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Write out job metadata to a file in the same directory as refined.cif #132

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Merged
k-chrispens merged 2 commits into from
Mar 5, 2026
Merged

Write out job metadata to a file in the same directory as refined.cif #132

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3239abc
Write out job metadata to a file in the same directory as refined.cif
marcuscollins Mar 5, 2026
d60f807
Make sure output directory exists
marcuscollins Mar 5, 2026
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17 changes: 8 additions & 9 deletions src/sampleworks/utils/guidance_script_utils.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
from __future__ import annotations

import argparse
import json
import os
import pickle
import traceback
Expand Down Expand Up @@ -387,6 +388,9 @@ def run_guidance(
log_path = getattr(args, "log_path", None) or os.path.join(args.output_dir, "run.log")
os.makedirs(os.path.dirname(log_path) or ".", exist_ok=True)

# just in case log_path does not go to args.output_dir, make sure the latter exists
os.makedirs(args.output_dir, exist_ok=True)

# separate logs for each guidance run
handle = logger.add(
log_path, level="INFO", filter=lambda rec: rec["extra"].get("special", False) is True
Expand Down Expand Up @@ -578,17 +582,12 @@ def run_guidance_job_queue(job_queue_path: str) -> list[JobResult]:
job_results = []
for i, job in enumerate(job_queue):
logger.info(f"Running job {i + 1}/{len(job_queue)}: {job}")
# TODO: I think it is safe now to re-use the wrapper, it might save us some time.
# The model wrapper can persist state across runs, so we need to re-initialize it each run.
# if job.model_checkpoint is None:
# raise ValueError(
# "Running guidance requires that you specify a model checkpoint, not None"
# )
# device, model_wrapper = get_model_and_device(
# str(device), job.model_checkpoint, job.model, job.method, model_wrapper.model
# )

job_result = run_guidance(job, job.guidance_type, model_wrapper, device)
# write out the job parameters to a JSON file in the same directory as the refined.cif file
with open(Path(job_result.output_dir) / "job_metadata.json", "w") as fp:
json.dump(job.__dict__, fp)
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job_results.append(job_result)
torch.cuda.empty_cache() # just in case

Expand Down