{ "cells": [ { "cell_type": "markdown", "id": "8d730d2d-c6dd-4c40-bb16-1f0cba9afc4a", "metadata": {}, "source": [ "# Using the Edge Impulse Python SDK with SageMaker Studio\n", "\n", "" ] }, { "cell_type": "markdown", "id": "90c2c25d-7493-44a5-9765-f8c944dc2e9c", "metadata": {}, "source": [ "[![View in Edge Impulse docs](https://raw.githubusercontent.com/edgeimpulse/notebooks/main/.assets/images/ei-badge.svg)](https://docs.edgeimpulse.com/docs/edge-impulse-python-sdk/python-sdk-with-sagemaker-studio)\n", "[![View on GitHub](https://raw.githubusercontent.com/edgeimpulse/notebooks/main/.assets/images/badge-view-on-github.svg)](https://github.com/edgeimpulse/notebooks/blob/main/notebooks/python-sdk-with-sagemaker-studio.ipynb)\n", "[![Download notebook](https://raw.githubusercontent.com/edgeimpulse/notebooks/main/.assets/images/badge-download-notebook.svg)](https://raw.githubusercontent.com/edgeimpulse/notebooks/main/notebooks/python-sdk-with-sagemaker-studio.ipynb) " ] }, { "attachments": {}, "cell_type": "markdown", "id": "8edef800-3a91-46c0-836b-42e0518241ad", "metadata": {}, "source": [ "Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio.\n", "\n", "![SageMaker Studio](https://raw.githubusercontent.com/edgeimpulse/notebooks/main/.assets/images/python-sdk-sagemaker-studio.png)\n", "\n", "To learn more about using the Python SDK, please see: [Edge Impulse Python SDK Overview](https://docs.edgeimpulse.com/docs/edge-impulse-python-sdk/overview).\n", "\n", "This guide has been built from AWS reference project **Introduction to SageMaker TensorFlow - Image Classification**, please have a look at this [AWS documentation page](https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification-tensorflow.html). \n", "\n", "Below are the changes made to the original training script and configuration:\n", "\n", "- The `Python 3 (Data Science 3.0)` kernel was used.\n", "- The dataset has been changed to classify images as `car` vs `unknown`. You can download the dataset from this Edge Impulse [public project](https://studio.edgeimpulse.com/public/210613/latest) and store it in your S3 bucket.\n", "- The dataset has been imported in the Edge Impulse S3 bucket configured when creating the SageMaker Studio domain. Make sure to adapt to your path or use the AWS reference project.\n", "- The training instance used is `ml.m5.large`." ] }, { "attachments": {}, "cell_type": "markdown", "id": "6caec400", "metadata": {}, "source": [ "