536 lines
19 KiB
Python
536 lines
19 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Export API routes: checkpoint discovery and model export operations.
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"""
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import asyncio
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import json
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import os
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import sys
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import time
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from pathlib import Path
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from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple
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from fastapi import APIRouter, Depends, HTTPException, Query, Request
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from fastapi.responses import StreamingResponse
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import structlog
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from loggers import get_logger
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# Add backend directory to path
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backend_path = Path(__file__).parent.parent.parent
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if str(backend_path) not in sys.path:
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sys.path.insert(0, str(backend_path))
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# Auth
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from auth.authentication import get_current_subject
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# Import backend functions
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try:
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from core.export import get_export_backend
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except ImportError:
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parent_backend = backend_path.parent / "backend"
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if str(parent_backend) not in sys.path:
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sys.path.insert(0, str(parent_backend))
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from core.export import get_export_backend
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# Import Pydantic models
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from models import (
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LoadCheckpointRequest,
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ExportStatusResponse,
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ExportOperationResponse,
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ExportMergedModelRequest,
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ExportBaseModelRequest,
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ExportGGUFRequest,
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ExportLoRAAdapterRequest,
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)
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router = APIRouter()
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logger = get_logger(__name__)
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@router.post("/load-checkpoint", response_model = ExportOperationResponse)
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async def load_checkpoint(
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request: LoadCheckpointRequest,
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current_subject: str = Depends(get_current_subject),
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):
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"""
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Load a checkpoint into the export backend.
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Wraps ExportBackend.load_checkpoint.
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"""
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try:
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# Version switching is handled automatically by the subprocess-based
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# export backend — no need for ensure_transformers_version() here.
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# Free GPU memory: shut down any running inference/training subprocesses
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# before loading the export checkpoint (they'd compete for VRAM).
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try:
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from core.inference import get_inference_backend
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inf = get_inference_backend()
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if inf.active_model_name:
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logger.info(
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"Unloading inference model '%s' to free GPU memory for export",
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inf.active_model_name,
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)
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inf._shutdown_subprocess()
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inf.active_model_name = None
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inf.models.clear()
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except Exception as e:
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logger.warning("Could not unload inference model: %s", e)
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# Also unload any active GGUF llama-server (the inference unload
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# above only covers the safetensors / Unsloth backend; GGUF
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# chat runs as a separate subprocess).
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try:
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from routes.inference import get_llama_cpp_backend
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llama = get_llama_cpp_backend()
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if getattr(llama, "is_loaded", False):
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logger.info("Unloading GGUF chat model to free GPU memory for export")
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llama.unload_model()
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except Exception as e:
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logger.debug("llama-server unload skipped for export: %s", e)
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# Also unload any active diffusion pipeline (Images page); it
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# competes for the same GPU and would survive the inference
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# shutdown above. Best effort; silently skip if the module is
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# absent.
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try:
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from core.inference.diffusion import get_diffusion_backend
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diff = get_diffusion_backend()
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if diff.is_loaded:
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logger.info("Unloading diffusion model to free GPU memory for export")
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diff.unload_model()
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except Exception as e:
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logger.debug("diffusion unload skipped for export: %s", e)
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try:
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from core.training import get_training_backend
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trn = get_training_backend()
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if trn.is_training_active():
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logger.info("Stopping active training to free GPU memory for export")
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trn.stop_training()
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# Wait for training subprocess to actually exit before proceeding,
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# otherwise it may still hold GPU memory when export tries to load.
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for _ in range(60): # up to 30s
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if not trn.is_training_active():
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break
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import time
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time.sleep(0.5)
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else:
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logger.warning(
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"Training subprocess did not exit within 30s, proceeding anyway"
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)
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except Exception as e:
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logger.warning("Could not stop training: %s", e)
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backend = get_export_backend()
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# load_checkpoint spawns and waits on a subprocess and can take
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# minutes. Run it in a worker thread so the event loop stays
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# free to serve the live log SSE stream concurrently.
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success, message = await asyncio.to_thread(
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backend.load_checkpoint,
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checkpoint_path = request.checkpoint_path,
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max_seq_length = request.max_seq_length,
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load_in_4bit = request.load_in_4bit,
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trust_remote_code = request.trust_remote_code,
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)
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if not success:
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raise HTTPException(status_code = 400, detail = message)
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return ExportOperationResponse(success = True, message = message)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error loading checkpoint: {e}", exc_info = True)
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raise HTTPException(
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status_code = 500,
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detail = f"Failed to load checkpoint: {str(e)}",
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)
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@router.post("/cleanup", response_model = ExportOperationResponse)
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async def cleanup_export_memory(
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current_subject: str = Depends(get_current_subject),
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):
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"""
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Cleanup export-related models from memory (GPU/CPU).
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Wraps ExportBackend.cleanup_memory.
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"""
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try:
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backend = get_export_backend()
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success = await asyncio.to_thread(backend.cleanup_memory)
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if not success:
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raise HTTPException(
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status_code = 500,
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detail = "Memory cleanup failed. See server logs for details.",
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)
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return ExportOperationResponse(
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success = True,
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message = "Memory cleanup completed successfully",
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error during export memory cleanup: {e}", exc_info = True)
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raise HTTPException(
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status_code = 500,
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detail = f"Failed to cleanup export memory: {str(e)}",
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)
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@router.get("/status", response_model = ExportStatusResponse)
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async def get_export_status(
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current_subject: str = Depends(get_current_subject),
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):
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"""
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Get current export backend status (loaded checkpoint, model type, PEFT flag).
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"""
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try:
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backend = get_export_backend()
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return ExportStatusResponse(
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current_checkpoint = backend.current_checkpoint,
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is_vision = bool(getattr(backend, "is_vision", False)),
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is_peft = bool(getattr(backend, "is_peft", False)),
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)
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except Exception as e:
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logger.error(f"Error getting export status: {e}", exc_info = True)
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raise HTTPException(
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status_code = 500,
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detail = f"Failed to get export status: {str(e)}",
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)
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def _export_details(output_path: Optional[str]) -> Optional[Dict[str, Any]]:
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"""Return the export path relative to exports_root so the install path is not leaked."""
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if not output_path:
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return None
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try:
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from utils.paths.storage_roots import exports_root
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rel = os.path.relpath(output_path, exports_root())
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if rel.startswith(".."):
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rel = os.path.basename(output_path)
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return {"output_path": rel}
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except Exception:
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return {"output_path": os.path.basename(output_path)}
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@router.post("/export/merged", response_model = ExportOperationResponse)
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async def export_merged_model(
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request: ExportMergedModelRequest,
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current_subject: str = Depends(get_current_subject),
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):
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"""
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Export a merged PEFT model (e.g., 16-bit or 4-bit) and optionally push to Hub.
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Wraps ExportBackend.export_merged_model.
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"""
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try:
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backend = get_export_backend()
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success, message, output_path = await asyncio.to_thread(
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backend.export_merged_model,
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save_directory = request.save_directory,
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format_type = request.format_type,
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push_to_hub = request.push_to_hub,
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repo_id = request.repo_id,
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hf_token = request.hf_token,
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private = request.private,
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)
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if not success:
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raise HTTPException(status_code = 400, detail = message)
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return ExportOperationResponse(
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success = True,
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message = message,
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details = _export_details(output_path),
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error exporting merged model: {e}", exc_info = True)
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raise HTTPException(
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status_code = 500,
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detail = f"Failed to export merged model: {str(e)}",
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)
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@router.post("/export/base", response_model = ExportOperationResponse)
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async def export_base_model(
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request: ExportBaseModelRequest,
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current_subject: str = Depends(get_current_subject),
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):
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"""
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Export a non-PEFT base model and optionally push to Hub.
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Wraps ExportBackend.export_base_model.
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"""
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try:
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backend = get_export_backend()
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success, message, output_path = await asyncio.to_thread(
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backend.export_base_model,
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save_directory = request.save_directory,
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push_to_hub = request.push_to_hub,
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repo_id = request.repo_id,
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hf_token = request.hf_token,
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private = request.private,
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base_model_id = request.base_model_id,
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)
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if not success:
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raise HTTPException(status_code = 400, detail = message)
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return ExportOperationResponse(
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success = True,
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message = message,
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details = _export_details(output_path),
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error exporting base model: {e}", exc_info = True)
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raise HTTPException(
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status_code = 500,
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detail = f"Failed to export base model: {str(e)}",
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)
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@router.post("/export/gguf", response_model = ExportOperationResponse)
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async def export_gguf(
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request: ExportGGUFRequest,
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current_subject: str = Depends(get_current_subject),
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):
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"""
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Export the current model to GGUF format and optionally push to Hub.
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Wraps ExportBackend.export_gguf.
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"""
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try:
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backend = get_export_backend()
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success, message, output_path = await asyncio.to_thread(
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backend.export_gguf,
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save_directory = request.save_directory,
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quantization_method = request.quantization_method,
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push_to_hub = request.push_to_hub,
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repo_id = request.repo_id,
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hf_token = request.hf_token,
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)
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if not success:
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raise HTTPException(status_code = 400, detail = message)
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return ExportOperationResponse(
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success = True,
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message = message,
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details = _export_details(output_path),
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error exporting GGUF model: {e}", exc_info = True)
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raise HTTPException(
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status_code = 500,
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detail = f"Failed to export GGUF model: {str(e)}",
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)
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@router.post("/export/lora", response_model = ExportOperationResponse)
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async def export_lora_adapter(
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request: ExportLoRAAdapterRequest,
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current_subject: str = Depends(get_current_subject),
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):
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"""
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Export only the LoRA adapter (if the loaded model is PEFT).
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Wraps ExportBackend.export_lora_adapter.
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"""
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try:
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backend = get_export_backend()
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success, message, output_path = await asyncio.to_thread(
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backend.export_lora_adapter,
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save_directory = request.save_directory,
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push_to_hub = request.push_to_hub,
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repo_id = request.repo_id,
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hf_token = request.hf_token,
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private = request.private,
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)
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if not success:
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raise HTTPException(status_code = 400, detail = message)
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return ExportOperationResponse(
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success = True,
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message = message,
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details = _export_details(output_path),
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error exporting LoRA adapter: {e}", exc_info = True)
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raise HTTPException(
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status_code = 500,
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detail = f"Failed to export LoRA adapter: {str(e)}",
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)
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# ─────────────────────────────────────────────────────────────────────
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# Live export log stream (Server-Sent Events)
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# ─────────────────────────────────────────────────────────────────────
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#
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# The export worker subprocess redirects its stdout/stderr into a pipe
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# that a reader thread forwards to the orchestrator as log entries (see
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# core/export/worker.py::_setup_log_capture and
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# core/export/orchestrator.py::_append_log). This endpoint streams
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# those entries to the browser so the export dialog can show a live
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# terminal-style output panel while load_checkpoint / export_merged /
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# export_gguf / export_lora / export_base run.
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#
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# Shape follows the training progress SSE endpoint
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# (routes/training.py::stream_training_progress): each event carries
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# `id`, `event`, and `data` fields, the stream starts with a `retry:`
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# directive, and `Last-Event-ID` is honored on reconnect.
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def _format_sse(data: str, event: str, event_id: Optional[int] = None) -> str:
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"""Format a single SSE message with id/event/data fields."""
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lines = []
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if event_id is not None:
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lines.append(f"id: {event_id}")
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lines.append(f"event: {event}")
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lines.append(f"data: {data}")
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lines.append("")
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lines.append("")
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return "\n".join(lines)
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@router.get("/logs/stream")
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async def stream_export_logs(
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request: Request,
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since: Optional[int] = Query(
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None,
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description = "Return log entries with seq strictly greater than this cursor.",
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),
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current_subject: str = Depends(get_current_subject),
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):
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"""
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Stream live stdout/stderr output from the export worker subprocess
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as Server-Sent Events.
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Events:
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- `log` : a single log line (data: {"stream","line","ts"})
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- `heartbeat`: periodic keepalive when no new lines are available
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- `complete` : emitted once the export worker is idle and no new
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lines arrived for ~1 second. Clients should close.
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- `error` : unrecoverable server-side error
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The `id:` field on each event is the log entry's monotonic seq
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number so the browser can resume via `Last-Event-ID` on reconnect.
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"""
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backend = get_export_backend()
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# Determine starting cursor. Explicit `since` wins, then
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# Last-Event-ID header on reconnect, otherwise start from the
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# run-start snapshot captured by clear_logs() so the client sees
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# every line emitted since the current run began -- even if the
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# SSE connection opened after the POST that kicked off the export.
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# Using get_current_log_seq() here would lose the early bootstrap
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# lines that arrive in the gap between POST and SSE connect.
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last_event_id = request.headers.get("last-event-id")
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if since is None and last_event_id is not None:
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try:
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since = int(last_event_id)
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except ValueError:
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pass
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if since is None:
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cursor = backend.get_run_start_seq()
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else:
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cursor = max(0, int(since))
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async def event_generator() -> AsyncGenerator[str, None]:
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nonlocal cursor
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# Tell the browser to reconnect after 3 seconds if the
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# connection drops mid-export.
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yield "retry: 3000\n\n"
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last_yield = time.monotonic()
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idle_since: Optional[float] = None
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try:
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while True:
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if await request.is_disconnected():
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return
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entries, new_cursor = backend.get_logs_since(cursor)
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if entries:
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for entry in entries:
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payload = json.dumps(
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{
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"stream": entry.get("stream", "stdout"),
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"line": entry.get("line", ""),
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"ts": entry.get("ts"),
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}
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)
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yield _format_sse(
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payload,
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event = "log",
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event_id = int(entry.get("seq", 0)),
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)
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cursor = new_cursor
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last_yield = time.monotonic()
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idle_since = None
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else:
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now = time.monotonic()
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if now - last_yield > 10.0:
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yield _format_sse("{}", event = "heartbeat")
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last_yield = now
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if not backend.is_export_active():
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# Give the reader thread a moment to drain any
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# trailing lines the worker process printed
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# just before signalling done.
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if idle_since is None:
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idle_since = now
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elif now - idle_since > 1.0:
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yield _format_sse(
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"{}",
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event = "complete",
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event_id = cursor,
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)
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return
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else:
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idle_since = None
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await asyncio.sleep(0.1)
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except asyncio.CancelledError:
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# Client disconnected mid-yield. Don't re-raise, just end
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# the generator cleanly so StreamingResponse finalizes.
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return
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except Exception as exc:
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logger.error("Export log stream failed: %s", exc, exc_info = True)
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try:
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yield _format_sse(
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json.dumps({"error": str(exc)}),
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event = "error",
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)
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except Exception:
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pass
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return StreamingResponse(
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event_generator(),
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media_type = "text/event-stream",
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headers = {
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no",
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},
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)
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