将数据发送到Google Cloud ML自定义预测例程时发生未知错误 [英] Unknown Error Sending Data to Google Cloud ML Custom Prediction Routine

问题描述

我正在尝试在AI平台上编写自定义ML预测例程，以从客户端获取文本数据，进行一些自定义预处理，将其传递到模型中，然后运行模型.我能够成功地将此代码打包并部署到Google云上.但是，每次我尝试从node.js向其发送请求时，都会返回data: { error: 'Prediction failed: unknown error.' },.

I am trying to write a custom ML prediction routine on AI Platform to get text data from a client, do some custom preprocessing, pass it into the model, and run the model. I was able to package and deploy this code on Google cloud successfully. However, every time I try to send a request to it from node.js, I get back data: { error: 'Prediction failed: unknown error.' },.

这是我相关的自定义预测例程代码.请注意，我在客户端的文本中设置了instances，然后在自定义预测例程中对其进行标记化和预处理.

Here is my relevant custom prediction routine code. Note that I set instances to my text in the client and then tokenize and preprocess it in the custom prediction routine.

def __init__(self, model, session, saver, dictionary):
    self.model = model
    self.sess = session

@classmethod
def from_path(cls, model_dir):
    m = Model(learning_rate=0.1)
    session = tf.Session()
    session.run(tf.global_variables_initializer())
    session.run(tf.local_variables_initializer())
    saver = tf.train.Saver(max_to_keep=0)
    saver.restore(session, (os.path.join(model_dir, 'model.ckpt')))
    return cls(m, session)

def predict(self, instances, **kwargs):
    utterance = nltk.word_tokenize(instances)
    utterance = self.preprocess_utterance(utterance)

    preds = self.sess.run([self.model['preds'], feed_dict={'input_data': utterance)
    return preds

这是我的Node.js代码:

Here is my Node.js code:

   text_string = "Hello how are you?"
   google.auth.getApplicationDefault(function (err, authClient, projectId) {
        if (err) {
            console.log('Authentication failed because of ', err);
            return;
        }
        if (authClient.createScopedRequired && authClient.createScopedRequired()) {
            var scopes = ['https://www.googleapis.com/auth/cloud-platform'];
            authClient = authClient.createScoped(scopes);
        }
        var request = {
            name: "projects/" + projectId + "/models/classifier",
            resource: {"instances": [message_string]},

            // This is a "request-level" option
            auth: authClient
        };

        machinelearning.projects.predict(request, function (err, result) {

            console.log(result)

            if (err) {
                console.log(err);
            } else {
                console.log(result);
                res.status(200).send('Hello, world! This is the prediction: ' + JSON.stringify(result)).end();
            }
        });
    });

在这段代码中，我只是将文本发送到Google Cloud模型.请求正文为: body: '{"instances":["Hello how are you?"]}',

In this code I am just sending the text to the google Cloud model. The request body is: body: '{"instances":["Hello how are you?"]}',

有人知道为什么它会失败吗?

Does anyone have an idea of why it's failing?

如果没有，那么有人对我如何调试它有任何想法吗?未知错误消息根本没有用.

If not, then does anyone have any idea of how I can debug this? An unknown error message is not useful at all.

这是saved_model_cli中带有--all选项的输出.

Here is the output from saved_model_cli with the --all option.

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['length_input'] tensor_info:
        dtype: DT_INT32
        shape: ()
        name: Placeholder_3:0
    inputs['seqlen'] tensor_info:
        dtype: DT_INT32
        shape: (-1)
        name: Placeholder_2:0
    inputs['indicator'] tensor_info:
        dtype: DT_INT32
        shape: (-1, 2)
        name: Placeholder_1:0
    inputs['input_data'] tensor_info:
        dtype: DT_INT32
        shape: (-1, -1)
        name: Placeholder:0
    inputs['y'] tensor_info:
        dtype: DT_INT32
        shape: (-1, -1)
        name: Placeholder_4:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['preds'] tensor_info:
        dtype: DT_INT32
        shape: (-1, -1)
        name: Cast:0
  Method name is: tensorflow/serving/predict

基于此，我应该提供此字典作为输入，但是它不起作用.

Based on this, I should provide this dictionary as input, but it does not work.

{"instances": [ { "input_data": [138, 30, 66], "length_input": 1, "indicator": [[0, 0]], "seqlen": [3], "y": [138, 30, 66] } ]}

{"instances": [ { "input_data": [138, 30, 66], "length_input": 1, "indicator": [[0, 0]], "seqlen": [3], "y": [138, 30, 66] } ]}

推荐答案

我发现了问题所在.问题不在于输入数据的格式.而是在NLTK中. NLTK.word_tokenize引发错误，因为它没有进行标记化所需的数据.我必须将数据上传到Google Cloud或使用不需要任何数据文件的令牌化方法来解决此问题.

I figured out the issue. The issue was not the formatting of the input data. Rather it was in NLTK. NLTK.word_tokenize was throwing an error because it did not have the data necessary to do the tokenization. I had to upload the data to Google Cloud or use a tokenization method that didn't require any data files to solve this problem.

我不知道为什么这个Google Cloud自定义预测例程软件不会告诉用户正在发生的错误，但是通过我的所有努力，只要出现问题，它总是只返回Unknown error.如果我确切地知道错误是什么，那将是一个简单的解决方法.

I don't know why this Google Cloud custom prediction routine software doesn't tell its users the errors that are happening, but through all my efforts it always just returns Unknown error whenever something goes wrong. If I had known precisely what the error was, this would have been an easy fix.

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