ValueError while deploying tensorflow model to Amazon SageMaker

I want to deploy my trained tensorflow model to the amazon sagemaker, I am following the official guide here: https://aws.amazon.com/blogs/machine-learning/deploy-trained-keras-or-tensorflow-models-using-amazon-sagemaker/ to deploy my model using jupyter notebook.

But when I try to use code:

predictor = sagemaker_model.deploy(initial_instance_count=1, instance_type='ml.t2.medium')

It gives me the following error message:

ValueError: Error hosting endpoint sagemaker-tensorflow-2019-08-07-22-57-59-547: Failed Reason: The image '520713654638.dkr.ecr.us-west-1.amazonaws.com/sagemaker-tensorflow:1.12-cpu-py3 ' does not exist.

I think the tutorial does not tell me to create an image, and I do not know what to do.

import boto3, re
from sagemaker import get_execution_role

role = get_execution_role()

# make a tar ball of the model data files
import tarfile
with tarfile.open('model.tar.gz', mode='w:gz') as archive:
    archive.add('export', recursive=True)

# create a new s3 bucket and upload the tarball to it
import sagemaker

sagemaker_session = sagemaker.Session()
inputs = sagemaker_session.upload_data(path='model.tar.gz', key_prefix='model')

from sagemaker.tensorflow.model import TensorFlowModel
sagemaker_model = TensorFlowModel(model_data = 's3://' + sagemaker_session.default_bucket() + '/model/model.tar.gz',
                                  role = role,
                                  framework_version = '1.12',
                                  entry_point = 'train.py',
                                  py_version='py3')

%%time
#here I fail to deploy the model and get the error message
predictor = sagemaker_model.deploy(initial_instance_count=1,
                                   instance_type='ml.m4.xlarge')

Solution 1:

https://github.com/aws/sagemaker-python-sdk/issues/912#issuecomment-510226311

As mentioned in the issue

Python 3 isn't supported using the TensorFlowModel object, as the container uses the TensorFlow serving api library in conjunction with the GRPC client to handle making inferences, however the TensorFlow serving api isn't supported in Python 3 officially, so there are only Python 2 versions of the containers when using the TensorFlowModel object.

If you need Python 3 then you will need to use the Model object defined in #2 above. The inference script format will change if you need to handle pre and post processing. https://github.com/aws/sagemaker-tensorflow-serving-container#prepost-processing.