为TensorFlow Serving REST API生成实例或输入 [英] Generate instances or inputs for TensorFlow Serving REST API

问题描述

我准备尝试基于已保存的模型试用TensorFlow Serving REST API，并且想知道是否存在一种简单的方法来生成需要与之一起发送的JSON实例(基于行)或输入(列)我的请求.

I'm ready to try out my TensorFlow Serving REST API based on a saved model, and was wondering if there was an easy way to generate the JSON instances (row-based) or inputs (columnar) I need to send with my request.

我的模型中有数千个功能，我不希望手动输入JSON.有没有一种方法可以使用现有数据来提供可用于预测API的序列化数据?

I have several thousand features in my model and I would hate to manually type in a JSON. Is there a way I can use existing data to come up with serialized data I can throw at the predict API?

我在整个管道(包括tf.Transform)中都使用了TFX，所以我不确定是否可以使用TFX内建的整洁方法.

I'm using TFX for the entire pipeline (incl. tf.Transform), so I'm not sure if there is a neat way built into TFX I can use.

saved_model_cli的输出是这样的:

The given SavedModel SignatureDef contains the following input(s):
  inputs['examples'] tensor_info:
      dtype: DT_STRING
      shape: (-1)
      name: input_example_tensor:0

没有告诉我太多.

推荐答案

您可以使用Python REST客户端以编程方式进行调用，而无需手动编写请求.这是tensorflow_serving github中的示例代码:

You can use a Python REST client to make the call programatically, instead of manually composing the request. This is a sample code in tensorflow_serving github:

https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/resnet_client.py

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