Fix GPU runbook: neural path must not import joblib

scripts/gpu/run_neural.sh

@@ -35,7 +35,7 @@ mkdir -p "$OUT_DIR"
 # 1. Environment: Python 3.12 with CUDA PyTorch from PyPI. --no-sources skips the CPU-only torch
 #    index that pyproject.toml pins for Linux development machines.
 command -v uv >/dev/null || python3 -m pip install --quiet "uv==$UV_VERSION"
-uv venv --quiet --python 3.12 .venv-gpu
+uv venv --quiet --allow-existing --python 3.12 .venv-gpu
 uv pip install --quiet --python .venv-gpu --no-sources -e ".[neural]"
 if [ -n "${TORCH_INDEX:-}" ]; then
   uv pip install --quiet --python .venv-gpu --reinstall-package torch \

src/triggermap/models/crf/model.py

@@ -12,7 +12,6 @@
 
 from __future__ import annotations
 
-import hashlib
 import json
 from collections import Counter
 from collections.abc import Sequence
@@ -42,6 +41,7 @@
     split_at_lines,
     tokenize,
 )
+from triggermap.models.provenance import training_hash, training_origins
 from triggermap.schema.models import (
     AdvisoryAnnotation,
     Category,
@@ -136,17 +136,6 @@ def _pairs(
     return out
 
 
-def training_hash(records: Sequence[AdvisoryAnnotation]) -> str:
-    lines = sorted(r.model_dump_json() for r in records)
-    return hashlib.sha256("\n".join(lines).encode()).hexdigest()
-
-
-def training_origins(records: Sequence[AdvisoryAnnotation]) -> dict[str, int]:
-    """How many training records each label origin contributed (``unrecorded`` before 1.1)."""
-    counts = Counter(r.origin.value if r.origin else "unrecorded" for r in records)
-    return dict(sorted(counts.items()))
-
-
 class CrfExtractor:
     def __init__(
         self,

src/triggermap/models/provenance.py

@@ -0,0 +1,21 @@
+"""Training-data provenance shared by every learner (no learner dependencies)."""
+
+from __future__ import annotations
+
+import hashlib
+from collections import Counter
+from collections.abc import Sequence
+
+from triggermap.schema.models import AdvisoryAnnotation
+
+
+def training_hash(records: Sequence[AdvisoryAnnotation]) -> str:
+    """SHA-256 of the training records, independent of their order."""
+    lines = sorted(r.model_dump_json() for r in records)
+    return hashlib.sha256("\n".join(lines).encode()).hexdigest()
+
+
+def training_origins(records: Sequence[AdvisoryAnnotation]) -> dict[str, int]:
+    """How many training records each label origin contributed (``unrecorded`` before 1.1)."""
+    counts = Counter(r.origin.value if r.origin else "unrecorded" for r in records)
+    return dict(sorted(counts.items()))

src/triggermap/models/transformer/train.py

@@ -25,7 +25,7 @@
 from transformers import AutoModel, AutoTokenizer, get_linear_schedule_with_warmup
 
 from triggermap.eval.metrics import collect, summary
-from triggermap.models.crf.model import training_hash, training_origins
+from triggermap.models.provenance import training_hash, training_origins
 from triggermap.models.transformer.data import (
     BIO,
     CATEGORIES,

tests/test_transformer.py

@@ -340,3 +340,33 @@ def test_scope_index_matches_linear_scope_of() -> None:
     for start in range(len(doc.text)):
         span = (start, min(start + 3, len(doc.text)))
         assert index.scope(span) == scope_of(doc, span)
+
+
+def test_neural_path_needs_only_the_neural_extra() -> None:
+    """The GPU runbook installs only `.[neural]`: training and prediction must not import
+    baseline (CRF, spaCy, scikit-learn) or serving packages."""
+    import subprocess
+    import sys
+
+    blocked = ["joblib", "sklearn", "pycrfsuite", "spacy", "fastapi", "uvicorn", "onnxruntime"]
+    modules = [
+        "triggermap.cli",
+        "triggermap.models.transformer.train",
+        "triggermap.models.transformer.extractor",
+        "triggermap.models.experiment",
+        "triggermap.models.registry",
+        "triggermap.corpus.splits",
+        "triggermap.eval.runner",
+        "triggermap.eval.report",
+        "triggermap.serve.policy",
+        "triggermap.schema.validate",
+        "triggermap.ingest.build",
+        "triggermap.text.preprocessing",
+    ]
+    code = (
+        "import importlib, sys\n"
+        f"for name in {blocked!r}: sys.modules[name] = None\n"
+        f"for name in {modules!r}: importlib.import_module(name)\n"
+    )
+    result = subprocess.run([sys.executable, "-c", code], capture_output=True, text=True)
+    assert result.returncode == 0, result.stderr[-2000:]