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code-review-fix task 15.3: replace datetime.utcnow() with datetime.now(timezone.utc) in main.py

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Marko Djordjevic 2026-02-18 20:58:11 +01:00
parent 9c08ffc44d
commit 059c436717
1 changed files with 7 additions and 7 deletions
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14
services/ml/app/main.py
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@ -14,7 +14,7 @@ import uuid
from contextlib import asynccontextmanager
from pathlib import Path
from typing import Optional, Dict, Any, List
from datetime import datetime
from datetime import datetime, timezone
import json
import requests as http_requests
@ -81,7 +81,7 @@ async def lifespan(app: FastAPI):
.where(TrainingRun.status == "running")
.values(
status="failed",
completed_at=datetime.utcnow(),
completed_at=datetime.now(timezone.utc),
metrics_summary={"error": "Service restarted while training was in progress"},
)
)
@ -1226,7 +1226,7 @@ def _run_training_background(run_id: str, model_type: str, config: PipelineConfi
.where(TrainingRun.run_id == run_id)
.values(
status="failed",
completed_at=datetime.utcnow(),
completed_at=datetime.now(timezone.utc),
metrics_summary={
"error": "Training timed out after 30 minutes"
},
@ -1268,7 +1268,7 @@ def _run_training_background(run_id: str, model_type: str, config: PipelineConfi
"model": model_instance,
"metadata": {
"model_type": model_type,
"trained_at": datetime.utcnow().isoformat(),
"trained_at": datetime.now(timezone.utc).isoformat(),
"run_id": run_id,
"feature_columns": feature_cols,
"labels": (
@ -1288,7 +1288,7 @@ def _run_training_background(run_id: str, model_type: str, config: PipelineConfi
.where(TrainingRun.run_id == run_id)
.values(
status="completed",
completed_at=datetime.utcnow(),
completed_at=datetime.now(timezone.utc),
metrics_summary=metrics,
)
)
@ -1306,7 +1306,7 @@ def _run_training_background(run_id: str, model_type: str, config: PipelineConfi
.where(TrainingRun.run_id == run_id)
.values(
status="failed",
completed_at=datetime.utcnow(),
completed_at=datetime.now(timezone.utc),
metrics_summary={"error": str(exc)},
)
)
@ -1368,7 +1368,7 @@ async def training_start(request: TrainingStartRequest):
experiment_name=config.stages.training.mlflow.experiment_name,
pipeline_config_hash=config_hash,
status="running",
created_at=datetime.utcnow(),
created_at=datetime.now(timezone.utc),
metrics_summary={},
)
db.add(training_run)