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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# BYOC\n", | ||
| "\n", | ||
| "Examples on how to use your own model serving containers or extend pre-built containers on SageMaker." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## TensorFlow\n", | ||
| "---\n", | ||
| "\n", | ||
| "### Elastic inference\n", | ||
| "\n", | ||
| "* [Using Amazon Elastic Inference with a pre-trained TensorFlow Serving model on SageMaker](../sagemaker-python-sdk/tensorflow_serving_using_elastic_inference_with_your_own_model/tensorflow_serving_pretrained_model_elastic_inference.ipynb)\n", | ||
| "\n", | ||
| "### TensorFlow Serving container\n", | ||
| "\n", | ||
| "* [Using the SageMaker TensorFlow Serving Container](../sagemaker-python-sdk/tensorflow_serving_container/tensorflow_serving_container.ipynb)\n", | ||
| "\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "instance_type": "ml.t3.medium", | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (Data Science)", | ||
| "language": "python", | ||
| "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.7.10" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Data Types" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Image\n", | ||
| "---\n", | ||
| "\n", | ||
| "* [MNIST Training using PyTorch](../sagemaker-python-sdk/pytorch_mnist/pytorch_mnist.ipynb)\n", | ||
| "* [Using the SageMaker TensorFlow Serving Container](../sagemaker-python-sdk/tensorflow_serving_container/tensorflow_serving_container.ipynb)\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Tabular\n", | ||
| "---\n", | ||
| "\n", | ||
| "* [Iris Training and Prediction with Sagemaker Scikit-learn](../sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform.ipynb)\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Text\n", | ||
| "---\n", | ||
| "\n", | ||
| "* [Hosting and Deployment of Pre-Trained Text Models using SageMaker Endpoint and BlazingText](../introduction_to_amazon_algorithms/blazingtext_hosting_pretrained_fasttext/blazingtext_hosting_pretrained_fasttext.ipynb)\n", | ||
| "\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Time series\n", | ||
| "---\n", | ||
| "\n", | ||
| "* [Time series forecasting with DeepAR - Synthetic data](../introduction_to_amazon_algorithms/deepar_synthetic/deepar_synthetic.ipynb)\n", | ||
| "\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "instance_type": "ml.t3.medium", | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (Data Science)", | ||
| "language": "python", | ||
| "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.7.10" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Get started with endpoints\n", | ||
| "\n", | ||
| "Examples on how to use your own model serving containers or extend pre-built containers on SageMaker.\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## A/B testing\n", | ||
| "---\n", | ||
| "\n", | ||
| "* [A/B Testing with Amazon SageMaker](../sagemaker_endpoints/a_b_testing/a_b_testing.ipynb)\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Multi-model endpoints\n", | ||
| "---\n", | ||
| "\n", | ||
| "* [Amazon SageMaker Multi-Model Endpoints using Scikit Learn](../advanced_functionality/multi_model_sklearn_home_value/sklearn_multi_model_endpoint_home_value.ipynb)\n", | ||
| "* [Amazon SageMaker Multi-Model Endpoints using XGBoost](../advanced_functionality/multi_model_xgboost_home_value/xgboost_multi_model_endpoint_home_value.ipynb)\n", | ||
| "\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "instance_type": "ml.t3.medium", | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (Data Science)", | ||
| "language": "python", | ||
| "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.7.10" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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