Description

# SageMaker Studio SageMaker Studio is an open source library for interacting with Amazon SageMaker Unified Studio resources. With the library, you can access these resources such as domains, projects, connections, and databases, all in one place with minimal code. ## Table of Contents 1. [Installation](#installation) 2. [Usage](#usage) 1. [Setting Up Credentials and ClientConfig](#credentials--client-config) 1. [Using ClientConfig](#using-clientconfig) 2. [Domain](#domain) 3. [Domain Properties](#domain-properties) 3. [Project](#project) 1. [Properties](#project-properties) 1. [IAM Role ARN](#iam-role) 2. [KMS Key ARN](#kms-key-arn) 3. [MLflow Tracking Server ARN](#mlflow-tracking-server-arn) 4. [S3 Path](#s3-path) 2. [Connections](#connections) 1. [Connection Data](#connection-data) 2. [Secrets](#secrets) 3. [Catalogs](#catalogs) 4. [Databases and Tables](#databases-and-tables) 1. [Databases](#databases) 2. [Tables](#tables) 4. [Utils Methods](#utils-methods) 1. [SQL Utilities](#sql-utilities) 2. [DataFrame Utils](#dataframe-utils) 3. [Spark Utilities](#spark-utilities) 5. [Execution APIs](#execution-apis) 1. [Local Execution APIs](#local-execution-apis) 1. [StartExecution API](#startexecution) 2. [GetExecution API](#getexecution) 3. [ListExecutions API](#listexecutions) 4. [StopExecution API](#stopexecution) 2. [Remote Execution APIs](#remote-execution-apis) 1. [StartExecution API](#startexecution-1) 2. [GetExecution API](#getexecution-1) 3. [ListExecutions API](#listexecutions-1) 4. [StopExecution API](#stopexecution-1) ## 1) Installation The SageMaker Studio is built to PyPI, and the latest version of the library can be installed using the following command: ```bash pip install sagemaker-studio ``` #### Supported Python Versions SageMaker Studio supports Python versions 3.10 and newer. #### Licensing SageMaker Studio is licensed under the Apache 2.0 License. It is copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. The license is available at: http://aws.amazon.com/apache2.0/ ## 2) Usage ### Setting Up Credentials and ClientConfig If SageMaker Studio is being used within Amazon SageMaker Unified Studio JupyterLab, the library will automatically pull your latest credentials from the environment. If you are using the library elsewhere, or if you want to use different credentials within the SageMaker Unified Studio JupyterLab, you will need to first retrieve your SageMaker Unified Studio credentials and make them available in the environment through either: 1. Storing them within an [AWS named profile](https://docs.aws.amazon.com/sdkref/latest/guide/file-format.html). If using a profile name other than `default`, you will need to supply the profile name by: 1. Supplying it during initialization of the SageMaker Studio `ClientConfig` object 2. Setting the AWS profile name as an environment variable (e.g. `export AWS_PROFILE="my_profile_name"`) 2. Initializing a [boto3 `Session`](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html) object and supplying it when initializing a SageMaker Studio `ClientConfig` object ##### AWS Named Profile To use the AWS named profile, you can update your AWS `config` file with your profile name and any other settings you would like to use: ```config [my_profile_name] region = us-east-1 ``` Your `credentials` file should have the credentials stored for your profile: ```config [my_profile_name] aws_access_key_id=AKIAIOSFODNN7EXAMPLE aws_secret_access_key=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY aws_session_token=IQoJb3JpZ2luX2IQoJb3JpZ2luX2IQoJb3JpZ2luX2IQoJb3JpZ2luX2IQoJb3JpZVERYLONGSTRINGEXAMPLE ``` Finally, you can pass in the profile when initializing the `ClientConfig` object. ```python from sagemaker_studio import ClientConfig conf = ClientConfig(profile_name="my_profile_name") ``` You can also set the profile name as an environment variable: ```bash export AWS_PROFILE="my_profile_name" ``` ##### Boto3 Session To use a [boto3 `Session`](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html) object for credentials, you will need to initialize the `Session` and supply it to `ClientConfig`. ```python from boto3 import Session from sagemaker_studio import ClientConfig my_session = Session(...) conf = ClientConfig(session=my_session) ``` #### Using ClientConfig If using `ClientConfig` for supplying credentials or changing the AWS region name, the `ClientConfig` object will need to be supplied when initializing any further SageMaker Studio objects, such as `Domain` or `Project`. If using non prod endpoint for an AWS service, it can also be supplied in the `ClientConfig`. Note: In sagemaker space, datazone endpoint is by default fetched from t