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xc-adapt-agent-2174/main.py

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"""ModelHub XC 适配智能体 —— 多任务提交器(本批次 4 条)
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启动后依次提交 4 条已查重的模型适配验证任务,随后保持 /health 存活等待平台停止。
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批次清单(提交前均查过:模型列表 0 条 / 全站任务 0 条 / 详情接口无验证记录):
1. mrm8488/DeepSeek-R1-Distill-Qwen-1.5B-grpo-limo-2e Qwen2 1.5B Ascend_910-b4 vllm
2. HollowInky/skill-search-qwen3-4b-unmasked-grpo Qwen3 4B Ascend_910-b4 vllm
3. dmis-lab/OSP-1.4B-100B-Muon Llama 1.4B Ascend_910-b4 vllm
4. qnguyen3/quan-1.8b-chat Llama 1.8B Ascend_910-b3 vllm
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平台契约:根目录 Dockerfile,监听 8080,GET /health 探活,读取 STRATEGY_ID,处理 SIGTERM。
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"""
import json
import os
import signal
import threading
import time
from datetime import datetime
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
import requests
BASE_URL = os.environ.get("BASE_URL", "https://modelhub.org.cn")
ADD_TASK_ENDPOINT = "/api/adapt/task/add"
XC_TOKEN = os.environ.get("XC_TOKEN", "6616dfe75c874e61987b1f7c908e6d77")
STRATEGY_ID = os.environ.get("STRATEGY_ID", "")
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# 910B4 模板(max_model_len 2048)
CFG_B4 = """framework: vllm
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api: completion
max_tokens: 1024
temperature: 0.7
repetition_penalty: 1.2
top_p: 0.9
lang: zh
max_model_len: 2048
sut_config:
gpu_num: 1
values:
command:
- vllm
- serve
- /model
- --port
- '8000'
- --served-model-name
- llm
- --max-model-len
- '2048'
- --dtype
- auto
- --gpu-memory-utilization
- '0.95'
- -tp
- '1'
- --enforce-eager
- --trust-remote-code
ref_config:
gpu_num: 1
values:
command:
- vllm
- serve
- /model
- --port
- '8000'
- --served-model-name
- llm
- --max-model-len
- '2048'
- --dtype
- auto
- --gpu-memory-utilization
- '0.95'
- -tp
- '1'
- --enforce-eager
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- --trust-remote-code"""
# 910B3 模板(max_model_len 4096)
CFG_B3 = """framework: vllm
api: completion
lang: en
max_model_len: 4096
max_tokens: 1024
temperature: 0.7
repetition_penalty: 1.1
top_p: 0.9
sut_config:
gpu_num: 1
values:
command:
- vllm
- serve
- /model
- --port
- '20644'
- --served-model-name
- llm
- --max-model-len
- '4096'
- -tp
- '1'
- --enforce-eager
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- --trust-remote-code
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ref_config:
gpu_num: 1
values:
command:
- vllm
- serve
- /model
- --port
- '80'
- --served-model-name
- llm
- --max-model-len
- '4096'
- -tp
- '1'
- --enforce-eager
- --trust-remote-code"""
TASKS = [
{
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"label": "OSP-1.4B-100B-Adam",
"model": "https://www.modelscope.cn/models/dmis-lab/OSP-1.4B-100B-Adam",
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"gpu": "Ascend_910-b4",
"config": CFG_B4,
},
{
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"label": "OSP-1.4B-1T-Adam",
"model": "https://www.modelscope.cn/models/dmis-lab/OSP-1.4B-1T-Adam",
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"gpu": "Ascend_910-b4",
"config": CFG_B4,
},
]
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TASK_TYPE = "text-generation"
FRAMEWORK = "vllm"
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HEADERS = {"Content-Type": "application/json", "Xc-Token": XC_TOKEN}
HTTP_HOST, HTTP_PORT = "0.0.0.0", 8080
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GAP = 4 # 两条之间的间隔秒数
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_state = {
"strategy_id": STRATEGY_ID,
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"phase": "starting", # starting | submitting | done
"total": len(TASKS),
"submitted": 0,
"failed": 0,
"results": [],
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"started_at": None,
"finished_at": None,
}
_shutdown = threading.Event()
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def submit_one(task):
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payload = {
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"configParams": task["config"],
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"framework": FRAMEWORK,
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"modelAddress": task["model"],
"targetGpu": task["gpu"],
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"taskType": TASK_TYPE,
"strategyId": STRATEGY_ID,
}
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try:
resp = requests.post(BASE_URL + ADD_TASK_ENDPOINT, headers=HEADERS,
json=payload, timeout=90)
body = resp.json()
except Exception as e: # noqa: BLE001
body = {"code": -1, "message": "exception: %s" % e}
code = body.get("code")
data = body.get("data") or {}
print("[submit] %s gpu=%s code=%s message=%s taskId=%s"
% (task["label"], task["gpu"], code, body.get("message"), data.get("id")), flush=True)
return code, body.get("message"), data.get("id")
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def run_batch():
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_state["phase"] = "submitting"
_state["started_at"] = datetime.utcnow().isoformat()
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for i, task in enumerate(TASKS, 1):
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if _shutdown.is_set():
break
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code, msg, task_id = submit_one(task)
_state["results"].append({
"index": i,
"label": task["label"],
"model": task["model"],
"gpu": task["gpu"],
"code": code,
"message": msg,
"task_id": task_id,
"time": datetime.utcnow().isoformat(),
})
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if code == 0:
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_state["submitted"] += 1
else:
_state["failed"] += 1
if i < len(TASKS):
time.sleep(GAP)
_state["phase"] = "done"
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_state["finished_at"] = datetime.utcnow().isoformat()
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print("[batch] 完成 submitted=%d failed=%d total=%d"
% (_state["submitted"], _state["failed"], _state["total"]), flush=True)
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class Handler(BaseHTTPRequestHandler):
def do_GET(self):
if self.path == "/health":
self._json({"status": "ok"})
elif self.path in ("/", "/status"):
self._json(_state)
else:
self._json({"error": "not found"}, 404)
def _json(self, body, code=200):
raw = json.dumps(body, ensure_ascii=False, default=str).encode()
self.send_response(code)
self.send_header("Content-Type", "application/json")
self.send_header("Content-Length", str(len(raw)))
self.end_headers()
self.wfile.write(raw)
def log_message(self, fmt, *args):
print(self.address_string() + " - " + (fmt % args), flush=True)
def _handle_signal(signum, _frame):
print("[main] 收到信号 %s,准备退出" % signum, flush=True)
_shutdown.set()
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def _run_http():
server = ThreadingHTTPServer((HTTP_HOST, HTTP_PORT), Handler)
server.timeout = 1
print("[http] 监听 %s:%d" % (HTTP_HOST, HTTP_PORT), flush=True)
while not _shutdown.is_set():
server.handle_request()
server.server_close()
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def main():
signal.signal(signal.SIGTERM, _handle_signal)
signal.signal(signal.SIGINT, _handle_signal)
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print("[main] strategy_id=%s tasks=%d" % (STRATEGY_ID, len(TASKS)), flush=True)
for t in TASKS:
print("[main] 计划: %s @ %s" % (t["label"], t["gpu"]), flush=True)
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http_thread = threading.Thread(target=_run_http, daemon=False)
http_thread.start()
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worker = threading.Thread(target=run_batch, daemon=True)
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worker.start()
_shutdown.wait()
if __name__ == "__main__":
main()