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import glob
import io
import json
import os
import time
from dataclasses import dataclass
import pandas as pd
from huggingface_hub import HfApi, hf_hub_download, snapshot_download
from loguru import logger
from competitions.enums import SubmissionStatus
from competitions.info import CompetitionInfo
from competitions.utils import run_evaluation
_DOCKERFILE = """
FROM huggingface/competitions:latest
CMD uvicorn competitions.api:api --port 7860 --host 0.0.0.0
"""
# format _DOCKERFILE
_DOCKERFILE = _DOCKERFILE.replace("\n", " ").replace(" ", "\n").strip()
@dataclass
class JobRunner:
competition_id: str
token: str
output_path: str
def __post_init__(self):
self.competition_info = CompetitionInfo(competition_id=self.competition_id, autotrain_token=self.token)
self.competition_id = self.competition_info.competition_id
self.competition_type = self.competition_info.competition_type
self.metric = self.competition_info.metric
self.submission_id_col = self.competition_info.submission_id_col
self.submission_cols = self.competition_info.submission_cols
self.submission_rows = self.competition_info.submission_rows
self.time_limit = self.competition_info.time_limit
self.dataset = self.competition_info.dataset
self.submission_filenames = self.competition_info.submission_filenames
def get_pending_subs(self):
submission_jsons = snapshot_download(
repo_id=self.competition_id,
allow_patterns="submission_info/*.json",
token=self.token,
repo_type="dataset",
)
submission_jsons = glob.glob(os.path.join(submission_jsons, "submission_info/*.json"))
pending_submissions = []
for _json in submission_jsons:
_json = json.load(open(_json, "r", encoding="utf-8"))
team_id = _json["id"]
for sub in _json["submissions"]:
if sub["status"] == SubmissionStatus.PENDING.value:
pending_submissions.append(
{
"team_id": team_id,
"submission_id": sub["submission_id"],
"datetime": sub["datetime"],
"submission_repo": sub["submission_repo"],
"space_id": sub["space_id"],
}
)
if len(pending_submissions) == 0:
return None
logger.info(f"Found {len(pending_submissions)} pending submissions.")
pending_submissions = pd.DataFrame(pending_submissions)
pending_submissions["datetime"] = pd.to_datetime(pending_submissions["datetime"])
pending_submissions = pending_submissions.sort_values("datetime")
pending_submissions = pending_submissions.reset_index(drop=True)
return pending_submissions
def _queue_submission(self, team_id, submission_id):
team_fname = hf_hub_download(
repo_id=self.competition_id,
filename=f"submission_info/{team_id}.json",
token=self.token,
repo_type="dataset",
)
with open(team_fname, "r", encoding="utf-8") as f:
team_submission_info = json.load(f)
for submission in team_submission_info["submissions"]:
if submission["submission_id"] == submission_id:
submission["status"] = SubmissionStatus.QUEUED.value
break
team_submission_info_json = json.dumps(team_submission_info, indent=4)
team_submission_info_json_bytes = team_submission_info_json.encode("utf-8")
team_submission_info_json_buffer = io.BytesIO(team_submission_info_json_bytes)
api = HfApi(token=self.token)
api.upload_file(
path_or_fileobj=team_submission_info_json_buffer,
path_in_repo=f"submission_info/{team_id}.json",
repo_id=self.competition_id,
repo_type="dataset",
)
def mark_submission_failed(self, team_id, submission_id):
team_fname = hf_hub_download(
repo_id=self.competition_id,
filename=f"submission_info/{team_id}.json",
token=self.token,
repo_type="dataset",
)
with open(team_fname, "r", encoding="utf-8") as f:
team_submission_info = json.load(f)
for submission in team_submission_info["submissions"]:
if submission["submission_id"] == submission_id:
submission["status"] = SubmissionStatus.FAILED.value
team_submission_info_json = json.dumps(team_submission_info, indent=4)
team_submission_info_json_bytes = team_submission_info_json.encode("utf-8")
team_submission_info_json_buffer = io.BytesIO(team_submission_info_json_bytes)
api = HfApi(token=self.token)
api.upload_file(
path_or_fileobj=team_submission_info_json_buffer,
path_in_repo=f"submission_info/{team_id}.json",
repo_id=self.competition_id,
repo_type="dataset",
)
def run_local(self, team_id, submission_id, submission_repo):
self._queue_submission(team_id, submission_id)
eval_params = {
"competition_id": self.competition_id,
"competition_type": self.competition_type,
"metric": self.metric,
"token": self.token,
"team_id": team_id,
"submission_id": submission_id,
"submission_id_col": self.submission_id_col,
"submission_cols": self.submission_cols,
"submission_rows": self.submission_rows,
"output_path": self.output_path,
"submission_repo": submission_repo,
"time_limit": self.time_limit,
"dataset": self.dataset,
"submission_filenames": self.submission_filenames,
}
eval_params = json.dumps(eval_params)
eval_pid = run_evaluation(eval_params, local=True, wait=True)
logger.info(f"New evaluation process started with pid {eval_pid}.")
def _create_readme(self, project_name):
_readme = "---\n"
_readme += f"title: {project_name}\n"
_readme += "emoji: 🚀\n"
_readme += "colorFrom: green\n"
_readme += "colorTo: indigo\n"
_readme += "sdk: docker\n"
_readme += "pinned: false\n"
_readme += "duplicated_from: autotrain-projects/autotrain-advanced\n"
_readme += "---\n"
_readme = io.BytesIO(_readme.encode())
return _readme
def create_space(self, team_id, submission_id, submission_repo, space_id):
api = HfApi(token=self.token)
params = {
"competition_id": self.competition_id,
"competition_type": self.competition_type,
"metric": self.metric,
"token": self.token,
"team_id": team_id,
"submission_id": submission_id,
"submission_id_col": self.submission_id_col,
"submission_cols": self.submission_cols,
"submission_rows": self.submission_rows,
"output_path": self.output_path,
"submission_repo": submission_repo,
"time_limit": self.time_limit,
"dataset": self.dataset,
"submission_filenames": self.submission_filenames,
}
api.add_space_secret(repo_id=space_id, key="PARAMS", value=json.dumps(params))
readme = self._create_readme(space_id.split("/")[-1])
api.upload_file(
path_or_fileobj=readme,
path_in_repo="README.md",
repo_id=space_id,
repo_type="space",
)
_dockerfile = io.BytesIO(_DOCKERFILE.encode())
api.upload_file(
path_or_fileobj=_dockerfile,
path_in_repo="Dockerfile",
repo_id=space_id,
repo_type="space",
)
self._queue_submission(team_id, submission_id)
def run(self):
while True:
pending_submissions = self.get_pending_subs()
if pending_submissions is None:
time.sleep(5)
continue
if self.competition_type == "generic":
for _, row in pending_submissions.iterrows():
team_id = row["team_id"]
submission_id = row["submission_id"]
submission_repo = row["submission_repo"]
self.run_local(team_id, submission_id, submission_repo)
elif self.competition_type == "script":
for _, row in pending_submissions.iterrows():
team_id = row["team_id"]
submission_id = row["submission_id"]
submission_repo = row["submission_repo"]
space_id = row["space_id"]
try:
self.create_space(team_id, submission_id, submission_repo, space_id)
except Exception as e:
logger.error(
f"Failed to create space for {team_id} {submission_id} {submission_repo} {space_id}: {e}"
)
# mark submission as failed
self.mark_submission_failed(team_id, submission_id)
logger.error(f"Marked submission {submission_id} as failed.")
continue
time.sleep(5)
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