NNI 示例 BatchTuner

Posted 2022-12-05 甘木甘木

NNI 小白入门

使用最简单的BatchTuner进行演示

参考本博客前，需要先浏览过NNI的官网，了解大致流程

GridSearch 参考另一篇 https://blog.csdn.net/weixin_44110392/article/details/113257885

运行代码框架

• a simple implement of linear regression

  不用在意线性回归的实现

  • 假如需要调参的是 batch_size, epochs_number, learning_rate

    • 为了方便演示，假设上面三个参数从给定的范围中选取，所以使用BatchTuner

      • 参数范围：

        epochs = [i for i in range(10, 20)]
        batch_sizes = [i for i in range(10, 20, 5)]
        learning_rates = [i*0.001 for i in range(1,3)]
        

NNI配置

config.yml

NNI 环境配置

• trialConcurrency: 并发数量
• maxTrialNum: 调参的参数组的总数。例如上面给出的10*2*2=40
• Trail-command: 每一次执行程序的命令

authorName: default
experimentName: example_Linear_Regression
trialConcurrency: 5
maxExecDuration: 1h
maxTrialNum: 40
#choice: local, remote, pai
trainingServicePlatform: local
searchSpacePath: search_space.json
#choice: true, false
useAnnotation: false
tuner:
  builtinTunerName: BatchTuner
trial:
  command: python3 linear_regression.py
  codeDir: .
  gpuNum: 0

search_space.json

参数的搜索空间

本例中使用BatchTuner,所以就是不同的参数组成的list

• 下面的写法是固定的，默认batchTuner只能这样写，能更改的只有_value中的内容（类型必须是list）
  • 下面value中的list省略了一些参数（太长不好看）

    "combine_params": 
        "_type": "choice",
        "_value": ["epochs": 10, "batch_size": 10, "learning_rate": 0.001, "epochs": 									10, "batch_size": 10, "learning_rate": 0.002, "epochs": 10, 													"batch_size": 15, "learning_rate": 0.001"learning_rate": 0.002]
    

执行文件

• main函数
  • log是为了方便debug
  • nni.get_next_parameter()得到tuner中的下一组参数
    • 由于我写的参数是用dict的形式存储，所以我先生成了一组默认参数然后用读取到的参数update
• train函数
  • 要在开始的时候用读入的参数初始化parameters
  • 最后要report_final_reault()，这里读入的数值会在最终的交互界面上画图
• 其余函数与NNI无关

import torch as t
import numpy as np
import random
import nni
import logging

LOG = logging.getLogger('linear_regression')

np.random.seed(1)
random.seed(1)


def generate_date(true_w, true_b, num_examples):
    '''
    generate synthetic data
    '''
    x = np.random.normal(0, 1, (num_examples, len(true_w)))
    y = np.dot(x, true_w) + true_b
    # add noise
    y += np.random.normal(0, 0.01, y.shape)

    return t.Tensor(x), t.Tensor(y)


def data_iter(batch_size, features, labels):
    '''
    reading data iteration by iteration
    '''
    num_examples = len(labels)
    indices = list(range(num_examples))
    # examples are read at random
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size):
        batch_indices = np.array(indices[i:min(i + batch_size, num_examples)])
        yield features[batch_indices], labels[batch_indices]


def loss_func(y_hat, y):
    '''
    square loss
    '''
    return (y_hat - y)**2 / 2


def sgd(params, lr, batch_size):
    '''
    minibatch stochastic gradient descent
    '''
    with t.no_grad():
        for param in params:
            param -= lr * param.grad / batch_size
            param.grad.zero_()


def linreg(X, w, b):
    """The linear regression model."""
    return t.matmul(X, w) + b


def train(ARGS, features, labels):
    lr = ARGS['learning_rate']
    num_epochs = ARGS['epochs']
    net = linreg
    loss = loss_func
    batch_size = ARGS['batch_size']
    w = t.Tensor([1, 1])
    w.requires_grad_(True)
    b = t.Tensor([0])
    b.requires_grad_(True)

    for epoch in range(num_epochs):
        for X, y in data_iter(batch_size, features, labels):
            l = loss(net(X, w, b), y)  # Minibatch loss in `X` and `y`
            # Compute gradient on `l` with respect to [`w`, `b`]
            l.sum().backward()
            sgd([w, b], lr,
                batch_size)  # Update parameters using their gradient
        # with t.no_grad():
        #     train_l = loss(net(features, w, b), labels)
        # print(f'epoch epoch + 1, loss float(train_l.mean()):f')
    res_loss = loss(net(features, w, b), labels)
    # print(w, b)
    loss_mean = res_loss.mean()
    nni.report_final_result(float(loss_mean))

def generate_default_params():
    """
    Generate default hyper parameters
    """
    return "epochs": 10, "batch_size": 10, "learning_rate": 0.003


if __name__ == "__main__":

    # generate data
    true_w = [2, -3.4]
    true_b = 4.2
    features, labels = generate_date(true_w=true_w,
                                     true_b=true_b,
                                     num_examples=200)

    # get parameters from tuner
    RECEIVED_PARAMS = nni.get_next_parameter()
    LOG.debug(RECEIVED_PARAMS)
    PARAMS = generate_default_params()
    PARAMS.update(RECEIVED_PARAMS)
    # train
    train(PARAMS, features, labels)

结果