Training and Serving Merlin on AWS SageMaker#
With AWS Sagemaker, you can package your own models that you can train and deploy in the SageMaker environment. The following notebook shows you how to use NVIDIA Merlin for training and inference in the SageMaker environment.
This notebook assumes that readers are familiar with some basic concepts in NVIDIA Merlin, such as:
Using NVTabular to GPU-accelerate preprocessing and feature engineering.
Training a ranking model using Merlin Models.
Making Inference with the Triton Inference Server and Merlin Models for Tensorflow.
Running the Example Notebook#
Additionally, you need to configure basic AWS settings.
For setting up AWS credentials, refer to
Configuring the AWS CLI
in the AWS documentation.
After you configure basic AWS settings, you can mount your AWS credentials
-v $HOME/.aws:/root/.aws to your Docker command in Step 1 of
Running the Example Notebooks:
docker run --gpus all --rm -it \
-p 8888:8888 -p 8797:8787 -p 8796:8786 --ipc=host \
-v $HOME/.aws:/root/.aws \
<docker container> /bin/bash
and follow the remaining steps in Running the Example Notebooks.