Sagemaker 批量转换“ValueError：无法将字符串转换为浮点数”

Question

我正在使用 sagemaker 和批量转换运行本地转换器。但是，转换似乎没有调用我的自定义代码。

以下是SKlearn init

from sagemaker.sklearn.estimator import SKLearn
source_dir = 'train'
script_path = 'train.py'

sklearn = SKLearn(
    entry_point=script_path,
    train_instance_type="local_gpu",
    source_dir=source_dir,
    role=role,
    sagemaker_session=sagemaker_session)
sklearn.fit({'train': "file://test.csv"})

train.py 是一个 python 脚本，用于加载训练数据，并将模型保存到 S3

批量转换为：

transformer = sklearn.transformer(instance_count=1,
                                  entry_point=source_dir+"/"+script_path,
                                  instance_type='local_gpu',
                                  strategy='MultiRecord',
                                  assemble_with='Line'
                                  )
transformer.transform("file://test_messages", content_type='text/csv', split_type='Line')
print('Waiting for transform job: ' + transformer.latest_transform_job.job_name)
transformer.wait()

file://test_messages 包含一个 csv，它是一个字符串列表

完整的错误是

algo-1-6c5rl_1  | 172.18.0.1 - - [30/Jan/2020:14:14:30 +0000] "GET /ping HTTP/1.1" 200 0 "-" "-"
algo-1-6c5rl_1  | 172.18.0.1 - - [30/Jan/2020:14:14:30 +0000] "GET /execution-parameters HTTP/1.1" 404 232 "-" "-"
algo-1-6c5rl_1  | 2020-01-30 14:14:30,846 ERROR - train - Exception on /invocations [POST]
algo-1-6c5rl_1  | Traceback (most recent call last):
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/sagemaker_containers/_functions.py", line 93, in wrapper
algo-1-6c5rl_1  |     return fn(*args, **kwargs)
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/sagemaker_sklearn_container/serving.py", line 56, in default_input_fn
algo-1-6c5rl_1  |     return np_array.astype(np.float32) if content_type in content_types.UTF8_TYPES else np_array
algo-1-6c5rl_1  | ValueError: could not convert string to float: 'IMPORTANT - You could be entitled up to �3,160 in compensation from mis-sold PPI on a credit card or loan. Please reply PPI for info or STOP to opt out.'
algo-1-6c5rl_1  | 
algo-1-6c5rl_1  | During handling of the above exception, another exception occurred:
algo-1-6c5rl_1  | 
algo-1-6c5rl_1  | Traceback (most recent call last):
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/flask/app.py", line 2446, in wsgi_app
algo-1-6c5rl_1  |     response = self.full_dispatch_request()
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/flask/app.py", line 1951, in full_dispatch_request
algo-1-6c5rl_1  |     rv = self.handle_user_exception(e)
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/flask/app.py", line 1820, in handle_user_exception
algo-1-6c5rl_1  |     reraise(exc_type, exc_value, tb)
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/flask/_compat.py", line 39, in reraise
algo-1-6c5rl_1  |     raise value
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/flask/app.py", line 1949, in full_dispatch_request
algo-1-6c5rl_1  |     rv = self.dispatch_request()
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/flask/app.py", line 1935, in dispatch_request
algo-1-6c5rl_1  |     return self.view_functions[rule.endpoint](**req.view_args)
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/sagemaker_containers/_transformer.py", line 200, in transform
algo-1-6c5rl_1  |     self._model, request.content, request.content_type, request.accept
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/sagemaker_containers/_transformer.py", line 227, in _default_transform_fn
algo-1-6c5rl_1  |     data = self._input_fn(content, content_type)
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/sagemaker_containers/_functions.py", line 95, in wrapper
algo-1-6c5rl_1  |     six.reraise(error_class, error_class(e), sys.exc_info()[2])
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/six.py", line 692, in reraise
algo-1-6c5rl_1  |     raise value.with_traceback(tb)
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/sagemaker_containers/_functions.py", line 93, in wrapper
algo-1-6c5rl_1  |     return fn(*args, **kwargs)
algo-1-6c5rl_1  |   File "/miniconda3/lib/python3.7/site-packages/sagemaker_sklearn_container/serving.py", line 56, in default_input_fn
algo-1-6c5rl_1  |     return np_array.astype(np.float32) if content_type in content_types.UTF8_TYPES else np_array
algo-1-6c5rl_1  | sagemaker_containers._errors.ClientError: could not convert string to float: 'IMPORTANT - You could be entitled up to �3,160 in compensation from mis-sold PPI on a credit card or loan. Please reply PPI for info or STOP to opt out.'
algo-1-6c5rl_1  | 172.18.0.1 - - [30/Jan/2020:14:14:30 +0000] "POST /invocations HTTP/1.1" 500 290 "-" "-"
.Waiting for transform job: sagemaker-scikit-learn-2020-01-30-14-14-30-490

它似乎无法处理我的字符串。我在 train.py 中有使用 TfidfVectorizer 转换字符串的代码，但没有调用该代码

Answer 1

我是 AWS SageMaker 的一名工程师。感谢您提供 Estimator/Transformer 设置的详细信息以及完整的错误日志。

查看具体错误，好像是default_input_fn中的Scikit-learn容器失败了。谢天谢地，SageMaker Scikit-learn 是开源的，因此我们可以直接访问源代码 sagemaker_sklearn_container/serving.py#L56 以帮助了解它的工作原理。

容器选择在发送到模型之前执行“默认”输入函数来处理输入。显然，默认实现不适用于您想要的输入格式。

与训练类似，您需要提供自定义 Python 代码来控制 SageMaker Scikit-learn 如何在服务/推理模式下处理您的模型。如果您想覆盖默认值，您需要在您的自定义 Python 代码中实现 input_fn。您可以选择将此添加到您的 train.py 脚本中，或者在 Transformer 中传递不同的 Python 文件。

这个文档应该有助于编写input_fn: https://sagemaker.readthedocs.io/en/stable/using_sklearn.html#process-input

如果仍有问题，您可以分享自定义代码中的示例。