sagemaker-studio1.1.5

Published 17 days ago

Python library to interact with Amazon SageMaker Unified Studio

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.

Installation
Usage

1) Installation

The SageMaker Studio is built to PyPI, and the latest version of the library can be installed using the following command:

pip install sagemaker-studio

Supported Python Versions

SageMaker Studio supports Python versions 3.10 and newer.

Licensing

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:

Storing them within an AWS named profile. 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")
Initializing a boto3 Session 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:

[my_profile_name]
region = us-east-1

Your credentials file should have the credentials stored for your profile:

[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.

from sagemaker_studio import ClientConfig

conf = ClientConfig(profile_name="my_profile_name")

You can also set the profile name as an environment variable:

export AWS_PROFILE="my_profile_name"

Boto3 Session

To use a boto3 Session object for credentials, you will need to initialize the Session and supply it to ClientConfig.

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 the metadata json file.

from sagemaker_studio import ClientConfig, Project

conf = ClientConfig(region="eu-west-1")
proj = Project(config=conf)

Domain

Domain can be initialized as follows.

from sagemaker_studio import Domain

dom = Domain()

If you are not using the SageMaker Studio within SageMaker Unified Studio Jupyter Lab, you will need to provide the ID of the domain you want to use.

dom = Domain(id="123456")

Domain Properties

A Domain object has several string properties that can provide information about the domain that you are using.

dom.id
dom.root_domain_unit_id
dom.name
dom.domain_execution_role
dom.status
dom.portal_url

Project

Project can be initialized as follows.

from sagemaker_studio import Project

proj = Project()

If you are not using the SageMaker Studio within the SageMaker Unified Studio Jupyter Lab or Data Notebook, you will need to provide either the ID or name of the project you would like to use and the domain ID of the project.

proj = Project(name="my_proj_name", domain_id="123456")

Project Properties

A Project object has several string properties that can provide information about the project that you are using.

proj.id
proj.name
proj.domain_id,
proj.project_status,
proj.domain_unit_id,
proj.project_profile_id
proj.user_id

IAM Role ARN

To retrieve the project IAM role ARN, you can retrieve the iam_role field. This gets the IAM role ARN of the default IAM connection within your project.

proj.iam_role

KMS Key ARN

If you are using a KMS key within your project, you can retrieve the kms_key_arn field.

proj.kms_key_arn

MLflow Tracking Server ARN

If you are using an MLflow tracking server within your project, you can retrieve the mlflow_tracking_server_arn field.

Usage

proj.mlflow_tracking_server_arn

S3 Path

One of the properties of a Project is s3. You can access various S3 paths that exist within your project.

# S3 path of project root directory
proj.s3.root

# S3 path of datalake consumer Glue DB directory (requires DataLake environment)
proj.s3.datalake_consumer_glue_db

# S3 path of Athena workgroup directory (requires DataLake environment)
proj.s3.datalake_athena_workgroup

# S3 path of workflows output directory (requires Workflows environment)
proj.s3.workflow_output_directory

# S3 path of workflows temp storage directory (requires Workflows environment)
proj.s3.workflow_temp_storage

# S3 path of EMR EC2 log destination directory (requires EMR EC2 environment)
proj.s3.emr_ec2_log_destination

# S3 path of EMR EC2 log bootstrap directory (requires EMR EC2 environment)
proj.s3.emr_ec2_certificates

# S3 path of EMR EC2 log bootstrap directory (requires EMR EC2 environment)
proj.s3.emr_ec2_log_bootstrap

Other Environment S3 Paths

You can also access the S3 path of a different environment by providing an environment ID.

proj.s3.environment_path(environment_id="env_1234")

Connections

You can retrieve a list of connections for a project, or you can retrieve a single connection by providing its name. If no name is passed, it refers to project's default IAM connection.

proj_connections: List[Connection] = proj.connections
proj_iam_conn = proj.connection()
proj_redshift_conn = proj.connection("<my_redshift_connection_name>")

Each Connection object has several properties that can provide information about the connection.

proj_redshift_conn.name
proj_redshift_conn.id
proj_redshift_conn.physical_endpoints[0].host
proj_redshift_conn.iam_role

Connection Data

To retrieve all properties of a Connection, you can access the data field to get a ConnectionData object. ConnectionData fields can be accessed using the dot notation (e.g. conn_data.top_level_field). For retrieving further nested data within ConnectionData, you can access it as a dictionary. (e.g. conn_data.top_level_field["nested_field"]).

conn_data: ConnectionData = proj_redshift_conn.data
red_temp_dir = conn_data.redshiftTempDir
lineage_sync = conn_data.lineageSync
lineage_job_id = lineage_sync["lineageJobId"]

spark_conn = proj.connection("<my_spark_glue_connection_name>")
id = spark_conn.id
env_id = spark_conn.environment_id
glue_conn = spark_conn.data.glue_connection_name
workers = spark_conn.data.number_of_workers
glue_version = spark_conn.data.glue_version

# Fetching tracking server ARN and tracking server name from an MLFlow connection
ml_flow_conn = proj.connection('<my_ml_flow_connection_name>')
tracking_server_arn = ml_flow_conn.data.tracking_server_arn
tracking_server_name = ml_flow_conn.data.tracking_server_name

Catalogs

If your Connection is of the LAKEHOUSE or IAM type, you can retrieve a list of catalogs, or a single catalog by providing its id.

conn_catalogs: List[Catalog] = proj.connection().catalogs
my_default_catalog: Catalog = proj.connection().catalog()
my_catalog: Catalog = proj.connection().catalog("1234567890:catalog1/sub_catalog")
proj.connection("<lakehouse_connection_name>").catalogs

Each Catalog object has several properties that can provide information about the catalog.

my_catalog.name
my_catalog.id
my_catalog.type
my_catalog.spark_catalog_name
my_catalog.resource_arn

Secrets

Retrieve the secret (username, password, other connection related metadata) for the connection using this property.

snowflake_connection: Connection = proj.connection("<snowflake_connection_name>")
secret = snowflake_connection.secret

Secret can be a dictionary containing credentials, or a single string depending on the connection type.

AWS Clients

You can retrieve a Boto3 AWS client initialized with the connection's credentials.

redshift_connection: Connection = proj.connection("<redshift_connection_name>")
redshift_client = redshift_connection.create_client()

Some connections are directly associated with an AWS service, and will default to using that AWS service's client if no service name is specified. Those connections are listed in the below table.

Connection Type	AWS Service Name
ATHENA	athena
DYNAMODB	dynamodb
REDSHIFT	redshift
S3	s3
S3_FOLDER	s3

For other connection types, you must specify an AWS service name.

iam_connection: Connection = proj.connection("<iam_connection_name>")
glue_client = iam_connection.create_client("glue")

Databases and Tables

Databases

Within a catalog, you can retrieve a list of databases, or a single database by providing its name.

my_catalog: Catalog

catalog_dbs: List[Database] = my_catalog.databases
my_db: Database = my_catalog.database("my_db")

Each Database object has several properties that can provide information about the database.

my_db.name
my_db.catalog_id
my_db.location_uri
my_db.project_id
my_db.domain_id

Tables

You can also retrieve either a list of tables or a specific table within a Database.

my_db_tables: List[Table] = my_db.tables
my_table: Table = my_db.table("my_table")

Each Table object has several properties that can provide information about the table.

my_table.name
my_table.database_name
my_table.catalog_id
my_table.location

You can also retrieve a list of the columns within a table. Column contains the column name and the data type of the column.

my_table_columns: List[Column] = my_table.columns
col_0: Column = my_table_columns[0]
col_0.name
col_0.type

Utils Methods

The SageMaker Studio SDK provides utility modules for common data operations including SQL execution, DataFrame operations, and Spark session management.

SQL Utilities

The SQL utilities module provides a simple interface for executing SQL queries against various database engines within SageMaker Studio. When no connection is specified, queries are executed locally using DuckDB.

Supported Database Engines

The following database engines are supported:

Amazon Athena
Amazon Redshift
MySQL
PostgreSQL
Snowflake
Google BigQuery
Amazon DynamoDB
Microsoft SQL Server
DuckDB (default when no connection specified)

Basic Usage

Import the SQL utilities

from sagemaker_studio import sqlutils

Execute SQL with DuckDB (No Connection)

When no connection is specified, queries are executed locally using DuckDB:

# Simple SELECT query
result = sqlutils.sql("SELECT 1 as test_column")
result

# Query with literal values
result = sqlutils.sql("SELECT * FROM table WHERE id = 123")

Execute SQL with Project Connections

Use existing project connections by specifying either connection name or ID:

# Using connection name
result = sqlutils.sql(
    "SELECT * FROM my_table",
    connection_name="my_athena_connection"
)

# Using connection ID
result = sqlutils.sql(
    "SELECT * FROM my_table",
    connection_id="conn_12345"
)

Examples by Database Engine

Amazon Athena

# Query Athena using project connection with parameters
result = sqlutils.sql(
    """
    SELECT customer_id, order_date, total_amount
    FROM orders
    WHERE order_date >= :start_date
    """,
    parameters={"start_date": "2024-01-01"},
    connection_name="project.athena"
)

# Create external table in Athena
sqlutils.sql(
    """
    CREATE EXTERNAL TABLE sales_data (
        customer_id bigint,
        order_date date,
        amount decimal(10,2)
    )
    LOCATION 's3://my-bucket/sales-data/'
    """,
    connection_name="project.athena"
)

# Insert data using Create Table As Select (CTAS)
sqlutils.sql(
    """
    CREATE TABLE monthly_sales AS
    SELECT
        DATE_TRUNC('month', order_date) as month,
        SUM(amount) as total_sales
    FROM sales_data
    GROUP BY DATE_TRUNC('month', order_date)
    """,
    connection_name="project.athena"
)

Amazon Redshift

# Query Redshift with parameters
result = sqlutils.sql(
    """
    SELECT product_name, category, price
    FROM products
    WHERE category = :category
    AND price > :min_price
    """,
    parameters={"category": "Electronics", "min_price": 100},
    connection_name="project.redshift"
)

# Create table in Redshift
sqlutils.sql(
    """
    CREATE TABLE customer_summary (
        customer_id INTEGER PRIMARY KEY,
        total_orders INTEGER,
        total_spent DECIMAL(10,2),
        last_order_date DATE
    )
    """,
    connection_name="project.redshift"
)

# Insert aggregated data
sqlutils.sql(
    """
    INSERT INTO customer_summary
    SELECT
        customer_id,
        COUNT(*) as total_orders,
        SUM(amount) as total_spent,
        MAX(order_date) as last_order_date
    FROM orders
    GROUP BY customer_id
    """,
    connection_name="project.redshift"
)

# Update existing records
sqlutils.sql(
    """
    UPDATE products
    SET price = price * 1.1
    WHERE category = 'Electronics'
    """,
    connection_name="project.redshift"
)

Advanced Usage

Working with DataFrames

The sql function returns pandas DataFrames for SELECT queries, and row counts for DML operations:

import pandas as pd

# Execute query and get DataFrame
df = sqlutils.sql("SELECT * FROM sales_data", connection_name="redshift_conn")

# Use pandas operations
summary = df.groupby('region')['sales'].sum()
print(summary)

# Save to file
df.to_csv('sales_report.csv', index=False)

# DML operations return row counts
rows_affected = sqlutils.sql(
    "UPDATE inventory SET quantity = quantity - 1 WHERE product_id = 123",
    connection_name="redshift_conn"
)
print(f"Updated {rows_affected} inventory records")

Parameterized Queries

Use parameters to safely pass values to queries:

# Dictionary parameters (recommended)
result = sqlutils.sql(
    "SELECT * FROM orders WHERE customer_id = :customer_id AND status = :status",
    parameters={"customer_id": 12345, "status": "completed"},
    connection_name="redshift_connection"
)

# Athena with named parameters
result = sqlutils.sql(
    "SELECT * FROM products WHERE category = :category AND price > :min_price",
    parameters={"category": "Electronics", "min_price": 100},
    connection_name="athena_connection"
)

Getting Database Engine

You can also get the underlying SQLAlchemy engine for advanced operations:

# Get engine for a connection
engine = sqlutils.get_engine(connection_name="redshift_connection")

# Use engine directly with pandas
import pandas as pd

df = pd.read_sql("SELECT * FROM large_table LIMIT 1000", engine)

DuckDB Features

When using DuckDB (no connection specified), you get additional capabilities:

Python Integration

# DuckDB can access Python variables directly
import pandas as pd

my_df = pd.DataFrame({'id': [1, 2, 3], 'name': ['A', 'B', 'C']})

result = sqlutils.sql("SELECT * FROM my_df WHERE id > 1")

Notes

All queries return pandas DataFrames for easy data manipulation
DuckDB is automatically configured with S3 credentials from the environment
Connection credentials are managed through SageMaker Studio project connections
The module handles connection pooling and cleanup automatically

DataFrame Utils

Read from and write to catalog tables using pandas DataFrames with automatic format detection and database management.

Supported catalog types:

AwsDataCatalog
S3CatalogTables

Basic Usage

Import the Dataframe utilities

from sagemaker_studio import dataframeutils

Reading from Catalog Tables

Required Inputs:

database (str): Database name within the catalog
table (str): Table name

Optional Parameters:

catalog (str): Catalog identifier (defaults to AwsDataCatalog if not specified)
format (str): Data format - auto-detects from table metadata, falls back to parquet
**kwargs: Additional arguments
- for AwsDataCatalog, kwargs can be columns, chunked, etc
- for S3Tables, kwargs can be limit, row_filter, selected_fields, etc

import pandas as pd

# Read from AwsDataCatalog
df = pd.read_catalog_table(
    database="my_database",
    table="my_table"
)

# Read from S3 Tables
df = pd.read_catalog_table(
   database="my_database",
   table="my_table",
   catalog="s3tablescatalog/my_s3_tables_catalog",
)

Writing to Catalog Tables

Required Inputs:

database (str): Database name within the catalog
table (str): Table name

Optional Parameters:

catalog (str): Catalog identifier (defaults to AwsDataCatalog if not specified)
format (str): Data format used for AwsDataCatalog (default: parquet)
path (str): Custom S3 path for writing to AwsDataCatalog (auto-determined if not provided)
**kwargs: Additional arguments

Path Resolution Priority - S3 path is determined in this order:

User-provided path parameter
Existing database location + table name
Existing table location
Project default S3 location

import pandas as pd

# Create sample DataFrame
df = pd.DataFrame({
    'id': [1, 2, 3],
    'name': ['Alice', 'Bob', 'Charlie'],
    'value': [10.5, 20.3, 15.7]
})

# Write to AwsDataCatalog
df.to_catalog_table(
    database="my_database",
    table="my_table"
)

# Write to S3 Table Catalog
df.to_catalog_table(
    database="my_database",
    table="my_table",
    catalog="s3tablescatalog/my_s3_tables_catalog"
)

Usage with optional parameters

Reading from Catalog Tables

import pandas as pd

# Read from AwsDataCatalog by explicitly specifying catalogID and format
df = pd.read_catalog_table(
    database="my_database",
    table="my_table",
    catalog="123456789012",
    format="parquet"
)

# Read from AwsDataCatalog by explicitly specifying catalogID, format, and additional args -> columns
df = pd.read_catalog_table(
    database="my_database",
    table="my_table",
    catalog="123456789012",
    format="parquet",
    columns=['<column_name_1>, <column_name_2>']
)

# Read from S3 Tables with additional args -> limit
df = pd.read_catalog_table(
   database="my_database",
   table="my_table",
   catalog="s3tablescatalog/my_s3_tables_catalog",
   limit=500
)

# Read from S3 Tables with additional args -> selected_fields
df = pd.read_catalog_table(
   database="my_database",
   table="my_table",
   catalog="s3tablescatalog/my_s3_tables_catalog",
   selected_fields=['<field_name_1>, <field_name_2>']
)

Writing to Catalog Tables

# Write to AwsDataCatalog with csv format
df.to_catalog_table(
    database="my_database",
    table="my_table",
    format="csv"
)

# Write to AwsDataCatalog at user specified s3 path
df.to_catalog_table(
    database="my_database",
    table="my_table",
    path="s3://my-bucket/custom/path/"
)

# Write to AwsDataCatalog with additional argument -> compression
df.to_catalog_table(
    database="my_database",
    table="my_table",
    compression='gzip'
)

Spark Utilities

The Spark utilities module provides a simple interface for working with Spark Connect sessions and managing Spark configurations for various data sources within SageMaker Studio. When no connection is specified, a Spark Connect session is created using the default Athena Spark connection.

Basic Usage

Import the Spark utilities

from sagemaker_studio import sparkutils

Initialize Spark Session

Supported connection types:

Spark connect

Optional Parameters:

connection_name (str): Name of the connection to execute query against (e.g., "my_redshift_connection")

When no connection is specified, a default Athena Spark session is created:

# Default session
spark = sparkutils.init()

# Session with specific connection
spark = sparkutils.init(connection_name="my_spark_connection")

Working with Spark Options

Supported connection types:

Amazon DocumentDB
Amazon DynamoDB
Amazon Redshift
Aurora MySQL
Aurora PostgreSQL
Azure SQL
Google BigQuery
Microsoft SQL Server
MySQL
PostgreSQL
Oracle
Snowflake

Required Inputs:

connection_name (str): Name of the connection to get Spark options for (e.g., "my_redshift_connection")

Get formatted Spark options for connecting to data sources:

# Get options for Redshift connection
options = sparkutils.get_spark_options("my_redshift_connection")

Examples by Operation Type

Reading and Writing Data

# Create sample DataFrame
df_to_write = spark.createDataFrame(
    [(1, "Alice"), (2, "Bob")],
    ["id", "name"]
)

# Get spark options for Redshift connection
spark_options = sparkutils.get_spark_options("my_redshift_connection")

# Write DataFrame using JDBC
df_to_write.write \
    .format("jdbc") \
    .options(**spark_options) \
    .option("dbtable", "sample_table") \
    .save()

# Read DataFrame using JDBC
df_to_read = spark.read \
    .format('jdbc') \
    .options(**spark_options) \
    .option('dbtable', 'sample_table') \
    .load()

# Display results
df_to_read.show()

Notes

Spark sessions are automatically configured for Athena spark compute
Connection credentials are managed through SageMaker Studio project connections
The module handles session management and cleanup automatically
Spark options are formatted appropriately for each supported data source

Execution APIs

Execution APIs provide you the ability to start an execution to run a notebook headlessly either within the same user space or on remote compute.

Local Execution APIs

Use these APIs to start/stop/get/list executions within the user's space.

StartExecution

You can start a notebook execution headlessly within the same user space.

from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio import ClientConfig

config = ClientConfig(overrides={
            "execution": {
                "local": True,
            }
        })
sagemaker_studio_api = SageMakerStudioAPI(config)

result = sagemaker_studio_api.execution_client.start_execution(
    execution_name="my-execution",
    input_config={"notebook_config": {
        "input_path": "src/folder2/test.ipynb"}},
    execution_type="NOTEBOOK",
    output_config={"notebook_config": {
        "output_formats": ["NOTEBOOK", "HTML"]
    }}
)
print(result)

GetExecution

You can retrieve details about a local execution using the GetExecution API.

from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio import ClientConfig

config = ClientConfig(region="us-west-2", overrides={
            "execution": {
                "local": True,
            }
        })
sagemaker_studio_api = SageMakerStudioAPI(config)

get_response = sagemaker_studio_api.execution_client.get_execution(execution_id="asdf-3b998be2-02dd-42af-8802-593d48d04daa")
print(get_response)

ListExecutions

You can use ListExecutions API to list all the executions that ran in the user's space.

from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio import ClientConfig

config = ClientConfig(region="us-west-2", overrides={
            "execution": {
                "local": True,
            }
        })
sagemaker_studio_api = SageMakerStudioAPI(config)

list_executions_response = sagemaker_studio_api.execution_client.list_executions(status="COMPLETED")
print(list_executions_response)

StopExecution

You can use StopExecution API to stop an execution that's running in the user space.

from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio import ClientConfig

config = ClientConfig(region="us-west-2", overrides={
            "execution": {
                "local": True,
            }
        })
sagemaker_studio_api = SageMakerStudioAPI(config)

stop_response = sagemaker_studio_api.execution_client.stop_execution(execution_id="asdf-3b998be2-02dd-42af-8802-593d48d04daa")
print(stop_response)

Remote Execution APIs

Use these APIs to start/stop/get/list executions running on remote compute.

StartExecution

You can start a notebook execution headlessly on a remote compute specified in the StartExecution request.

from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio import ClientConfig

config = ClientConfig(region="us-west-2")
sagemaker_studio_api = SageMakerStudioAPI(config)

result = sagemaker_studio_api.execution_client.start_execution(
    execution_name="my-execution",
    execution_type="NOTEBOOK",
    input_config={"notebook_config": {"input_path": "src/folder2/test.ipynb"}},
    output_config={"notebook_config": {"output_formats": ["NOTEBOOK", "HTML"]}},
    termination_condition={"max_runtime_in_seconds": 9000},
    compute={
        "instance_type": "ml.c5.xlarge",
        "image_details": {
            # provide either ecr_uri or (image_name and image_version)
            "image_name": "sagemaker-distribution-embargoed-prod",
            "image_version": "2.2",
            "ecr_uri": "ECR-registry-account.dkr.ecr.us-west-2.amazonaws.com/repository-name[:tag]",
        }
    }
)
print(result)

GetExecution

You can retrieve details about an execution running on remote compute using the GetExecution API.

from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio import ClientConfig

config = ClientConfig(region="us-west-2")
sagemaker_studio_api = SageMakerStudioAPI(config)

get_response = sagemaker_studio_api.execution_client.get_execution(execution_id="asdf-3b998be2-02dd-42af-8802-593d48d04daa")
print(get_response)

ListExecutions

You can use ListExecutions API to list all the headless executions that ran on remote compute.

from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio import ClientConfig

config = ClientConfig(region="us-west-2")
sagemaker_studio_api = SageMakerStudioAPI(config)

list_executions_response = sagemaker_studio_api.execution_client.list_executions(status="COMPLETED")
print(list_executions_response)

StopExecution

You can use StopExecution API to stop an execution that's running on remote compute.

from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio import ClientConfig

config = ClientConfig(region="us-west-2")
sagemaker_studio_api = SageMakerStudioAPI(config)

stop_response = sagemaker_studio_api.execution_client.stop_execution(execution_id="asdf-3b998be2-02dd-42af-8802-593d48d04daa")
print(stop_response)