mirror of
https://github.com/mudler/LocalAI.git
synced 2026-02-05 04:02:45 -05:00
547 lines
22 KiB
Python
547 lines
22 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
LocalAI ACE-Step Backend
|
|
|
|
gRPC backend for ACE-Step 1.5 music generation. Aligns with upstream acestep API:
|
|
- LoadModel: initializes AceStepHandler (DiT) and LLMHandler, parses Options.
|
|
- SoundGeneration: uses create_sample (simple mode), format_sample (optional), then
|
|
generate_music from acestep.inference. Writes first output to request.dst.
|
|
- Fail hard: no fallback WAV on error; exceptions propagate to gRPC.
|
|
"""
|
|
from concurrent import futures
|
|
import argparse
|
|
import shutil
|
|
import signal
|
|
import sys
|
|
import os
|
|
import tempfile
|
|
|
|
import backend_pb2
|
|
import backend_pb2_grpc
|
|
import grpc
|
|
from acestep.inference import (
|
|
GenerationParams,
|
|
GenerationConfig,
|
|
generate_music,
|
|
create_sample,
|
|
format_sample,
|
|
)
|
|
from acestep.handler import AceStepHandler
|
|
from acestep.llm_inference import LLMHandler
|
|
|
|
|
|
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
|
|
MAX_WORKERS = int(os.environ.get("PYTHON_GRPC_MAX_WORKERS", "1"))
|
|
|
|
# Model name -> HuggingFace/ModelScope repo (from upstream api_server.py)
|
|
MODEL_REPO_MAPPING = {
|
|
"acestep-v15-turbo": "ACE-Step/Ace-Step1.5",
|
|
"acestep-5Hz-lm-0.6B": "ACE-Step/Ace-Step1.5",
|
|
"acestep-5Hz-lm-1.7B": "ACE-Step/Ace-Step1.5",
|
|
"vae": "ACE-Step/Ace-Step1.5",
|
|
"Qwen3-Embedding-0.6B": "ACE-Step/Ace-Step1.5",
|
|
"acestep-v15-base": "ACE-Step/acestep-v15-base",
|
|
"acestep-v15-sft": "ACE-Step/acestep-v15-sft",
|
|
"acestep-v15-turbo-shift3": "ACE-Step/acestep-v15-turbo-shift3",
|
|
"acestep-5Hz-lm-4B": "ACE-Step/acestep-5Hz-lm-4B",
|
|
}
|
|
DEFAULT_REPO_ID = "ACE-Step/Ace-Step1.5"
|
|
|
|
|
|
def _can_access_google(timeout=3.0):
|
|
"""Check if Google is accessible (to choose HuggingFace vs ModelScope)."""
|
|
import socket
|
|
try:
|
|
socket.setdefaulttimeout(timeout)
|
|
socket.socket(socket.AF_INET, socket.SOCK_STREAM).connect(("www.google.com", 443))
|
|
return True
|
|
except (socket.timeout, socket.error, OSError):
|
|
return False
|
|
|
|
|
|
def _download_from_huggingface(repo_id, local_dir, model_name):
|
|
"""Download model from HuggingFace Hub."""
|
|
from huggingface_hub import snapshot_download
|
|
is_unified = repo_id == DEFAULT_REPO_ID or repo_id == "ACE-Step/Ace-Step1.5"
|
|
if is_unified:
|
|
download_dir = local_dir
|
|
print(f"[ace-step] Downloading unified repo {repo_id} to {download_dir}...", file=sys.stderr)
|
|
else:
|
|
download_dir = os.path.join(local_dir, model_name)
|
|
os.makedirs(download_dir, exist_ok=True)
|
|
print(f"[ace-step] Downloading {model_name} from {repo_id} to {download_dir}...", file=sys.stderr)
|
|
snapshot_download(
|
|
repo_id=repo_id,
|
|
local_dir=download_dir,
|
|
local_dir_use_symlinks=False,
|
|
)
|
|
return os.path.join(local_dir, model_name)
|
|
|
|
|
|
def _download_from_modelscope(repo_id, local_dir, model_name):
|
|
"""Download model from ModelScope."""
|
|
from modelscope import snapshot_download
|
|
is_unified = repo_id == DEFAULT_REPO_ID or repo_id == "ACE-Step/Ace-Step1.5"
|
|
if is_unified:
|
|
download_dir = local_dir
|
|
print(f"[ace-step] Downloading unified repo {repo_id} from ModelScope to {download_dir}...", file=sys.stderr)
|
|
else:
|
|
download_dir = os.path.join(local_dir, model_name)
|
|
os.makedirs(download_dir, exist_ok=True)
|
|
print(f"[ace-step] Downloading {model_name} from ModelScope {repo_id} to {download_dir}...", file=sys.stderr)
|
|
snapshot_download(
|
|
model_id=repo_id,
|
|
local_dir=download_dir,
|
|
)
|
|
return os.path.join(local_dir, model_name)
|
|
|
|
|
|
def _ensure_model_downloaded(model_name, checkpoint_dir):
|
|
"""
|
|
Ensure model is present; download from HuggingFace or ModelScope if missing.
|
|
model_name: e.g. "acestep-v15-turbo", "vae", "acestep-5Hz-lm-0.6B"
|
|
checkpoint_dir: directory that will contain model_name as a subdir.
|
|
Returns path to the model directory.
|
|
"""
|
|
return None
|
|
if not model_name or not checkpoint_dir:
|
|
return None
|
|
model_path = os.path.join(checkpoint_dir, model_name)
|
|
if os.path.exists(model_path) and os.listdir(model_path):
|
|
print(f"[ace-step] Model {model_name} already at {model_path}", file=sys.stderr)
|
|
return model_path
|
|
repo_id = MODEL_REPO_MAPPING.get(model_name, DEFAULT_REPO_ID)
|
|
print(f"[ace-step] Model {model_name} not found, downloading...", file=sys.stderr)
|
|
use_hf = _can_access_google()
|
|
if use_hf:
|
|
try:
|
|
return _download_from_huggingface(repo_id, checkpoint_dir, model_name)
|
|
except Exception as e:
|
|
print(f"[ace-step] HuggingFace download failed: {e}, trying ModelScope", file=sys.stderr)
|
|
return _download_from_modelscope(repo_id, checkpoint_dir, model_name)
|
|
else:
|
|
try:
|
|
return _download_from_modelscope(repo_id, checkpoint_dir, model_name)
|
|
except Exception as e:
|
|
print(f"[ace-step] ModelScope download failed: {e}, trying HuggingFace", file=sys.stderr)
|
|
return _download_from_huggingface(repo_id, checkpoint_dir, model_name)
|
|
|
|
|
|
def _is_float(s):
|
|
try:
|
|
float(s)
|
|
return True
|
|
except (ValueError, TypeError):
|
|
return False
|
|
|
|
|
|
def _is_int(s):
|
|
try:
|
|
int(s)
|
|
return True
|
|
except (ValueError, TypeError):
|
|
return False
|
|
|
|
|
|
def _parse_timesteps(s):
|
|
if s is None or (isinstance(s, str) and not s.strip()):
|
|
return None
|
|
if isinstance(s, (list, tuple)):
|
|
return [float(x) for x in s]
|
|
try:
|
|
return [float(x.strip()) for x in str(s).split(",") if x.strip()]
|
|
except (ValueError, TypeError):
|
|
return None
|
|
|
|
|
|
def _parse_options(opts_list):
|
|
"""Parse repeated 'key:value' options into a dict. Coerce numeric and bool."""
|
|
out = {}
|
|
for opt in opts_list or []:
|
|
if ":" not in opt:
|
|
continue
|
|
key, value = opt.split(":", 1)
|
|
key = key.strip()
|
|
value = value.strip()
|
|
if _is_int(value):
|
|
out[key] = int(value)
|
|
elif _is_float(value):
|
|
out[key] = float(value)
|
|
elif value.lower() in ("true", "false"):
|
|
out[key] = value.lower() == "true"
|
|
else:
|
|
out[key] = value
|
|
return out
|
|
|
|
|
|
def _generate_audio_sync(servicer, payload, dst_path):
|
|
"""
|
|
Run full ACE-Step pipeline using acestep.inference:
|
|
- If sample_mode/sample_query: create_sample() for caption/lyrics/metadata.
|
|
- If use_format and caption/lyrics: format_sample().
|
|
- Build GenerationParams and GenerationConfig, then generate_music().
|
|
Writes the first generated audio to dst_path. Raises on failure.
|
|
"""
|
|
|
|
opts = servicer.options
|
|
dit_handler = servicer.dit_handler
|
|
llm_handler = servicer.llm_handler
|
|
|
|
for key, value in opts.items():
|
|
if key not in payload:
|
|
payload[key] = value
|
|
|
|
def _opt(name, default):
|
|
return opts.get(name, default)
|
|
|
|
lm_temperature = _opt("temperature", 0.85)
|
|
lm_cfg_scale = _opt("lm_cfg_scale", _opt("cfg_scale", 2.0))
|
|
lm_top_k = opts.get("top_k")
|
|
lm_top_p = _opt("top_p", 0.9)
|
|
if lm_top_p is not None and lm_top_p >= 1.0:
|
|
lm_top_p = None
|
|
inference_steps = _opt("inference_steps", 8)
|
|
guidance_scale = _opt("guidance_scale", 7.0)
|
|
batch_size = max(1, int(_opt("batch_size", 1)))
|
|
|
|
use_simple = bool(payload.get("sample_query") or payload.get("text"))
|
|
sample_mode = use_simple and (payload.get("thinking") or payload.get("sample_mode"))
|
|
sample_query = (payload.get("sample_query") or payload.get("text") or "").strip()
|
|
use_format = bool(payload.get("use_format"))
|
|
caption = (payload.get("prompt") or payload.get("caption") or "").strip()
|
|
lyrics = (payload.get("lyrics") or "").strip()
|
|
vocal_language = (payload.get("vocal_language") or "en").strip()
|
|
instrumental = bool(payload.get("instrumental"))
|
|
bpm = payload.get("bpm")
|
|
key_scale = (payload.get("key_scale") or "").strip()
|
|
time_signature = (payload.get("time_signature") or "").strip()
|
|
audio_duration = payload.get("audio_duration")
|
|
if audio_duration is not None:
|
|
try:
|
|
audio_duration = float(audio_duration)
|
|
except (TypeError, ValueError):
|
|
audio_duration = None
|
|
|
|
if sample_mode and llm_handler and getattr(llm_handler, "llm_initialized", False):
|
|
parsed_language = None
|
|
if sample_query:
|
|
for hint in ("english", "en", "chinese", "zh", "japanese", "ja"):
|
|
if hint in sample_query.lower():
|
|
parsed_language = "en" if hint == "english" or hint == "en" else hint
|
|
break
|
|
vocal_lang = vocal_language if vocal_language and vocal_language != "unknown" else parsed_language
|
|
sample_result = create_sample(
|
|
llm_handler=llm_handler,
|
|
query=sample_query or "NO USER INPUT",
|
|
instrumental=instrumental,
|
|
vocal_language=vocal_lang,
|
|
temperature=lm_temperature,
|
|
top_k=lm_top_k,
|
|
top_p=lm_top_p,
|
|
use_constrained_decoding=True,
|
|
)
|
|
if not sample_result.success:
|
|
raise RuntimeError(f"create_sample failed: {sample_result.error or sample_result.status_message}")
|
|
caption = sample_result.caption or caption
|
|
lyrics = sample_result.lyrics or lyrics
|
|
bpm = sample_result.bpm
|
|
key_scale = sample_result.keyscale or key_scale
|
|
time_signature = sample_result.timesignature or time_signature
|
|
if sample_result.duration is not None:
|
|
audio_duration = sample_result.duration
|
|
if getattr(sample_result, "language", None):
|
|
vocal_language = sample_result.language
|
|
|
|
if use_format and (caption or lyrics) and llm_handler and getattr(llm_handler, "llm_initialized", False):
|
|
user_metadata = {}
|
|
if bpm is not None:
|
|
user_metadata["bpm"] = bpm
|
|
if audio_duration is not None and float(audio_duration) > 0:
|
|
user_metadata["duration"] = int(audio_duration)
|
|
if key_scale:
|
|
user_metadata["keyscale"] = key_scale
|
|
if time_signature:
|
|
user_metadata["timesignature"] = time_signature
|
|
if vocal_language and vocal_language != "unknown":
|
|
user_metadata["language"] = vocal_language
|
|
format_result = format_sample(
|
|
llm_handler=llm_handler,
|
|
caption=caption,
|
|
lyrics=lyrics,
|
|
user_metadata=user_metadata if user_metadata else None,
|
|
temperature=lm_temperature,
|
|
top_k=lm_top_k,
|
|
top_p=lm_top_p,
|
|
use_constrained_decoding=True,
|
|
)
|
|
if format_result.success:
|
|
caption = format_result.caption or caption
|
|
lyrics = format_result.lyrics or lyrics
|
|
if format_result.duration is not None:
|
|
audio_duration = format_result.duration
|
|
if format_result.bpm is not None:
|
|
bpm = format_result.bpm
|
|
if format_result.keyscale:
|
|
key_scale = format_result.keyscale
|
|
if format_result.timesignature:
|
|
time_signature = format_result.timesignature
|
|
if getattr(format_result, "language", None):
|
|
vocal_language = format_result.language
|
|
|
|
thinking = bool(payload.get("thinking"))
|
|
use_cot_metas = not sample_mode
|
|
params = GenerationParams(
|
|
task_type=payload.get("task_type", "text2music"),
|
|
instruction=payload.get("instruction", "Fill the audio semantic mask based on the given conditions:"),
|
|
reference_audio=payload.get("reference_audio_path"),
|
|
src_audio=payload.get("src_audio_path"),
|
|
audio_codes=payload.get("audio_code_string", ""),
|
|
caption=caption,
|
|
lyrics=lyrics,
|
|
instrumental=instrumental or (not lyrics or str(lyrics).strip().lower() in ("[inst]", "[instrumental]")),
|
|
vocal_language=vocal_language or "unknown",
|
|
bpm=bpm,
|
|
keyscale=key_scale,
|
|
timesignature=time_signature,
|
|
duration=float(audio_duration) if audio_duration and float(audio_duration) > 0 else -1.0,
|
|
inference_steps=inference_steps,
|
|
seed=int(payload.get("seed", -1)),
|
|
guidance_scale=guidance_scale,
|
|
use_adg=bool(payload.get("use_adg")),
|
|
cfg_interval_start=float(payload.get("cfg_interval_start", 0.0)),
|
|
cfg_interval_end=float(payload.get("cfg_interval_end", 1.0)),
|
|
shift=float(payload.get("shift", 1.0)),
|
|
infer_method=(payload.get("infer_method") or "ode").strip(),
|
|
timesteps=_parse_timesteps(payload.get("timesteps")),
|
|
repainting_start=float(payload.get("repainting_start", 0.0)),
|
|
repainting_end=float(payload.get("repainting_end", -1)) if payload.get("repainting_end") is not None else -1,
|
|
audio_cover_strength=float(payload.get("audio_cover_strength", 1.0)),
|
|
thinking=thinking,
|
|
lm_temperature=lm_temperature,
|
|
lm_cfg_scale=lm_cfg_scale,
|
|
lm_top_k=lm_top_k or 0,
|
|
lm_top_p=lm_top_p if lm_top_p is not None and lm_top_p < 1.0 else 0.9,
|
|
lm_negative_prompt=payload.get("lm_negative_prompt", "NO USER INPUT"),
|
|
use_cot_metas=use_cot_metas,
|
|
use_cot_caption=bool(payload.get("use_cot_caption", True)),
|
|
use_cot_language=bool(payload.get("use_cot_language", True)),
|
|
use_constrained_decoding=True,
|
|
)
|
|
|
|
config = GenerationConfig(
|
|
batch_size=batch_size,
|
|
allow_lm_batch=bool(payload.get("allow_lm_batch", False)),
|
|
use_random_seed=bool(payload.get("use_random_seed", True)),
|
|
seeds=payload.get("seeds"),
|
|
lm_batch_chunk_size=max(1, int(payload.get("lm_batch_chunk_size", 8))),
|
|
constrained_decoding_debug=bool(payload.get("constrained_decoding_debug")),
|
|
audio_format=(payload.get("audio_format") or "flac").strip() or "flac",
|
|
)
|
|
|
|
save_dir = tempfile.mkdtemp(prefix="ace_step_")
|
|
try:
|
|
result = generate_music(
|
|
dit_handler=dit_handler,
|
|
llm_handler=llm_handler if (llm_handler and getattr(llm_handler, "llm_initialized", False)) else None,
|
|
params=params,
|
|
config=config,
|
|
save_dir=save_dir,
|
|
progress=None,
|
|
)
|
|
if not result.success:
|
|
raise RuntimeError(result.error or result.status_message or "generate_music failed")
|
|
|
|
audios = result.audios or []
|
|
if not audios:
|
|
raise RuntimeError("generate_music returned no audio")
|
|
|
|
first_path = audios[0].get("path") or ""
|
|
if not first_path or not os.path.isfile(first_path):
|
|
raise RuntimeError("first generated audio path missing or not a file")
|
|
|
|
shutil.copy2(first_path, dst_path)
|
|
finally:
|
|
try:
|
|
shutil.rmtree(save_dir, ignore_errors=True)
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
class BackendServicer(backend_pb2_grpc.BackendServicer):
|
|
def __init__(self):
|
|
self.model_path = None
|
|
self.checkpoint_dir = None
|
|
self.project_root = None
|
|
self.options = {}
|
|
self.dit_handler = None
|
|
self.llm_handler = None
|
|
|
|
def Health(self, request, context):
|
|
return backend_pb2.Reply(message=b"OK")
|
|
|
|
def LoadModel(self, request, context):
|
|
try:
|
|
self.options = _parse_options(list(getattr(request, "Options", []) or []))
|
|
model_path = getattr(request, "ModelPath", None) or ""
|
|
model_name = (request.Model or "").strip()
|
|
model_file = (getattr(request, "ModelFile", None) or "").strip()
|
|
|
|
if model_path and model_name:
|
|
self.checkpoint_dir = model_path
|
|
self.project_root = os.path.dirname(model_path)
|
|
self.model_path = os.path.join(model_path, model_name)
|
|
elif model_file:
|
|
self.model_path = model_file
|
|
self.checkpoint_dir = os.path.dirname(model_file)
|
|
self.project_root = os.path.dirname(self.checkpoint_dir)
|
|
else:
|
|
self.model_path = model_name or "."
|
|
self.checkpoint_dir = os.path.dirname(self.model_path) if self.model_path else "."
|
|
self.project_root = os.path.dirname(self.checkpoint_dir) if self.checkpoint_dir else "."
|
|
|
|
config_path = model_name or os.path.basename(self.model_path.rstrip("/\\"))
|
|
|
|
# Auto-download DiT model and VAE if missing (same as upstream)
|
|
if config_path:
|
|
try:
|
|
_ensure_model_downloaded(config_path, self.checkpoint_dir)
|
|
except Exception as e:
|
|
print(f"[ace-step] Warning: DiT model download failed: {e}", file=sys.stderr)
|
|
try:
|
|
_ensure_model_downloaded("vae", self.checkpoint_dir)
|
|
except Exception as e:
|
|
print(f"[ace-step] Warning: VAE download failed: {e}", file=sys.stderr)
|
|
|
|
self.dit_handler = AceStepHandler()
|
|
device = self.options.get("device", "auto")
|
|
use_flash = self.options.get("use_flash_attention", True)
|
|
if isinstance(use_flash, str):
|
|
use_flash = str(use_flash).lower() in ("1", "true", "yes")
|
|
offload = self.options.get("offload_to_cpu", False)
|
|
if isinstance(offload, str):
|
|
offload = str(offload).lower() in ("1", "true", "yes")
|
|
status_msg, ok = self.dit_handler.initialize_service(
|
|
project_root=self.project_root,
|
|
config_path=config_path,
|
|
device=device,
|
|
use_flash_attention=use_flash,
|
|
compile_model=False,
|
|
offload_to_cpu=offload,
|
|
offload_dit_to_cpu=bool(self.options.get("offload_dit_to_cpu", False)),
|
|
)
|
|
if not ok:
|
|
return backend_pb2.Result(success=False, message=f"DiT init failed: {status_msg}")
|
|
|
|
self.llm_handler = None
|
|
if self.options.get("init_lm", True):
|
|
lm_model = self.options.get("lm_model_path", "acestep-5Hz-lm-0.6B")
|
|
if lm_model:
|
|
try:
|
|
_ensure_model_downloaded(lm_model, self.checkpoint_dir)
|
|
except Exception as e:
|
|
print(f"[ace-step] Warning: LM model download failed: {e}", file=sys.stderr)
|
|
self.llm_handler = LLMHandler()
|
|
lm_backend = (self.options.get("lm_backend") or "vllm").strip().lower()
|
|
if lm_backend not in ("vllm", "pt"):
|
|
lm_backend = "vllm"
|
|
lm_status, lm_ok = self.llm_handler.initialize(
|
|
checkpoint_dir=self.checkpoint_dir,
|
|
lm_model_path=lm_model,
|
|
backend=lm_backend,
|
|
device=device,
|
|
offload_to_cpu=offload,
|
|
dtype=getattr(self.dit_handler, "dtype", None),
|
|
)
|
|
if not lm_ok:
|
|
self.llm_handler = None
|
|
print(f"[ace-step] LM init failed (optional): {lm_status}", file=sys.stderr)
|
|
|
|
print(f"[ace-step] LoadModel: model={self.model_path}, options={list(self.options.keys())}", file=sys.stderr)
|
|
return backend_pb2.Result(success=True, message="Model loaded successfully")
|
|
except Exception as err:
|
|
return backend_pb2.Result(success=False, message=f"LoadModel error: {err}")
|
|
|
|
def SoundGeneration(self, request, context):
|
|
if not request.dst:
|
|
return backend_pb2.Result(success=False, message="request.dst is required")
|
|
|
|
use_simple = bool(request.text)
|
|
if use_simple:
|
|
payload = {
|
|
"sample_query": request.text or "",
|
|
"sample_mode": True,
|
|
"thinking": True,
|
|
"vocal_language": request.language or request.GetLanguage() or "en",
|
|
"instrumental": request.GetInstrumental() if request.HasField("instrumental") else False,
|
|
}
|
|
else:
|
|
caption = request.caption or request.GetCaption() or request.text
|
|
payload = {
|
|
"prompt": caption,
|
|
"lyrics": request.lyrics or request.GetLyrics() or "",
|
|
"thinking": request.GetThink() if request.HasField("think") else False,
|
|
"vocal_language": request.language or request.GetLanguage() or "en",
|
|
}
|
|
if request.HasField("bpm"):
|
|
payload["bpm"] = request.GetBpm()
|
|
if request.HasField("keyscale") and request.GetKeyscale():
|
|
payload["key_scale"] = request.GetKeyscale()
|
|
if request.HasField("timesignature") and request.GetTimesignature():
|
|
payload["time_signature"] = request.GetTimesignature()
|
|
if request.HasField("duration") and request.duration:
|
|
payload["audio_duration"] = int(request.duration) if request.duration else None
|
|
if request.Src:
|
|
payload["src_audio_path"] = request.Src
|
|
|
|
_generate_audio_sync(self, payload, request.dst)
|
|
return backend_pb2.Result(success=True, message="Sound generated successfully")
|
|
|
|
def TTS(self, request, context):
|
|
if not request.dst:
|
|
return backend_pb2.Result(success=False, message="request.dst is required")
|
|
payload = {
|
|
"sample_query": request.text,
|
|
"sample_mode": True,
|
|
"thinking": False,
|
|
"vocal_language": (request.language if request.language else "") or "en",
|
|
"instrumental": False,
|
|
}
|
|
_generate_audio_sync(self, payload, request.dst)
|
|
return backend_pb2.Result(success=True, message="TTS (music fallback) generated successfully")
|
|
|
|
|
|
def serve(address):
|
|
server = grpc.server(
|
|
futures.ThreadPoolExecutor(max_workers=MAX_WORKERS),
|
|
options=[
|
|
("grpc.max_message_length", 50 * 1024 * 1024),
|
|
("grpc.max_send_message_length", 50 * 1024 * 1024),
|
|
("grpc.max_receive_message_length", 50 * 1024 * 1024),
|
|
],
|
|
)
|
|
backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
|
|
server.add_insecure_port(address)
|
|
server.start()
|
|
print(f"[ace-step] Server listening on {address}", file=sys.stderr)
|
|
|
|
def shutdown(sig, frame):
|
|
server.stop(0)
|
|
sys.exit(0)
|
|
|
|
signal.signal(signal.SIGINT, shutdown)
|
|
signal.signal(signal.SIGTERM, shutdown)
|
|
|
|
try:
|
|
while True:
|
|
import time
|
|
time.sleep(_ONE_DAY_IN_SECONDS)
|
|
except KeyboardInterrupt:
|
|
server.stop(0)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--addr", default="localhost:50051", help="Listen address")
|
|
args = parser.parse_args()
|
|
serve(args.addr)
|