SageMaker Studio Python Library

1.1.11 · active · verified Sun Mar 29

The sagemaker-studio Python library is an open-source tool designed to interact with Amazon SageMaker Unified Studio resources. It provides a simplified interface to programmatically access and manage entities such as domains, projects, connections, and databases. The library also includes utility modules for common data operations, including SQL execution, DataFrame manipulation, and Spark session management. The current version is 1.1.11, and it maintains an active development and release cadence, with updates often reflecting the evolution of the broader SageMaker Unified Studio platform.

Warnings

gotcha When using `sagemaker-studio` outside of an Amazon SageMaker Unified Studio JupyterLab environment, you must explicitly provide AWS credentials and region. This can be done by storing them in an AWS named profile or by passing a `ClientConfig` object during initialization of library components.
Fix: Use environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION), configure an AWS named profile, or initialize `ClientConfig(region_name='...', profile_name='...')` and pass it to object constructors.
gotcha The `sagemaker-studio` library (specifically for IAM-based domains) is supported only when using Space Distribution Image versions 2.11+ or 3.6+ in JupyterLab Notebooks or the Code Editor. Older image versions will not support the library.
Fix: Ensure your SageMaker Studio Space is running a compatible Space Distribution Image version (2.11+ or 3.6+). Upgrade your image if necessary via the SageMaker Studio console.
gotcha Forgetting to shut down compute instances or applications within SageMaker Studio can lead to unexpected AWS costs. This is an operational consideration for the platform, but directly impacts users leveraging this library for resource management.
Fix: Always shut down inactive JupyterLab applications, kernel sessions, and other compute resources in the SageMaker Studio console when no longer in use. Consider implementing lifecycle configurations for automatic shutdown.
gotcha Custom Python modules or local files within your SageMaker Studio environment might require careful path handling. The Studio home folder is typically mapped to `/root` inside the notebook container, so direct relative paths may not work as expected.
Fix: When importing local modules, use `sys.path.insert(0, os.path.abspath('path/to/module'))` or ensure your modules are placed in a location discoverable by Python's path, considering the `/root` mapping.

Install

pip install sagemaker-studio Install latest version

Imports

ClientConfig
```
from sagemaker_studio import ClientConfig
```
Used for configuring AWS credentials and region when not running within a SageMaker Unified Studio JupyterLab environment.
Domain
```
from sagemaker_studio import Domain
```
Represents an Amazon SageMaker Domain, which is an administrative boundary for Studio resources.
Project
```
from sagemaker_studio import Project
```
Represents a SageMaker Studio Project, used to organize ML workflows and resources.
sqlutils
```
from sagemaker_studio import sqlutils
```
Provides utilities for SQL execution against various data sources.
dataframeutils
```
from sagemaker_studio import dataframeutils
```
Offers utilities for DataFrame operations, including reading from and writing to catalog tables.

Quickstart

This quickstart demonstrates how to initialize `ClientConfig`, `Domain`, and `Project` objects using the `sagemaker-studio` library. It highlights the importance of credential configuration, either automatically within SageMaker Studio JupyterLab or explicitly via `ClientConfig` with an AWS profile. It then shows how to retrieve basic properties of a SageMaker Domain and Project. Note that running this code successfully requires valid AWS credentials and existing SageMaker Domain and Project IDs, and appropriate IAM permissions.

import os
from sagemaker_studio import ClientConfig, Domain, Project

# --- Configure ClientConfig if not in SageMaker Studio JupyterLab ---
# In a SageMaker Studio JupyterLab environment, credentials are automatically pulled.
# Otherwise, provide AWS credentials and region.
# Replace 'us-east-1' and 'your-aws-profile' as needed.
# For demonstration, we use os.environ.get for robustness.
aws_region = os.environ.get('AWS_REGION', 'us-east-1')
aws_profile = os.environ.get('AWS_PROFILE', 'default')

# Instantiate ClientConfig (optional, if running outside of Studio or with specific profile)
try:
    client_config = ClientConfig(region_name=aws_region, profile_name=aws_profile)
except Exception as e:
    print(f"Could not initialize ClientConfig, proceeding assuming environment provides credentials: {e}")
    client_config = None # Or handle more robustly

# --- Interact with a SageMaker Domain and Project ---
# To interact with a Domain or Project, you typically need its ID.
# Replace 'your-domain-id' and 'your-project-id' with actual values.
# If running in Studio, these might be discoverable from the environment.
domain_id = os.environ.get('SAGEMAKER_DOMAIN_ID', 'd-xxxxxxxxxxxx') # Placeholder
project_id = os.environ.get('SAGEMAKER_PROJECT_ID', 'p-yyyyyyyyyyyy') # Placeholder

print(f"Attempting to interact with Domain ID: {domain_id}")
print(f"Attempting to interact with Project ID: {project_id}")

if client_config:
    # Initialize Domain with client_config
    domain = Domain(id=domain_id, client_config=client_config)
    project = Project(id=project_id, domain_id=domain_id, client_config=client_config)
else:
    # Initialize Domain without client_config (assumes environment provides)
    domain = Domain(id=domain_id)
    project = Project(id=project_id, domain_id=domain_id)

# Accessing some properties (these calls might fail if IDs are invalid or permissions are insufficient)
try:
    print(f"Domain ID: {domain.id}")
    print(f"Project Name: {project.name}")
    print(f"Project S3 Path: {project.s3_path}")
except Exception as e:
    print(f"Error accessing domain/project properties: {e}")
    print("Please ensure the provided Domain/Project IDs are valid and your AWS credentials/permissions are correctly configured.")

# Example of using a utility module (requires an actual connection and query context)
# from sagemaker_studio import sqlutils
# try:
#     result = sqlutils.sql("SELECT 1", connection_name="your-connection-name")
#     print(f"SQL Query Result: {result}")
# except Exception as e:
#     print(f"Error executing SQL query: {e}")

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