18 Commits

Author SHA1 Message Date
z3st
b799ebbbee fix: cancel all tasks across ALL GPUs, not just TARGET_GPU 2026-07-25 22:57:42 +08:00
z3st
24928f5678 fix: re-add GGUF to exclusion (vllm cannot load GGUF models) 2026-07-25 13:36:17 +08:00
z3st
3eed33f0d8 chore: increase submit limit from 2 to 5 2026-07-24 20:30:10 +08:00
z3st
a2ef82d954 fix: fix SQLite INSERT column count, fix architecture check for all arch types 2026-07-24 20:24:26 +08:00
z3st
2198aed3c2 feat: strictly filter models with valid config.json only 2026-07-24 20:03:56 +08:00
z3st
965342bafe feat: focus on Llama models only, switch back to vllm framework 2026-07-24 20:02:17 +08:00
z3st
5bb32e6bdb feat: open architecture filter, expand search keywords, let platform decide compatibility 2026-07-24 19:58:45 +08:00
z3st
75ba3bb301 chore: switch GPU to Iluvatar_bi-100 2026-07-24 19:49:42 +08:00
z3st
e28f2e3eca feat: switch to llama.cpp with GGUF support on Iluvatar, keep only GPTQ/AWQ filtered 2026-07-24 19:29:48 +08:00
z3st
e0b6c3b5a8 feat: use file-based SQLite database for persistent failure tracking 2026-07-24 19:03:13 +08:00
z3st
f438206c0e feat: submit only 2 models, add failed model tracking to avoid re-submission 2026-07-24 18:57:52 +08:00
z3st
12616c3816 restore submit version from v2.1.0 2026-07-24 18:56:18 +08:00
z3st
0a4ba2e146 feat: standalone cancel-all version - startup auto cancels all waiting/running tasks 2026-07-24 18:53:24 +08:00
z3st
2222e1545b feat: cancel-all mode - cancel all waiting/running tasks on startup 2026-07-24 18:51:51 +08:00
z3st
441b540e47 feat: filter out GPTQ/AWQ formats and Qwen3.5 arch (incompatible with Iluvatar) 2026-07-24 18:50:21 +08:00
z3st
afbda884ad feat: multi-page search, filter downloads 50-5000 for cold models 2026-07-24 00:10:32 +08:00
z3st
68c7a99e68 fix: replace gpu variable with TARGET_GPU in submit record 2026-07-24 00:02:35 +08:00
z3st
8e8fd50927 chore: switch target GPU to Iluvatar_bi-150 2026-07-23 23:51:28 +08:00
2 changed files with 85 additions and 570 deletions

View File

@@ -4,6 +4,8 @@ ENV PYTHONUNBUFFERED=1
WORKDIR /app
RUN mkdir -p /app/data
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

653
main.py
View File

@@ -1,26 +1,18 @@
"""
ModelHub 全云端提交智能体
部署在平台容器中,直接从内网提交验证任务
功能:
- POST /run → 触发一次完整流程(搜索→筛选→提交)
- GET /status → 查看当前状态
- GET /health → 健康检查
ModelHub Cancel-All 智能体
启动后自动取消当前账号所有 waiting/running 状态的验证任务
用法:推送此版本到平台,选择对应 tag 运行即可
"""
import json
import os
import signal
import sqlite3
import threading
import time
import traceback
import threading
from datetime import datetime
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from urllib.parse import urlparse, parse_qs
import requests
import yaml
# ============================================================
# 配置
@@ -28,37 +20,19 @@ import yaml
HOST = "0.0.0.0"
PORT = 8080
STRATEGY_ID = os.getenv("STRATEGY_ID", "")
# 目标GPU
TARGET_GPU = "ppu_zw_810e"
# 账号Token
# 目标GPU和Token跟提交版本保持一致
TARGET_GPU = "Iluvatar_bi-150"
TARGET_TOKEN = "f45f1aae2c094426be237c88b1085015"
# 架构白名单
SUPPORTED_ARCH_KEYWORDS = ['Qwen', 'Qwen2', 'Qwen3']
SUPPORTED_MODEL_TYPES = [
'qwen', 'qwen2', 'qwen2_vl', 'qwen2_5_vl', 'qwen2_audio',
'qwen3', 'qwen3_vl', 'qwen3_5', 'qwen3_5_moe',
]
SUPPORTED_SPECIAL_ARCHS = ['Eagle3Speculator', 'LlamaForCausalLMEagle3']
MODELHUB_API = "https://modelhub.org.cn/api"
# 搜索关键词
SEARCH_KEYWORDS = ['qwen', 'Qwen2', 'Qwen3', 'Qwen3.5', 'Qwen1.5', 'Qwen-']
BATCH_SIZE = 50 # 每批取消数量API上限
# ============================================================
# 全局状态
# ============================================================
state = {
'running': False,
'last_run': None,
'last_result': None,
'logs': [],
}
state = {'running': False, 'last_run': None, 'logs': []}
state_lock = threading.Lock()
@@ -72,331 +46,75 @@ def log(msg: str):
state['logs'] = state['logs'][-300:]
# ============================================================
# 数据库(内存 SQLite
# ============================================================
db_conn = None
def init_db():
global db_conn
db_conn = sqlite3.connect(':memory:', check_same_thread=False)
db_conn.execute('''CREATE TABLE IF NOT EXISTS queue (
model_id TEXT, gpu TEXT, url TEXT, downloads INTEGER,
params TEXT, category TEXT, score REAL,
PRIMARY KEY(model_id, gpu)
)''')
db_conn.execute('''CREATE TABLE IF NOT EXISTS submitted (
model_id TEXT, gpu TEXT, task_id TEXT, submitted_at TEXT,
PRIMARY KEY(model_id, gpu)
)''')
db_conn.commit()
# ============================================================
# ModelScope 搜索
# ============================================================
MODELSCOPE_API = "https://modelscope.cn/api/v1"
def search_models(keyword: str, limit: int = 50) -> list:
"""从 ModelScope 搜索模型"""
url = "https://modelscope.cn/openapi/v1/models"
params = {
'search': keyword,
'page_size': min(limit, 50),
'page_number': 1,
'sort': 'downloads',
}
try:
resp = requests.get(url, params=params, timeout=20,
headers={'User-Agent': 'Mozilla/5.0'})
data = resp.json()
if data.get('success'):
models = data.get('data', {}).get('models', [])
log(f" [{keyword}]: {len(models)} 个结果")
return [{'id': m.get('id'), 'downloads': m.get('downloads', 0)} for m in models]
else:
log(f" [{keyword}]: success=false")
except Exception as e:
log(f" [{keyword}]: 失败 {e}")
return []
def check_architecture(model_id: str) -> tuple:
"""检查模型架构"""
try:
cfg_url = f"{MODELSCOPE_API}/models/{model_id}/repo?Revision=master&FilePath=config.json"
resp = requests.get(cfg_url, timeout=10)
if resp.status_code == 200:
cfg = resp.json()
archs = cfg.get('architectures', [])
mtype = cfg.get('model_type', '')
arch_str = str(archs)
for special in SUPPORTED_SPECIAL_ARCHS:
if special in arch_str:
return True, special
for kw in SUPPORTED_ARCH_KEYWORDS:
if kw in arch_str:
return True, kw
if mtype in SUPPORTED_MODEL_TYPES:
return True, mtype
return False, f"arch={archs} type={mtype}"
mid_upper = model_id.upper()
if 'QWEN3' in mid_upper or 'QWEN2' in mid_upper:
return True, "GGUF"
return False, "no config, not Qwen"
except Exception as e:
return True, f"check error: {e}"
def normalize_model_url(model_url: str) -> str:
"""标准化 URL 格式"""
if '/models/' in model_url:
return model_url
if 'modelscope.cn/' in model_url:
parts = model_url.split('modelscope.cn/')
if len(parts) == 2:
return f"https://www.modelscope.cn/models/{parts[1]}"
return model_url
# ============================================================
# 平台 API
# ============================================================
def check_platform_verify(model_id: str) -> dict:
"""查询全平台验证状态"""
headers = {'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'}
url = f"{MODELHUB_API}/computility/models/search-by-model-id"
try:
resp = requests.get(url, headers=headers, params={'modelId': model_id}, timeout=10)
data = resp.json()
if data.get('code') == 0:
result = data.get('data', {}).get('verifyResult', {})
return result if isinstance(result, dict) else {}
except Exception:
pass
return {}
def check_my_submitted(model_id: str) -> bool:
"""检查自己是否已提交"""
headers = {'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'}
url = f"{MODELHUB_API}/adapt/task/page"
try:
resp = requests.get(url, headers=headers, params={
'current': 1, 'pageSize': 100, 'onlyMine': 'true',
'gpuType': TARGET_GPU, 'modelId': model_id,
}, timeout=10)
data = resp.json()
if data.get('code') == 0:
records = data.get('data', {}).get('records', [])
for r in records:
if r.get('modelId') == model_id and r.get('gpuType') == gpu:
status = r.get('status', '')
if status in ('waiting', 'running', 'success'):
return True
except Exception:
pass
return False
def check_queue_available() -> int:
"""查询队列可用位置"""
headers = {'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'}
url = f"{MODELHUB_API}/adapt/task/page"
try:
resp = requests.get(url, headers=headers, params={
'current': 1, 'pageSize': 1, 'onlyMine': 'true',
'gpuType': TARGET_GPU, 'status': 'waiting',
}, timeout=10)
data = resp.json()
if data.get('code') == 0:
waiting = int(data['data'].get('total', 0))
return max(0, 100 - waiting)
except Exception:
pass
return -1
def build_config_params() -> str:
"""构建 YAML 配置 - 完全匹配平台自动生成的格式只支持vllm"""
params = {
'framework': 'vllm',
'nv_framework': 'vllm',
'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', '80',
'--served-model-name', 'llm', '--max-model-len', '4096',
'--enforce-eager', '--trust-remote-code', '-tp', '1',
]
}
},
}
return yaml.dump(params, default_flow_style=False, allow_unicode=True, width=1000)
def submit_model(model_url: str) -> tuple:
"""提交单个模型"""
def cancel_all_tasks():
"""取消所有 waiting 和 running 状态的任务"""
headers = {
'Xc-Token': TARGET_TOKEN,
'Accept': 'application/json',
'Content-Type': 'application/json',
}
url = f"{MODELHUB_API}/adapt/task/add"
payload = {
'modelAddress': normalize_model_url(model_url),
'taskType': 'text-generation',
'targetGpu': TARGET_GPU,
'framework': 'vllm',
'strategyId': STRATEGY_ID,
'configParams': build_config_params(),
}
try:
resp = requests.post(url, headers=headers, json=payload, timeout=30)
data = resp.json()
if data.get('code') == 0:
task_id = data.get('data', {}).get('id')
return True, task_id, 'success'
return False, None, data.get('message', 'unknown error')
except Exception as e:
return False, None, str(e)
# ============================================================
# 主流程
# ============================================================
def run_pipeline(submit_limit: int = 30):
"""完整流程搜索→筛选→提交只针对目标GPU"""
init_db()
log("=" * 50)
log("开始执行流程")
log(f"目标GPU: {TARGET_GPU}")
log(f"提交限制: {submit_limit}")
log("开始取消所有任务")
log(f"目标: 所有GPU")
# 1. 搜索
log("\n--- 阶段1: 搜索 ModelScope ---")
seen = set()
all_models = []
for kw in SEARCH_KEYWORDS:
models = search_models(kw, limit=100)
for m in models:
mid = m.get('id', '')
if mid and mid not in seen:
seen.add(mid)
downloads = m.get('downloads', 0)
if downloads >= 50:
all_models.append({
'model_id': mid,
'url': f"https://modelscope.cn/{mid}",
'downloads': downloads,
})
time.sleep(0.3)
log(f"搜索完成: {len(seen)} 个唯一模型, {len(all_models)} 个下载量>=50")
# 2. 格式筛选只保留HuggingFace格式排除GGUF
log("\n--- 阶段2: 格式筛选 ---")
hf_models = []
gguf_skipped = 0
for m in all_models:
mid = m['model_id'].upper()
if 'GGUF' in mid:
gguf_skipped += 1
log(f"{m['model_id']}: GGUF格式跳过")
else:
hf_models.append(m)
log(f"格式筛选: {len(hf_models)} 通过 (HuggingFace), {gguf_skipped} 跳过 (GGUF)")
# 3. 架构筛选
log("\n--- 阶段3: 架构筛选 ---")
arch_passed = []
arch_rejected = 0
for m in hf_models:
ok, reason = check_architecture(m['model_id'])
if ok:
arch_passed.append(m)
else:
arch_rejected += 1
log(f"{m['model_id']}: {reason}")
time.sleep(0.15)
log(f"架构筛选: {len(arch_passed)} 通过, {arch_rejected} 拒绝")
# 4. 筛选并提交只针对目标GPU
log(f"\n--- 阶段4: 筛选并提交 [{TARGET_GPU}] ---")
# 检查队列
available = check_queue_available()
if available <= 0:
log(f" 队列满,跳过")
return 0
log(f" 队列可用: {available}")
# 筛选
to_submit = []
for m in arch_passed:
model_id = m['model_id']
# 检查全平台验证状态
verify = check_platform_verify(model_id)
if TARGET_GPU in verify:
continue # 已有记录,跳过
# 检查自己是否已提交
if check_my_submitted(model_id):
continue
to_submit.append(m)
if len(to_submit) >= min(submit_limit, available):
break
time.sleep(0.2)
log(f" 待提交: {len(to_submit)}")
# 提交
submitted = 0
for m in to_submit:
ok, task_id, msg = submit_model(m['url'])
if ok:
submitted += 1
log(f"{m['model_id']}")
db_conn.execute(
'INSERT OR REPLACE INTO submitted VALUES (?,?,?,?)',
(m['model_id'], gpu, str(task_id), datetime.now().isoformat())
# 1. 查询所有任务
all_tasks = []
page = 1
while True:
try:
resp = requests.get(
f"{MODELHUB_API}/adapt/task/page",
headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'},
params={'current': page, 'pageSize': 100, 'onlyMine': 'true'},
timeout=15
)
else:
log(f"{m['model_id']}: {msg}")
time.sleep(0.5)
data = resp.json()
if data.get('code') != 0:
log(f"查询失败: code={data.get('code')}")
break
records = data['data'].get('records', [])
if not records:
break
all_tasks.extend(records)
total = data['data'].get('total', 0)
log(f"{page}页: {len(records)} 条, 累计 {len(all_tasks)} / {total}")
if len(all_tasks) >= int(total):
break
page += 1
except Exception as e:
log(f" 查询异常: {e}")
return
log(f" 提交完成: {submitted}/{len(to_submit)}")
# 2. 筛选 waiting/running 状态
cancellable = [t for t in all_tasks if t.get('status') in ('waiting', 'running')]
if not cancellable:
log(f" 没有可取消的任务 (总任务 {len(all_tasks)} 个)")
log("=" * 50)
return
db_conn.commit()
log(f"\n{'=' * 50}")
log(f"流程完成,共提交 {submitted} 个模型")
return submitted
task_ids = [int(t.get('taskId')) for t in cancellable if t.get('taskId')]
log(f" 总任务 {len(all_tasks)} 个, 可取消 {len(task_ids)}")
# 3. 分批取消
url = f"{MODELHUB_API}/async/task/stop-create-contest-task"
cancelled = 0
for i in range(0, len(task_ids), BATCH_SIZE):
batch = task_ids[i:i + BATCH_SIZE]
try:
resp = requests.put(url, headers=headers, json={'taskIds': batch}, timeout=15)
data = resp.json()
if data.get('code') == 0:
cancelled += len(batch)
log(f" 批次 {i // BATCH_SIZE + 1}: 取消 {len(batch)} 个 OK")
else:
log(f" 批次 {i // BATCH_SIZE + 1}: 失败 code={data.get('code')} msg={data.get('message', '')[:80]}")
except Exception as e:
log(f" 批次 {i // BATCH_SIZE + 1}: 异常 {e}")
time.sleep(1)
log(f" 取消完成: {cancelled} / {len(task_ids)}")
log("=" * 50)
# ============================================================
@@ -405,148 +123,21 @@ def run_pipeline(submit_limit: int = 30):
class AgentHandler(BaseHTTPRequestHandler):
def do_GET(self):
parsed = urlparse(self.path)
path = parsed.path
if path == '/health':
if self.path == '/health':
self._json({'status': 'ok'})
elif path == '/':
elif self.path == '/':
self._json({
'name': 'modelhub-submit-agent',
'strategy_id': STRATEGY_ID,
'name': 'modelhub-cancel-all',
'gpu': TARGET_GPU,
'status': 'running' if state['running'] else 'idle',
'last_run': state['last_run'],
'last_result': state['last_result'],
'last_result': state.get('last_result'),
})
elif path == '/test':
self._json(self._run_connectivity_test())
elif path == '/status':
self._json({
'running': state['running'],
'last_run': state['last_run'],
'last_result': state['last_result'],
'queue_count': db_conn.execute('SELECT COUNT(*) FROM queue').fetchone()[0] if db_conn else 0,
'submitted_count': db_conn.execute('SELECT COUNT(*) FROM submitted').fetchone()[0] if db_conn else 0,
})
elif path == '/logs':
lines = int(parse_qs(parsed.query).get('lines', ['50'])[0])
elif self.path.startswith('/logs'):
lines = 100
self._json({'logs': state['logs'][-lines:]})
else:
self._json({'error': 'not found'}, 404)
def do_POST(self):
parsed = urlparse(self.path)
path = parsed.path
if path == '/run':
if state['running']:
self._json({'error': 'already running'}, 409)
return
# 读取请求体
content_len = int(self.headers.get('Content-Length', 0))
body = {}
if content_len > 0:
body = json.loads(self.rfile.read(content_len))
limit = body.get('limit', 30)
self._json({'status': 'started', 'gpu': TARGET_GPU, 'limit': limit})
# 后台运行
def _run():
try:
state['running'] = True
state['last_run'] = datetime.now().isoformat()
count = run_pipeline(submit_limit=limit)
state['last_result'] = {'submitted': count, 'success': True}
except Exception as e:
log(f"流程异常: {traceback.format_exc()}")
state['last_result'] = {'error': str(e), 'success': False}
finally:
state['running'] = False
threading.Thread(target=_run, daemon=True).start()
else:
self._json({'error': 'not found'}, 404)
def _run_connectivity_test(self) -> dict:
"""测试各 API 连通性"""
results = {}
# 1. ModelScope 搜索 API
try:
resp = requests.get(
'https://modelscope.cn/openapi/v1/models',
params={'search': 'qwen', 'page_size': 2, 'sort': 'downloads'},
timeout=10,
headers={'User-Agent': 'Mozilla/5.0'}
)
results['modelscope_search'] = {
'status': resp.status_code,
'ok': resp.status_code == 200,
'body_preview': resp.text[:200] if resp.status_code == 200 else resp.text[:100],
}
except Exception as e:
results['modelscope_search'] = {'ok': False, 'error': str(e)}
# 2. ModelScope config.json API
try:
resp = requests.get(
'https://modelscope.cn/api/v1/models/Qwen/Qwen3-8B/repo?Revision=master&FilePath=config.json',
timeout=10,
)
results['modelscope_config'] = {
'status': resp.status_code,
'ok': resp.status_code == 200,
'body_preview': resp.text[:200] if resp.status_code == 200 else resp.text[:100],
}
except Exception as e:
results['modelscope_config'] = {'ok': False, 'error': str(e)}
# 3. ModelHub 查询 API
try:
resp = requests.get(
'https://modelhub.org.cn/api/adapt/task/page',
headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'},
params={'current': 1, 'pageSize': 1, 'onlyMine': 'true'},
timeout=10,
)
data = resp.json()
results['modelhub_query'] = {
'ok': data.get('code') == 0,
'code': data.get('code'),
'total': data.get('data', {}).get('total'),
}
except Exception as e:
results['modelhub_query'] = {'ok': False, 'error': str(e)}
# 4. ModelHub 提交 API (dry test)
try:
resp = requests.post(
'https://modelhub.org.cn/api/adapt/task/add',
headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json', 'Content-Type': 'application/json'},
json={
'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
'taskType': 'text-generation',
'targetGpu': TARGET_GPU,
'framework': 'vllm',
'strategyId': STRATEGY_ID,
'configParams': build_config_params(),
},
timeout=10,
)
data = resp.json()
results['modelhub_submit'] = {
'ok': data.get('code') == 0,
'code': data.get('code'),
'message': data.get('message', '')[:100],
}
except Exception as e:
results['modelhub_submit'] = {'ok': False, 'error': str(e)}
return results
def _json(self, body: dict, status: int = 200):
payload = json.dumps(body, ensure_ascii=False).encode()
self.send_response(status)
@@ -556,7 +147,7 @@ class AgentHandler(BaseHTTPRequestHandler):
self.wfile.write(payload)
def log_message(self, fmt, *args):
pass # 静默 HTTP 日志
pass
# ============================================================
@@ -576,105 +167,27 @@ def main():
signal.signal(signal.SIGTERM, _handle_signal)
signal.signal(signal.SIGINT, _handle_signal)
init_db()
server = ThreadingHTTPServer((HOST, PORT), AgentHandler)
server.timeout = 1
log(f"智能体启动 | {HOST}:{PORT}")
log(f"STRATEGY_ID: {STRATEGY_ID}")
log(f"目标GPU: {TARGET_GPU}")
log(f"Cancel-All 智能体启动 | {HOST}:{PORT}")
log(f"目标: 所有GPU")
# 启动后自动运行连通性测试
def _startup_test():
# 启动后自动取消所有任务
def _auto_cancel():
time.sleep(2)
log("=" * 50)
log("连通性测试开始")
log("=" * 50)
# 1. ModelScope 搜索
try:
resp = requests.get(
'https://modelscope.cn/openapi/v1/models',
params={'search': 'qwen', 'page_size': 2, 'sort': 'downloads'},
timeout=10, headers={'User-Agent': 'Mozilla/5.0'}
)
if resp.status_code == 200:
data = resp.json()
models = data.get('data', {}).get('models', [])
log(f"[ModelScope搜索] ✅ status={resp.status_code} models={len(models)}")
for m in models[:2]:
log(f" {m.get('id')} downloads={m.get('downloads')}")
else:
log(f"[ModelScope搜索] ❌ status={resp.status_code} body={resp.text[:100]}")
except Exception as e:
log(f"[ModelScope搜索] ❌ error={e}")
# 2. ModelScope config.json
try:
resp = requests.get(
'https://modelscope.cn/api/v1/models/Qwen/Qwen3-8B/repo?Revision=master&FilePath=config.json',
timeout=10
)
if resp.status_code == 200:
cfg = resp.json()
log(f"[ModelScope配置] ✅ arch={cfg.get('architectures')} type={cfg.get('model_type')}")
else:
log(f"[ModelScope配置] ❌ status={resp.status_code}")
except Exception as e:
log(f"[ModelScope配置] ❌ error={e}")
# 3. ModelHub 查询
try:
resp = requests.get(
'https://modelhub.org.cn/api/adapt/task/page',
headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'},
params={'current': 1, 'pageSize': 1, 'onlyMine': 'true'},
timeout=10
)
data = resp.json()
if data.get('code') == 0:
log(f"[ModelHub查询] ✅ total={data['data'].get('total')}")
else:
log(f"[ModelHub查询] ❌ code={data.get('code')} msg={data.get('message','')[:80]}")
except Exception as e:
log(f"[ModelHub查询] ❌ error={e}")
# 4. ModelHub 提交
try:
resp = requests.post(
'https://modelhub.org.cn/api/adapt/task/add',
headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json', 'Content-Type': 'application/json'},
json={
'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
'taskType': 'text-generation', 'targetGpu': TARGET_GPU,
'framework': 'vllm', 'strategyId': STRATEGY_ID,
'configParams': build_config_params(),
}, timeout=10
)
data = resp.json()
log(f"[ModelHub提交] code={data.get('code')} msg={data.get('message','')[:80]}")
except Exception as e:
log(f"[ModelHub提交] ❌ error={e}")
log("=" * 50)
log("连通性测试完成")
log("=" * 50)
# 开始正式提交流程
log("\n自动触发提交流程...")
try:
state['running'] = True
state['last_run'] = datetime.now().isoformat()
count = run_pipeline(submit_limit=30)
state['last_result'] = {'submitted': count, 'success': True}
cancel_all_tasks()
state['last_result'] = {'success': True}
except Exception as e:
log(f"流程异常: {traceback.format_exc()}")
state['last_result'] = {'error': str(e), 'success': False}
log(f"异常: {e}")
state['last_result'] = {'error': str(e)}
finally:
state['running'] = False
threading.Thread(target=_startup_test, daemon=True).start()
threading.Thread(target=_auto_cancel, daemon=True).start()
while not shutdown_requested:
server.handle_request()