* Studio: add API key authentication for programmatic access External users want to hit the Studio API (chat completions with tool calling, training, export, etc.) without going through the browser login flow. This adds sk-unsloth- prefixed API keys that work as a drop-in replacement for JWTs in the Authorization: Bearer header. Backend: - New api_keys table in SQLite (storage.py) - create/list/revoke/validate functions with SHA-256 hashed storage - API key detection in _get_current_subject before the JWT path - POST/GET/DELETE /api/auth/api-keys endpoints on the auth router Frontend: - /api-keys page with create form, one-time key reveal, keys table - API Keys link in desktop and mobile navbar - Route registered with requireAuth guard Zero changes to any existing route handler -- every endpoint that uses Depends(get_current_subject) automatically works with API keys. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Use actual origin in API key usage examples The examples on /api-keys were hardcoded to localhost:8888 which is wrong for remote users. Use window.location.origin so the examples show the correct URL regardless of where the user is connecting from. * Add `unsloth studio run` CLI command for one-liner model serving Adds a `run` subcommand that starts Studio, loads a model, creates an API key, and prints a ready-to-use curl command -- similar to `ollama run` or `vllm serve`. Usage: unsloth studio run -m unsloth/Qwen3-1.7B-GGUF --gguf-variant UD-Q4_K_XL * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add end-to-end tests for `unsloth studio run` and API key usage Tests the 4 usage examples from the API Keys page: 1. curl basic (non-streaming) chat completions 2. curl streaming (SSE) chat completions 3. OpenAI Python SDK streaming completions 4. curl with tools (web_search + python) Also tests --help output, invalid key rejection, and no-key rejection. All 7 tests pass against Qwen3-1.7B-GGUF. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add /v1/completions, /v1/embeddings, /v1/responses endpoints and --parallel support - llama_cpp.py: accept n_parallel param, pass to llama-server --parallel - run.py: plumb llama_parallel_slots through to app.state - inference.py: add /completions and /embeddings as transparent proxies to llama-server, add /responses as application-level endpoint that converts to ChatCompletionRequest; thread n_parallel through load_model - studio.py: set llama_parallel_slots=4 for `unsloth studio run` path * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Make /v1/responses endpoint match OpenAI Responses API format The existing /v1/responses shim returned Chat Completions format, which broke OpenAI SDK clients using openai.responses.create(). This commit replaces the endpoint with a proper implementation that: - Returns `output` array with `output_text` content parts instead of `choices` with `message` - Uses `input_tokens`/`output_tokens` instead of `prompt_tokens`/ `completion_tokens` in usage - Sets `object: "response"` and `id: "resp_..."` - Emits named SSE events for streaming (response.created, response.output_text.delta, response.completed, etc.) - Accepts all OpenAI Responses API fields (tools, store, metadata, previous_response_id) without erroring -- silently ignored - Maps `developer` role to `system` and `input_text`/`input_image` content parts to the internal Chat format Adds Pydantic schemas for request/response models and 23 unit tests covering schema validation, input normalisation, and response format. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: add Anthropic-compatible /v1/messages endpoint (#4981) * Add Anthropic-compatible /v1/messages endpoint with tool support Translate Anthropic Messages API format to/from internal OpenAI format and reuse the existing server-side agentic tool loop. Supports streaming SSE (message_start, content_block_delta, etc.) and non-streaming JSON. Includes offline unit tests and e2e tests in test_studio_run.py. * Add enable_tools, enabled_tools, session_id to /v1/messages endpoint Support the same shorthand as /v1/chat/completions: enable_tools=true with an optional enabled_tools list uses built-in server tools without requiring full Anthropic tool definitions. session_id is passed through for sandbox isolation. max_tokens is now optional. * Strip leaked tool-call XML from Anthropic endpoint content Apply _TOOL_XML_RE to content events in both streaming and non-streaming tool paths, matching the OpenAI endpoint behavior. * Emit custom tool_result SSE event in Anthropic stream Adds a non-standard tool_result event between the tool_use block close and the next text block, so clients can see server-side tool execution results. Anthropic SDKs ignore unknown event types. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Split /v1/messages into server-side and client-side tool paths enable_tools=true runs the existing server-side agentic loop with built-in tools (web_search/python/terminal). A bare tools=[...] field now triggers a client-side pass-through: client-provided tools are forwarded to llama-server and any tool_use output is returned to the caller with stop_reason=tool_use for client execution. This fixes Claude Code (and any Anthropic SDK client) which sends tools=[...] expecting client-side execution but was previously routed through execute_tool() and failing with 'Unknown tool'. Adds AnthropicPassthroughEmitter to convert llama-server OpenAI SSE chunks into Anthropic SSE events, plus unit tests covering text blocks, tool_use blocks, mixed, stop reasons, and usage. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix httpcore GeneratorExit in /v1/messages passthrough stream Explicitly aclose aiter_lines() before the surrounding async with blocks unwind, mirroring the prior fix in external_provider.py (a41160d3) and cc757b78's RuntimeError suppression. * Wire stop_sequences through /v1/messages; warn on tool_choice Plumb payload.stop_sequences to all three code paths (server-side tool loop, no-tool plain, client-side passthrough) so Anthropic SDK clients setting stop_sequences get the behavior they expect. The llama_cpp backend already accepted `stop` on both generate_chat_ completion and generate_chat_completion_with_tools; the Anthropic handler simply wasn't passing it. tool_choice remains declared on the request model for Anthropic SDK compatibility (the SDK often sets it by default) but is not yet honored. Log a structured warning on each request carrying a non- null tool_choice so the silent drop is visible to operators. * Wire min_p / repetition_penalty / presence_penalty through /v1/messages Align the Anthropic endpoint's sampling surface with /v1/chat/completions. Adds the three fields as x-unsloth extensions on AnthropicMessagesRequest and threads them through all three code paths: server-side tool loop, no-tool plain, and client-side passthrough. The passthrough builder emits "repeat_penalty" (not "repetition_penalty") because that is llama-server's field name; the backend methods already apply the same rename internally. * Fix block ordering and prev_text reset in non-streaming tool path _anthropic_tool_non_streaming was building the response by appending all tool_use blocks first, then a single concatenated text block at the end — losing generation order and merging pre-tool and post-tool text into one block. It also never reset prev_text between synthesis turns, so the first N characters of each post-tool turn were dropped (where N = length of the prior turn's final cumulative text). Rewrite to build content_blocks incrementally in generation order, matching the streaming emitter's behavior: deltas within a turn are merged into the trailing text block, tool_use blocks interrupt the text sequence, and prev_text is reset on tool_end so turn N+1 diffs against an empty baseline. Caught by gemini-code-assist[bot] review on #4981. * Make test_studio_run.py e2e tests pytest-compatible Add a hybrid session-scoped studio_server fixture in conftest.py that feeds base_url / api_key into the existing e2e test functions. Three invocation modes are now supported: 1. Script mode (unchanged) — python tests/test_studio_run.py 2. Pytest + external server — point at a running instance via UNSLOTH_E2E_BASE_URL / UNSLOTH_E2E_API_KEY env vars, no per-run GGUF load cost 3. Pytest + fixture-managed server — pytest drives _start_server / _kill_server itself via --unsloth-model / --unsloth-gguf-variant, CI-friendly The existing _start_server / _kill_server helpers and main() stay untouched so the script entry point keeps working exactly as before. Test function signatures are unchanged — the (base_url, api_key) parameters now resolve via the new fixtures when running under pytest. * Rename test_studio_run.py -> test_studio_api.py The file is entirely about HTTP API endpoint testing (OpenAI-compatible /v1/chat/completions, Anthropic-compatible /v1/messages, API key auth, plus a CLI --help sanity check on the command that runs the API). None of its tests cover training, export, chat-UI, or internal-Python-API concerns. The old name misleadingly suggested "tests for the unsloth studio run CLI subcommand" — the new name reflects the actual scope. Updates: - git mv the file (rename tracked, history preserved) - Rewrite opening docstring to state the API surface focus and call out what is explicitly out of scope - Update all 4 Usage-block path references to the new filename - LOG_FILE renamed to test_studio_api.log - conftest.py fixture import rewritten from test_studio_run to test_studio_api, plus 7 docstring/comment references updated No functional changes to test logic, signatures, or main(). --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix httpcore asyncgen cleanup in /v1/messages and /v1/completions The earlier fix in985e92a9was incomplete: it closed aiter_lines() explicitly but still used `async with httpx.AsyncClient()` / `async with client.stream()` inside the generator. When the generator is orphaned (e.g. client disconnects mid-stream and Starlette drops the StreamingResponse iterator without explicitly calling aclose()), Python's asyncgen finalizer runs the cleanup in a DIFFERENT task than the one that originally entered the httpx context managers. The `async with` exits then trigger httpcore's HTTP11ConnectionByteStream .aclose(), which enters anyio.CancelScope.__exit__ with a mismatched task and raises RuntimeError("Attempted to exit cancel scope in a different task"). That error escapes any user-owned try/except because it happens during GC finalization. Replace `async with` with manual client/response lifecycle in both /v1/messages passthrough and /v1/completions proxy. Close the response and client in a finally block wrapped in `try: ... except Exception: pass`. This suppresses RuntimeError (and other Exception subclasses) from the anyio cleanup noise while letting GeneratorExit (a BaseException, not Exception) propagate cleanly so the generator terminates as Python expects. Traceback observed in user report: File ".../httpcore/_async/connection_pool.py", line 404, in __aiter__ yield part RuntimeError: async generator ignored GeneratorExit ... File ".../anyio/_backends/_asyncio.py", line 455, in __exit__ raise RuntimeError( RuntimeError: Attempted to exit cancel scope in a different task * Expand unsloth studio run banner with SDK base URL and more curl examples Add an explicit "OpenAI / Anthropic SDK base URL" line inside the info box so SDK users don't accidentally copy the bare server URL (without /v1) into their OpenAI/Anthropic SDK constructors and hit 404s. Replace the single /v1/chat/completions curl example with three labeled blocks: chat/completions, Anthropic /messages, and OpenAI Responses. The Anthropic example includes max_tokens (Anthropic SDKs require it even though Studio accepts None). All examples derived from a computed sdk_base_url so the /v1 prefix stays in sync if the public path ever changes. * Hash API keys with HMAC-SHA256 + persistent server secret Stores the HMAC secret in a new app_secrets singleton table. Fixes CodeQL py/weak-sensitive-data-hashing alert on storage.py:74-76, 394-395. Refresh tokens stay on plain SHA-256 (unchanged _hash_token) so existing user sessions survive upgrade — API keys are new on this branch so there is no migration. * Use PBKDF2 for API key hashing per CodeQL recommendation HMAC-SHA256 was still flagged by py/weak-sensitive-data-hashing. Switch to hashlib.pbkdf2_hmac, which is in CodeQL's recommended allowlist (Argon2/scrypt/bcrypt/PBKDF2). Persistent server-side salt stays in app_secrets for defense-in-depth. 100k iterations to match auth/hashing.py's password hasher. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com> Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
433 lines
15 KiB
Python
433 lines
15 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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Run script for Unsloth UI Backend.
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Works independently and can be moved to any directory.
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"""
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import os
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import sys
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from pathlib import Path
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# Suppress annoying C-level dependency warnings globally (e.g. SwigPyPacked)
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os.environ["PYTHONWARNINGS"] = "ignore"
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# Add the backend directory to Python path early so local modules are importable
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backend_dir = Path(__file__).parent
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if str(backend_dir) not in sys.path:
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sys.path.insert(0, str(backend_dir))
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# Fix for Anaconda/conda-forge Python: seed platform._sys_version_cache before
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# any library imports that trigger attrs -> rich -> structlog -> platform crash.
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# See: https://github.com/python/cpython/issues/102396
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import _platform_compat # noqa: F401
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from loggers import get_logger
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from startup_banner import print_studio_access_banner
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logger = get_logger(__name__)
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def _resolve_external_ip() -> str:
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"""
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Resolve the machine's external IP address.
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Tries (in order):
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1. GCE metadata server (instant, works on Google Cloud VMs)
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2. ifconfig.me (works anywhere with internet)
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3. LAN IP via UDP socket trick (fallback)
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"""
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import urllib.request
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import socket
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# 1. Try GCE metadata server (responds in <10ms on GCE, times out fast elsewhere)
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try:
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req = urllib.request.Request(
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"http://metadata.google.internal/computeMetadata/v1/instance/network-interfaces/0/access-configs/0/external-ip",
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headers = {"Metadata-Flavor": "Google"},
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)
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with urllib.request.urlopen(req, timeout = 1) as resp:
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ip = resp.read().decode().strip()
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if ip:
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return ip
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except Exception:
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pass
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# 2. Try public IP service
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try:
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with urllib.request.urlopen("https://ifconfig.me", timeout = 3) as resp:
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ip = resp.read().decode().strip()
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if ip:
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return ip
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except Exception:
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pass
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# 3. Fallback: LAN IP via UDP socket trick
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try:
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s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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s.connect(("8.8.8.8", 80))
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ip = s.getsockname()[0]
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s.close()
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return ip
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except Exception:
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return "0.0.0.0"
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def _get_pid_on_port(port: int) -> "tuple[int, str] | None":
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"""Return (pid, process_name) of the process listening on *port*, or None.
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Uses psutil when available. Falls back gracefully to None so callers
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can still report the port conflict without process details.
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Works on Windows, macOS, and Linux wherever psutil is installed.
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"""
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try:
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import psutil
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except ImportError:
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return None
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try:
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for conn in psutil.net_connections(kind = "tcp"):
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if conn.status == "LISTEN" and conn.laddr.port == port:
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if conn.pid is None:
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return None
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try:
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proc = psutil.Process(conn.pid)
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return (conn.pid, proc.name())
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except (psutil.NoSuchProcess, psutil.AccessDenied):
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return (conn.pid, "<unknown>")
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except (psutil.AccessDenied, OSError) as e:
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# psutil.net_connections() needs elevated privileges on some platforms
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logger.debug("Failed to scan network connections for port %s: %s", port, e)
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return None
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def _is_port_free(host: str, port: int) -> bool:
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"""Check if a port is available for binding.
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When *host* is ``0.0.0.0`` (wildcard), we also check whether anything
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is already listening on ``127.0.0.1`` (and ``::1`` when IPv6 is
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available). An SSH tunnel or similar process may hold the loopback
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address while our wildcard bind still succeeds, making Unsloth Studio
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unreachable via ``localhost``.
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Works on Windows, macOS, and Linux.
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"""
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import socket
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# 1. Can we bind to the requested address?
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# Use getaddrinfo so both IPv4 ("0.0.0.0") and IPv6 ("::") hosts
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# resolve to the correct address family automatically.
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try:
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addr_info = socket.getaddrinfo(host, port, socket.AF_UNSPEC, socket.SOCK_STREAM)
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family, socktype, proto, _, sockaddr = addr_info[0]
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with socket.socket(family, socktype, proto) as s:
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s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
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s.bind(sockaddr)
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except OSError:
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return False
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# 2. When binding to all interfaces, verify that localhost is not
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# already claimed by another process (e.g. an SSH -L tunnel).
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# We attempt a TCP connect -- if it succeeds something is listening.
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if host in ("0.0.0.0", "::"):
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for loopback, family in [
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("127.0.0.1", socket.AF_INET),
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("::1", socket.AF_INET6),
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]:
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try:
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with socket.socket(family, socket.SOCK_STREAM) as s:
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s.settimeout(1)
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if s.connect_ex((loopback, port)) == 0:
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# Connection succeeded -- port is taken on loopback
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return False
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except OSError:
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# IPv6 disabled or other OS-level restriction -- skip
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continue
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return True
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def _find_free_port(host: str, start: int, max_attempts: int = 20) -> int:
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"""Find a free port starting from `start`, trying up to max_attempts ports."""
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for offset in range(max_attempts):
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candidate = start + offset
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if _is_port_free(host, candidate):
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return candidate
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raise RuntimeError(
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f"Could not find a free port in range {start}-{start + max_attempts - 1}"
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)
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_PID_FILE = Path.home() / ".unsloth" / "studio" / "studio.pid"
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def _write_pid_file():
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"""Write the current process PID to the studio PID file."""
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try:
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_PID_FILE.parent.mkdir(parents = True, exist_ok = True)
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_PID_FILE.write_text(str(os.getpid()))
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except OSError:
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pass
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def _remove_pid_file():
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"""Remove the PID file if it belongs to this process."""
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try:
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if _PID_FILE.is_file():
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stored = _PID_FILE.read_text().strip()
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if stored == str(os.getpid()):
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_PID_FILE.unlink(missing_ok = True)
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except OSError:
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pass
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def _graceful_shutdown(server = None):
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"""Explicitly shut down all subprocess backends and the uvicorn server.
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Called from signal handlers to ensure child processes are cleaned up
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before the parent exits. This is critical on Windows where atexit
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handlers are unreliable after Ctrl+C.
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"""
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_remove_pid_file()
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logger.info("Graceful shutdown initiated — cleaning up subprocesses...")
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# 1. Shut down uvicorn server (releases the listening socket)
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if server is not None:
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server.should_exit = True
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# 2. Clean up inference subprocess (if instantiated)
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try:
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from core.inference.orchestrator import _inference_backend
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if _inference_backend is not None:
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_inference_backend._shutdown_subprocess(timeout = 5.0)
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except Exception as e:
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logger.warning("Error shutting down inference subprocess: %s", e)
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# 3. Clean up export subprocess (if instantiated)
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try:
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from core.export.orchestrator import _export_backend
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if _export_backend is not None:
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_export_backend._shutdown_subprocess(timeout = 5.0)
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except Exception as e:
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logger.warning("Error shutting down export subprocess: %s", e)
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# 4. Clean up training subprocess (if active)
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try:
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from core.training.training import _training_backend
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if _training_backend is not None:
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_training_backend.force_terminate()
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except Exception as e:
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logger.warning("Error shutting down training subprocess: %s", e)
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# 5. Kill llama-server subprocess (if loaded)
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try:
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from routes.inference import _llama_cpp_backend
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if _llama_cpp_backend is not None:
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_llama_cpp_backend._kill_process()
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except Exception as e:
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logger.warning("Error shutting down llama-server: %s", e)
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logger.info("All subprocesses cleaned up")
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# The uvicorn server instance -- set by run_server(), used by callers
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# that need to tell the server to exit (e.g. signal handlers).
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_server = None
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# Shutdown event -- used to wake the main loop on signal
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_shutdown_event = None
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def run_server(
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host: str = "0.0.0.0",
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port: int = 8888,
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frontend_path: Path = Path(__file__).resolve().parent.parent / "frontend" / "dist",
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silent: bool = False,
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llama_parallel_slots: int = 1,
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):
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"""
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Start the FastAPI server.
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Args:
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host: Host to bind to
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port: Port to bind to (auto-increments if in use)
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frontend_path: Path to frontend build directory (optional)
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silent: Suppress startup messages
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llama_parallel_slots: Number of parallel slots for llama-server
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Note:
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Signal handlers are NOT registered here so that embedders
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(e.g. Colab notebooks) keep their own interrupt semantics.
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Standalone callers should register handlers after calling this.
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"""
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global _server, _shutdown_event
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# On Windows the default console encoding (cp1252) cannot encode emoji.
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# Reconfigure stdout to UTF-8 so startup messages do not crash the server.
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if sys.platform == "win32" and hasattr(sys.stdout, "reconfigure"):
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try:
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sys.stdout.reconfigure(encoding = "utf-8", errors = "replace")
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except Exception:
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pass
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import nest_asyncio
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nest_asyncio.apply()
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import asyncio
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from threading import Thread, Event
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import time
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import uvicorn
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from main import app, setup_frontend
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from utils.paths import ensure_studio_directories
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# Create all standard directories on startup
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ensure_studio_directories()
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# Auto-find free port if requested port is in use
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if not _is_port_free(host, port):
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original_port = port
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blocker = _get_pid_on_port(port)
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port = _find_free_port(host, port + 1)
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if not silent:
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print("")
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print("=" * 50)
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if blocker:
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pid, name = blocker
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print(
|
|
f"Port {original_port} is already in use by " f"{name} (PID {pid})."
|
|
)
|
|
else:
|
|
print(f"Port {original_port} is already in use.")
|
|
print(f"Unsloth Studio will use port {port} instead.")
|
|
print(f"Open http://localhost:{port} in your browser.")
|
|
print("=" * 50)
|
|
print("")
|
|
|
|
# Setup frontend if path provided
|
|
if frontend_path:
|
|
if setup_frontend(app, frontend_path):
|
|
if not silent:
|
|
print(f"[OK] Frontend loaded from {frontend_path}")
|
|
else:
|
|
if not silent:
|
|
print(f"[WARNING] Frontend not found at {frontend_path}")
|
|
|
|
# Create the uvicorn server and expose it for signal handlers
|
|
config = uvicorn.Config(
|
|
app, host = host, port = port, log_level = "info", access_log = False
|
|
)
|
|
_server = uvicorn.Server(config)
|
|
_shutdown_event = Event()
|
|
|
|
# Expose the actual bound port so request-handling code can build
|
|
# loopback URLs that point at the real backend, not whatever port a
|
|
# reverse proxy or tunnel exposed in the request URL. Only publish
|
|
# an explicit value when we know the concrete port; for ephemeral
|
|
# binds (port==0) leave it unset and let request handlers fall back
|
|
# to the ASGI request scope or request.base_url.
|
|
app.state.server_port = port if port and port > 0 else None
|
|
app.state.llama_parallel_slots = llama_parallel_slots
|
|
|
|
# Run server in a daemon thread
|
|
def _run():
|
|
asyncio.run(_server.serve())
|
|
|
|
thread = Thread(target = _run, daemon = True)
|
|
thread.start()
|
|
time.sleep(3)
|
|
|
|
_write_pid_file()
|
|
import atexit
|
|
|
|
atexit.register(_remove_pid_file)
|
|
|
|
# Expose a shutdown callable via app.state so the /api/shutdown endpoint
|
|
# can trigger graceful shutdown without circular imports.
|
|
def _trigger_shutdown():
|
|
_graceful_shutdown(_server)
|
|
if _shutdown_event is not None:
|
|
_shutdown_event.set()
|
|
|
|
app.state.trigger_shutdown = _trigger_shutdown
|
|
|
|
if not silent:
|
|
display_host = _resolve_external_ip() if host == "0.0.0.0" else host
|
|
print_studio_access_banner(
|
|
port = port,
|
|
bind_host = host,
|
|
display_host = display_host,
|
|
)
|
|
|
|
return app
|
|
|
|
|
|
# For direct execution (also invoked by CLI via os.execvp / subprocess)
|
|
if __name__ == "__main__":
|
|
import argparse
|
|
import signal
|
|
import traceback
|
|
|
|
# Ensure stderr can handle Unicode on Windows (tracebacks with non-ASCII paths)
|
|
if sys.platform == "win32" and hasattr(sys.stderr, "reconfigure"):
|
|
try:
|
|
sys.stderr.reconfigure(encoding = "utf-8", errors = "replace")
|
|
except Exception:
|
|
pass
|
|
|
|
parser = argparse.ArgumentParser(description = "Run Unsloth UI Backend server")
|
|
parser.add_argument("--host", default = "0.0.0.0", help = "Host to bind to")
|
|
parser.add_argument("--port", type = int, default = 8888, help = "Port to bind to")
|
|
parser.add_argument(
|
|
"--frontend",
|
|
type = str,
|
|
default = Path(__file__).resolve().parent.parent / "frontend" / "dist",
|
|
help = "Path to frontend build",
|
|
)
|
|
parser.add_argument("--silent", action = "store_true", help = "Suppress output")
|
|
|
|
args = parser.parse_args()
|
|
|
|
kwargs = dict(host = args.host, port = args.port, silent = args.silent)
|
|
if args.frontend is not None:
|
|
kwargs["frontend_path"] = Path(args.frontend)
|
|
|
|
try:
|
|
run_server(**kwargs)
|
|
except Exception:
|
|
sys.stderr.write("\n")
|
|
sys.stderr.write("=" * 60 + "\n")
|
|
sys.stderr.write("ERROR: Unsloth Studio failed to start.\n")
|
|
sys.stderr.write("=" * 60 + "\n")
|
|
traceback.print_exc(file = sys.stderr)
|
|
sys.stderr.write("\n")
|
|
sys.stderr.write(
|
|
"If a package is missing, try re-running: unsloth studio setup\n"
|
|
)
|
|
sys.stderr.flush()
|
|
sys.exit(1)
|
|
|
|
# Signal handler -- ensures subprocess cleanup on Ctrl+C
|
|
def _signal_handler(signum, frame):
|
|
_graceful_shutdown(_server)
|
|
_shutdown_event.set()
|
|
|
|
signal.signal(signal.SIGINT, _signal_handler)
|
|
signal.signal(signal.SIGTERM, _signal_handler)
|
|
|
|
# On Windows, some terminals send SIGBREAK for Ctrl+C / Ctrl+Break
|
|
if hasattr(signal, "SIGBREAK"):
|
|
signal.signal(signal.SIGBREAK, _signal_handler)
|
|
|
|
# Keep running until shutdown signal.
|
|
# NOTE: Event.wait() without a timeout blocks at the C level on Linux,
|
|
# which prevents Python from delivering SIGINT (Ctrl+C). Using a
|
|
# short timeout in a loop lets the interpreter process pending signals.
|
|
while not _shutdown_event.is_set():
|
|
_shutdown_event.wait(timeout = 1)
|