17 Commits

Author SHA1 Message Date
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
z3st
5a6862f8da fix: revert to ModelScope search (HF unreachable in container), fix ACCOUNTS reference 2026-07-23 23:32:33 +08:00
z3st
727f0f678f feat: single GPU, HuggingFace only, vllm only
- Target single GPU (ppu_zw_810e) instead of 4 GPUs
- Search only from HuggingFace API (removed ModelScope)
- Framework fixed to vllm (removed llama.cpp)
- Filter out GGUF format models
- Simplified config and functions
2026-07-23 23:19:20 +08:00
z3st
207d44b8f2 fix: add nv_framework back for API safety 2026-07-23 22:43:22 +08:00
z3st
1d33394dac fix: remove nv_framework, let platform auto-assign 2026-07-23 22:42:45 +08:00
z3st
1811a66aee fix: match platform auto-generated configParams format
- Remove /bin/bash -ic wrapper from sut_config.command
- Use list format for all command arguments (matches platform auto-gen)
- Add nv_framework field
- Remove unnecessary fields: docker_image, nv_docker_image, modelFormat, model, cpu_num
- Fix ref_config to match platform format (port 80, max_model_len 4096)
2026-07-23 22:35:50 +08:00
z3st
12bf6d637f fix: move gpu_num/cpu_num outside values block to match API spec 2026-07-23 22:10:17 +08:00
z3st
85c456afe0 fix: yaml.dump width=1000 to prevent command string wrapping 2026-07-22 22:43:35 +08:00
z3st
b3b52848c2 fix: verifyResult can be None, ensure dict return 2026-07-22 01:39:33 +08:00
z3st
d6e9f2e7a0 fix: page_size limit 50 for ModelScope API 2026-07-22 01:13:26 +08:00
z3st
821edbc8f5 debug: add logging to search function 2026-07-22 01:08:03 +08:00
z3st
5e2df178e1 feat: re-enable auto-run pipeline after connectivity test 2026-07-21 20:45:26 +08:00

719
main.py
View File

@@ -1,26 +1,18 @@
""" """
ModelHub 全云端提交智能体 ModelHub Cancel-All 智能体
部署在平台容器中,直接从内网提交验证任务 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
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
""" """
import json import json
import os import os
import signal import signal
import sqlite3
import threading
import time import time
import traceback import threading
from datetime import datetime from datetime import datetime
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from urllib.parse import urlparse, parse_qs
import requests import requests
import yaml
# ============================================================ # ============================================================
# 配置 # 配置
@@ -28,60 +20,19 @@ import yaml
HOST = "0.0.0.0" HOST = "0.0.0.0"
PORT = 8080 PORT = 8080
STRATEGY_ID = os.getenv("STRATEGY_ID", "")
# 四个账号的 Token # 目标GPU和Token跟提交版本保持一致
ACCOUNTS = { TARGET_GPU = "Iluvatar_bi-150"
'MetaX_c-500': 'f8e60d1dac7f4472967e7ca40145747b', TARGET_TOKEN = "f45f1aae2c094426be237c88b1085015"
'Kunlunxin_p-800': 'f45f1aae2c094426be237c88b1085015',
'Ascend_910-b4': 'f3c05879e7c34bbba92f399f12884183',
'hygon_k100-ai': 'b88507029b884ad3b4bad8ba09e6546e',
}
# GPU 引擎配置
GPU_CONFIGS = {
'MetaX_c-500': {
'framework': 'vllm',
'docker_image': 'modelhubxc-4pd.tencentcloudcr.com/enginex/enginex-metax/vllm:0.9.1',
},
'Kunlunxin_p-800': {
'framework': 'vllm',
'docker_image': 'modelhubxc-4pd.tencentcloudcr.com/enginex/sunjichen/xc-llm-kunlun:latest',
},
'Ascend_910-b4': {
'framework': 'vllm',
'docker_image': 'git.modelhub.org.cn:9443/enginex-ascend/vllm-ascend:v0.11.0rc0',
},
'hygon_k100-ai': {
'framework': 'llama.cpp',
'docker_image': 'modelhubxc-4pd.tencentcloudcr.com/enginex/enginex-hygon/hygon-llama.cpp:b7516',
},
}
# 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']
MODELSCOPE_API = "https://modelscope.cn/api/v1"
MODELHUB_API = "https://modelhub.org.cn/api" MODELHUB_API = "https://modelhub.org.cn/api"
BATCH_SIZE = 50 # 每批取消数量API上限
# 搜索关键词
SEARCH_KEYWORDS = ['qwen', 'Qwen2', 'Qwen3', 'Qwen3.5', 'Qwen1.5', 'Qwen-']
# ============================================================ # ============================================================
# 全局状态 # 全局状态
# ============================================================ # ============================================================
state = { state = {'running': False, 'last_run': None, 'logs': []}
'running': False,
'last_run': None,
'last_result': None,
'logs': [],
}
state_lock = threading.Lock() state_lock = threading.Lock()
@@ -95,373 +46,76 @@ def log(msg: str):
state['logs'] = state['logs'][-300:] state['logs'] = state['logs'][-300:]
# ============================================================ def cancel_all_tasks():
# 数据库(内存 SQLite """取消所有 waiting 和 running 状态的任务"""
# ============================================================
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
# ============================================================
def search_models(keyword: str, limit: int = 100) -> list:
"""从 ModelScope 搜索模型"""
url = "https://modelscope.cn/openapi/v1/models"
params = {
'search': keyword,
'page_size': limit,
'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'):
return data.get('data', {}).get('models', [])
except Exception as e:
log(f" 搜索失败 [{keyword}]: {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': list(ACCOUNTS.values())[0], '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:
return data.get('data', {}).get('verifyResult', {})
except Exception:
pass
return {}
def check_my_submitted(model_id: str, gpu: str, token: str) -> bool:
"""检查自己是否已提交"""
headers = {'Xc-Token': 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': 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(gpu: str, token: str) -> int:
"""查询队列可用位置"""
headers = {'Xc-Token': 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': 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(gpu: str) -> str:
"""构建 YAML 配置"""
config = GPU_CONFIGS.get(gpu, {})
framework = config.get('framework', 'vllm')
docker_image = config.get('docker_image', '')
if framework == 'llama.cpp':
params = {
'framework': 'llama.cpp',
'docker_image': docker_image,
'nv_docker_image': docker_image,
'api': 'completion',
'max_tokens': 1024,
'temperature': 0.7,
'repetition_penalty': 1.2,
'top_p': 0.9,
'lang': 'zh',
'max_model_len': 4096,
'modelFormat': 'GGUF',
'sut_config': {
'values': {
'gpu_num': 1,
'command': [
'/bin/bash', '-ic',
'llama-server --model /model --alias llm --threads 20 '
'--n-gpu-layers 999 --prio 3 --min_p 0.01 '
'--ctx-size 4096 --host 0.0.0.0 --port 8000 --jinja --flash-attn off'
]
}
},
'ref_config': {
'values': {
'cpu_num': 2, 'gpu_num': 1,
'command': [
'llama-server', '--model', '/model', '--alias', 'llm',
'--threads', '20', '--n-gpu-layers', '999',
'--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000',
]
}
},
'model': 'llm',
}
else:
params = {
'framework': 'vllm',
'docker_image': docker_image,
'nv_docker_image': docker_image,
'api': 'completion',
'max_tokens': 1024,
'temperature': 0.7,
'repetition_penalty': 1.2,
'top_p': 0.9,
'lang': 'zh',
'max_model_len': 2048,
'modelFormat': 'HuggingFace',
'sut_config': {
'values': {
'gpu_num': 1,
'command': [
'/bin/bash', '-ic',
'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': {
'values': {
'cpu_num': 2, 'gpu_num': 1,
'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',
]
}
},
'model': 'llm',
}
return yaml.dump(params, default_flow_style=False, allow_unicode=True)
def submit_model(model_url: str, gpu: str, token: str) -> tuple:
"""提交单个模型"""
headers = { headers = {
'Xc-Token': token, 'Xc-Token': TARGET_TOKEN,
'Accept': 'application/json', 'Accept': 'application/json',
'Content-Type': 'application/json', 'Content-Type': 'application/json',
} }
url = f"{MODELHUB_API}/adapt/task/add"
config = GPU_CONFIGS.get(gpu, {})
payload = {
'modelAddress': normalize_model_url(model_url),
'taskType': 'text-generation',
'targetGpu': gpu,
'framework': config.get('framework', 'vllm'),
'strategyId': STRATEGY_ID,
'configParams': build_config_params(gpu),
}
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(gpus: list = None, submit_limit: int = 30):
"""完整流程:搜索→筛选→提交"""
if gpus is None:
gpus = list(GPU_CONFIGS.keys())
init_db()
log("=" * 50) log("=" * 50)
log("开始执行流程") log("开始取消所有任务")
log(f"目标GPU: {', '.join(gpus)}") log(f"目标GPU: {TARGET_GPU}")
log(f"提交限制: 每GPU {submit_limit}")
# 1. 搜索 # 1. 查询所有任务
log("\n--- 阶段1: 搜索 ModelScope ---") all_tasks = []
seen = set() page = 1
all_models = [] while True:
for kw in SEARCH_KEYWORDS: try:
models = search_models(kw, limit=100) resp = requests.get(
for m in models: f"{MODELHUB_API}/adapt/task/page",
mid = m.get('id', '') headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'},
if mid and mid not in seen: params={'current': page, 'pageSize': 100, 'onlyMine': 'true',
seen.add(mid) 'gpuType': TARGET_GPU},
downloads = m.get('downloads', 0) timeout=15
if downloads >= 50: )
all_models.append({ data = resp.json()
'model_id': mid, if data.get('code') != 0:
'url': f"https://modelscope.cn/{mid}", log(f"查询失败: code={data.get('code')}")
'downloads': downloads,
'params': m.get('params', ''),
'category': 'quantized' if 'GGUF' in mid.upper() else 'standard',
})
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. 架构筛选
log("\n--- 阶段2: 架构筛选 ---")
arch_passed = []
arch_rejected = 0
for m in all_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")
# 3. 按 GPU 筛选并提交
log("\n--- 阶段3: 筛选并提交 ---")
total_submitted = 0
for gpu in gpus:
token = ACCOUNTS.get(gpu)
if not token:
continue
log(f"\n[{gpu}]")
# 检查队列
available = check_queue_available(gpu, token)
if available <= 0:
log(f" 队列满,跳过")
continue
log(f" 队列可用: {available}")
# 筛选
to_submit = []
for m in arch_passed:
model_id = m['model_id']
# hygon 只接受 GGUF
if gpu == 'hygon_k100-ai' and 'GGUF' not in model_id.upper():
continue
# 检查全平台验证状态
verify = check_platform_verify(model_id)
if 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, gpu, token):
continue
to_submit.append(m)
if len(to_submit) >= min(submit_limit, available):
break break
time.sleep(0.2) 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" 待提交: {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
# 提交 task_ids = [int(t.get('taskId')) for t in cancellable if t.get('taskId')]
submitted = 0 log(f" 总任务 {len(all_tasks)} 个, 可取消 {len(task_ids)}")
for m in to_submit:
ok, task_id, msg = submit_model(m['url'], gpu, token) # 3. 分批取消
if ok: url = f"{MODELHUB_API}/async/task/stop-create-contest-task"
submitted += 1 cancelled = 0
log(f"{m['model_id']}") for i in range(0, len(task_ids), BATCH_SIZE):
db_conn.execute( batch = task_ids[i:i + BATCH_SIZE]
'INSERT OR REPLACE INTO submitted VALUES (?,?,?,?)', try:
(m['model_id'], gpu, str(task_id), datetime.now().isoformat()) 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: else:
log(f" {m['model_id']}: {msg}") log(f" 批次 {i // BATCH_SIZE + 1}: 失败 code={data.get('code')} msg={data.get('message', '')[:80]}")
time.sleep(0.5) except Exception as e:
log(f" 批次 {i // BATCH_SIZE + 1}: 异常 {e}")
time.sleep(1)
log(f" 提交完成: {submitted}/{len(to_submit)}") log(f" 取消完成: {cancelled} / {len(task_ids)}")
total_submitted += submitted log("=" * 50)
db_conn.commit()
log(f"\n{'=' * 50}")
log(f"流程完成,共提交 {total_submitted} 个模型")
return total_submitted
# ============================================================ # ============================================================
@@ -470,151 +124,21 @@ def run_pipeline(gpus: list = None, submit_limit: int = 30):
class AgentHandler(BaseHTTPRequestHandler): class AgentHandler(BaseHTTPRequestHandler):
def do_GET(self): def do_GET(self):
parsed = urlparse(self.path) if self.path == '/health':
path = parsed.path
if path == '/health':
self._json({'status': 'ok'}) self._json({'status': 'ok'})
elif path == '/': elif self.path == '/':
self._json({ self._json({
'name': 'modelhub-submit-agent', 'name': 'modelhub-cancel-all',
'strategy_id': STRATEGY_ID, 'gpu': TARGET_GPU,
'status': 'running' if state['running'] else 'idle', 'status': 'running' if state['running'] else 'idle',
'last_run': state['last_run'], 'last_result': state.get('last_result'),
'last_result': state['last_result'],
}) })
elif path == '/test': elif self.path.startswith('/logs'):
self._json(self._run_connectivity_test()) lines = 100
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])
self._json({'logs': state['logs'][-lines:]}) self._json({'logs': state['logs'][-lines:]})
else: else:
self._json({'error': 'not found'}, 404) 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))
gpus = body.get('gpus', list(GPU_CONFIGS.keys()))
limit = body.get('limit', 30)
self._json({'status': 'started', 'gpus': gpus, 'limit': limit})
# 后台运行
def _run():
try:
state['running'] = True
state['last_run'] = datetime.now().isoformat()
count = run_pipeline(gpus=gpus, 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:
token = list(ACCOUNTS.values())[0]
resp = requests.get(
'https://modelhub.org.cn/api/adapt/task/page',
headers={'Xc-Token': 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:
token = list(ACCOUNTS.values())[0]
resp = requests.post(
'https://modelhub.org.cn/api/adapt/task/add',
headers={'Xc-Token': token, 'Accept': 'application/json', 'Content-Type': 'application/json'},
json={
'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
'taskType': 'text-generation',
'targetGpu': 'Kunlunxin_p-800',
'framework': 'vllm',
'strategyId': STRATEGY_ID,
'configParams': 'framework: vllm\n',
},
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): def _json(self, body: dict, status: int = 200):
payload = json.dumps(body, ensure_ascii=False).encode() payload = json.dumps(body, ensure_ascii=False).encode()
self.send_response(status) self.send_response(status)
@@ -624,7 +148,7 @@ class AgentHandler(BaseHTTPRequestHandler):
self.wfile.write(payload) self.wfile.write(payload)
def log_message(self, fmt, *args): def log_message(self, fmt, *args):
pass # 静默 HTTP 日志 pass
# ============================================================ # ============================================================
@@ -644,94 +168,27 @@ def main():
signal.signal(signal.SIGTERM, _handle_signal) signal.signal(signal.SIGTERM, _handle_signal)
signal.signal(signal.SIGINT, _handle_signal) signal.signal(signal.SIGINT, _handle_signal)
init_db()
server = ThreadingHTTPServer((HOST, PORT), AgentHandler) server = ThreadingHTTPServer((HOST, PORT), AgentHandler)
server.timeout = 1 server.timeout = 1
log(f"智能体启动 | {HOST}:{PORT}") log(f"Cancel-All 智能体启动 | {HOST}:{PORT}")
log(f"STRATEGY_ID: {STRATEGY_ID}") log(f"目标GPU: {TARGET_GPU}")
log(f"GPU: {', '.join(GPU_CONFIGS.keys())}")
# 启动后自动运行连通性测试 # 启动后自动取消所有任务
def _startup_test(): def _auto_cancel():
time.sleep(2) time.sleep(2)
log("=" * 50)
log("连通性测试开始")
log("=" * 50)
# 1. ModelScope 搜索
try: try:
resp = requests.get( state['running'] = True
'https://modelscope.cn/openapi/v1/models', state['last_run'] = datetime.now().isoformat()
params={'search': 'qwen', 'page_size': 2, 'sort': 'downloads'}, cancel_all_tasks()
timeout=10, headers={'User-Agent': 'Mozilla/5.0'} state['last_result'] = {'success': True}
)
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: except Exception as e:
log(f"[ModelScope搜索] ❌ error={e}") log(f"异常: {e}")
state['last_result'] = {'error': str(e)}
finally:
state['running'] = False
# 2. ModelScope config.json threading.Thread(target=_auto_cancel, daemon=True).start()
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:
token = list(ACCOUNTS.values())[0]
resp = requests.get(
'https://modelhub.org.cn/api/adapt/task/page',
headers={'Xc-Token': 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:
token = list(ACCOUNTS.values())[0]
resp = requests.post(
'https://modelhub.org.cn/api/adapt/task/add',
headers={'Xc-Token': token, 'Accept': 'application/json', 'Content-Type': 'application/json'},
json={
'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
'taskType': 'text-generation', 'targetGpu': 'Kunlunxin_p-800',
'framework': 'vllm', 'strategyId': STRATEGY_ID,
'configParams': 'framework: vllm\n',
}, 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,请查看上方结果")
log("=" * 50)
threading.Thread(target=_startup_test, daemon=True).start()
while not shutdown_requested: while not shutdown_requested:
server.handle_request() server.handle_request()