train-submit" in result.output
+
+
+def test_comment_and_provenance_recorded_in_meta(tmp_path: Path):
+    out_dir = tmp_path / "run2"
+    result = runner.invoke(
+        app,
+        ["new-run", "--out", str(out_dir), "--comment", "quick test"],
+    )
+    assert result.exit_code == 0, result.output
+
+    with open(out_dir / "config.toml", "rb") as f:
+        cfg = tomllib.load(f)
+
+    assert cfg["meta"]["comment"] == "quick test"
+    assert cfg["meta"]["created_by"] == "giant new-run"
+    assert "created_at" in cfg["meta"]
+    assert "git_hash" in cfg["meta"]
+
+
+def test_data_flag_fills_printed_next_step_commands(tmp_path: Path):
+    out_dir = tmp_path / "run3"
+    result = runner.invoke(
+        app,
+        ["new-run", "--out", str(out_dir), "--data", "/ceph/lbogner/train.parquet"],
+    )
+    assert result.exit_code == 0, result.output
+    assert "/ceph/lbogner/train.parquet" in result.output
+    assert "<data.parquet>" not in result.output
+
+
+def test_dry_run_writes_nothing(tmp_path: Path):
+    out_dir = tmp_path / "run4"
+    result = runner.invoke(
+        app,
+        ["new-run", "--out", str(out_dir), "--hidden-dim", "512", "--dry-run"],
+    )
+    assert result.exit_code == 0, result.output
+    assert "dry-run" in result.output
+    assert "hidden_dim = 512" in result.output
+    assert not out_dir.exists()
+
+
+def test_force_guard_refuses_to_clobber_existing_checkpoints(tmp_path: Path):
+    out_dir = tmp_path / "run5"
+    out_dir.mkdir()
+    (out_dir / "last.pt").touch()
+
+    result = runner.invoke(app, ["new-run", "--out", str(out_dir), "--mode", "ddpm"])
+    assert result.exit_code != 0
+    assert "already has last.pt" in result.output
+    assert not (out_dir / "config.toml").exists()
+
+    result = runner.invoke(
+        app, ["new-run", "--out", str(out_dir), "--mode", "ddpm", "--force"]
+    )
+    assert result.exit_code == 0, result.output
+    assert (out_dir / "config.toml").exists()
+
+
+def test_default_out_dir_used_when_out_omitted(tmp_path: Path, monkeypatch):
+    monkeypatch.chdir(tmp_path)
+    result = runner.invoke(app, ["new-run", "--hidden-dim", "64"])
+    assert result.exit_code == 0, result.output
+
+    checkpoints_dir = tmp_path / "checkpoints"
+    run_dirs = list(checkpoints_dir.iterdir()) if checkpoints_dir.exists() else []
+    assert len(run_dirs) == 1
+    assert (run_dirs[0] / "config.toml").exists()

tests/test_cli_predict.py

@@ -58,6 +58,17 @@ def test_each_call_produces_a_distinct_uuid(tmp_path):
     assert uuid1 != uuid2
 
 
+def test_pinned_pred_uuid_is_used_as_is(tmp_path):
+    # `rollout-submit` pins the uuid at submit time so its condor run-dir,
+    # the output filename, and the eventual YAML sidecar all agree.
+    data = tmp_path / "data.parquet"
+    pinned = "abcd1234-abcd-4abc-9abc-abcdabcdabcd"
+    out, _, pred_uuid = _resolve_prediction_output(data, None, pred_uuid=pinned)
+
+    assert pred_uuid == pinned
+    assert out.name == f"{pinned}.parquet"
+
+
 def test_dataset_path_is_resolved(tmp_path):
     data = tmp_path / "data.parquet"
     _, dataset_path, _ = _resolve_prediction_output(data, None)

tests/test_condor_gpu.py

@@ -0,0 +1,216 @@
+"""Tests for GPU-job HTCondor submission (`giant train-submit` / `giant rollout-submit`)."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from typer.testing import CliRunner
+
+from giant import config as gconfig
+from giant.cli import app
+from giant.condor import (
+    CondorJobMeta,
+    GpuSubmitConfig,
+    parse_cluster_id,
+    write_gpu_submit,
+)
+
+runner = CliRunner()
+
+_MINIMAL_TOML = """\
+[train]
+mode = "flow"
+epochs = 1
+
+[model]
+hidden_dim = 8
+n_blocks = 2
+"""
+
+
+# ---------------------------------------------------------------------------
+# default_out_dir
+# ---------------------------------------------------------------------------
+
+
+def test_default_out_dir_encodes_hyperparams():
+    cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, None, {}, {})
+    out_dir = gconfig.default_out_dir(cfg)
+    name = out_dir.name
+    assert f"_h{cfg['model']['hidden_dim']}_" in name
+    assert f"_b{cfg['model']['n_blocks']}_" in name
+    assert f"_c{cfg['model']['conditioning']}_" in name
+
+
+def test_default_out_dir_avoids_collisions():
+    cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, None, {}, {})
+    a = gconfig.default_out_dir(cfg)
+    b = gconfig.default_out_dir(cfg)
+    assert a != b
+    # same hyperparam-derived prefix, differing only in the uuid tag
+    assert a.name.rsplit("_", 1)[0] == b.name.rsplit("_", 1)[0]
+
+
+# ---------------------------------------------------------------------------
+# giant/condor.py: GpuSubmitConfig / write_gpu_submit
+# ---------------------------------------------------------------------------
+
+
+def test_write_gpu_submit_description(tmp_path: Path):
+    cfg = GpuSubmitConfig(
+        run_dir=tmp_path / "condor",
+        accounting_group="cms",
+        repo_dir=tmp_path,
+        command="uv run giant train data.parquet --config c.toml --out out --device cuda",
+    )
+    sub = write_gpu_submit(cfg, "#!/bin/bash\necho hi\n")
+    txt = sub.read_text()
+
+    assert "universe = docker" in txt
+    assert "docker_image = mschnepf/slc7-condocker" in txt
+    assert "+RemoteJob = True" in txt
+    assert "RequestGPUs = 1" in txt
+    assert "accounting_group = cms" in txt
+    assert "requirements = (TARGET.ProvidesEtpCeph =?= True)" in txt
+    assert "ProvidesETPResources" not in txt
+    assert "queue 1" in txt
+
+    wrapper = cfg.run_dir / "run.sh"
+    assert wrapper.exists()
+    assert wrapper.stat().st_mode & 0o111
+    assert wrapper.read_text() == "#!/bin/bash\necho hi\n"
+
+
+def test_gpu_requirements_include_type_and_memory_pins(tmp_path: Path):
+    cfg = GpuSubmitConfig(
+        run_dir=tmp_path / "condor",
+        accounting_group="cms",
+        repo_dir=tmp_path,
+        command="uv run giant train ...",
+        request_gpus=2,
+        gpu_type="Tesla V100-PCIE-32GB",
+        gpu_memory_mb=16000,
+    )
+    txt = write_gpu_submit(cfg, "#!/bin/bash\n").read_text()
+
+    assert "RequestGPUs = 2" in txt
+    assert 'TARGET.GPUs_DeviceName =?= "Tesla V100-PCIE-32GB"' in txt
+    assert "TARGET.GPUs_GlobalMemoryMb >= 16000" in txt
+
+
+# ---------------------------------------------------------------------------
+# CondorJobMeta / parse_cluster_id
+# ---------------------------------------------------------------------------
+
+
+def test_condor_job_meta_round_trip(tmp_path: Path):
+    meta = CondorJobMeta(
+        accounting_group="cms",
+        request_gpus=1,
+        gpu_type=None,
+        gpu_memory_mb=None,
+        request_memory_mb=16384,
+        request_walltime_s=172800,
+        docker_image="mschnepf/slc7-condocker",
+        repo_dir="/ceph/lbogner/giant",
+        command="uv run giant train ...",
+        submitted_at="2026-07-24T00:00:00+00:00",
+    )
+    path = tmp_path / "submission.json"
+    meta.save(path)
+    loaded = CondorJobMeta.load(path)
+    assert loaded == meta
+    assert loaded.cluster_id is None
+
+    loaded.cluster_id = 123456
+    loaded.save(path)
+    assert CondorJobMeta.load(path).cluster_id == 123456
+
+
+def test_parse_cluster_id():
+    assert parse_cluster_id("1 job(s) submitted to cluster 123456.\n") == 123456
+    assert parse_cluster_id("ERROR: something went wrong\n") is None
+
+
+# ---------------------------------------------------------------------------
+# CLI: giant train-submit / giant rollout-submit (--dry-run only, no cluster contact)
+# ---------------------------------------------------------------------------
+
+
+def test_train_submit_dry_run_writes_condor_dir(tmp_path: Path):
+    config = tmp_path / "config.toml"
+    config.write_text(_MINIMAL_TOML)
+    data = tmp_path / "train.parquet"
+    out_dir = tmp_path / "ckpt"
+
+    result = runner.invoke(
+        app,
+        [
+            "train-submit",
+            str(data),
+            "--config",
+            str(config),
+            "--accounting-group",
+            "cms",
+            "--out",
+            str(out_dir),
+            "--dry-run",
+        ],
+    )
+    assert result.exit_code == 0, result.output
+
+    run_dir = out_dir / "condor"
+    sub_txt = (run_dir / "job.sub").read_text()
+    assert "+RemoteJob = True" in sub_txt
+    assert "requirements = (TARGET.ProvidesEtpCeph =?= True)" in sub_txt
+
+    wrapper = (run_dir / "run.sh").read_text()
+    assert "--device cuda" in wrapper
+    assert "last.pt" in wrapper
+    assert "RESUME" in wrapper
+
+    meta = CondorJobMeta.load(run_dir / "submission.json")
+    assert meta.accounting_group == "cms"
+    assert meta.cluster_id is None
+
+
+def test_rollout_submit_dry_run_writes_condor_dir(tmp_path: Path):
+    data = tmp_path / "seed.parquet"
+    checkpoint = tmp_path / "best.pt"
+    geometry = tmp_path / "oracle.pkl"
+    out = tmp_path / "rollout.parquet"
+
+    result = runner.invoke(
+        app,
+        [
+            "rollout-submit",
+            str(data),
+            "--checkpoint",
+            str(checkpoint),
+            "--geometry",
+            str(geometry),
+            "--accounting-group",
+            "cms",
+            "--out",
+            str(out),
+            "--dry-run",
+        ],
+    )
+    assert result.exit_code == 0, result.output
+
+    condor_dirs = list(tmp_path.glob("condor_*"))
+    assert len(condor_dirs) == 1
+    run_dir = condor_dirs[0]
+
+    sub_txt = (run_dir / "job.sub").read_text()
+    assert "+RemoteJob = True" in sub_txt
+    assert "requirements = (TARGET.ProvidesEtpCeph =?= True)" in sub_txt
+
+    wrapper = (run_dir / "run.sh").read_text()
+    assert "--device cuda" in wrapper
+    assert "--prediction-id" in wrapper
+    assert "last.pt" not in wrapper
+    assert "RESUME" not in wrapper
+
+    meta = CondorJobMeta.load(run_dir / "submission.json")
+    assert meta.accounting_group == "cms"