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AutoML

一、 安装

x86

pip install nni

other 源码安装

git clone .git
cd nni
pip install --upgrade setuptools pip wheel
python setup.py develop

二、 用法示例

2.1 提交服务命令:

nnictl create --config config.yml --port 8080

2.2 定义超参空间:

{"C": {"_type":"uniform","_value":[0.1, 1]},"kernel": {"_type":"choice","_value":["linear", "rbf", "poly", "sigmoid"]},"degree": {"_type":"choice","_value":[1, 2, 3, 4]},"gamma": {"_type":"uniform","_value":[0.01, 0.1]},"coef0": {"_type":"uniform","_value":[0.01, 0.1]}
}

2.3 定义运行参数:

运行时间限制

运行测试

运行脚本

运行环境

search_space:features:_type: choice_value: [ 128, 256, 512, 1024 ]lr:_type: loguniform_value: [ 0.0001, 0.1 ]momentum:_type: uniform_value: [ 0, 1 ]trial_command: python model.py
trial_code_directory: .trial_concurrency: 2
max_trial_number: 10tuner:name: TPEclass_args:optimize_mode: maximizetraining_service:platform: local

2.4 定义执行算法脚本:

import nni
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_digits
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import logging
import numpy as npLOG = logging.getLogger('sklearn_classification')def load_data():'''Load dataset, use 20newsgroups dataset'''digits = load_digits()X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25)ss = StandardScaler()X_train = ss.fit_transform(X_train)X_test = ss.transform(X_test)return X_train, X_test, y_train, y_testdef get_default_parameters():'''get default parameters'''params = {'C': 1.0,'kernel': 'linear','degree': 3,'gamma': 0.01,'coef0': 0.01}return paramsdef get_model(PARAMS):'''Get model according to parameters'''model = SVC()model.C = PARAMS.get('C')model.kernel = PARAMS.get('kernel')model.degree = PARAMS.get('degree')model.gamma = PARAMS.get('gamma')model.coef0 = PARAMS.get('coef0')return modeldef run(X_train, X_test, y_train, y_test, model):'''Train model and predict result'''model.fit(X_train, y_train)score = model.score(X_test, y_test)LOG.debug('score: %s', score)nni.report_final_result(score)if __name__ == '__main__':X_train, X_test, y_train, y_test = load_data()try:# get parameters from tunerRECEIVED_PARAMS = nni.get_next_parameter()LOG.debug(RECEIVED_PARAMS)PARAMS = get_default_parameters()PARAMS.update(RECEIVED_PARAMS)LOG.debug(PARAMS)model = get_model(PARAMS)run(X_train, X_test, y_train, y_test, model)except Exception as exception:LOG.exception(exception)raise

2.5 Web portal UI

可以设置训练时常

portal 8080 服务自动根据yml 文件去做参数筛选，优化算法提交脚本

不同训练结果准确率展示

三、总结

优点：

  1. 代码结构清晰，能灵活增加算法

  2. 参数配置灵活自定义

  3. 支持不同平台环境算法训练

  4. 有较好的UI及结果展示界面

缺点：

  1. 是通过专门web服务的方式分次进行提交任务，yml,conf 等配置文件依次提交，不太适合我们的提交方式

  2. 获取结果得从对应的服务获取结果，按照定义的配置执行

本文标签： AutoML

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