问题
I have deployed a TensorFlow model on AWS SageMaker, and I want to be able to invoke it using a csv file as the body of the call. The documentation says about creating a serving_input_function like the one below:
def serving_input_fn(hyperparameters):
# Logic to the following:
# 1. Defines placeholders that TensorFlow serving will feed with inference requests
# 2. Preprocess input data
# 3. Returns a tf.estimator.export.ServingInputReceiver or tf.estimator.export.TensorServingInputReceiver,
# which packages the placeholders and the resulting feature Tensors together.
In step 2, where it says preprocess input data, how do I get a handle on input data to process them?
回答1:
I had the same problem but I wanted to handle jpeg requests.
Once you have your model_data ready, you can deploy it with the following lines.
from sagemaker.tensorflow.model import TensorFlowModel
sagemaker_model = TensorFlowModel(
model_data = 's3://path/to/model/model.tar.gz',
role = role,
framework_version = '1.12',
entry_point = 'train.py',
source_dir='my_src',
env={'SAGEMAKER_REQUIREMENTS': 'requirements.txt'}
)
predictor = sagemaker_model.deploy(
initial_instance_count=1,
instance_type='ml.m4.xlarge',
endpoint_name='resnet-tensorflow-classifier'
)
Your notebook should have a my_src directory which contains a file train.py and a requirements.txt file. The train.py file should have a function input_fn defined. For me, that function handled image/jpeg content:
CSV_CONTENT_TYPE = 'text/csv'
# Deserialize the Invoke request body into an object we can perform prediction on
def input_fn(request_body, content_type=CSV_CONTENT_TYPE):
# process an image uploaded to the endpoint
if content_type == CSV_CONTENT_TYPE:
##handle input
return handled_input
else:
raise errors.UnsupportedFormatError(content_type)
If yourtrain.py code imports some modules, you must supply requirements.txt defining those dependencies (that was the part I had trouble finding in the docs).
Hope this helps someone in the future.
回答2:
You can preprocess input data by adding an input_fn which will be invoked every time u invoke and endpoint. It receives the input data and the content type of the data.
def input_fn(data, content_type):
// do some data preprocessing.
return preprocessed_data
This article explains it in more depth: https://docs.aws.amazon.com/sagemaker/latest/dg/tf-training-inference-code-template.html
来源：https://stackoverflow.com/questions/49775557/how-can-i-invoke-a-sagemaker-model-trained-with-tensorflow-using-a-csv-file-in