使用SageMaker时使用Keras进行早期停止和回调 [英] Early Stopping and Callbacks with Keras when using SageMaker

问题描述

我正在使用Sagemaker来训练keras模型.训练模型时，我需要实施早期停止方法.

I am using sagemaker to train a keras model. I need to implement early stoping approach when training the model.

有没有办法传递诸如EarlyStopping，Histories..etc之类的回调.

Is there a way to pass callbacks such as EarlyStopping, Histories..etc.

以传统方式，我们通常将此参数作为参数传递给keras的fit函数:

In traditional way, we used to pass this as a parameter to keras's fit function:

results = model.fit(train_x_trim, train_y_trim, 
                    validation_data=(test_x, test_y), 
                    epochs=FLAGS.epoch,  
                    verbose=0, 
                    callbacks=[tboard, checkpointer, early_stopping, history])

但是，如果使用SageMaker，则需要调用SageMaker的fit函数，该函数不支持回调.

However, if using SageMaker, we need to call SageMaker's fit function instead which doesn't support callbacks.

from sagemaker.tensorflow import TensorFlow 
iris_estimator = TensorFlow(entry_point='training_code.py', 
                            role=role, output_path=model_location, 
                            code_location=custom_code_upload_location, 
                            train_instance_count=1, 
                            train_instance_type='ml.c4.xlarge', 
                            training_steps=1000, 
                            evaluation_steps=100)

任何想法如何在SageMaker中实现回调?

Any idea how to implement callbacks in SageMaker ?

推荐答案

对于抱歉的答复，我深表歉意.

I apologize for the late response.

看起来您在上面指定的Keras代码本质上是您的算法代码.这将在您的用户脚本中定义，在您提供的SageMaker Python SDK示例中为"training_code.py".

It looks like the Keras code you specified above is essentially your algorithm code. This would be defined in your user script, which would be "training_code.py" in the SageMaker Python SDK example you provided.

从TensorFlow 1.11开始，SageMaker预定义的TensorFlow容器支持脚本模式".您应该可以在用户脚本中指定Keras回调.

Starting with TensorFlow 1.11, the SageMaker predefined TensorFlow containers have support for "script mode". You should be able to specify your Keras callbacks within your user script.

有关更多信息: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/tensorflow/README.rst#tensorflow-sagemaker-estimators-and-models

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