jim800121chen
efa67d59a4
- Frontend: rewrite Home.vue to match backend POST /jobs API (remove single-stage options) - Frontend: add Monitor page (/monitor) for queue and job monitoring - Frontend: add job history with localStorage tracking (per-browser) - Frontend: fix Nginx proxy rewrite (/api -> /) and add 500MB upload limit - Backend: add MinIO storage support (STORAGE_BACKEND=minio) alongside local mode - Backend: add GET /queues/stats API for queue monitoring - Backend: fix download handler for MinIO (buffer mode for Node 18 compat) - Workers: add S3/MinIO download/upload in consumer.py with isolated temp dirs - Workers: add s3_storage.py helper with lifecycle rule (7-day TTL) - Docker: add docker-compose.yml with all services (web, scheduler, redis, workers) - Docker: ports mapped to 9500 (web) and 9501 (scheduler) - Config: add .env to .gitignore to protect secrets Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
123 lines
3.5 KiB
Python
123 lines
3.5 KiB
Python
"""
|
|
Stub implementations of worker core functions.
|
|
|
|
Used when WORKER_MODE=stub, allowing development and testing of
|
|
Scheduler / Queue / UI without requiring the Kneron Toolchain environment.
|
|
|
|
Each stub:
|
|
- Sleeps to simulate processing time
|
|
- Creates a minimal output file
|
|
- Returns a result dict matching the real core function signature
|
|
"""
|
|
|
|
import os
|
|
import time
|
|
from typing import Any, Dict
|
|
|
|
|
|
def process_onnx_core_stub(
|
|
input_paths: Dict[str, str],
|
|
output_path: str,
|
|
parameters: Dict[str, Any],
|
|
) -> Dict[str, Any]:
|
|
"""Stub ONNX processing: sleep 2s, create a fake out.onnx."""
|
|
file_path = input_paths["file_path"]
|
|
if not os.path.exists(file_path):
|
|
raise FileNotFoundError(f"Input file not found: {file_path}")
|
|
|
|
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
|
|
|
time.sleep(2)
|
|
|
|
# Create minimal valid-looking output
|
|
with open(output_path, "wb") as f:
|
|
f.write(b"STUB_ONNX_OUTPUT_" + os.path.basename(file_path).encode())
|
|
|
|
return {
|
|
"file_path": output_path,
|
|
"file_size": os.path.getsize(output_path),
|
|
"eval_report": "",
|
|
"model_info": {
|
|
"model_id": parameters.get("model_id"),
|
|
"version": parameters.get("version"),
|
|
"platform": parameters.get("platform"),
|
|
},
|
|
}
|
|
|
|
|
|
def process_bie_core_stub(
|
|
input_paths: Dict[str, str],
|
|
output_path: str,
|
|
parameters: Dict[str, Any],
|
|
) -> Dict[str, Any]:
|
|
"""Stub BIE processing: sleep 3s, create a fake out.bie."""
|
|
onnx_file_path = input_paths["onnx_file_path"]
|
|
data_dir = input_paths["data_dir"]
|
|
|
|
if not os.path.exists(onnx_file_path):
|
|
raise FileNotFoundError(f"ONNX file not found: {onnx_file_path}")
|
|
|
|
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
|
|
|
# Count ref images (if any)
|
|
img_count = 0
|
|
if os.path.isdir(data_dir):
|
|
img_count = len([f for f in os.listdir(data_dir) if os.path.isfile(os.path.join(data_dir, f))])
|
|
|
|
time.sleep(3)
|
|
|
|
with open(output_path, "wb") as f:
|
|
f.write(b"STUB_BIE_OUTPUT")
|
|
|
|
return {
|
|
"file_path": output_path,
|
|
"file_size": os.path.getsize(output_path),
|
|
"model_info": {
|
|
"model_id": parameters.get("model_id"),
|
|
"version": parameters.get("version"),
|
|
"platform": parameters.get("platform"),
|
|
},
|
|
"analysis_info": {
|
|
"input_name": "stub_input",
|
|
"batch_size": 1,
|
|
"channels": 3,
|
|
"height": 224,
|
|
"width": 224,
|
|
},
|
|
"processed_images": img_count,
|
|
}
|
|
|
|
|
|
def process_nef_core_stub(
|
|
input_paths: Dict[str, str],
|
|
output_path: str,
|
|
parameters: Dict[str, Any],
|
|
) -> Dict[str, Any]:
|
|
"""Stub NEF processing: sleep 2s, create a fake out.nef."""
|
|
bie_file_path = input_paths["bie_file_path"]
|
|
|
|
if not os.path.exists(bie_file_path):
|
|
raise FileNotFoundError(f"BIE file not found: {bie_file_path}")
|
|
|
|
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
|
|
|
time.sleep(2)
|
|
|
|
with open(output_path, "wb") as f:
|
|
f.write(b"STUB_NEF_OUTPUT")
|
|
|
|
return {
|
|
"file_path": output_path,
|
|
"file_size": os.path.getsize(output_path),
|
|
"model_info": {
|
|
"model_id": parameters.get("model_id"),
|
|
"version": parameters.get("version"),
|
|
"platform": parameters.get("platform"),
|
|
},
|
|
"compilation_info": {
|
|
"optimization_level": "stub",
|
|
"memory_usage": "stub",
|
|
"inference_speed": "stub",
|
|
},
|
|
}
|