你需要在部署模型时首先实际建立一个正确的签名。
此外，你还需要用tensorflow服务进行部署。
在推理时，你还需要在请求时给出一个合适的输入格式：基本上，sagemaker docker服务器会接受请求的输入，并将其传递给tensorflow服务。所以，输入的内容需要符合
TF serving inputs
.
下面是一个简单的例子，在Tensorflow服务中使用Sagemaker部署Keras多输入多输出模型，以及之后如何进行推理。
import tarfile
from tensorflow.python.saved_model import builder
from tensorflow.python.saved_model.signature_def_utils import predict_signature_def
from tensorflow.python.saved_model import tag_constants
from keras import backend as K
import sagemaker
#nano ~/.aws/config
#get_ipython().system('nano ~/.aws/config')
from sagemaker import get_execution_role
from sagemaker.tensorflow.serving import Model
def serialize_to_tf_and_dump(model, export_path):
serialize a Keras model to TF model
:param model: compiled Keras model
:param export_path: str, The export path contains the name and the version of the model
:return:
# Build the Protocol Buffer SavedModel at 'export_path'
save_model_builder = builder.SavedModelBuilder(export_path)
# Create prediction signature to be used by TensorFlow Serving Predict API
signature = predict_signature_def(
inputs={
"input_type_1": model.input[0],
"input_type_2": model.input[1],
outputs={
"decision_output_1": model.output[0],
"decision_output_2": model.output[1],
"decision_output_3": model.output[2]
with K.get_session() as sess:
# Save the meta graph and variables
save_model_builder.add_meta_graph_and_variables(
sess=sess, tags=[tag_constants.SERVING], signature_def_map={"serving_default": signature})
save_model_builder.save()
# instanciate model
model = ....
# convert to tf model
serialize_to_tf_and_dump(model, 'model_folder/1')
# tar tf model
with tarfile.open('model.tar.gz', mode='w:gz') as archive:
archive.add('model_folder', recursive=True)
# upload it to s3
sagemaker_session = sagemaker.Session()
inputs = sagemaker_session.upload_data(path='model.tar.gz')
# convert to sagemaker model
role = get_execution_role()
sagemaker_model = Model(model_data = inputs,
name='DummyModel',
role = role,
framework_version = '1.12')
predictor = sagemaker_model.deploy(initial_instance_count=1,
instance_type='ml.t2.medium', endpoint_name='MultiInputMultiOutputModel')
在推理中，这里是如何请求预测的。
import json
import boto3
x_inputs = ... # list with 2 np arrays of size (batch_size, ...)
data={
'inputs':{
"input_type_1": x[0].tolist(),
"input_type_2": x[1].tolist()
endpoint_name = 'MultiInputMultiOutputModel'
client = boto3.client('runtime.sagemaker')
response = client.invoke_endpoint(EndpointName=endpoint_name, Body=json.dumps(data), ContentType='application/json')
predictions = json.loads(response['Body'].read())
