代码即引擎：蓝耘 MaaS分布式开发实战，从数据分片到模型并行，破解百亿参数训练的算力密码

import requests
import json

# 配置 API 参数（需根据实际接口文档修改）
API_URL = "https://api.lanying.com/massapi"  # 接口地址（需替换）
API_KEY = "your_api_key_here"               # API 密钥（需替换）
MODEL_NAME = "your_model_name"              # 模型名称（如：image-classifier）

# 请求头（需根据接口要求添加认证信息）
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# 输入数据示例（请根据实际需求修改）
data = {
    "input": "your_input_data_here",  # 可能是图像路径、文本、数值等
    "parameters": {
        "batch_size": 1,
        "threshold": 0.8
    }
}

try:
    # 发送 POST 请求调用 API
    response = requests.post(
        API_URL,
        headers=headers,
        data=json.dumps(data),
        timeout=10  # 设置超时时间
    )
    
    # 检查响应状态码
    if response.status_code == 200:
        result = response.json()
        print("API 返回结果:", result)
        # 在此处处理输出结果（如：保存、可视化等）
    else:
        print(f"请求失败，状态码: {response.status_code}")
        print("错误信息:", response.text)

except requests.exceptions.RequestException as e:
    print(f"网络请求异常: {e}")

# 下载安装包并赋予执行权限  
wget https://lanyun-mass.com/downloads/mass-v3.2.1-linux-x64.run  
chmod +x mass-v3.2.1-linux-x64.run  
# 执行安装（支持自定义路径，默认安装至/usr/local/mass）  
./mass-v3.2.1-linux-x64.run --install  

import mass  
print(mass.__version__)  # 输出版本号，如3.2.1  

import numpy as np  
def matrix_mult(a, b):  
    return np.dot(a, b)  
a = np.random.rand(10000, 10000)  
b = np.random.rand(10000, 10000)  
matrix_mult(a, b)  

from mass import MassContext  
# 初始化分布式环境  
ctx = MassContext(nodes=4, gpus_per_node=8)  
# 转换为分布式矩阵（a按行拆分，b按列拆分）  
a_dist = ctx.from_numpy(a, split_dim=0)  
b_dist = ctx.from_numpy(b, split_dim=1)  
# 分布式计算与结果汇聚  
c_dist = a_dist.dot(b_dist)  
c = c_dist.to_numpy()  

import mass  
from mass.nn import ResNet50, CrossEntropyLoss, SGD  
from mass.data import ImageFolderDataset, DataLoader  

ctx = mass.init()  # 初始化分布式环境  
# 加载数据集并自动分片（支持数据预处理流水线）  
dataset = ImageFolderDataset("/data/imagenet", transform=transforms.Compose([  
    transforms.Resize(224),  
    transforms.ToTensor()  
]))  
dataloader = DataLoader(dataset, batch_size=256, shuffle=True)  

# 定义模型与优化器（参数自动同步至所有节点）  
model = ResNet50(num_classes=1000).to(ctx.device)  
loss_fn = CrossEntropyLoss()  
optimizer = SGD(model.parameters(), lr=0.01)  

# 分布式训练循环（自动处理梯度同步）  
for epoch in range(10):  
    for inputs, labels in dataloader:  
        inputs, labels = inputs.to(ctx.device), labels.to(ctx.device)  
        outputs = model(inputs)  
        loss = loss_fn(outputs, labels)  
        loss.backward()  
        optimizer.step()  
        optimizer.zero_grad()  

from mass.nn.parallel import ModelParallel  
class DistributedGPT(ModelParallel):  
    def __init__(self, num_layers, local_layers, start_layer=0):  
        super().__init__()  
        self.layers = nn.ModuleList([  
            TransformerLayer() for _ in range(local_layers)  
        ])  
        self.start_layer = start_layer  
    def forward(self, x):  
        for layer in self.layers:  
            x = layer(x)  
        return x  

# 节点0加载前3层，节点1加载中间3层，节点2加载后3层  
if ctx.rank == 0:  
    model = DistributedGPT(num_layers=9, local_layers=3)  
elif ctx.rank == 1:  
    model = DistributedGPT(num_layers=9, local_layers=3, start_layer=3)  
elif ctx.rank == 2:  
    model = DistributedGPT(num_layers=9, local_layers=3, start_layer=6)  

ctx = MassContext(gradient_compression=True, compression_ratio=0.1)  # 压缩率10%  

with ctx.async_communication():  
    loss.backward()  # 计算梯度（CPU/GPU）  
    ctx.all_reduce(gradients)  # 异步通信（网络）  

from mass.data import GridData  
# 三维网格定义（经度×纬度×高度）  
grid = GridData(shape=(3600, 1800, 50), split_dims=(0, 1))  # 按经纬度拆分  
grid.data = np.random.rand(3600, 1800, 50)  # 初始化气压场数据  

def advection_solver(grid):  
    u, v = grid[:, :, :, 0], grid[:, :, :, 1]  # 提取风速分量  
    # 跨节点边界数据同步（东-西、南-北方向）  
    u = ctx.edge_sync(u, direction='EW')  
    v = ctx.edge_sync(v, direction='NS')  
    # 扩散计算（伪代码，实际调用高性能内核）  
    new_grid = grid.copy()  
    new_grid[:, :, :, 0] = ctx.diffuse(u, alpha=0.1)  
    return new_grid  

sudo ufw allow 8080-8090/tcp  # 开放通信端口  
ssh node2 "echo test"  # 验证节点间SSH连通性  

ctx = MassContext(use_amp=True)  # 启用混合精度训练，减少显存占用  
model = LargeModel(shard=True, shard_size=1e9)  # 按1GB分片存储模型参数