Sagemaker command line interface. For most use-cases, pass the raw string.

Sagemaker command line interface One of the challenges is setting up authentication and fine-grained permissions for users based on their roles and activities. For more information about deploying a model endpoint, see Deploy the Model to SageMaker AI Hosting Services. Please Note: This project Sep 24, 2021 · Create a lifecycle configuration entity via the AWS Command Line Interface (AWS CLI). Access a space using AWS Identity and Access Management (IAM) or AWS IAM Identity Center authentication. For example, MLOps engineers typically perform model deployment activities, […] Jun 3, 2025 · This configuration enables secure package access from the isolated SageMaker JupyterLab environment while maintaining control over external dependencies. In this post, we showcase how to configure using the boto3 SDK for Python and outline different scaling policies and patterns. Feb 25, 2024 · Explore serverless deployment with AWS ECR and SageMaker. Learn to optimize your workflow efficiently. NET AWS SDK for C++ AWS SDK for Go v2 AWS SDK for Java V2 AWS SDK for JavaScript V3 AWS SDK for Kotlin AWS SDK for PHP V3 AWS SDK for Python AWS SDK for Ruby V3. For more information, see Onboard to Amazon SageMaker AI domain. Dec 1, 2023 · As organizations scale the adoption of machine learning (ML), they are looking for efficient and reliable ways to deploy new infrastructure and onboard teams to ML environments. SageMaker HyperPod CLI offers Apr 29, 2020 · Hope this helps! From within the SageMaker Studio interface, click the upload button and upload the ZIP file into SageMaker Studio: Next, go to File -> New -> Terminal to open a Terminal in the SageMaker Studio interface. Complete all prerequisites - To access AWS services with the AWS CLI, you need at minimum an AWS account and IAM credentials. CloudSensei - Amazon SageMaker HyperPod introduces CLI and SDK for AI Workflows: We are excited to announce the general availability of the Amazon SageMaker HyperPod Command Line Interface (CLI) and Software Development Kit (SDK). For information about updating the AWS CLI, see Install or update to the latest version of the AWS Command Line Interface. Jupyter Notebook and Lab: To run the provided notebooks. With this CLI, you can access SageMaker Unified Studio resources and run both local and remote executions. Customers can configure auto-scaling policies through the AWS SDK for Python (Boto3), SageMaker Python SDK, or the AWS Command Line Interface (AWS CLI). , $ run-notebook run weather. In the notebook cell, enter the following code: May 14, 2023 · It is not possible to install docker in the SageMaker Studio. This project is designed to be a user-friendly and efficient solution for handling machine learning tasks with Amazon SageMaker using FastAPI, a modern web framework for building APIs in Python. This chapter provides steps to get started with version 2 of the AWS Command Line Interface (AWS CLI) and provides links to the relevant instructions. Aug 7, 2025 · CLI to interact with SageMaker StudioSageMaker Studio CLI SageMaker Studio CLI is an open source command-line utility for interacting with Amazon SageMaker Unified Studio. AWS CLI: Install the AWS Command Line Interface (CLI) and configure it with your AWS credentials. 4. Each model is referred to as a production (model) variant. 93. You can use them in combination if you choose. We’ve looked at the AWS services that compose SageMaker and how these services are tied together. Learn about Jupyter AI main capabilities. 35 to run the sagemaker list-endpoints command. You can make batch inferences from an Autopilot model using the SageMaker Python SDK, the Autopilot user interface (UI), the AWS SDK for Python (boto3), or the AWS Command Line Interface (AWS CLI). You can create an AWS PrivateLink interface endpoint to connect to SageMaker AI or to SageMaker AI Runtime using either the AWS Management Console or AWS Command Line Interface (AWS CLI). Sep 14, 2022 · SageMaker studio provides a workspace for users with hosted Jupyter notebooks and flexible compute resources. df -h RAM utilization and availability: free -m Use CloudWatch metrics to view SageMaker AI resource utilization Note: If you receive errors when you run AWS Command Line Interface (AWS CLI) commands, then see Troubleshooting errors for the AWS CLI. Dec 1, 2020 · You can configure some of these policies using the Amazon SageMaker console, the AWS Command Line Interface (AWS CLI), or the AWS SDK’s Application Auto Scaling API for the advanced options. Jun 16, 2025 · In this post, we demonstrate how you can bring transactional data from AWS OLTP data stores like Amazon Relational Database Service (Amazon RDS) and Amazon Aurora flowing into Redshift using zero-ETL integrations to SageMaker Lakehouse Federated Catalog (Bring your own Amazon Redshift into SageMaker Lakehouse). e. Use the following AWS Command Line Interface (AWS CLI) commands to change the default storage size. For more information about accessing spaces using the AWS CLI, see Accessing spaces using the AWS Command Line Interface in Amazon SageMaker Studio spaces. For more information about This customization includes installing custom packages, configuring extensions, preloading datasets, and setting up source code repositories The following instructions use the AWS Command Line Interface (AWS CLI) to create, attach, debug, and detach lifecycle configurations for the CodeEditor application type: Use the AWS CLI 2. Learn how to deploy a model with AWS SageMaker using this comprehensive step-by-step guide, covering setup, training, and deployment. May 10, 2023 · After this command runs, you can start JupyterLab by running jupyter lab. You can use many services from SageMaker Studio, AWS SDK for Python (Boto3), or AWS Command Line Interface (AWS CLI), including the following: IDEs on SageMaker Studio to perform complete ML development with a broad set of fully managed IDEs, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code – Open Source), and RStudio For usage examples, see Pagination in the AWS Command Line Interface User Guide . Use the following AWS CLI command to update the domain: To troubleshoot the failed pipeline execution in SageMaker, complete the following steps: Run the AWS Command Line Interface (AWS CLI) command list-pipeline-executions. Any editor that inserts Windows or other special characters potentially will cause scripts to fail. You can create, describe, stop, and list the compilation jobs. Also, make sure that you're using the most recent AWS CLI version. To activate auto scaling for a model, you can use the SageMaker AI console, the AWS Command Line Interface (AWS CLI), or an AWS SDK through the Application Auto Scaling API. SageMaker Studio has made some instance types available as “fast launch”. In the notebook menu, choose the + icon to add a new cell. If the total number of items available is more than the value specified, a NextToken is provided in the command’s Set up the Docker application. You use the AWS Command Line Interface (AWS CLI) or the the console to automate customization for your JupyterLab environment. With lifecycle configurations, system administrators can apply automated controls to their SageMaker Studio domains and their users. 2. We also examined various ways to call the SageMaker API including the Python SDK, the boto3 library, and the command line interface. Aug 8, 2023 · A utility command line interface (CLI) called sm-spark-cli is also provided for interacting with the Spark UI from the SageMaker Studio system terminal. 6+ Git Docker Command Line Clone and set up the GitHub repository for GenAI application Configure the AWS credentials in the host you are using for your setup. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed. Docker: Docker must be installed and running on your local machine if you plan to build the custom Docker container for deployment. $ aws sagemaker list-pipeline-executions --pipeline-name test-pipeline-p Use the following AWS Command Line Interface (AWS CLI) commands to delete resources within a domain: May 30, 2025 · An existing Amazon SageMaker Studio domain with an associated Amazon SageMaker user profile. The prerequisites for following along with this post include: AWS Account and Administrator access to AWS Account Node. The Amazon SageMaker Studio UI does not use the default instance type value set here. Table of Contents Installation Usage Setting up Credentials AWS Named Profile Commands credentials get-domain Amazon SageMaker AI provides APIs, SDKs, and a command line interface that you can use to create and manage notebook instances and train and deploy models. I want to launch a presigned URL on an Amazon SageMaker AI notebook instance or Amazon SageMaker Studio domain, but I receive errors. This repo contains scripts to re-run common tweaks on a fresh (i. For example, a person can use commands to start an Amazon EC2 instance, connect an Amazon EC2 instance to a specific Auto Scaling group, and This section provides guidance on managing SageMaker HyperPod through the SageMaker AI console UI or the AWS Command Line Interface (CLI). SageMaker provides algorithms for training machine learning models, classifying images, detecting objects, analyzing text, forecasting time series, reducing data dimensionality, and clustering data groups. For any workshop module that requires use of the AWS Command Line Interface (see above), you also will need a plain text editor for writing Bash scripts. This page's topics demonstrate how to launch Amazon SageMaker Studio from the Amazon SageMaker AI console and the AWS Command Line Interface (AWS CLI). --generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. This can help prevent the AWS service calls from timing out. Have a SageMaker AI Studio domain. With this library, you have three ways to run, schedule, and monitor notebook execution: You can perform operations from the shell using a command-line interface designed explicitly for running notebooks (e. g. You'll learn how to perform various tasks related to SageMaker HyperPod, whether you prefer a visual interface or working with commands. We recommend using the latest version of the AWS Command Line Interface. Permissions to access the Amazon Elastic Container Registry (Amazon ECR) service. Create an application and specify CodeEditor as the app-type using the following command. *SageMaker Endpoint*: An existing SageMaker endpoint. js and the Npm command line interface AWS CDK and version 2 of the AWS CLI Git command line interface installed on your computer for cloning the repository Complete CDK Bootstrapping in AWS Account with instructions in this link Nov 25, 2024 · Implementing Scale Down to Zero is simple with SageMaker Inference Components. After running these scripts your default command-line terminal will go from this: To something like this: Once installed, everytime you access a newly restarted notebook instance, you Nov 6, 2020 · To see how you can build your container image using SageMaker AI Studio, see Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks. 39 to run the sagemaker list-user-profiles command. Most inputs to these utilities are actually CSV strings that are processed left-to-right. If the total number of items available is more than the value specified, a NextToken is provided in the command’s For usage examples, see Pagination in the AWS Command Line Interface User Guide . --max-items (integer) The total number of items to return in the command’s output. With this integration, you can now seamlessly onboard the changed data from OLTP Topics 窶｢ Access Amazon SageMaker Uni・‘d Studio 窶｢ Create a project 窶｢ Get started with Amazon Bedrock in SageMaker Uni・‘d Studio 窶｢ Get started with the query editor in Amazon SageMaker Uni・‘d Studio 窶｢ Get started adding on-demand Amazon EMR on EC2 instances 窶｢ Use the sample notebook 窶｢ Getting started The SageMaker notebook instances help create the environment by initiating Jupyter servers on Amazon Elastic Compute Cloud (Amazon EC2) and providing preconfigured kernels with the following packages: the Amazon SageMaker Python SDK, AWS SDK for Python (Boto3), AWS Command Line Interface (AWS CLI), Conda, Pandas, deep learning framework Jul 16, 2024 · 3. You can securely connect to SageMaker training containers through AWS Systems Manager (SSM). This gives you a shell-level access to debug training jobs that are running within the container. For more advanced use-cases, pass a CSV string of operations Start with a SageMaker HyperPod command-line interface The Amazon SageMaker HyperPod command-line interface (HyperPod CLI) is a tool that helps manage clusters, training jobs, and inference endpoints on the SageMaker HyperPod clusters orchestrated by Amazon EKS. The following tabs show three options for deploying your model: Using APIs, Autopilot UI, or using APIs to deploy from different accounts. We cover core concepts of SageMaker Studio and provide code examples of how to apply lifecycle configuration to […] AWS Command Line Interface V2 AWS SDK for . Optional: Configure the AWS Command Line Interface (AWS CLI) if you intend to manage your AWS services and resources for the account using the AWS CLI. The solution consists of shell scripts that perform the following actions: 1 day ago · Amazon SageMaker Catalog, which is now built in to Amazon SageMaker, can help you collect and organize your data with the accompanying business context people need to understand it. 1 day ago · Amazon SageMaker Catalog now offers column-level metadata forms and enforced glossary requirements, enabling organizations to improve data classification, discoverability, and governance through standardized business metadata. Implementing Scale Down to Zero is simple with SageMaker Inference Components . This section shows how to manage Amazon SageMaker Neo compilation jobs for machine learning models using Amazon Command Line Interface (CLI). Then, use the container image for model training in AWS Step Functions. You can use either the Studio UI or the AWS Command Line Interface (AWS CLI) to update the image. For more information, see Get started in the Docker documentation. Dec 27, 2024 · AWS CLI: Command Line Interface - A Complete List of Commands The AWS Command Line Interface (CLI) is a powerful tool that allows users to manage and automate tasks in AWS directly from the command line. Built-in algorithms and pretrained models in Amazon SageMaker SageMaker provides algorithms for training machine learning models, classifying images, detecting objects, analyzing text, forecasting time series, reducing data dimensionality, and clustering data groups. This section shows how to manage Amazon SageMaker Neo compilation jobs for machine learning models using AWS Command Line Interface (CLI). Use the following AWS Command Line Interface (AWS CLI) command to update the domain. Is there a way to install and use it? $ sudo yum install docker Loaded plugins: ovl, priorities No package docker available. The correct response option is AWS Command Line Interface. The sm-spark-cli enables managing Spark History Server without leaving SageMaker Studio. Nov 26, 2024 · This post serves as a step-by-step guide on how to set up lifecycle configurations for your Amazon SageMaker Studio domains. A command line interface (CLI) A Python library A JupyterLab extension that can be enabled for JupyterLab running locally, in SageMaker Studio, or on a SageMaker notebook instance Each of the interfaces has the same functionality, so which to use is a matter of preference. The CLI eliminates the need to manually set up and connect to Docker build environments for building container images […] Package custom dependencies in a Docker container image by using Amazon SageMaker and Amazon Elastic Container Registry (Amazon ECR). Prerequisites This topic includes instructions for creating and associating a lifecycle configuration with JupyterLab. This will install the unzip package, which we need in the The SageMaker FastAPI project provides a seamless way to preprocess data, invoke a SageMaker endpoint hosted on AWS, and perform postprocessing on the prediction results. Sep 11, 2023 · Docker: Make sure Docker is installed , as we’ll be creating a custom container image. 31. For instructions, see Installing the AWS. Submit a notebook job After the Jul 27, 2021 · To get started, create a new SageMaker Project from the SageMaker Studio or the command-line interface using the new project templates that provide out-of-the box integration with these third party tools. Install the latest AWS CLI by following the steps in Getting started with the AWS CLI in the AWS Command Line Interface User Guide for Version 2. Start the Studio app with the specified lifecycle configuration. SageMakerImageArn -> (string) For more information about accessing spaces using the Amazon CLI, see Accessing spaces using the Amazon Command Line Interface in Amazon SageMaker Studio spaces. Learn how to delete a Amazon SageMaker AI domain using the Amazon SageMaker AI console and the AWS Command Line Interface. Delete Code Editor resources using the AWS CLI You can delete your Code Editor application and space using the AWS Command Line Interface (AWS CLI). You can use the AWS Command Line Interface (AWS CLI) to launch Amazon SageMaker Studio Classic by creating a presigned domain URL. If you need to establish a connection between Amazon Q Developer and your VPC, see Creating an interface VPC endpoint for Amazon Q . A Unix terminal with the AWS Command Line Interface (AWS CLI) and Terraform installed. Install the AWS Command Line Interface (AWS CLI) on your local machine. Nov 27, 2024 · For this post, we demonstrate SMP implementation on SageMaker trainings jobs. Prerequisites Before you begin, complete the following prerequisites: Onboard to Amazon SageMaker AI domain. Jul 8, 2019 · Conclusion In this post we’ve discussed the SageMaker architecture. To copy the CodeArtifact login AWS Command Line Interface (AWS CLI) command: Open the private repository, in the Packages section, choose View connection instructions. See Use quick setup for Amazon SageMaker AI. These tools make it easier for developers and ML practitioners to build, train, and deploy large-scale AI models on SageMaker HyperPod. Following the tutorial, you'll create a HyperPod cluster with three Slurm nodes, my-controller-group, my-login-group, and worker-group-1. For more information, see Delete unused resources. Write the script The following sample script installs pyarrow using the pip package manager. Note The Amazon SageMaker Studio UI does not use the default instance type value set here. The following tutorial demonstrates how to create a new SageMaker HyperPod cluster and set it up with Slurm through the AWS CLI commands for SageMaker HyperPod. 38 sagemaker commands. Learn how to register your model for auto scaling using the AWS Command Line Interface or Application Auto Scaling API. The SageMaker Studio domain must have SageMaker Projects enabled. If you’re installing the extension from within the JupyterLab terminal, restart the Jupyter server to load the extension. The instances in your VPC do not need to connect to the public internet in order to communicate with the SageMaker API or SageMaker AI Runtime. This documentation serves as a reference for the available HyperPod CLI commands. It also adds a visual interface for many advanced SageMaker features such as data wrangler, feature store, pipeline, model registry, and so on. The %%ai command applies your instructions to the entire cell, whereas %ai apply them to the specific line. Using %%ai and %ai magic commands, you can interact with the language model of your choice from your notebook cells or any IPython command line interface. For more information, see Amazon ECR Managed Policies in the Amazon When I use my Amazon SageMaker Studio environment in VPC-only mode, I experience connectivity issues with my JupyterLab and Code Editor spaces. For usage examples, see Pagination in the AWS Command Line Interface User Guide . The following page provides instructions on how to detach your custom images and clean up the related resources using the Amazon SageMaker AI console or the AWS Command Line Interface (AWS CLI). Automation with AWS Lambda and CloudWatch Events Ideal for complex automation scenarios, such as scheduled stopping or idle timeout-based stopping. Nov 10, 2025 · AWS CLI: Make sure the AWS Command Line Interface (AWS CLI) is installed and configured with credentials that have the necessary permissions. Associate the lifecycle configuration to a domain or user profile. Learn about the AWS CLI 2. disable-sagemaker-servicecatalog-portfolio disassociate-trial-component enable-sagemaker-servicecatalog-portfolio get-device-fleet-report get-lineage-group-policy get-model-package-group-policy get-sagemaker-servicecatalog-portfolio-status get-search-suggestions list-actions list-algorithms list-app-image-configs list-apps list-artifacts list You can create and attach lifecycle configurations using either the AWS Management Console or the AWS Command Line Interface. 0+) Working with the AWS CDK in Python Python ≥ 3. It automatically documents assets generated by AWS Glue and Amazon Redshift, and it connects directly with Amazon Quick Sight, Amazon Simple Storage Service (Amazon S3) buckets, Dec 17, 2024 · SageMaker Studio Convenient for managing multiple notebook instances within a unified interface. It is assumed that you have an administrative user for many of the administrative tasks throughout the SageMaker AI developer guide. Use the AWS CLI 2. Nov 21, 2023 · Explore the text generation AI model Prerequisites AWS Command Line Interface (AWS CLI) version 2 AWS CDK Toolkit (version 2. If the string includes a comma, it should be double-quoted. Dec 1, 2021 · You can use SageMaker Inference Recommender from SageMaker Studio, the AWS Command Line Interface (CLI), or the AWS SDK, and within minutes, get recommendations to deploy your ML model. ## Setting Up the Environment Variables May 6, 2024 · Optional: AWS CLI or SDK: Although not strictly required, familiarity with the AWS Command Line Interface (CLI) or AWS SDKs can be helpful for advanced users who want to automate tasks or interact This command enables the modern SageMaker Studio experience whereby your user settings and access can be reconfigured to take advantage of JupyterLab 4 and modern UI enhancements. *AWS CLI*: AWS Command Line Interface installed and configured. The following example illustrates an %%ai magic command invoking an Anthropic Claude model Feb 4, 2018 · For usage examples, see Pagination in the AWS Command Line Interface User Guide . Launching a machine learning (ML) training cluster with Amazon SageMaker training jobs is a seamless process that begins with a straightforward API call, AWS Command Line Interface (AWS CLI) command, or AWS SDK interaction. ipynb -p place="Seattle, WA"). Use a lifecycle script. You can use this solution to promote consistency of the analytical environments for data science teams across your enterprise. You can restart the Jupyter server by choosing Shut Down on the File menu from your JupyterLab, and starting JupyterLab from your command line by running jupyter lab. The Amazon SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio JupyterLab notebooks via CLI. The following example demonstrates the use of the Git CLI: 1. To learn more visit our documentation page. Update the JupyterLab spaces that you've already created to use the latest version of the SageMaker Distribution Image to access the latest features. Once the Terminal is open, type sudo yum install -y unzip. , newly created or rebooted) SageMaker classic notebook instance, to make the notebook instance a little bit more ergonomic for prolonged usage. Command-Line Interface ¶ The aws-sagemaker-remote CLI provides utilities to compliment processing, training, and other scripts. 35 to run the sagemaker list-pipelines command. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. pip install -U sagemaker To use local mode and Docker capabilities, set the following parameter of the domain’s DockerSettings using the AWS Command Line Interface (AWS CLI): Sep 14, 2020 · April 2025: This post was reviewed and updated for accuracy. For most use-cases, pass the raw string. The AWS Command Line Interface (AWS CLI) provides the capability to control multiple AWS services directly from the command line within one tool. To stop accruing charges from resources, delete any additional resources. Lifecycle configurations are shell scripts triggered by JupyterLab lifecycle events, such as starting a new JupyterLab notebook. Apr 16, 2021 · Working with the CodeCommit repository on SageMaker Studio (using the Git CLI) You can also work with the Git command line interface (CLI) on Studio. Jan 23, 2025 · In this post, we show how to create an automated continuous integration and delivery (CI/CD) pipeline solution to build, scan, and deploy custom Docker images to SageMaker Studio domains. It enables you to interact with AWS services, provision resources, and automate repetitive tasks all from a terminal, making it a key tool for developers, system administrators, and DevOps This can help prevent the AWS service calls from timing out. Note: Use the AWS CloudShell console if you don't have AWS CLI configured in your local machine. mcktm dmonm dqhgp sqeatl uam rjx pts frazg qefxczj lflav wwkr phgfw oeeawo bcxhhdsu ytjyeiw