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ca67170 ad067af ca67170 276a433 ca67170 ef2699c ca67170 ac56a3e ca67170 276a433 ca67170 ac56a3e ca67170 276a433 ca67170 538c63e ca67170 ef2699c ca67170 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | import os
import shutil
from datetime import datetime, timezone
from typing import Optional
from src.envs import API, QUEUE_REPO, PROJ_DIR
def _file_name(path_or_obj) -> str:
if path_or_obj is None:
return ""
if isinstance(path_or_obj, dict) and "name" in path_or_obj:
return os.path.basename(path_or_obj["name"])
if hasattr(path_or_obj, "name"):
return os.path.basename(path_or_obj.name)
return os.path.basename(str(path_or_obj))
def queue_student_submission(
group_id: str,
alias: Optional[str],
state_dict_file: Optional[str],
model_py_file: str,
preproc_py_file: str,
) -> tuple[str, str]:
"""
Uploads submitted files to the private queue dataset for offline evaluation.
Layout in repo:
{PROJ_DIR}/{group_id} + {alias}/{timestamp}/model.py
{PROJ_DIR}/{group_id} + {alias}/{timestamp}/preprocess.py
{PROJ_DIR}/{group_id} + {alias}/{timestamp}/model.pt (optional)
{PROJ_DIR}/{group_id} + {alias}/{timestamp}/request.json
"""
if not group_id or not group_id.strip():
raise ValueError("Group ID is required.")
group_id = group_id.strip()
alias = alias.strip() if isinstance(alias, str) else alias
ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
alias_sanitized = (alias or "NA").replace("/", "_")
base_path = f"{PROJ_DIR}/{group_id} + {alias_sanitized}/{ts}"
# Prepare a small request.json metadata
request_json = {
"group_id": group_id,
"alias": alias,
"timestamp": ts,
"status": "PENDING",
"datasets": ["img_val"],
"has_weights": bool(state_dict_file),
}
# Save metadata to a temp file for upload
tmp_dir = os.path.abspath(os.path.join(".", ".tmp_queue_upload"))
os.makedirs(tmp_dir, exist_ok=True)
req_local = os.path.join(tmp_dir, f"request_{group_id}_{ts}.json")
with open(req_local, "w") as f:
import json
json.dump(request_json, f)
# Upload request
API.upload_file(
path_or_fileobj=req_local,
path_in_repo=f"{base_path}/request.json",
repo_id=QUEUE_REPO,
repo_type="dataset",
commit_message=f"Queue request {group_id}/{ts}",
)
# Upload model and preprocess
API.upload_file(
path_or_fileobj=model_py_file,
path_in_repo=f"{base_path}/model.py",
repo_id=QUEUE_REPO,
repo_type="dataset",
commit_message=f"Upload model.py for {group_id}/{ts}",
)
API.upload_file(
path_or_fileobj=preproc_py_file,
path_in_repo=f"{base_path}/preprocess.py",
repo_id=QUEUE_REPO,
repo_type="dataset",
commit_message=f"Upload preprocess.py for {group_id}/{ts}",
)
if state_dict_file:
API.upload_file(
path_or_fileobj=state_dict_file,
path_in_repo=f"{base_path}/model.pt",
repo_id=QUEUE_REPO,
repo_type="dataset",
commit_message=f"Upload model.pt for {group_id}/{ts}",
)
# Cleanup temporary
try:
shutil.rmtree(tmp_dir, ignore_errors=True)
except Exception:
pass
return f"Submission queued for Group '{group_id}' at {ts}. Your model will be evaluated shortly.", ts
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