* Studio: exclude /api/export/status from request access logs The frontend polls /api/export/status every 5s to detect export start, so it fires continuously even when idle. Each poll emitted an info request_completed access line, making up most of the server access logs. Add it to _EXCLUDED_PATHS alongside /api/train/status. The endpoint is unchanged; export state is still logged by the export modules and streamed over SSE, so no signal is lost. * Studio: collapse hub download-progress polls in the access log download-status and gguf-download-progress (plus the dataset equivalents) are polled about twice a second for the whole download, so each emitted an info request_completed line. Add them to _QUIET_POLL_PATHS so they collapse to one heartbeat line per 10s instead of one per poll. * Studio: log hub download progress at 10% steps The access log carried no real progress, only poll pings. Emit one hub_download_progress line per 10% step from the shared snapshot progress reader, so an active download shows actual percentage without a line per poll. Throttled per job and resynced if the same download restarts. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: drop successful chat thread/project CRUD from the access log A single chat turn fans out about twenty requests under /api/chat/threads and /api/chat/projects (list, fetch, per-message forks, and the message writes) that only reflect the UI re-rendering. Suppress their 2xx access line so the log keeps the signal (generation, tool calls, code execution, engine stats) and errors. Non-2xx on these paths still log. * Studio: silence transformers torch_dtype deprecation warning transformers logs "`torch_dtype` is deprecated! Use `dtype` instead!" once at model-config load via logger.warning_once (logging, not warnings), so a warnings filter cannot catch it. Attach a small logging.Filter in setup_logging, which runs before any model config is parsed, to drop that record on the transformers loggers that emit it. * Studio: quiet inference load-progress polls and log throttled load progress The frontend polls /api/inference/load-progress about twice a second for the whole model load, so each emitted a request_completed line. Add it to _QUIET_POLL_PATHS (heartbeat) and emit one inference_load_progress line per 10% step from the load-progress route, so a load shows real percentage instead of a line per poll. * Studio: fully suppress download/load progress poll access lines The download-status, download-progress, gguf-download-progress, active-downloads and transport-status polls (model and dataset), plus inference load-progress, fire ~2x/s for the whole download or load. Their progress is now reported by the hub_download_progress / inference_load_progress events (and the viewer's progress line), so the per-poll access line adds nothing. Drop it on 2xx and keep it on errors, instead of the prior 10s heartbeat. Chat CRUD suppression is folded into the same _is_quiet_success helper. * Studio: suppress training-tab model/dataset download-progress polls The training tab polls /api/models/download-progress and /api/datasets/download-progress about twice a second for the whole prep phase. These are separate routes from the /api/hub equivalents and only scan the cache, so their 2xx access line adds nothing (on Windows they always read 0 since the bytes live in snapshots/, not blobs/). Suppress the 2xx line and keep errors, alongside /api/models/gguf-download-progress. * Studio: drop transient pre-auth 401 on chat thread/project polls On first load the SPA fires chat thread/project GETs before the initial token refresh, so they 401 until /api/auth/refresh runs and the retries succeed. That pre-auth 401 is a bootstrap artifact, not an error; suppress it alongside the already-quiet 2xx line. Genuine 4xx/5xx on these paths, the download/load poll 401s, and all /api/auth/* still log. * Studio: quiet tab-switch list polls and per-poll scan/reconnect logs Switching between the Train, Export, and Chat tabs refetches list endpoints on a timer, and each hit re-logs internal detail. Heartbeat /api/train/runs, /api/models/checkpoints, /api/models/local and /api/rag/knowledge-bases (10s window, first hit and errors still log), and downgrade two per-poll INFO lines to debug: the checkpoints scan summary ("Found N training runs") and the per-reconnect SSE resume line. The meaningful "replayed N missed steps" line, logged only when steps were actually replayed, stays at info. * Studio: enable tokenizer parallelism for dataset prep on Windows/macOS TOKENIZERS_PARALLELISM was forced off everywhere to stop datasets' forked map() workers from deadlocking, but that fork only happens on Linux. On spawn platforms (Windows/macOS) dataset.map() runs in-process (dataset_map_num_proc returns None), so disabling tokenizer parallelism leaves the fast tokenizer single-threaded and dataset prep runs serially on one core. Keep it off on Linux (fork safety) and on for spawn platforms, where there is no fork to deadlock. Measured ~7x faster tokenization (12.5s -> 1.7s for 20k rows on a 32-core Windows box). * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: log throttled training status to the server log Training step/loss/epoch only went to the UI via SSE, so the server log showed inference engine_stats and train/runs heartbeats but nothing about the actual run. Emit one throttled training_progress line (step/total, percent, loss, epoch, eta) from the CUDA event pump: the first step, then at most every 30s, plus the final step, resyncing when a new run restarts the counter. Per-step UI streaming is unchanged. * Studio: quiet llama.cpp update-status polls and log throttled update progress The prebuilt llama.cpp update polls /api/llama/update-status about twice a second for the whole download and install. Suppress its 2xx access line (errors still log) and emit one throttled llama_update_progress line per 10% step from the status route, so the update shows progress without a line per poll. The existing "llama update: installing" and "llama update: success" events still bracket it. * Studio: quiet the export log-tail poll The Export tab polls /api/export/logs about once a second to stream the export subprocess output into the UI panel. Suppress its 2xx access line; the real progress is already logged as event-driven "Export subprocess status: <phase>" lines plus the subprocess start and checkpoint-loaded events, and errors still log. * studio: keep errors and mutations visible in access-log suppression Make the quiet-success access-log suppression GET-only so chat thread/project mutations (POST/PUT/DELETE) still log; only their list-poll 2xx and the transient pre-auth 401 are dropped. Suppress /api/export/status 2xx only (move it out of the all-status exclude set) so a 401/403/500 on it stays visible. Legacy /api/models and /api/datasets download-progress polls emit no hub_download_progress events, so heartbeat them via the 10s quiet-poll window instead of suppressing outright, keeping download visibility (notably on Linux). The event-emitting /api/hub download polls stay fully suppressed. Update and extend the middleware tests to cover GET-only suppression, the export-status error path, and the legacy download heartbeat. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: tighten access-log and training-progress comments Comment-only pass: collapse the multi-line explanations in the logging middleware and the throttled training-progress logger to fewer lines while keeping the rationale. No behavior change. * studio: log structured export_progress phases Emit a structured export_progress event per phase (consolidated in the server log like training and download progress) instead of a plain status string, and add a phase milestone at the start of the heavy export step so the merge/save/convert is visible in the server log, not only in the forwarded stdout panel. * Studio: reset training-progress log throttle on each new run start_training rebuilds the per-run progress state but left _last_progress_log_ts/_last_progress_log_step at their prior values. A run started within 30s of a previous one whose last logged step matched the new run's first step would hit the step == prev short-circuit and drop the promised first training_progress line, then stay suppressed until the old 30s window expired. Reset both fields when a new job is accepted. * Studio: keep post-bootstrap chat 401s visible in the access log The chat thread/project 401 suppression dropped every GET 401 on those prefixes, so a genuine expired-session 401 vanished alongside the transient pre-auth race. Gate the 401 drop on a per-middleware bootstrap latch that flips once /api/auth/refresh first succeeds: before that the 401s are the pre-refresh race and are suppressed; after it any chat 401 is a real failure and logs. Add a test for the post-refresh case. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: limit chat access-log suppression to the exact list polls The chat thread/project suppression matched by startswith, so it also dropped the 2xx access line for detail and message reads (/threads/{id}, /threads/{id}/messages, /threads/{id}/messages/{id}, /projects/{id}) that are not the high-frequency list polls, losing their access and latency logging. Match the two list paths exactly instead, so only the intended list polls (and their pre-auth 401 race) are suppressed while detail and message reads keep their access line. Add a regression test. * Studio: reset inference load-progress throttle for each load The load-progress throttle (_last_load_progress_step) is a module global that persisted across loads, so a cached or small load whose first sampled /api/inference/load-progress response already reported fraction=1.0 hit step == prev (10) from a prior completed load and emitted no inference_load_progress line, while that endpoint's access log is suppressed, leaving the new load with no progress signal. Arm the throttle at load initiation in _load_model_impl so each load's first step always logs. Add a regression test. * Studio: tighten logging comments Collapse a few verbose comments (tokenizer-parallelism note, torch_dtype filter, legacy download-poll heartbeat, chat list-path suppression) to fewer lines without changing intent or code. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
115 lines
4.1 KiB
Python
115 lines
4.1 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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"""llama.cpp prebuilt update endpoints.
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GET /api/llama/update-status -> is a newer prebuilt available + job state
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POST /api/llama/update -> download + atomically swap to the latest
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Detection reuses utils.llama_cpp_freshness; the swap reuses
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install_llama_prebuilt.py via utils.llama_cpp_update. Both fail open so the UI
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never blocks on a missing marker / offline GitHub.
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"""
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from __future__ import annotations
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import asyncio
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import threading
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from typing import Optional
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from fastapi import APIRouter, Depends, Query
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from pydantic import BaseModel, Field
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from auth.authentication import get_current_subject
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from loggers import get_logger
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from utils.llama_cpp_update import get_update_status, start_update
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logger = get_logger(__name__)
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router = APIRouter()
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class LlamaUpdateJob(BaseModel):
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state: str = Field("idle", description = "idle | running | success | error")
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message: str = ""
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from_tag: Optional[str] = None
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to_tag: Optional[str] = None
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reload_required: Optional[bool] = None
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error: Optional[str] = None
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progress: Optional[float] = Field(None, description = "0..1 while running, 1 on success.")
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started_at: Optional[str] = None
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finished_at: Optional[str] = None
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class LlamaUpdateStatusResponse(BaseModel):
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supported: bool = Field(
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False,
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description = "True when the install came from an Unsloth prebuilt (has a marker).",
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)
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update_available: bool = Field(
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False, description = "True when the latest release is genuinely newer than the install."
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)
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stale: bool = Field(
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False, description = "Update available AND install older than the staleness threshold."
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)
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installed_tag: Optional[str] = None
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latest_tag: Optional[str] = None
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published_repo: Optional[str] = None
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installed_at_utc: Optional[str] = None
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age_days: Optional[int] = None
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source_build: bool = Field(
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False, description = "True when there is no marker (source build) but a prebuilt is offered."
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)
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update_size_bytes: Optional[int] = Field(
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None, description = "Download size of the prebuilt Update would fetch, in bytes."
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)
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job: LlamaUpdateJob = Field(default_factory = LlamaUpdateJob)
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class LlamaUpdateActionResponse(BaseModel):
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started: bool
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reason: Optional[str] = None
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message: Optional[str] = None
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job: LlamaUpdateJob = Field(default_factory = LlamaUpdateJob)
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_llama_update_lock = threading.Lock()
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_last_llama_update_step = -1
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def _log_llama_update_progress(job: LlamaUpdateJob) -> None:
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"""One llama_update_progress line per 10% step so a prebuilt update reports
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progress without a line per poll. Resyncs when a new update starts."""
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global _last_llama_update_step
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if job.state != "running" or job.progress is None:
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return
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step = int(max(0.0, min(float(job.progress), 1.0)) * 10)
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with _llama_update_lock:
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prev = _last_llama_update_step
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if step == prev:
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return
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_last_llama_update_step = step
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if step < prev:
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return # new update; resync without logging
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logger.info("llama_update_progress", to_tag = job.to_tag or "", percent = step * 10)
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@router.get("/update-status", response_model = LlamaUpdateStatusResponse)
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async def llama_update_status(
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force_refresh: bool = Query(
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False, description = "Bypass the 24h release cache for an explicit check."
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),
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current_subject: str = Depends(get_current_subject),
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) -> LlamaUpdateStatusResponse:
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# Off the event loop: detection may probe the host and read GitHub.
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status = await asyncio.to_thread(get_update_status, force_refresh = force_refresh)
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resp = LlamaUpdateStatusResponse(**status)
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_log_llama_update_progress(resp.job)
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return resp
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@router.post("/update", response_model = LlamaUpdateActionResponse)
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async def llama_update(
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current_subject: str = Depends(get_current_subject),
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) -> LlamaUpdateActionResponse:
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action = await asyncio.to_thread(start_update)
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return LlamaUpdateActionResponse(**action)
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