The best options to use an Inception neural network architecture instead of a ResNet architecture for image classification in Amazon SageMaker are:
Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training. This option allows users to customize the training environment and use any TensorFlow model they want. Users can create a Docker image that contains the TensorFlow Estimator API and the Inception model from the TensorFlow Hub, and push it to Amazon ECR. Then, users can use the SageMaker Estimator class to train the model using the custom Docker image and the training data from Amazon S3.
Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training. This option allows users to use the built-in TensorFlow container provided by SageMaker and write custom code to load and train the Inception model. Users can use the TensorFlow Estimator class to specify the custom code and the training data from Amazon S3. The custom code can use the TensorFlow Hub module to load the Inception model and fine-tune it on the training data.
The other options are not feasible for this scenario because:
Customize the built-in image classification algorithm to use Inception and use this for model training. This option is not possible because the built-in image classification algorithm in SageMaker does not support customizing the neural network architecture. The built-in algorithm only supports ResNet models with different depths and widths.
Create a support case with the SageMaker team to change the default image classification algorithm to Inception. This option is not realistic because the SageMaker team does not provide such a service. Users cannot request the SageMaker team to change the default algorithm or add new algorithms to the built-in ones.
Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker. This option is not advisable because it does not leverage the benefits of SageMaker, such as managed training and deployment, distributed training, and automatic model tuning. Users would have to manually install and configure the Inception network code and the TensorFlow framework on the EC2 instance, and run the training and inference code on the same instance, which may not be optimal for performance and scalability.
References:
Use Your Own Algorithms or Models with Amazon SageMaker
Use the SageMaker TensorFlow Serving Container
TensorFlow Hub