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ADD file via upload

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qiwang 2025-07-21 18:53:40 +08:00
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import concurrent.futures
import time
import logging
import threading
from uuid import uuid4
from typing import Dict, List, Optional
import requests
import os
import json
import yaml # 用于处理YAML文件
# -------------------------- 全局配置与常量定义 --------------------------
# 日志配置
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[logging.StreamHandler()]
)
logger = logging.getLogger(__name__)
# 任务状态定义
TASK_STATUS = {
"SUBMITTED": "待提交", # 初始状态
"SUBMITTING": "提交中", # 提交过程中
"SUCCEED": "提交成功", # 云际确认成功
"FAILED": "提交失败", # 云际确认失败
"RETRY_EXHAUSTED": "重试耗尽" # 超过最大失败次数
}
# 全局任务字典（key=target_id，value=任务详情）
task_map: Dict[str, Dict] = {}
task_map_lock = threading.Lock() # 任务字典线程锁
# 全局数据集映射（key=file_location，value=DatasetInfo实例）
dataset_map: Dict[str, Dict] = {}
dataset_lock = threading.Lock() # 数据集映射线程锁
# 子算法-集群-数据集映射（从YAML加载）
ALGORITHM_MAPPING: Dict[int, Dict] = {} # 结构: {son_code_id: {"clusters": [], "file_location": ""}}
# 任务模板（从YAML加载）
TASK_TEMPLATES: List[Dict] = []
# API配置
API_CONFIG = {
"login": {
"url": "http://119.45.255.234:30180/jcc-admin/admin/login",
"timeout": 10
},
"create_package": {
"url": "http://119.45.255.234:30180/jsm/jobSet/createPackage",
"timeout": 15
},
"upload_file": {
"url": "http://121.36.5.116:32010/object/upload",
"timeout": 3000
},
"notify_upload": {
"url": "http://119.45.255.234:30180/jsm/jobSet/notifyUploaded",
"timeout": 15
},
"bind_cluster": {
"url": "http://119.45.255.234:30180/jsm/jobSet/binding",
"timeout": 15
},
"query_binding": {
"url": "http://119.45.255.234:30180/jsm/jobSet/queryBinding",
"timeout": 15
},
"submit_task": {
"url": "http://119.45.255.234:30180/jsm/jobSet/submit",
"timeout": 100
},
"task_details": {
"url": "http://119.45.255.234:30180/pcm/v1/core/task/details",
"timeout": 15
}
}
# 集群资源配置（key=集群ID，value=总资源/可用资源）
cluster_resources: Dict[str, Dict] = {
"1790300942428540928": { # modelarts集群
"total": {"CPU": 1024, "MEMORY": 2048, "NPU": 56},
"available": {"CPU": 1024, "MEMORY": 2048, "NPU": 56}
},
"1790300942428540929": { # 新增集群示例
"total": {"CPU": 512, "MEMORY": 1024, "NPU": 32},
"available": {"CPU": 512, "MEMORY": 1024, "NPU": 32}
}
}
cluster_lock = threading.Lock() # 集群资源线程锁
# -------------------------- 数据结构定义 --------------------------
class DatasetInfo(dict):
"""数据集信息结构"""
def __init__(self, file_location: str, name: str, size: float, **kwargs):
super().__init__()
self["file_location"] = file_location # 本地路径（主键）
self["id"] = kwargs.get("id", str(uuid4())) # 数据集唯一标识
self["name"] = name # 数据集名称
self["size"] = size # 大小（字节）
self["is_uploaded"] = kwargs.get("is_uploaded", False) # 是否已上传
self["upload_cluster"] = kwargs.get("upload_cluster", []) # 上传的集群
self["upload_time"] = kwargs.get("upload_time") # 上传时间
self["description"] = kwargs.get("description") # 描述
self["dataset_target_id"] = kwargs.get("dataset_target_id") # 绑定ID
class TaskInfo(dict):
"""任务信息结构"""
def __init__(self, task_name: str, dataset_name: str, son_code_id: int, resource: Dict, **kwargs):
super().__init__()
self["target_id"] = kwargs.get("target_id", str(uuid4())) # 任务唯一ID
self["task_name"] = task_name # 任务名称
self["package_name"] = kwargs.get("package_name", f"{task_name.lower()}-pkg") # 文件夹名称
self["dataset_name"] = dataset_name # 关联数据集名称
self["son_code_id"] = son_code_id # 子算法ID
self["resource"] = resource # 资源需求（CPU/MEMORY/NPU等）
self["status"] = TASK_STATUS["SUBMITTED"] # 初始状态：待提交
self["submit_time"] = kwargs.get("submit_time", time.strftime("%Y-%m-%d %H:%M:%S")) # 提交时间
self["success_time"] = None # 成功时间（成功时填充）
self["third_party_task_id"] = "" # 云际任务ID（提交后填充）
self["file_location"] = kwargs.get("file_location", "") # 本地文件路径（从映射获取）
self["error_msg"] = "" # 错误信息
self["fail_count"] = 0 # 失败次数
self["max_fail_threshold"] = kwargs.get("max_fail_threshold", 3) # 最大失败阈值
self["cluster_id"] = "" # 提交的集群ID（从映射获取）
# -------------------------- YAML配置文件处理 --------------------------
def load_yaml_config(yaml_path: str = "sonCode_cluster__mapping.yaml") -> None:
"""加载YAML配置文件（包含算法映射和任务模板）"""
# 加载配置文件
try:
with open(str, "r", encoding="utf-8") as f:
data = yaml.safe_load(f)
ALGORITHM_MAPPING = data.get("algorithm_mapping", {})
TASK_TEMPLATES = data.get("task_templates", [])
logger.info(
f"成功加载配置文件 | 子算法映射: {len(ALGORITHM_MAPPING)} 条 | 任务模板: {len(TASK_TEMPLATES)} 条")
except Exception as e:
logger.error(f"加载配置文件失败: {str(e)}", exc_info=True)
# -------------------------- 工具方法 --------------------------
def generate_task_templates() -> List[Dict]:
"""从全局变量返回任务模板（实际从YAML加载）"""
return TASK_TEMPLATES
def load_tasks_to_queue(templates: List[Dict]) -> None:
"""将任务静态数据加载到任务队列（从映射获取file_location）"""
global task_map
with task_map_lock:
task_map.clear()
for idx, template in enumerate(templates):
try:
# 检查必填字段
required_fields = ["task_name_template", "prefix", "dataset_name", "son_code_id", "resource"]
missing_fields = [f for f in required_fields if f not in template]
if missing_fields:
logger.warning(f"跳过无效任务模板（索引{idx}）：缺少字段 {missing_fields}")
continue
# 从映射获取file_location
son_code_id = template["son_code_id"]
mapping = ALGORITHM_MAPPING.get(son_code_id)
if not mapping:
logger.warning(f"子算法ID {son_code_id} 无映射配置，跳过任务模板（索引{idx}）")
continue
file_location = mapping["file_location"]
# 生成任务名称
task_name = template["task_name_template"].format(prefix=template["prefix"])
# 创建任务实例
task = TaskInfo(
task_name=task_name,
dataset_name=template["dataset_name"],
son_code_id=son_code_id,
resource=template["resource"],
file_location=file_location # 从映射填充
)
task_map[task["target_id"]] = task
logger.info(
f"任务入队 | task_name: {task_name} | 子算法ID: {son_code_id} | 数据集路径: {file_location}")
except Exception as e:
logger.error(f"加载任务模板失败（索引{idx}）：{str(e)}")
logger.info(f"任务队列加载完成 | 共 {len(task_map)} 个有效任务")
def select_cluster(task_resource: Dict, son_code_id: int) -> Optional[str]:
"""根据任务资源需求和子算法ID选择合适的集群（优先从映射中选择）"""
# 1. 从映射获取该子算法支持的集群
mapping = ALGORITHM_MAPPING.get(son_code_id)
if not mapping:
logger.warning(f"子算法ID {son_code_id} 无集群映射，尝试所有集群")
candidate_clusters = list(cluster_resources.keys())
else:
candidate_clusters = mapping["clusters"]
with cluster_lock:
# 2. 检查候选集群是否满足资源需求
for cluster_id in candidate_clusters:
if cluster_id not in cluster_resources:
logger.warning(f"映射中集群 {cluster_id} 不存在于资源配置中，跳过")
continue
cluster = cluster_resources[cluster_id]
# 检查资源是否满足
resource_match = True
for res_type, required in task_resource.items():
available = cluster["available"].get(res_type, 0)
if available < required:
resource_match = False
break
if resource_match:
# 占用资源
for res_type, required in task_resource.items():
if res_type in cluster["available"]:
cluster["available"][res_type] -= required
logger.info(f"选中集群 {cluster_id}（子算法 {son_code_id} 映射）| 更新后可用资源: {cluster['available']}")
return cluster_id
# 3. 若映射中的集群不满足，尝试其他集群
all_clusters = list(cluster_resources.keys())
for cluster_id in all_clusters:
if cluster_id in candidate_clusters:
continue # 已检查过
cluster = cluster_resources[cluster_id]
resource_match = True
for res_type, required in task_resource.items():
available = cluster["available"].get(res_type, 0)
if available < required:
resource_match = False
break
if resource_match:
for res_type, required in task_resource.items():
if res_type in cluster["available"]:
cluster["available"][res_type] -= required
logger.info(f"选中集群 {cluster_id}（非映射）| 更新后可用资源: {cluster['available']}")
return cluster_id
logger.warning(f"无满足资源需求的集群 | 任务需求: {task_resource} | 子算法: {son_code_id}")
return None
# -------------------------- 数据集上传判断与处理方法 --------------------------
def check_and_handle_dataset(file_location: str, dataset_name: str, cluster_id: str) -> Optional[str]:
"""检查数据集是否已上传到指定集群，未上传则执行上传"""
global dataset_map
# 步骤1: 检查数据集是否已上传
dataset = get_dataset_status(file_location, dataset_name, cluster_id)
# 步骤2: 若未上传，则执行上传
if not dataset or not dataset["is_uploaded"] or cluster_id not in dataset["upload_cluster"]:
dataset = upload_dataset(file_location, dataset_name, cluster_id)
if not dataset:
return None # 上传失败
return dataset["dataset_target_id"]
def get_dataset_status(file_location: str, dataset_name: str, cluster_id: str) -> Optional[Dict]:
"""检查数据集状态（是否已上传到指定集群）"""
with dataset_lock:
if file_location in dataset_map:
dataset = dataset_map[file_location]
# 验证是否已上传到目标集群
if dataset["is_uploaded"] and cluster_id in dataset["upload_cluster"]:
logger.info(
f"数据集 {dataset_name} 已上传到集群 {cluster_id} | target_id: {dataset['dataset_target_id']}")
return dataset
return None
def upload_dataset(file_location: str, dataset_name: str, cluster_id: str) -> Optional[Dict]:
"""执行数据集上传流程"""
dataset_path = os.path.join(file_location, dataset_name)
# 检查本地文件是否存在
if not os.path.exists(dataset_path):
logger.error(f"数据集本地文件不存在 | path: {dataset_path}")
return None
# 计算文件大小（字节）
try:
file_size = os.path.getsize(dataset_path)
except OSError as e:
logger.error(f"获取文件大小失败 | path: {dataset_path} | 错误: {str(e)}")
return None
logger.info(f"开始上传数据集 {dataset_name} 到集群 {cluster_id} | path: {dataset_path}")
try:
# 获取认证Token
token = get_token()
if not token:
logger.error("获取Token失败，无法上传数据集")
return None
headers = {'Authorization': f'Bearer {token}'}
package_name = f"dataset-{dataset_name.split('.')[0]}-{uuid4().hex[:6]}" # 生成唯一文件夹名
# 1. 创建数据集文件夹
create_payload = {
"userID": 5,
"name": package_name,
"dataType": "dataset",
"packageID": 0,
"uploadPriority": {"type": "specify", "clusters": [cluster_id]},
"bindingInfo": {
"clusterIDs": [cluster_id],
"name": package_name,
"category": "image",
"type": "dataset",
"imageID": "",
"chip": ["ASCEND"],
"packageID": 0
}
}
create_resp = requests.post(
API_CONFIG["create_package"]["url"],
json=create_payload,
headers=headers,
timeout=API_CONFIG["create_package"]["timeout"]
)
create_resp.raise_for_status()
create_result = create_resp.json()
if create_result.get("code") != "OK":
raise ValueError(f"创建文件夹失败 | 响应: {create_result}")
packageID = create_result["data"]["newPackage"]["packageID"]
logger.info(f"数据集文件夹创建成功 | packageID: {packageID}")
# 2. 上传文件
info_data = {
"userID": 5,
"packageID": packageID,
"loadTo": [3],
"loadToPath": [f"/dataset/5/{package_name}/"]
}
with open(dataset_path, 'rb') as f:
form_data = {"info": (None, json.dumps(info_data)), "files": f}
upload_resp = requests.post(
API_CONFIG["upload_file"]["url"],
files=form_data,
headers=headers,
timeout=API_CONFIG["upload_file"]["timeout"]
)
upload_resp.raise_for_status()
upload_result = upload_resp.json()
if upload_result.get("code") != "OK":
raise ValueError(f"文件上传失败 | 响应: {upload_result}")
object_id = upload_result["data"]["uploadeds"][0]["objectID"]
logger.info(f"数据集文件上传成功 | objectID: {object_id}")
# 3. 通知上传完成
notify_payload = {
"userID": 5,
"packageID": packageID,
"uploadParams": {
"dataType": "dataset",
"uploadInfo": {"type": "local", "localPath": dataset_name, "objectIDs": [object_id]}
}
}
notify_resp = requests.post(
API_CONFIG["notify_upload"]["url"],
json=notify_payload,
headers=headers,
timeout=API_CONFIG["notify_upload"]["timeout"]
)
notify_resp.raise_for_status()
# 4. 绑定到集群
bind_payload = {
"userID": 5,
"info": {"type": "dataset", "packageID": packageID, "clusterIDs": [cluster_id]}
}
bind_resp = requests.post(
API_CONFIG["bind_cluster"]["url"],
json=bind_payload,
headers=headers,
timeout=API_CONFIG["bind_cluster"]["timeout"]
)
bind_resp.raise_for_status()
# 5. 查询绑定ID（dataset_target_id）
query_resp = requests.post(
API_CONFIG["query_binding"]["url"],
json={"dataType": "dataset", "param": {"userID": 5, "bindingID": -1, "type": "private"}},
headers=headers,
timeout=API_CONFIG["query_binding"]["timeout"]
).json()
if query_resp.get("code") != "OK":
raise ValueError(f"查询绑定ID失败 | 响应: {query_resp}")
dataset_target_id = None
for item in query_resp["data"]["datas"]:
if item["info"]["name"] == package_name:
dataset_target_id = item["ID"]
break
if not dataset_target_id:
raise ValueError(f"未找到数据集 {package_name} 的绑定ID")
# 上传成功，创建并保存数据集信息
dataset = DatasetInfo(
file_location=file_location,
name=dataset_name,
size=file_size,
is_uploaded=True,
upload_cluster=[cluster_id],
upload_time=time.strftime("%Y-%m-%d %H:%M:%S"),
dataset_target_id=dataset_target_id
)
with dataset_lock:
dataset_map[file_location] = dataset
logger.info(f"数据集 {dataset_name} 上传成功 | target_id: {dataset_target_id}")
return dataset
except Exception as e:
logger.error(f"数据集上传失败 | name: {dataset_name} | 错误: {str(e)}", exc_info=True)
return None
# -------------------------- API调用方法 --------------------------
def get_token() -> Optional[str]:
"""获取认证Token"""
login_payload = {"username": "admin", "password": "Nudt@123"}
try:
config = API_CONFIG["login"]
response = requests.post(config["url"], json=login_payload, timeout=config["timeout"])
response.raise_for_status()
result = response.json()
if result.get("code") == 200 and "data" in result and "token" in result["data"]:
logger.info("Token获取成功")
return result["data"]["token"]
else:
logger.error(f"Token获取失败 | 响应: {result}")
return None
except requests.exceptions.RequestException as e:
logger.error(f"登录请求异常: {str(e)}", exc_info=True)
return None
def submit_single_task(task: Dict) -> bool:
"""提交单个任务到集群"""
token = get_token()
if not token:
with task_map_lock:
task["status"] = TASK_STATUS["FAILED"]
task["error_msg"] = "获取Token失败"
return False
# 获取选择的集群ID
cluster_id = task.get("cluster_id")
if not cluster_id:
with task_map_lock:
task["status"] = TASK_STATUS["FAILED"]
task["error_msg"] = "未指定集群ID"
logger.error(f"[{task['task_name']}] 提交失败：未指定集群ID")
return False
# 数据集检查与上传
dataset_target_id = check_and_handle_dataset(
file_location=task["file_location"],
dataset_name=task["dataset_name"],
cluster_id=cluster_id
)
if not dataset_target_id:
# 数据集上传失败，释放集群资源
with cluster_lock:
if cluster_id in cluster_resources:
for res_type, required in task["resource"].items():
if res_type in cluster_resources[cluster_id]["available"]:
cluster_resources[cluster_id]["available"][res_type] += required
with task_map_lock:
task["status"] = TASK_STATUS["FAILED"]
task["error_msg"] = "数据集上传失败"
logger.error(f"[{task['task_name']}] 提交失败：数据集处理失败")
return False
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {token}'
}
task_name = task["task_name"]
package_name = task["package_name"]
son_code_id = task["son_code_id"] # 子算法ID
try:
# 1. 创建数据集文件夹
config = API_CONFIG["create_package"]
create_payload = {
"userID": 5,
"name": package_name,
"dataType": "dataset",
"packageID": 0,
"uploadPriority": {"type": "specify", "clusters": [cluster_id]},
"bindingInfo": {
"clusterIDs": [cluster_id],
"name": package_name,
"category": "image",
"type": "dataset",
"imageID": "",
"bias": [],
"region": [],
"chip": ["ASCEND"],
"selectedCluster": [],
"modelType": "",
"env": "",
"version": "",
"packageID": 0,
"points": 0
}
}
create_resp = requests.post(config["url"], json=create_payload, headers=headers, timeout=config["timeout"])
create_resp.raise_for_status()
create_result = create_resp.json()
if create_result.get("code") != "OK":
raise ValueError(f"创建文件夹失败 | API返回: {create_result}")
packageID = create_result["data"]["newPackage"]["packageID"]
logger.info(f"[{task_name}] 第一步：创建文件夹成功 | packageID: {packageID} | 集群: {cluster_id}")
# 2. 通知上传完成
config = API_CONFIG["notify_upload"]
notify_payload = {
"userID": 5,
"packageID": packageID,
"uploadParams": {
"dataType": "dataset",
"uploadInfo": {"type": "local", "localPath": task["dataset_name"], "objectIDs": []}
}
}
notify_resp = requests.post(config["url"], json=notify_payload, headers=headers, timeout=config["timeout"])
notify_resp.raise_for_status()
logger.info(f"[{task_name}] 第二步：通知上传完成成功")
# 3. 绑定数据集到集群
config = API_CONFIG["bind_cluster"]
bind_payload = {
"userID": 5,
"info": {"type": "dataset", "packageID": packageID, "clusterIDs": [cluster_id]}
}
bind_resp = requests.post(config["url"], json=bind_payload, headers=headers, timeout=config["timeout"])
bind_resp.raise_for_status()
logger.info(f"[{task_name}] 第三步：数据集绑定集群 {cluster_id} 成功")
# 4. 提交训练任务
config = API_CONFIG["submit_task"]
task_res = task["resource"]
submit_payload = {
"userID": 5,
"jobSetInfo": {
"jobs": [
{
"localJobID": "1",
"name": task_name,
"description": "自动提交的训练任务",
"type": "AI",
"files": {
"dataset": {"type": "Binding", "bindingID": dataset_target_id},
"model": {"type": "Binding", "bindingID": ""},
"image": {"type": "Image", "imageID": 11}
},
"jobResources": {
"scheduleStrategy": "dataLocality",
"clusters": [
{
"clusterID": cluster_id,
"runtime": {"envs": {}, "params": {}},
"code": {"type": "Binding", "bindingID": son_code_id},
"resources": [
{"type": "CPU", "name": "ARM", "number": task_res["CPU"]},
{"type": "MEMORY", "name": "RAM", "number": task_res["MEMORY"]},
{"type": "MEMORY", "name": "VRAM", "number": 32},
{"type": "STORAGE", "name": "DISK", "number": 886},
{"type": "NPU", "name": "ASCEND910", "number": task_res.get("NPU", 0)}
]
}
]
}
}
]
}
}
response = requests.post(
config["url"],
json=submit_payload,
headers=headers,
timeout=config["timeout"]
)
response.raise_for_status()
submit_resp = response.json()
if submit_resp.get("code") != "OK":
raise ValueError(f"任务提交失败 | API返回: {submit_resp}")
third_party_task_id = submit_resp.get('data', {}).get('jobSetID')
logger.info(f"[{task_name}] 第四步：任务提交至集群 {cluster_id} 成功 | 云际任务ID: {third_party_task_id}")
# 更新任务状态为成功
with task_map_lock:
task["status"] = TASK_STATUS["SUCCEED"]
task["third_party_task_id"] = third_party_task_id
return True
except Exception as e:
error_msg = f"提交失败: {str(e)}"
# 任务失败时释放集群资源
with cluster_lock:
if cluster_id in cluster_resources:
for res_type, required in task["resource"].items():
if res_type in cluster_resources[cluster_id]["available"]:
cluster_resources[cluster_id]["available"][res_type] += required
logger.info(f"任务失败，释放集群 {cluster_id} 资源: {task['resource']}")
with task_map_lock:
task["fail_count"] += 1
if task["fail_count"] >= task["max_fail_threshold"]:
task["status"] = TASK_STATUS["RETRY_EXHAUSTED"]
else:
task["status"] = TASK_STATUS["FAILED"]
task["error_msg"] = error_msg
logger.error(f"[{task_name}] {error_msg}", exc_info=True)
return False
def query_third_party_task_status(third_party_task_id: str) -> Optional[Dict]:
"""查询云际平台任务状态"""
if not third_party_task_id:
logger.warning("云际任务ID为空，无法查询状态")
return None
try:
url = "http://119.45.255.234:30180/pcm/v1/core/task/list"
params = {
"pageNum": 1,
"pageSize": 10,
"type": 1
}
token = get_token()
if not token:
logger.error("获取Token失败，无法查询任务状态")
return None
headers = {"Authorization": f"Bearer {token}"} if token else {}
response = requests.get(
url,
params=params,
headers=headers,
timeout=15
)
response.raise_for_status()
result = response.json()
if result.get("code") != 200 or "data" not in result or "list" not in result["data"]:
logger.error(f"查询任务状态接口返回异常 | 响应: {result}")
return None
task_list = result["data"]["list"]
target_task = None
for task in task_list:
if task.get("id") == third_party_task_id:
target_task = task
break
if not target_task:
logger.warning(f"未找到任务 {third_party_task_id}")
return None
task_status = target_task.get("status")
end_time = target_task.get("endTime")
logger.info(f"任务 {third_party_task_id} 状态: {task_status} | 结束时间: {end_time or '未结束'}")
return {
"status": task_status,
"end_time": end_time
}
except Exception as e:
logger.error(f"查询任务状态失败 | 任务ID: {third_party_task_id} | 错误: {str(e)}", exc_info=True)
return None
# -------------------------- 线程一：任务监控线程 --------------------------
class TaskMonitorThread(threading.Thread):
"""监控线程：专注监控任务状态"""
def __init__(self, check_interval: int = 10):
super().__init__(name="TaskMonitorThread")
self.check_interval = check_interval
self._stop_event = threading.Event()
def run(self) -> None:
logger.info(f"监控线程启动 | 监控间隔: {self.check_interval}秒")
while not self._stop_event.is_set():
with task_map_lock:
tasks = list(task_map.values())
for task in tasks:
with task_map_lock:
current_status = task["status"]
if current_status in [TASK_STATUS["SUBMITTED"], TASK_STATUS["RETRY_EXHAUSTED"]]:
continue
if current_status == TASK_STATUS["SUBMITTING"] and task["third_party_task_id"]:
third_party_info = query_third_party_task_status(task["third_party_task_id"])
if third_party_info:
with task_map_lock:
if third_party_info["status"] == "Succeeded":
task["status"] = TASK_STATUS["SUCCEED"]
task["success_time"] = third_party_info["end_time"]
logger.info(f"任务 {task['task_name']} 成功 | 时间: {task['success_time']}")
elif third_party_info["status"] in ["Failed", "Saved"]:
task["status"] = TASK_STATUS["FAILED"]
task["fail_count"] += 1
task["error_msg"] = f"云际任务失败（第{task['fail_count']}次）"
logger.warning(f"任务 {task['task_name']} 失败 | 次数: {task['fail_count']}")
if self._check_all_completed():
logger.info("所有任务已完成")
self.stop()
self._stop_event.wait(self.check_interval)
logger.info("监控线程结束")
def _check_all_completed(self) -> bool:
"""检查所有任务是否完成"""
with task_map_lock:
for task in task_map.values():
if task["status"] in [TASK_STATUS["SUBMITTED"], TASK_STATUS["SUBMITTING"]]:
return False
if task["status"] == TASK_STATUS["FAILED"] and task["fail_count"] < task["max_fail_threshold"]:
return False
return True
def stop(self) -> None:
self._stop_event.set()
# -------------------------- 线程二：任务提交线程 --------------------------
class TaskSubmitThread(threading.Thread):
"""提交线程：按状态判断是否提交"""
def __init__(self, max_workers: int = 3):
super().__init__(name="TaskSubmitThread")
self.max_workers = max_workers
self._stop_event = threading.Event()
def run(self) -> None:
logger.info(f"提交线程启动 | 并发数: {self.max_workers}")
while not self._stop_event.is_set():
# 筛选待提交任务
with task_map_lock:
pending_tasks = []
for task in task_map.values():
status = task["status"]
if status == TASK_STATUS["SUBMITTED"]:
pending_tasks.append(task)
elif status == TASK_STATUS["FAILED"] and task["fail_count"] < task["max_fail_threshold"]:
pending_tasks.append(task)
elif status == TASK_STATUS["FAILED"]:
logger.info(f"任务 {task['task_name']} 失败次数超限，停止提交")
# 并发提交
with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor:
futures = {executor.submit(self.commit_task, task): task for task in pending_tasks}
for future in concurrent.futures.as_completed(futures):
task = futures[future]
try:
future.result()
except Exception as e:
with task_map_lock:
task["status"] = TASK_STATUS["FAILED"]
task["fail_count"] += 1
task["error_msg"] = f"提交异常: {str(e)}"
logger.error(f"任务 {task['task_name']} 提交异常 | 错误: {str(e)}")
if self._check_all_completed():
logger.info("所有任务已完成，提交线程退出")
self.stop()
break
if not pending_tasks:
logger.info("无待提交任务，等待5秒")
self._stop_event.wait(5)
logger.info("提交线程结束")
def commit_task(self, task: Dict) -> None:
"""提交任务入口：基于映射选择集群"""
son_code_id = task["son_code_id"]
cluster_id = select_cluster(task["resource"], son_code_id)
if not cluster_id:
with task_map_lock:
task["status"] = TASK_STATUS["FAILED"]
task["fail_count"] += 1
task["error_msg"] = "无可用集群"
logger.error(f"任务 {task['task_name']} 提交失败：无可用集群")
return
# 标记为提交中
with task_map_lock:
task["status"] = TASK_STATUS["SUBMITTING"]
task["cluster_id"] = cluster_id
logger.info(f"任务 {task['task_name']} 开始提交至集群 {cluster_id}（子算法 {son_code_id}）")
# 执行提交
submit_success = submit_single_task(task)
if not submit_success:
logger.warning(f"任务 {task['task_name']} 提交失败，等待重试（当前次数：{task['fail_count']}）")
def _check_all_completed(self) -> bool:
"""检查所有任务是否完成"""
with task_map_lock:
for task in task_map.values():
if task["status"] in [TASK_STATUS["SUBMITTED"], TASK_STATUS["SUBMITTING"]]:
return False
if task["status"] == TASK_STATUS["FAILED"] and task["fail_count"] < task["max_fail_threshold"]:
return False
return True
def stop(self) -> None:
self._stop_event.set()
# -------------------------- 主程序 --------------------------
if __name__ == "__main__":
# 加载YAML配置（包含算法映射和任务模板）
load_yaml_config()
# 生成任务模板并加载到队列
task_templates = generate_task_templates()
load_tasks_to_queue(task_templates)
# 启动线程
monitor_thread = TaskMonitorThread(check_interval=10)
monitor_thread.start()
submit_thread = TaskSubmitThread(max_workers=3)
submit_thread.start()
# 等待线程完成
monitor_thread.join()
submit_thread.join()
logger.info("所有任务处理完毕，程序退出")