lars
cd73aa2966
Groundwork for the b2luigi pipeline orchestration in gitea #83, split out so the workflow package itself lands as a self-contained change: - new `workflow` optional-dependency extra (b2luigi, which pulls luigi + tenacity), included in `dev`. - deterministic rollout/predict sidecar path: with an explicit `--out`, the YAML goes to `out.with_suffix(".yaml")` instead of a uuid-named file under the checkpoint directory, so a workflow task can declare it as a target. The uuid behaviour is kept for the no-`--out` case, leaving ad-hoc runs and the /ceph predictions convention untouched. - epoch-aware shuffle seeding in StreamingStepsDataset (`seed` + `set_epoch`, the DistributedSampler convention). Shuffling previously drew from the global numpy state, which `run_train_job` reseeds from `train.seed` at process start — so a one-epoch-per-job chain would have replayed the same batch order every epoch. Seeding from `(seed, epoch, worker_id)` makes epoch k's order identical whether it runs inside one long `giant train` or as its own resumed job. The val-split seed is untouched. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
111 lines
2.2 KiB
TOML
111 lines
2.2 KiB
TOML
[project]
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name = "giant"
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version = "0.3.10"
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description = "Geant4 step-function surrogate via conditional flow matching"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"numpy>=1.26,<3",
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"pandas>=2.2,<4",
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"pyarrow>=16,<25",
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"tqdm>=4.60,<5",
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"typer>=0.12,<1",
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"pyyaml>=6,<7",
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"particle>=1.0,<2",
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]
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[project.optional-dependencies]
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cpu = [
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"torch>=2.3,<2.4",
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]
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cuda = [
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"torch>=2.3,<2.4",
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]
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dev = [
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"pytest>=8,<10",
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"pytest-cov>=5,<8",
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"ruff>=0.15,<1",
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"ty>=0.0.50,<0.1",
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"bump-my-version>=1.2,<2",
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"git-cliff>=2,<3",
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"giant[convert,analysis,geometry,wandb,workflow]",
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]
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geometry = [
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"scikit-learn>=1.4,<2",
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]
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wandb = [
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"wandb>=0.16,<1",
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]
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convert = [
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"uproot>=5.3,<6",
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"awkward>=2.6,<3",
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"polars>=1.0,<2",
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]
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analysis = [
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"matplotlib>=3.8,<4",
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"polars>=1.0,<2",
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"ipykernel>=7.3.0",
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# KIT matplotlib theme, published from git.larsbogner.de. Only the local
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# `giant analyze render` step imports it; compute workers never do.
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"plotstyle>=1.0.0",
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]
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# b2luigi pulls luigi + tenacity; the only sanctioned way to chain a
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# multi-step pipeline (see giant/workflow/).
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workflow = [
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"b2luigi>=1.0,<2",
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]
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[project.scripts]
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giant = "giant.cli:app"
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dwarf = "giant.tools.dwarf:app"
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[tool.ruff]
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line-length = 120
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[tool.coverage.run]
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source = ["giant"]
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omit = ["*/legacy/*"]
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[tool.coverage.report]
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exclude_also = [
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"if TYPE_CHECKING:",
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"raise NotImplementedError",
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]
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["giant"]
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[tool.uv]
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conflicts = [
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[
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{ extra = "cpu" },
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{ extra = "cuda" },
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],
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]
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[tool.uv.sources]
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torch = [
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{ index = "pytorch-cpu", extra = "cpu" },
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{ index = "pytorch-cu118", extra = "cuda" },
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]
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plotstyle = { index = "larsbogner" }
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[[tool.uv.index]]
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name = "pytorch-cpu"
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url = "https://download.pytorch.org/whl/cpu"
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explicit = true
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[[tool.uv.index]]
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name = "pytorch-cu118"
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url = "https://download.pytorch.org/whl/cu118"
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explicit = true
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[[tool.uv.index]]
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name = "larsbogner"
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url = "https://git.larsbogner.de/api/packages/lars/pypi/simple/"
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explicit = true
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