Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
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# Serve an ResNet Pytorch model on GPU with Amazon SageMaker Multi-model endpoints (MME)
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In this example, we will walk you through how to use NVIDIA Triton Inference Server on Amazon SageMaker MME with GPU feature to deploy Resnet Pytorch model for **Image Classification**.
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## Steps to run the notebook
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1. Launch SageMaker notebook instance with `g5.xlarge` instance. This example can also be run on a SageMaker studio notebook instance but the steps that follow will focus on the notebook instance.
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* For git repositories select the option `Clone a public git repository to this notebook instance only` and specify the Git repository URL
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2. Once JupyterLab is ready, launch the **resnet_pytorch_python_backend_MME.ipynb** notebook with **conda_python3** conda kernel and run through this notebook to learn how to host multiple CV models on `g5.2xlarge` GPU behind MME endpoint.
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