Amazon SageMaker: Developer Guide
- Length: 1770 pages
- Edition: 1
- Language: English
- Publisher: Amazon Web Services
- Publication Date: 2021
- ISBN-10: B07JVSBS9J
- Sales Rank: #9400 (See Top 100 Books)
This is official Amazon Web Services (AWS) documentation for Amazon SageMaker. Amazon SageMaker provides fully managed machine learning in the cloude. This guide demonstrates how to use Amazon SageMaker to build, train, and host machine learning models in the cloud. This documentation is offered here as a free Kindle book, or you can read it online or in PDF format at https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html.
What Is Amazon SageMaker? Machine Learning with Amazon SageMaker Explore, Analyze, and Process Data Train a Model with Amazon SageMaker Deploy a Model in Amazon SageMaker Get Inferences for an Entire Dataset with Batch Transform Validate a Machine Learning Model Monitoring a Model in Production Use Machine Learning Frameworks, Python, and R with Amazon SageMaker Use Apache MXNet with Amazon SageMaker Use Apache Spark with Amazon SageMaker Example 1: Use Amazon SageMaker for Training and Inference with Apache Spark Use Custom Algorithms for Model Training and Hosting on Amazon SageMaker with Apache Spark Use the SageMakerEstimator in a Spark Pipeline Additional Examples: Use Amazon SageMaker with Apache Spark Use Chainer with Amazon SageMaker Use PyTorch with Amazon SageMaker R User Guide to Amazon SageMaker Use Scikit-learn with Amazon SageMaker Use SparkML Serving with Amazon SageMaker Use TensorFlow with Amazon SageMaker Supported Regions and Quotas Get Started with Amazon SageMaker Set Up Amazon SageMaker Onboard to Amazon SageMaker Studio Onboard to SageMaker Studio Using Quick Start Onboard to SageMaker Studio Using AWS SSO Set Up AWS SSO for Use with SageMaker Studio Onboard to SageMaker Studio Using IAM Delete an SageMaker Studio Domain Get Started with Amazon SageMaker Studio Amazon SageMaker Studio Tour Get Started with Amazon SageMaker Notebook Instances and SDKs Step 1: Create an Amazon S3 Bucket Step 2: Create an Amazon SageMaker Notebook Instance Step 3: Create a Jupyter Notebook Step 4: Download, Explore, and Transform the Training Data Step 4.1: Download the MNIST Dataset Step 4.2: Explore the Training Dataset Step 4.3: Transform the Training Dataset and Upload It to Amazon S3 Step 5: Train a Model Step 6: Deploy the Model to Amazon SageMaker Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services Step 6.2: Deploy the Model with Batch Transform Step 7: Validate the Model Step 7.1: Validate a Model Deployed to Amazon SageMaker Hosting Services Step 7.2: Validate a Model Deployed with Batch Transform Step 8: Integrating Amazon SageMaker Endpoints into Internet-facing Applications Step 9: Clean Up Amazon SageMaker Studio Amazon SageMaker Studio UI Overview Use the SageMaker Studio Launcher Use Amazon SageMaker Studio Notebooks How Are Studio Notebooks Different from Notebook Instances? Get Started Create or Open an Amazon SageMaker Studio Notebook Use the SageMaker Studio Notebook Toolbar Share and Use a Studio Notebook Get Notebook and App Metadata Get Notebook Differences Manage Resources Change an Instance Type Change a SageMaker Image Create a Custom Kernel Shut Down Resources Usage Metering Available Resources Available Instance Types Available SageMaker Images Available SageMaker Kernels Perform Common Tasks in Studio Amazon SageMaker Studio Pricing Use Amazon SageMaker Notebook Instances Create a Notebook Instance Access Notebook Instances Control Root Access to a Notebook Instance Customize a Notebook Instance Using a Lifecycle Configuration Script Lifecycle Configuration Best Practices Install External Libraries and Kernels in Notebook Instances Notebook Instance Software Updates Control an Amazon EMR Spark Instance Using a Notebook Example Notebooks Set the Notebook Kernel Associate Git Repositories with Amazon SageMaker Notebook Instances Add a Git Repository to Your Amazon SageMaker Account Add a Git Repository to Your Amazon SageMaker Account (CLI) Create a Notebook Instance with an Associated Git Repository Create a Notebook Instance with an Associated Git Repository (CLI) Associate a CodeCommit Repository in a Different AWS Account with a Notebook Instance Use Git Repositories in a Notebook Instance Notebook Instance Metadata Monitor Jupyter Logs in Amazon CloudWatch Logs Automate model development with Amazon SageMaker Autopilot Create an Amazon SageMaker Autopilot experiment Get started with Amazon SageMaker Autopilot Samples: Explore modeling with Amazon SageMaker Autopilot Videos: Use Autopilot to automate and explore the machine learning process Tutorials: Get started with Amazon SageMaker Autopilot Amazon SageMaker Autopilot problem types Amazon SageMaker Autopilot notebook output Amazon SageMaker Autopilot container output API reference guide for Amazon SageMaker Autopilot Prepare and Label Data Process Data and Evaluate Models Process Data and Evaluate Models with scikit-learn Use Your Own Processing Code Run Scripts with Your Own Processing Container Build Your Own Processing Container (Advanced Scenario) Use Amazon SageMaker Ground Truth to Label Data Getting started Step 1: Before You Begin Step 2: Create a Labeling Job Step 3: Select Workers Step 4: Configure the Bounding Box Tool Step 5: Monitoring Your Labeling Job Label Images Bounding Box Image Semantic Segmentation Auto-Segmentation Tool Image Classification (Single Label) Image Classification (Multi-label) Label Verification Use Ground Truth to Label Text Named Entity Recognition Text Classification (Single Label) Text Classification (Multi-label) Label Videos and Video Frames Video Classification Label Video Frames Video Frame Object Detection Video Frame Object Tracking Video Frame Labeling Job Overview Worker Instructions Work on Video Frame Object Tracking Tasks Work on Video Frame Object Detection Tasks Use Ground Truth to Label 3D Point Clouds 3D Point Cloud Task types 3D Point Cloud Object Detection 3D Point Cloud Object Tracking 3D Point Cloud Semantic Segmentation 3D Point Cloud Labeling Jobs Overview Worker Instructions 3D Point Cloud Semantic Segmentation 3D Point Cloud Object Detection 3D Point Cloud Object Tracking Verify and Adjust Labels Creating Custom Labeling Workflows Step 1: Setting up your workforce Step 2: Creating your custom labeling task template Demo Template: Annotation of Images with crowd-bounding-box Demo Template: Labeling Intents with crowd-classifier Step 3: Processing with AWS Lambda Custom Workflows via the API Create a Labeling Job Built-in Task Types Creating Instruction Pages Create a Labeling Job (Console) Create a Labeling Job (API) Create a Labeling Category Configuration File with Label Category Attributes Use Input and Output Data Input Data Input Data Quotas Filter and Select Data for Labeling 3D Point Cloud Input Data Accepted Raw 3D Data Formats Create an Input Manifest File for a 3D Point Cloud Labeling Job Create a Point Cloud Frame Input Manifest File Create a Point Cloud Sequence Input Manifest Understand Coordinate Systems and Sensor Fusion Video Frame Input Data Choose Video Files or Video Frames for Input Data Input Data Setup Automated Video Frame Input Data Setup Manual Input Data Setup Output Data Enhanced Data Labeling Batches for Labeling Tasks Consolidate Annotations Automate Data Labeling Chaining Labeling Jobs Ground Truth Security and Permissions Assign IAM Permissions to Use Ground Truth Data and Storage Volume Encryption Workforce Authentication and Restrictions Monitor Labeling Job Status Create and Manage Workforces Using the Amazon Mechanical Turk Workforce Managing Vendor Workforces Use a Private Workforce Create and Manage Amazon Cognito Workforce Create a Private Workforce (Amazon Cognito) Create a Private Workforce (Amazon SageMaker Console) Create a Private Workforce (Amazon Cognito Console) Manage a Private Workforce (Amazon Cognito) Manage a Workforce (Amazon SageMaker Console) Manage a Private Workforce (Amazon Cognito Console) Create and Manage OIDC IdP Workforce Create a Private Workforce (OIDC IdP) Manage a Private Workforce (OIDC IdP) Manage Private Workforce Using the Amazon SageMaker API Track Worker Performance Create and manage Amazon SNS topics for your work teams Crowd HTML Elements Reference Train Models Choose an Algorithm Use Amazon SageMaker built-in algorithms Common elements of built-in algorithms Common parameters for built-in algorithms Common data formats for built-in algorithms Common data formats for training Common Data Formats for Inference Instance types for built-in algorithms Logs for built-In Algorithms BlazingText algorithm BlazingText Hyperparameters Tune a BlazingText Model DeepAR Forecasting Algorithm How the DeepAR Algorithm Works DeepAR Hyperparameters Tune a DeepAR Model DeepAR Inference Formats Factorization Machines Algorithm How Factorization Machines Work Factorization Machines Hyperparameters Tune a Factorization Machines Model Factorization Machine Response Formats Image Classification Algorithm How Image Classification Works Image Classification Hyperparameters Tune an Image Classification Model IP Insights Algorithm How IP Insights Works IP Insights Hyperparameters Tune an IP Insights Model IP Insights Data Formats IP Insights Training Data Formats IP Insights Inference Data Formats K-Means Algorithm How K-Means Clustering Works K-Means Hyperparameters Tune a K-Means Model K-Means Response Formats K-Nearest Neighbors (k-NN) Algorithm How the k-NN Algorithm Works k-NN Hyperparameters Tune a k-NN Model Data Formats for k-NN Training Input k-NN Request and Response Formats Latent Dirichlet Allocation (LDA) Algorithm How LDA Works LDA Hyperparameters Tune an LDA Model Linear learner algorithm How linear learner works Linear learner hyperparameters Tune a linear learner model Linear learner response formats Neural Topic Model (NTM) Algorithm NTM Hyperparameters Tune an NTM Model NTM Response Formats Object2Vec Algorithm How Object2Vec Works Object2Vec Hyperparameters Tune an Object2Vec Model Data Formats for Object2Vec Training Data Formats for Object2Vec Inference Encoder Embeddings for Object2Vec Object Detection Algorithm How Object Detection Works Object Detection Hyperparameters Tune an Object Detection Model Object Detection Request and Response Formats Principal Component Analysis (PCA) Algorithm How PCA Works PCA Hyperparameters PCA Response Formats Random Cut Forest (RCF) Algorithm How RCF Works RCF Hyperparameters Tune an RCF Model RCF Response Formats Semantic Segmentation Algorithm Semantic Segmentation Hyperparameters Sequence-to-Sequence Algorithm How Sequence-to-Sequence Works Sequence-to-Sequence Hyperparameters Tune a Sequence-to-Sequence Model XGBoost Algorithm How XGBoost Works XGBoost Hyperparameters Tune an XGBoost Model XGBoost Previous Versions XGBoost Release 0.72 Use Your Own Algorithms or Models with Amazon SageMaker Docker Container Basics Create Docker Containers with the Amazon SageMaker Containers Library Get Started: Build Your Custom Training Container with Amazon SageMaker Prebuilt Amazon SageMaker Docker Images for TensorFlow, MXNet, Chainer, and PyTorch Prebuilt Amazon SageMaker Docker Images for Scikit-learn and Spark ML Example Notebooks: Use Your Own Algorithm or Model Use Your Own Training Algorithms How Amazon SageMaker Runs Your Training Image How Amazon SageMaker Provides Training Information How Amazon SageMaker Signals Algorithm Success and Failure How Amazon SageMaker Processes Training Output Use Your Own Inference Code Use Your Own Inference Code with Hosting Services Use a Private Docker Registry for Real-Time Inference Containers Use Your Own Inference Code with Batch Transform Create Algorithm and Model Package Resources Create an Algorithm Resource Create a Model Package Resource Use Algorithm and Model Package Resources Use an Algorithm to Run a Training Job Use an Algorithm to Run a Hyperparameter Tuning Job Use a Model Package to Create a Model Use Reinforcement Learning with Amazon SageMaker Sample RL Workflow Using Amazon SageMaker RL RL Environments in Amazon SageMaker Distributed Training with Amazon SageMaker RL Hyperparameter Tuning with Amazon SageMaker RL Train a Deep Graph Network Manage Machine Learning with Amazon SageMaker Experiments Create a SageMaker Experiment View and Compare SageMaker Experiments, Trials, and Trial Components Track and Compare Tutorial Search Experiments Using Amazon SageMaker Studio Clean Up SageMaker Experiment Resources Search Using the SageMaker Console and API Amazon SageMaker Debugger Amazon SageMaker Studio Visualization Demos of Model Analysis with Debugger Use Debugger in AWS Containers Configure and Save Tensor Data Using the Debugger API Operations Use Debugger Built-in Rules for Training Job Analysis List of Debugger Built-in Rules Create Debugger Custom Rules for Training Job Analysis Amazon SageMaker Debugger Advanced Topics and Reference Documentation Use Debugger in Custom Training Containers Amazon SageMaker Debugger API Operations Use the SageMaker CreateTrainingJob and Debugger Configuration API Operations to Create and Debug Your Training Job Use Debugger Docker Images for Built-in or Custom Rules Amazon SageMaker Debugger Exceptions Known Limitations with Amazon SageMaker Debugger Perform Automatic Model Tuning How Hyperparameter Tuning Works Define Metrics Define Hyperparameter Ranges Tune Multiple Algorithms to Find the Best Model Get Started Managing Hyperparameter Tuning Jobs Create a new single or multi-algorithm HPO tuning job Example: Hyperparameter Tuning Job Create a Notebook Get the Amazon Sagemaker Boto 3 Client Get the Amazon SageMaker Execution Role Specify a Bucket and Data Output Location Download, Prepare, and Upload Training Data Configure and Launch a Hyperparameter Tuning Job Monitor the Progress of a Hyperparameter Tuning Job Clean up Stop Training Jobs Early Run a Warm Start Hyperparameter Tuning Job Best Practices for Hyperparameter Tuning Incremental Training in Amazon SageMaker Managed Spot Training in Amazon SageMaker Use Checkpoints in Amazon SageMaker Provide Dataset Metadata to Training Jobs with an Augmented Manifest File Monitor Amazon SageMaker Monitor and Analyze Training Jobs Using Metrics Monitor Amazon SageMaker with Amazon CloudWatch Log Amazon SageMaker Events with Amazon CloudWatch Log Amazon SageMaker API Calls with AWS CloudTrail Automating Amazon SageMaker with Amazon EventBridge Deploy Models for Inference Host Multiple Models with Multi-Model Endpoints Create a Multi-Model Endpoint Create a Multi-Model Endpoint (AWS SDK for Python (Boto)) Create a Multi-Model Endpoint (Console) Invoke a Multi-Model Endpoint Add or Remove Models Build Your Own Container with Multi Model Server Contract for Custom Containers to Serve Multiple Model How Multi-Model Endpoints Work Multi-Model Endpoint Security CloudWatch Metrics for Multi-Model Endpoint Deployments Amazon SageMaker Model Monitor Capture Data Create a Baseline Schedule Monitoring Jobs The cron Expression for Monitoring Schedule Amazon SageMaker Model Monitor Pre-built Container Interpret Results Schema for Statistics (statistics.json file) Schema for Violations (constraint_violations.json file) CloudWatch Metrics Visualize Results in Amazon SageMaker Studio Advanced Topics Customize Monitoring Preprocessing and Postprocessing Bring Your Own Containers Container Contract Inputs Container Contract Outputs Schema for Statistics (statistics.json file) Schema for Constraints (constraints.json file) CloudWatch Metrics Create a Monitoring Schedule with an AWS CloudFormation Custom Resource Deploy an Inference Pipeline Feature Processing with Spark ML and Scikit-learn Create a Pipeline Model Run Real-time Predictions with an Inference Pipeline Run Batch Transforms with Inference Pipelines Inference Pipeline Logs and Metrics Troubleshoot Inference Pipelines Compile and Deploy Models with Amazon SageMaker Neo Use Neo to Compile a Model Compile a Model (AWS Command Line Interface) Compile a Model (Amazon SageMaker Console) Compile a Model (Amazon SageMaker SDK) Deploy a Model Deploy a Model Compiled with Neo with Hosting Services Deploy a Model Compiled with Neo (AWS CLI) Deploy a Model Compiled with Neo (Console) Deploy a Model Compiled with Neo (Amazon SageMaker SDK) Deploy a Model Compiled with Neo (AWS IoT Greengrass) Request Inferences from a Deployed Service Troubleshooting Neo Compilation Errors Use Amazon SageMaker Elastic Inference (EI) Set Up to Use EI Attach EI to a Notebook Instance Use EI on Amazon SageMaker Hosted Endpoints Use Batch Transform Associate Prediction Results with Input Records Automatically Scale Amazon SageMaker Models Prerequisites Configure model autoscaling with the console Register a model Define a scaling policy Apply a scaling policy Edit a scaling policy Delete a scaling policy Query Endpoint Autoscaling History Update or delete endpoints that use automatic scaling Load testing your autoscaling configuration Use AWS CloudFormation to update autoscaling policies Test models in production Troubleshoot Amazon SageMaker Model Deployments Deployment Best Practices Host Instance Storage Volumes Amazon SageMaker Workflows Using Amazon Augmented AI for Human Review Get Started with Amazon Augmented AI Use Task Types Use Amazon Augmented AI with Amazon Textract Use Amazon Augmented AI with Amazon Rekognition Use Amazon Augmented AI with Custom Task Types Create a Flow Definition JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI Use Human Loop Activation Conditions JSON Schema with Amazon Textract Use Human Loop Activation Conditions JSON Schema with Amazon Rekognition Delete a Flow Definition Create and Start a Human Loop Create and Manage Worker Task Templates Create and Delete a Worker Task Templates Create Custom Worker Task Template Creating Good Worker Instructions Monitor and Manage Your Human Loop Permissions and Security in Amazon Augmented AI Use Amazon CloudWatch Events in Amazon Augmented AI Use APIs in Amazon Augmented AI Buy and Sell Amazon SageMaker Algorithms and Models in AWS Marketplace Sell Amazon SageMaker Algorithms and Model Packages Develop Algorithms and Models in Amazon SageMaker List Your Algorithm or Model Package on AWS Marketplace Find and Subscribe to Algorithms and Model Packages on AWS Marketplace Security in Amazon SageMaker Data Protection in Amazon SageMaker Protect Data at Rest Using Encryption Protecting Data in Transit with Encryption Protect Communications Between ML Compute Instances in a Distributed Training Job Key Management Internetwork Traffic Privacy Identity and Access Management for Amazon SageMaker How Amazon SageMaker Works with IAM Amazon SageMaker Identity-Based Policy Examples Amazon SageMaker Roles AWS Managed (Predefined) Policies for Amazon SageMaker Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference Troubleshooting Amazon SageMaker Identity and Access Logging and Monitoring Compliance Validation for Amazon SageMaker Resilience in Amazon SageMaker Infrastructure Security in Amazon SageMaker Connect a Notebook Instance to Resources in a VPC Training and Inference Containers Run in Internet-Free Mode Connect to Amazon SageMaker Through a VPC Interface Endpoint Connect to a Notebook Instance Through a VPC Interface Endpoint Give Amazon SageMaker Processing Jobs Access to Resources in Your Amazon VPC Give Amazon SageMaker Training Jobs Access to Resources in Your Amazon VPC Give Amazon SageMaker Hosted Endpoints Access to Resources in Your Amazon VPC Give Batch Transform Jobs Access to Resources in Your Amazon VPC API Reference Guide for Amazon SageMaker Programming Model for Amazon SageMaker Document History for Amazon SageMaker AWS glossary
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