unsloth/studio/backend/routes/export.py
Daniel Han c42c1d56e8
Studio: free chat model VRAM at training start only when the GPU is tight (#6243)
* Studio: free chat model VRAM at training start only when the GPU is tight

The training start route unconditionally tore down the transformers/MLX
inference subprocess before training, and never stopped the llama.cpp GGUF
server at all, so a loaded GGUF chat model kept holding VRAM for the whole
run. Conversely the HF model was always unloaded even when there was plenty
of room to keep it.

Make the unload VRAM aware and cover every inference backend:

- Add routes/training_vram.py with summarize_resident_chat(),
  can_keep_chat_during_training() and free_chat_models_for_training(). The
  keep/unload decision reuses the same estimator and live per device free
  VRAM reader the training GPU selection already uses (auto_select_gpu_ids,
  estimate_required_model_memory_gb, get_visible_gpu_utilization), so the
  probe agrees with the placement computed later in start_training.
- When a chat model is resident and training fits alongside it with a
  conservative margin (required_gb * 1.15 + 4 GB), keep it loaded so the
  user can train and chat at the same time; on a multi GPU box training
  lands on a different GPU and both coexist. Otherwise unload the HF/MLX
  orchestrator and the llama.cpp GGUF server before training starts.
- The export subprocess shutdown stays unconditional and now runs first so
  its freed VRAM is reflected in the decision.

Default deny: non CUDA backends, unestimable models, or any probe error
fall back to the previous always unload behavior.

Adds tests/test_training_vram_coexistence.py and updates two existing route
tests in test_gpu_selection.py.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: per-GPU floor for explicit GPU lists + don't unload chat on invalid gpu_ids

Address review feedback on the chat coexistence probe:

- Explicit gpu_ids mode now enforces a per-GPU floor in addition to the
  aggregate free-VRAM check, mirroring auto_select_gpu_ids' min_per_gpu_N.
  Without it, an uneven split such as free [45, 10] for a 40 GB job passed
  the aggregate threshold and kept chat loaded even though the 10 GB GPU
  could not hold its training shard, risking an OOM.
- Invalid explicit gpu_ids (ids outside the visible set, or a UUID/MIG
  mask) make resolve_requested_gpu_ids raise. That request is rejected with
  a 400 before training starts, so leave the resident chat model untouched
  instead of unloading it.
- Tighten the target_modules / gpu_ids type hints to List[str] / List[int].

Adds tests for the per-GPU floor (uneven split unloads, even split keeps)
and for invalid gpu_ids keeping the chat model loaded.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: only free chat VRAM once training will start; handle in-flight and CPU-only chat

Address the second review pass on the chat-coexistence path:

- Run the chat/export VRAM teardown as a before_spawn hook inside
  TrainingBackend.start_training, fired only after the start guards pass.
  Previously the route freed chat VRAM before calling start_training, so a
  refused start (e.g. a lingering pump thread) would tear down the resident
  chat model even though no training job began.
- Treat an in-flight HF chat load (loading_models set, no active model yet)
  as not safely sizeable: free it rather than risk both OOMing as the load
  keeps allocating after training starts.
- Do not count or tear down a GGUF llama-server confirmed to run entirely on
  CPU (_gpu_offload_active is False): it holds no VRAM, so killing it cannot
  help training fit.

Adds tests for the before_spawn hook (runs on start, skipped when a
subprocess is alive or a pump thread will not die, survives a hook error),
the in-flight load flag, and the CPU-only GGUF exclusion in both the resident
summary and the unload path.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: treat any in-flight chat load (HF swap / mid-start GGUF) as unsafe to keep

Tighten the in-flight detection in summarize_resident_chat so the keep check
never sizes a load that is still allocating:

- Flag loading on ANY non-empty loading_models, not only when active_model_name
  is empty. load_model adds the new model to loading_models before clearing the
  old active_model_name, so a replacement load during a swap was previously
  sized as a normal resident and could OOM as the new model finishes loading.
- Flag a GGUF server that is active but not yet healthy (is_loaded False) as
  in-flight: it is still mmaping/offloading layers, so its final VRAM footprint
  is unknown.

Consolidates the signal into a single resident["loading"] flag; the route frees
the chat model whenever it is set. Adds tests for the replacement HF load and
the mid-start GGUF cases.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: tighten comments in chat/training VRAM coexistence (comments only)

* Studio: run before_spawn VRAM hook only after GPU-selection validation

Reviewers found the before_spawn hook fired before prepare_gpu_selection
validated gpu_ids (and before config build), so a refused start (invalid
gpu_ids -> 400, or a bad grad-clip value) could still tear down chat/export
VRAM. Move the hook to immediately before proc.start(), once all synchronous
validation and process construction have passed. This also fixes the route's
in-flight-chat loading branch, since that teardown runs inside the same hook.

Add test_hook_skipped_when_gpu_selection_rejects.

* Studio: recompute GPU auto-selection after the before_spawn VRAM hook

Codex P2: with before_spawn moved after prepare_gpu_selection, placement was
frozen against the pre-teardown VRAM state while the hook freed export/chat
afterward. Auto-selection could pin training onto a GPU the hook then cleared
(or onto a kept chat model). Split validation from placement: explicit gpu_ids
are still validated before the hook (raise -> 400, no teardown; explicit
placement is VRAM-independent), but VRAM-dependent auto-selection now runs
after the hook so it sees the freed memory.

Add test_auto_placement_runs_after_hook and test_explicit_placement_validated_before_hook.

* Studio: allow chatting during training (lift sidebar gate + VRAM-aware load guard) (#6335)

* Studio: allow chatting during training (lift sidebar gate + VRAM-aware load guard)

The sidebar disabled New Chat, project, and home navigation while a training
run was active, so users could not chat during training even though the backend
serves inference fine alongside a run. This removes that gate and adds a backend
guard so the one genuinely risky operation, loading a new local chat model
mid-training, is refused with a clear 409 when it would not fit beside the run.

Frontend (app-sidebar.tsx): drop the chatDisabled = isTrainingRunning gate and
its consumers. Navigation triggers no model load on its own, so chat stays
usable during training.

Backend (routes/training_vram.py, routes/inference.py): add
can_load_chat_during_training plus a load/validate guard that sizes the same
effective load the loader performs (LoRA 4-bit to 16-bit resolved first, HF auto
placement via auto_select_gpu_ids, explicit multi-GPU per-GPU floor, GGUF sized
from on-disk shards and companions or the selected remote variant). It is a
no-op when training is inactive, never blocks external providers or
already-resident models, and default-denies only on a CUDA sizing failure so a
load can never OOM the run. Validate refuses early with the real settings so the
frontend does not unload the resident chat model for a load that would be
rejected.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: address review feedback for chat-during-training load guard

- Run the load/validate VRAM guard via asyncio.to_thread so the sync
  nvidia-smi + HF metadata work never blocks the event loop.
- Size the GGUF KV cache at the requested context (_estimate_gguf_kv_gb)
  and add it to the local GGUF estimate so large-context picks are not
  under-counted.
- Keep the requested quantization when adapter_config.json is malformed
  (not a JSON object) instead of raising in _effective_load_in_4bit.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: size the training load guard at the launcher's effective GGUF context

The GGUF KV-cache estimate used max_seq_length only, but the llama.cpp
launcher honors a user --ctx-size/-c in llama_extra_args. A load such as
max_seq_length=4096 with --ctx-size 131072 was sized against a 4k cache
while the server allocates 131k, so the guard could approve a long-context
GGUF load that then OOMs training. Size the guard's KV at the larger of
max_seq_length and the parsed --ctx-size (reusing the launcher's own
parse_ctx_override), keeping the conservative f16 cache so the estimate is
never smaller than what the server allocates.

The chat model picker also validated with the raw max_seq_length while
/load sizes with resolveLoadMaxSeqLength, so validate could pass, unload
the current model, then have /load reject the native-context load. Validate
now uses the same effective context; the load path is unchanged.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: size the GGUF training guard at the server parallel-slot count

The KV-cache estimate assumed a single slot, but llama-server allocates the
cache across --parallel slots (app.state.llama_parallel_slots). On a Studio
launched with --parallel N>1 the guard under-sized the cache N-fold and could
approve a GGUF chat load that then OOMs training. Thread the same slot count
the loader uses into the guard's KV estimate; default 1 leaves single-slot
setups unchanged.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Trim comments for chat-during-training guard

* Studio: keep chat generation alive across navigation; Train spinner + Return to Chat

Hoist the base chat runtime above the routed outlet so navigating to Train (or any tab) no longer aborts an in-flight generation; only an explicit Stop cancels. Add a Train sidebar spinner and swap New Chat to Return to Chat while a run is active, with a lightweight completion watch so the spinner clears from any tab. Also respawn a chat llama-server killed mid-session and guard unreadable HF cache dirs that 500'd the hub model list.

* Studio: show Return to Chat on the Train tab whenever a chat is live

Previously the top sidebar item only swapped to Return to Chat while training was running; on the Train tab with an idle/just-finished run it stayed New Chat, which started a fresh thread and cancelled an in-flight generation. Show Return to Chat (and navigate back, preserving the run) whenever a generation is running or its thread is still active, or training is in progress.

* Studio: keep a running chat alive when starting a New Chat

Starting a New Chat (or switching threads) while a generation was in flight
remounted the single-chat runtime provider, which detached the in-flight run
and cut the previous chat off (it showed up frozen / empty when reopened).

Key the single-chat view by project instead of by thread or new-chat nonce so
the provider stays mounted and assistant-ui switches to a fresh thread in place.
The previous generation keeps streaming in the background and autosaves on
completion, and returning to that thread reattaches the live run instead of
reloading a half-saved one.

Also:
- "Return to Chat" now lands on the thread that is still generating rather than
  the empty new chat that became active after New Chat.
- Skip the explicit /inference/cancel POST when an abort comes from a runtime
  detach (navigation / background switch) rather than an explicit Stop, so a
  backgrounded generation is never cancelled behind the scenes.

* Studio: make model export non-blocking and inline

The Export tab opened a full-screen modal that trapped focus, could not be
closed or cancelled while running, and showed no progress. It also stopped
training and unloaded the chat model before loading, so export could not run
alongside them.

Export now mirrors the training runtime pattern:

- Inline panel embedded where the Export Model button was, with no modal or
  backdrop, so the rest of the UI stays usable during an export.
- Global export runtime store plus an app-root lifecycle hook, so a run keeps
  going and streaming across navigation and is reflected on the Export nav item
  from any tab.
- The worker log stream now stays connected across the load to export phase
  boundary instead of stranding on "Waiting for worker output".
- Progress bar driven by phase and quant index (quant N of M for GGUF), with
  elapsed time and a working Cancel.
- load-checkpoint no longer stops training or unloads inference; export loads in
  its own subprocess in parallel and surfaces out-of-memory as a clear error.
- Add POST /api/export/cancel and is_export_active on /api/export/status.

* Studio: show Return to Chat on the Export tab too

Extend the New Chat to Return to Chat swap to the Export route so leaving a
running chat for Export offers a way back to the live generation, matching the
Train tab.

* Studio: smooth out Export animations and polish the panel

- Drop the height-based reveal animations (source switch, run panel, quant
  picker, hub fields) that caused flashing and reflow; use instant swaps and
  quick opacity fades instead.
- Method and quant cards now transition colors only, with no transition-all or
  hover lift, so selecting a method or quant is crisp instead of jumpy.
- Auto-scroll the export panel into view when it opens and add a scroll-to-bottom
  button when its output is below the fold, like Chat.
- Show Return to Chat on the Export tab while an export is running, matching how
  training drives it on the Train tab.
- Surface the current phase or stage in the live output before the first worker
  line arrives so the panel never looks stuck while progress is advancing.

* Studio: show Return to Chat on every non-chat tab

Generalize the Return to Chat swap from just Train/Export to any non-chat route
(Recipes, Projects, Hub, ...) so a running or active chat is always one click
away, instead of showing New Chat there.

* Studio: stream export logs over the Cloudflare tunnel; drop janky export animations

Exporting over a --secure Cloudflare quick tunnel showed "connecting..." with no
logs while the progress bar advanced. Cloudflare buffers text/event-stream and
only flushes when the stream closes, so the SSE log stream never reached the
browser during the run (direct localhost is unaffected, which is why this only
showed up over the tunnel).

Add a tunnel-safe JSON poll fallback (GET /api/export/logs?since=) that the
runtime lifecycle hook polls while a run is active. Short JSON responses are not
buffered by the proxy, so logs show up in near real time over the tunnel. It
shares the orchestrator's monotonic seq cursor with the SSE stream and the store
de-dupes by seq, so the two transports run together (SSE on localhost, poll over
the tunnel) without double-printing. A successful poll marks the panel
"streaming" instead of leaving it stuck on "connecting...".

Also remove the framer-motion AnimatePresence reveals from the export config and
run panel (quant picker, hub fields, the inline run panel, and the live log
section). The expand/slide animations flashed and felt clunky; the sections now
render in place.

* Studio: recover export over the Cloudflare tunnel when the blocking POST times out (524)

A model export over a --secure Cloudflare quick tunnel showed "Request failed
(524)" even though the export succeeded on the backend (the GGUF was written).
Cloudflare returns 524 when a single request takes longer than ~100s to respond,
and a GGUF conversion routinely runs for minutes, so the blocking per-method
export POST is cut off while the backend keeps going.

Confirm completion via short status polls instead of relying on the long POST
response (the same approach that fixed log streaming):

- The orchestrator records each finished op's outcome (status / output_path /
  error) with a monotonic seq, exposed on GET /api/export/status.
- parseJson now preserves the HTTP status; a 524/520/522/523/502/503 or a
  status-less network drop is classified as a recoverable transport error.
- runExport wraps each phase (load, every export method, each GGUF quant): on a
  recoverable failure it keeps the run alive (logs keep streaming, the panel
  shows "reconnecting...") and polls status until the still-running op finishes,
  then settles from the recorded result, recovering the output path for the
  success banner. A real 4xx still fails immediately; localhost still uses the
  fast POST response. applyBackendStatus also settles a reloaded run from the
  last-op record.

Verified over the tunnel: a 3m14s gemma-4-E4B-it GGUF export now ends on the
success banner with the output path instead of 524.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Studio: keep the export method + logs visible after navigating away mid-export

While an export was running, navigating to another tab and back to Export
remounted the page and reset the local form state (exportMethod, quant levels),
so the method card showed unselected and the run panel's log area was hidden
until the card was re-clicked. The run itself lives in the global store and was
unaffected.

Seed exportMethod / quantLevels from the active run's summary via lazy useState
initializers on (re)mount, and gate the panel's log area on the live run
(isExporting / logLines / the run's method) rather than only the local form
selection. The card stays selected and the logs/progress stay visible across
navigation; nothing changes when no run is active.

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Studio: address export/training review findings

- Export: guard Start against an empty GGUF quant selection so an inline-panel
  run with no quant can't settle as success with no file produced.
- Export: thread the source HF token into the background load so gated/private
  HF source exports (and gated bases) authenticate, matching the consent path.
- Export: only settle a recovered (non-owned) run as a finished export when the
  last backend op was an export, not a standalone load_checkpoint.
- Training: free the export subprocess whenever an export is active, not only
  once a checkpoint is loaded, so an in-flight export load can't race training
  for VRAM (current_checkpoint is unset during the load phase).

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2026-06-18 09:04:01 -07:00

548 lines
20 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Export API routes: checkpoint discovery and model export operations."""
import asyncio
import json
import os
import sys
import time
from pathlib import Path
from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple
from fastapi import APIRouter, Depends, HTTPException, Query, Request
from fastapi.responses import StreamingResponse
import structlog
from loggers import get_logger
backend_path = Path(__file__).parent.parent.parent
if str(backend_path) not in sys.path:
sys.path.insert(0, str(backend_path))
from auth.authentication import get_current_subject
from utils.utils import safe_error_detail
try:
from core.export import get_export_backend
except ImportError:
parent_backend = backend_path.parent / "backend"
if str(parent_backend) not in sys.path:
sys.path.insert(0, str(parent_backend))
from core.export import get_export_backend
from models import (
LoadCheckpointRequest,
ExportStatusResponse,
ExportOperationResponse,
ExportMergedModelRequest,
ExportBaseModelRequest,
ExportGGUFRequest,
ExportLoRAAdapterRequest,
)
router = APIRouter()
logger = get_logger(__name__)
@router.post("/load-checkpoint", response_model = ExportOperationResponse)
async def load_checkpoint(
request: LoadCheckpointRequest, current_subject: str = Depends(get_current_subject)
):
"""Load a checkpoint into the export backend (ExportBackend.load_checkpoint).
Export runs in its own subprocess and is allowed to run in parallel with
training and inference. We deliberately do NOT stop training or unload the
chat model here -- if the GPU runs out of memory the load/export fails with
a clear error instead of tearing down the user's other running workloads.
"""
try:
backend = get_export_backend()
# Run in a worker thread (spawns and waits on a subprocess, can take
# minutes) so the event loop stays free to serve the live log SSE stream.
success, message = await asyncio.to_thread(
backend.load_checkpoint,
checkpoint_path = request.checkpoint_path,
max_seq_length = request.max_seq_length,
load_in_4bit = request.load_in_4bit,
trust_remote_code = request.trust_remote_code,
approved_remote_code_fingerprint = request.approved_remote_code_fingerprint,
hf_token = request.hf_token,
)
if not success:
raise HTTPException(status_code = 400, detail = message)
return ExportOperationResponse(success = True, message = message)
except HTTPException:
raise
except Exception as e:
logger.error(f"Error loading checkpoint: {e}", exc_info = True)
raise HTTPException(
status_code = 500,
detail = "Failed to load checkpoint",
)
@router.post("/cleanup", response_model = ExportOperationResponse)
async def cleanup_export_memory(current_subject: str = Depends(get_current_subject)):
"""Cleanup export-related models from memory (ExportBackend.cleanup_memory)."""
try:
backend = get_export_backend()
success = await asyncio.to_thread(backend.cleanup_memory)
if not success:
raise HTTPException(
status_code = 500,
detail = "Memory cleanup failed. See server logs for details.",
)
return ExportOperationResponse(
success = True,
message = "Memory cleanup completed successfully",
)
except HTTPException:
raise
except Exception as e:
logger.error(f"Error during export memory cleanup: {e}", exc_info = True)
raise HTTPException(
status_code = 500,
detail = "Failed to cleanup export memory",
)
@router.post("/cancel", response_model = ExportOperationResponse)
async def cancel_export(current_subject: str = Depends(get_current_subject)):
"""Cancel the in-flight export by terminating its worker subprocess.
Only the export subprocess is killed; training and inference run in their
own subprocesses and keep going.
"""
try:
backend = get_export_backend()
cancelled = await asyncio.to_thread(backend.cancel_export)
return ExportOperationResponse(
success = True,
message = "Export cancelled" if cancelled else "No active export to cancel",
)
except Exception as e:
logger.error(f"Error cancelling export: {e}", exc_info = True)
raise HTTPException(
status_code = 500,
detail = "Failed to cancel export",
)
@router.get("/status", response_model = ExportStatusResponse)
async def get_export_status(current_subject: str = Depends(get_current_subject)):
"""Get export backend status (loaded checkpoint, model type, PEFT flag)."""
try:
backend = get_export_backend()
last_op = backend.get_last_op()
# Relativise the recovered output path the same way the per-op POST response
# does, so the success banner shows an identical path on either route.
last_op_output_path = None
if last_op and last_op.get("output_path"):
details = _export_details(last_op["output_path"])
last_op_output_path = (details or {}).get("output_path")
return ExportStatusResponse(
current_checkpoint = backend.current_checkpoint,
is_vision = bool(getattr(backend, "is_vision", False)),
is_peft = bool(getattr(backend, "is_peft", False)),
is_export_active = bool(backend.is_export_active()),
active_op_kind = backend.get_active_op_kind(),
last_op_seq = int(last_op["seq"]) if last_op else 0,
last_op_kind = last_op.get("kind") if last_op else None,
last_op_status = last_op.get("status") if last_op else None,
last_op_output_path = last_op_output_path,
last_op_error = last_op.get("error") if last_op else None,
)
except Exception as e:
logger.error(f"Error getting export status: {e}", exc_info = True)
raise HTTPException(
status_code = 500,
detail = "Failed to get export status",
)
@router.get("/logs")
async def get_export_logs(
since: Optional[int] = Query(
None,
description = "Return log entries with seq strictly greater than this cursor.",
),
current_subject: str = Depends(get_current_subject),
):
"""Tunnel-safe JSON fallback for the live export log stream.
The SSE endpoint (`/logs/stream`) is the low-latency path, but some reverse
proxies -- notably Cloudflare quick tunnels (`*.trycloudflare.com`) used by
`--secure` mode -- buffer `text/event-stream` responses and only flush when
the stream closes, so over the tunnel the browser sees nothing for the whole
export ("connecting..." with no logs). This endpoint returns the same
ring-buffer lines as a short, complete JSON response that no proxy buffers,
so the frontend can poll it and still show logs in near real time.
Shares the orchestrator's monotonic `seq` cursor with the SSE stream, so the
two transports can run together and the client de-dupes by seq.
"""
try:
backend = get_export_backend()
# No cursor on the first poll of a run: start from the run-start snapshot
# so the client gets every line since the run began (matches the SSE
# default), not the entire historical ring buffer.
if since is None:
cursor = backend.get_run_start_seq()
else:
cursor = max(0, int(since))
entries, new_cursor = backend.get_logs_since(cursor)
return {
"entries": [
{
"seq": int(entry.get("seq", 0)),
"stream": entry.get("stream", "stdout"),
"line": entry.get("line", ""),
"ts": entry.get("ts"),
}
for entry in entries
],
"cursor": new_cursor,
"active": bool(backend.is_export_active()),
}
except Exception as e:
logger.error(f"Error getting export logs: {e}", exc_info = True)
raise HTTPException(
status_code = 500,
detail = "Failed to get export logs",
)
def _try_register_external_export(path: Path) -> tuple[bool, Optional[str]]:
"""Best-effort registration so absolute exports show up in local scans."""
try:
from storage.studio_db import add_scan_folder
folder = add_scan_folder(str(path))
return True, str(folder.get("path") or path)
except Exception as exc:
logger.warning("Could not register export scan folder %s: %s", path, exc)
return False, None
def _export_details(output_path: Optional[str]) -> Optional[Dict[str, Any]]:
"""Return relative export paths, keeping external absolute paths visible."""
if not output_path:
return None
try:
from utils.paths.storage_roots import exports_root
path = Path(output_path)
# If it's outside exports_root, return the full absolute path
# so users can find their files on a different drive.
if path.is_absolute():
try:
path.resolve().relative_to(exports_root().resolve())
except ValueError:
registered, registered_path = _try_register_external_export(path)
return {
"output_path": str(path),
"scan_folder_registered": registered,
"scan_folder_path": registered_path,
}
rel = os.path.relpath(output_path, exports_root())
return {"output_path": rel}
except Exception:
return {"output_path": output_path}
@router.post("/export/merged", response_model = ExportOperationResponse)
async def export_merged_model(
request: ExportMergedModelRequest, current_subject: str = Depends(get_current_subject)
):
"""Export a merged PEFT model (16-bit or 4-bit), optionally pushing to Hub.
Wraps ExportBackend.export_merged_model.
"""
try:
backend = get_export_backend()
success, message, output_path = await asyncio.to_thread(
backend.export_merged_model,
save_directory = request.save_directory,
format_type = request.format_type,
push_to_hub = request.push_to_hub,
repo_id = request.repo_id,
hf_token = request.hf_token,
private = request.private,
)
if not success:
raise HTTPException(status_code = 400, detail = message)
return ExportOperationResponse(
success = True,
message = message,
details = _export_details(output_path),
)
except HTTPException:
raise
except Exception as e:
logger.error(f"Error exporting merged model: {e}", exc_info = True)
raise HTTPException(
status_code = 500,
detail = "Failed to export merged model",
)
@router.post("/export/base", response_model = ExportOperationResponse)
async def export_base_model(
request: ExportBaseModelRequest, current_subject: str = Depends(get_current_subject)
):
"""Export a non-PEFT base model, optionally pushing to Hub.
Wraps ExportBackend.export_base_model.
"""
try:
backend = get_export_backend()
success, message, output_path = await asyncio.to_thread(
backend.export_base_model,
save_directory = request.save_directory,
push_to_hub = request.push_to_hub,
repo_id = request.repo_id,
hf_token = request.hf_token,
private = request.private,
base_model_id = request.base_model_id,
)
if not success:
raise HTTPException(status_code = 400, detail = message)
return ExportOperationResponse(
success = True,
message = message,
details = _export_details(output_path),
)
except HTTPException:
raise
except Exception as e:
logger.error(f"Error exporting base model: {e}", exc_info = True)
raise HTTPException(
status_code = 500,
detail = "Failed to export base model",
)
@router.post("/export/gguf", response_model = ExportOperationResponse)
async def export_gguf(
request: ExportGGUFRequest, current_subject: str = Depends(get_current_subject)
):
"""Export the current model to GGUF format, optionally pushing to Hub.
Wraps ExportBackend.export_gguf.
"""
try:
backend = get_export_backend()
success, message, output_path = await asyncio.to_thread(
backend.export_gguf,
save_directory = request.save_directory,
quantization_method = request.quantization_method,
push_to_hub = request.push_to_hub,
repo_id = request.repo_id,
hf_token = request.hf_token,
)
if not success:
raise HTTPException(status_code = 400, detail = message)
return ExportOperationResponse(
success = True,
message = message,
details = _export_details(output_path),
)
except HTTPException:
raise
except Exception as e:
logger.error(f"Error exporting GGUF model: {e}", exc_info = True)
raise HTTPException(
status_code = 500,
detail = "Failed to export GGUF model",
)
@router.post("/export/lora", response_model = ExportOperationResponse)
async def export_lora_adapter(
request: ExportLoRAAdapterRequest, current_subject: str = Depends(get_current_subject)
):
"""Export only the LoRA adapter (if the loaded model is PEFT).
Wraps ExportBackend.export_lora_adapter.
"""
try:
backend = get_export_backend()
success, message, output_path = await asyncio.to_thread(
backend.export_lora_adapter,
save_directory = request.save_directory,
push_to_hub = request.push_to_hub,
repo_id = request.repo_id,
hf_token = request.hf_token,
private = request.private,
)
if not success:
raise HTTPException(status_code = 400, detail = message)
return ExportOperationResponse(
success = True,
message = message,
details = _export_details(output_path),
)
except HTTPException:
raise
except Exception as e:
logger.error(f"Error exporting LoRA adapter: {e}", exc_info = True)
raise HTTPException(
status_code = 500,
detail = "Failed to export LoRA adapter",
)
# Live export log stream (Server-Sent Events).
#
# The export worker's stdout/stderr is piped to the orchestrator as log
# entries (core/export/worker.py, orchestrator.py); this endpoint streams
# them to the browser for a live terminal panel during export operations.
#
# Shape follows routes/training.py::stream_training_progress: each event
# carries id/event/data, the stream starts with a `retry:` directive, and
# `Last-Event-ID` is honored on reconnect.
def _format_sse(
data: str,
event: str,
event_id: Optional[int] = None,
) -> str:
"""Format a single SSE message with id/event/data fields."""
lines = []
if event_id is not None:
lines.append(f"id: {event_id}")
lines.append(f"event: {event}")
lines.append(f"data: {data}")
lines.append("")
lines.append("")
return "\n".join(lines)
@router.get("/logs/stream")
async def stream_export_logs(
request: Request,
since: Optional[int] = Query(
None,
description = "Return log entries with seq strictly greater than this cursor.",
),
current_subject: str = Depends(get_current_subject),
):
"""
Stream live stdout/stderr from the export worker subprocess as
Server-Sent Events.
Events:
- `log` : a single log line (data: {"stream","line","ts"})
- `heartbeat`: periodic keepalive when no new lines are available
- `complete` : once the worker is idle and no new lines arrived for
~1 second. Clients should close.
- `error` : unrecoverable server-side error
Each event's `id:` field is the log entry's monotonic seq number so the
browser can resume via `Last-Event-ID` on reconnect.
"""
backend = get_export_backend()
# Starting cursor: explicit `since` wins, then Last-Event-ID on reconnect,
# else the run-start snapshot so the client sees every line since the run
# began even if the SSE connection opened after the export-kickoff POST.
last_event_id = request.headers.get("last-event-id")
if since is None and last_event_id is not None:
try:
since = int(last_event_id)
except ValueError:
pass
if since is None:
cursor = backend.get_run_start_seq()
else:
cursor = max(0, int(since))
async def event_generator() -> AsyncGenerator[str, None]:
nonlocal cursor
# Reconnect after 3 seconds if the connection drops mid-export.
yield "retry: 3000\n\n"
last_yield = time.monotonic()
idle_since: Optional[float] = None
try:
while True:
if await request.is_disconnected():
return
entries, new_cursor = backend.get_logs_since(cursor)
if entries:
for entry in entries:
payload = json.dumps(
{
"stream": entry.get("stream", "stdout"),
"line": entry.get("line", ""),
"ts": entry.get("ts"),
}
)
yield _format_sse(
payload,
event = "log",
event_id = int(entry.get("seq", 0)),
)
cursor = new_cursor
last_yield = time.monotonic()
idle_since = None
else:
now = time.monotonic()
if now - last_yield > 10.0:
yield _format_sse("{}", event = "heartbeat")
last_yield = now
if not backend.is_export_active():
# Let the reader thread drain trailing lines printed just
# before the worker signalled done.
if idle_since is None:
idle_since = now
elif now - idle_since > 1.0:
yield _format_sse(
"{}",
event = "complete",
event_id = cursor,
)
return
else:
idle_since = None
await asyncio.sleep(0.1)
except asyncio.CancelledError:
# Client disconnected mid-yield: end cleanly so StreamingResponse finalizes.
return
except Exception as exc:
logger.error("Export log stream failed: %s", exc, exc_info = True)
try:
yield _format_sse(
json.dumps({"error": safe_error_detail(exc)}),
event = "error",
)
except Exception:
pass
return StreamingResponse(
event_generator(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)