[AI学习笔记]自适应批处理：DeepSeek动态调度算法实战指南

def preprocess_state(raw_data):
    """
    状态预处理函数，将原始数据转换为模型输入向量
    """
    # 特征归一化
    normalized_data = normalize(raw_data)
    
    # 降维处理
    pca = PCA(n_components=64)
    reduced_data = pca.fit_transform(normalized_data)
    
    # 添加时间序列特征
    historical_features = generate_time_series_features(raw_data)
    
    # 合并特征向量
    final_state = np.concatenate([reduced_data, historical_features])
    
    return final_state

# 创建虚拟环境
python -m venv deepseek-env
source deepseek-env/bin/activate

# 安装依赖
pip install torch numpy pandas redis
pip install -U deepseek[full]  # 安装DeepSeek完整版

from deepseek import DeepSeekScheduler, ResourceConfig

# 配置资源参数
resource_config = ResourceConfig(
    min_instances=2,
    max_instances=20,
    cpu_per_instance=4,
    memory_per_instance=16
)

# 初始化调度器
scheduler = DeepSeekScheduler(
    model_path="models/deepseek_v3.pt",
    resource_config=resource_config,
    redis_host="localhost",
    redis_port=6379
)

from deepseek import Task

# 创建任务
task = Task(
    task_id="task-123",
    task_type="data_processing",
    priority=3,
    resource_request={"cpu": 2, "memory": 8},
    data_sources=["s3://bucket/data1.parquet"]
)

# 提交任务到调度器
scheduler.submit_task(task)

# 获取当前系统状态
current_state = scheduler.get_state()

# 手动触发调度决策（通常由系统自动触发）
decision = scheduler.make_decision(current_state)

# 应用决策
scheduler.apply_decision(decision)

# 部署示例
helm repo add deepseek https://deepseek.github.io/helm-charts
helm install deepseek-scheduler deepseek/deepseek-scheduler \
  --set redis.host=redis-master \
  --set resources.requests.cpu=2

import unittest
from deepseek import DeepSeekScheduler

class TestScheduler(unittest.TestCase):
    def setUp(self):
        self.scheduler = DeepSeekScheduler(
            model_path="test_model.pt",
            resource_config=ResourceConfig(min_instances=1, max_instances=5)
        )
    
    def test_resource_allocation(self):
        # 模拟高负载状态
        state = {"cpu_usage": 0.9, "task_queue": 15}
        decision = self.scheduler.make_decision(state)
        self.assertGreaterEqual(decision["add_instances"], 2)

def test_end_to_end():
    # 启动测试集群
    cluster = TestCluster()
    cluster.start()
    
    # 初始化调度器
    scheduler = DeepSeekScheduler(
        model_path="test_model.pt",
        resource_config=ResourceConfig(min_instances=1)
    )
    
    # 提交多个任务
    for i in range(20):
        task = Task(task_id=f"test-{i}", priority=i%5)
        scheduler.submit_task(task)
    
    # 等待处理完成
    time.sleep(60)
    
    # 验证结果
    metrics = scheduler.get_metrics()
    assert metrics["avg_latency"] < 10  # 单位：秒
    assert metrics["resource_utilization"] > 0.6
    
    # 清理环境
    cluster.stop()