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fix(training): output CoreML format and fix best-model finder

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ketos 7 defaults to safetensors output, but kraken's load_any() only
handles CoreML (.mlmodel). Adding --weights-format coreml ensures the
hot-swap after training produces a file that load_any() can parse.

Also fixed _find_best_model to look for best_<score>.mlmodel (produced
by --weights-format coreml) in addition to the previous checkpoint_*
pattern.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Marcel
2026-04-13 21:57:42 +02:00
parent 49c9022285
commit 669f2f8b98
1 changed files with 11 additions and 5 deletions
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16
ocr-service/main.py
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@@ -305,20 +305,25 @@ def _parse_best_checkpoint(checkpoint_dir: str) -> tuple[float | None, int]:
def _find_best_model(checkpoint_dir: str) -> str | None: def _find_best_model(checkpoint_dir: str) -> str | None:
"""Return the checkpoint file with the highest validation metric, or any model file.""" """Return the best final model file produced by ketos train.
pattern = re.compile(r"checkpoint_(\d+)-([0-9.]+)\.(ckpt|mlmodel)$")
With --weights-format coreml, ketos writes ``best_<score>.mlmodel``.
Falls back to any .mlmodel in the directory.
"""
# Prefer the named best file (e.g. best_0.8256.mlmodel or best_0.8256.safetensors)
best_pattern = re.compile(r"best_([0-9.]+)\.(mlmodel|safetensors)$")
best_acc: float | None = None best_acc: float | None = None
best_path: str | None = None best_path: str | None = None
for fname in os.listdir(checkpoint_dir): for fname in os.listdir(checkpoint_dir):
m = pattern.match(fname) m = best_pattern.match(fname)
if m: if m:
acc = float(m.group(2)) acc = float(m.group(1))
if best_acc is None or acc > best_acc: if best_acc is None or acc > best_acc:
best_acc = acc best_acc = acc
best_path = os.path.join(checkpoint_dir, fname) best_path = os.path.join(checkpoint_dir, fname)
if best_path: if best_path:
return best_path return best_path
# Fallback: any .mlmodel file in the directory # Fallback: any .mlmodel file
for fname in os.listdir(checkpoint_dir): for fname in os.listdir(checkpoint_dir):
if fname.endswith(".mlmodel"): if fname.endswith(".mlmodel"):
return os.path.join(checkpoint_dir, fname) return os.path.join(checkpoint_dir, fname)
@@ -369,6 +374,7 @@ async def train_model(
"ketos", "--workers", "0", "--device", "cpu", "--threads", "2", "ketos", "--workers", "0", "--device", "cpu", "--threads", "2",
"train", "train",
"-f", "page", "-f", "page",
"--weights-format", "coreml",
"-o", checkpoint_dir, "-o", checkpoint_dir,
"-q", "fixed", "-q", "fixed",
"-N", "10", "-N", "10",