# 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. [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 3.11 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 the metadata json file.
```python
from sagemaker_studio import ClientConfig, Project
conf = ClientConfig(region="eu-west-1")
proj = Project(config=conf)
```
### Domain
`Domain` can be initialized as follows.
```python
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.
```python
dom = Domain(id="123456")
```
#### Domain Properties
A `Domain` object has several string properties that can provide information about the domain that you are using.
```python
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.
```python
from sagemaker_studio import Project
proj = Project()
```
If you are not using the SageMaker Studio within the SageMaker Unified Studio Jupyter Lab, 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.
```python
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.
```python
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.
```python
proj.iam_role
```
##### KMS Key ARN
If you are using a KMS key within your project, you can retrieve the `kms_key_arn` field.
```python
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**
```python
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.
```python
# 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.
```python
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.
```python
proj_connections: List[Connection] = proj.connections
proj_redshift_conn = proj.connection("my_redshift_connection_name")
```
Each `Connection` object has several properties that can provide information about the connection.
```python
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"]`).
```python
conn_data: ConnectionData = proj_redshift_conn.data
red_temp_dir = conn_data.redshiftTempDir
lineage_sync = conn_data.lineageSync
lineage_job_id = lineage_sync["lineageJobId"]
```
```python
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
```
#### 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.
```python
conn_catalogs: List[Catalog] = proj.connection("project.iam").catalogs
my_catalog: Catalog = proj.connection("project.iam").catalog("1234567890:catalog1/sub_catalog")
proj.connection("project.default_lakehouse").catalogs
```
Each `Catalog` object has several properties that can provide information about the catalog.
```python
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.
```python
snowflake_connection: Connection = proj.connection("project.snowflake")
secret = snowflake_connection.secret
```
Secret can be a dictionary containing credentials, or a single string depending on the connection type.
#### Databases and Tables
##### Databases
Within a catalog, you can retrieve a list of databases, or a single database by providing its name.
```python
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.
```python
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`.
```python
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.
```python
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.
```python
my_table_columns: List[Column] = my_table.columns
col_0: Column = my_table_columns[0]
col_0.name
col_0.type
```
### 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.
```python
from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio._openapi.models import(
StartExecutionRequest,
)
from sagemaker_studio import ClientConfig
config = ClientConfig(overrides={
"execution_api": {
"local": True,
}
})
sagemaker_studio_api = SageMakerStudioAPI(config)
result = sagemaker_studio_api.execution_client.start_execution(
StartExecutionRequest(
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.
```python
from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio._openapi.models import(
GetExecutionRequest,
)
from sagemaker_studio import ClientConfig
config = ClientConfig(region="us-west-2", overrides={
"execution_api": {
"local": True,
}
})
sagemaker_studio_api = SageMakerStudioAPI(config)
get_response = sagemaker_studio_api.execution_client.get_execution(GetExecutionRequest(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.
```python
from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio._openapi.models import(
ListExecutionsRequest,
)
from sagemaker_studio import ClientConfig
config = ClientConfig(region="us-west-2", overrides={
"execution_api": {
"local": True,
}
})
sagemaker_studio_api = SageMakerStudioAPI(config)
list_executions_response = sagemaker_studio_api.execution_client.list_executions(ListExecutionsRequest(status="COMPLETED"))
print(list_executions_response)
```
##### StopExecution
You can use `StopExecution` API to stop an execution that's running in the user space.
```python
from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio._openapi.models import(
StopExecutionRequest,
)
from sagemaker_studio import ClientConfig
config = ClientConfig(region="us-west-2", overrides={
"execution_api": {
"local": True,
}
})
sagemaker_studio_api = SageMakerStudioAPI(config)
stop_response = sagemaker_studio_api.execution_client.stop_execution(StopExecutionRequest(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.
```python
from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio._openapi.models import(
StartExecutionRequest,
)
from sagemaker_studio import ClientConfig
config = ClientConfig(region="us-west-2")
sagemaker_studio_api = SageMakerStudioAPI(config)
result = sagemaker_studio_api.execution_client.start_execution(
StartExecutionRequest(
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-loadtest",
"image_version": "2.2",
"ecr_uri": "123456123456.dkr.ecr.us-west-2.amazonaws.com/sagemaker-distribution-embargoed-prod:3.0.0-reinvent2024-cpu"
}
}
)
)
print(result)
```
##### GetExecution
You can retrieve details about an execution running on remote compute using the `GetExecution` API.
```python
from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio._openapi.models import(
GetExecutionRequest,
)
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(GetExecutionRequest(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.
```python
from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio._openapi.models import(
ListExecutionsRequest,
)
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(ListExecutionsRequest(status="COMPLETED"))
print(list_executions_response)
```
##### StopExecution
You can use `StopExecution` API to stop an execution that's running on remote compute.
```python
from sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI
from sagemaker_studio._openapi.models import(
StopExecutionRequest,
)
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(StopExecutionRequest(execution_id="asdf-3b998be2-02dd-42af-8802-593d48d04daa"))
print(stop_response)
```
Raw data
{
"_id": null,
"home_page": "https://aws.amazon.com/sagemaker/",
"name": "sagemaker-studio",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": null,
"keywords": "AWS, Amazon, SageMaker, SageMaker Unified Studio, SDK",
"author": "Amazon Web Services",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/bc/e8/db97ece956dbf2c688ca96beccf3a58ad7996891e0b0825d69bb8ef295b1/sagemaker_studio-1.0.5.tar.gz",
"platform": "Linux",
"description": "# SageMaker Studio\n\nSageMaker 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. \n\n## Table of Contents\n\n1. [Installation](#installation)\n2. [Usage](#usage)\n 1. [Setting Up Credentials and ClientConfig](#credentials--client-config)\n 1. [Using ClientConfig](#using-clientconfig)\n 2. [Domain](#domain)\n 3. [Domain Properties](#domain-properties)\n 3. [Project](#project)\n 1. [Properties](#project-properties)\n 1. [IAM Role ARN](#iam-role)\n 2. [KMS Key ARN](#kms-key-arn)\n 3. [MLflow Tracking Server ARN](#mlflow-tracking-server-arn)\n 4. [S3 Path](#s3-path)\n 2. [Connections](#connections)\n 1. [Connection Data](#connection-data)\n 2. [Secrets](#secrets)\n 3. [Catalogs](#catalogs)\n 4. [Databases and Tables](#databases-and-tables)\n 1. [Databases](#databases)\n 2. [Tables](#tables)\n 4. [Execution APIs](#execution-apis)\n 1. [Local Execution APIs](#local-execution-apis)\n 1. [StartExecution API](#startexecution)\n 2. [GetExecution API](#getexecution)\n 3. [ListExecutions API](#listexecutions)\n 4. [StopExecution API](#stopexecution)\n 2. [Remote Execution APIs](#remote-execution-apis)\n 1. [StartExecution API](#startexecution-1)\n 2. [GetExecution API](#getexecution-1)\n 3. [ListExecutions API](#listexecutions-1)\n 4. [StopExecution API](#stopexecution-1)\n\n## 1) Installation\n\nThe SageMaker Studio is built to PyPI, and the latest version of the library can be installed using the following command:\n\n```bash\npip install sagemaker-studio\n```\n\n\n#### Supported Python Versions\nSageMaker Studio supports Python versions 3.11 and newer.\n\n#### Licensing\n\nSageMaker 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/\n\n## 2) Usage\n\n### Setting Up Credentials and ClientConfig\n\nIf SageMaker Studio is being used within Amazon SageMaker Unified Studio JupyterLab, the library will automatically pull your latest credentials from the environment.\n\nIf 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:\n1. 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:\n 1. Supplying it during initialization of the SageMaker Studio `ClientConfig` object \n 2. Setting the AWS profile name as an environment variable (e.g. `export AWS_PROFILE=\"my_profile_name\"`)\n2. 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\n\n##### AWS Named Profile\n\nTo 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:\n\n```config\n[my_profile_name]\nregion = us-east-1\n```\n\nYour `credentials` file should have the credentials stored for your profile:\n\n```config\n[my_profile_name]\naws_access_key_id=AKIAIOSFODNN7EXAMPLE\naws_secret_access_key=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY\naws_session_token=IQoJb3JpZ2luX2IQoJb3JpZ2luX2IQoJb3JpZ2luX2IQoJb3JpZ2luX2IQoJb3JpZVERYLONGSTRINGEXAMPLE\n```\n\nFinally, you can pass in the profile when initializing the `ClientConfig` object.\n\n```python\nfrom sagemaker_studio import ClientConfig\n\nconf = ClientConfig(profile_name=\"my_profile_name\")\n```\n\nYou can also set the profile name as an environment variable:\n\n```bash\nexport AWS_PROFILE=\"my_profile_name\"\n```\n\n\n##### Boto3 Session\n\nTo 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`.\n\n```python\nfrom boto3 import Session\nfrom sagemaker_studio import ClientConfig\n\nmy_session = Session(...)\nconf = ClientConfig(session=my_session)\n```\n\n#### Using ClientConfig\n\nIf 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. \n\n```python\nfrom sagemaker_studio import ClientConfig, Project\n\nconf = ClientConfig(region=\"eu-west-1\")\nproj = Project(config=conf)\n```\n\n\n### Domain\n\n`Domain` can be initialized as follows. \n\n```python\nfrom sagemaker_studio import Domain\n\ndom = Domain()\n```\n\nIf 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.\n\n```python\ndom = Domain(id=\"123456\")\n```\n\n#### Domain Properties\n\nA `Domain` object has several string properties that can provide information about the domain that you are using.\n\n```python\ndom.id\ndom.root_domain_unit_id\ndom.name\ndom.domain_execution_role\ndom.status\ndom.portal_url\n```\n\n\n\n\n\n### Project\n\n`Project` can be initialized as follows. \n\n```python\nfrom sagemaker_studio import Project\n\nproj = Project()\n```\n\nIf you are not using the SageMaker Studio within the SageMaker Unified Studio Jupyter Lab, 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.\n\n```python\nproj = Project(name=\"my_proj_name\", domain_id=\"123456\")\n```\n\n#### Project Properties\n\nA `Project` object has several string properties that can provide information about the project that you are using.\n\n```python\nproj.id\nproj.name\nproj.domain_id,\nproj.project_status,\nproj.domain_unit_id,\nproj.project_profile_id\nproj.user_id\n```\n\n##### IAM Role ARN\n\nTo 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.\n\n```python\nproj.iam_role\n```\n\n\n##### KMS Key ARN\n\nIf you are using a KMS key within your project, you can retrieve the `kms_key_arn` field.\n\n```python\nproj.kms_key_arn\n```\n\n### MLflow Tracking Server ARN\n\nIf you are using an MLflow tracking server within your project, you can retrieve the `mlflow_tracking_server_arn` field.\n\n**Usage**\n```python\nproj.mlflow_tracking_server_arn\n```\n\n\n##### S3 Path\n\nOne of the properties of a `Project` is `s3`. You can access various S3 paths that exist within your project.\n\n```python\n# S3 path of project root directory\nproj.s3.root\n\n# S3 path of datalake consumer Glue DB directory (requires DataLake environment)\nproj.s3.datalake_consumer_glue_db\n\n# S3 path of Athena workgroup directory (requires DataLake environment)\nproj.s3.datalake_athena_workgroup\n\n# S3 path of workflows output directory (requires Workflows environment)\nproj.s3.workflow_output_directory\n\n# S3 path of workflows temp storage directory (requires Workflows environment)\nproj.s3.workflow_temp_storage\n\n# S3 path of EMR EC2 log destination directory (requires EMR EC2 environment)\nproj.s3.emr_ec2_log_destination\n\n# S3 path of EMR EC2 log bootstrap directory (requires EMR EC2 environment)\nproj.s3.emr_ec2_certificates\n\n# S3 path of EMR EC2 log bootstrap directory (requires EMR EC2 environment)\nproj.s3.emr_ec2_log_bootstrap\n```\n\n###### Other Environment S3 Paths\n\nYou can also access the S3 path of a different environment by providing an environment ID.\n\n```python\nproj.s3.environment_path(environment_id=\"env_1234\")\n```\n\n\n#### Connections\n\nYou can retrieve a list of connections for a project, or you can retrieve a single connection by providing its name.\n\n```python\nproj_connections: List[Connection] = proj.connections\nproj_redshift_conn = proj.connection(\"my_redshift_connection_name\")\n```\n\nEach `Connection` object has several properties that can provide information about the connection.\n\n```python\nproj_redshift_conn.name\nproj_redshift_conn.id\nproj_redshift_conn.physical_endpoints[0].host\nproj_redshift_conn.iam_role\n```\n\n##### Connection Data\n\nTo 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\"]`).\n```python\nconn_data: ConnectionData = proj_redshift_conn.data\nred_temp_dir = conn_data.redshiftTempDir\nlineage_sync = conn_data.lineageSync\nlineage_job_id = lineage_sync[\"lineageJobId\"]\n```\n\n```python\nspark_conn = proj.connection(\"my_spark_glue_connection_name\")\nid = spark_conn.id\nenv_id = spark_conn.environment_id\nglue_conn = spark_conn.data.glue_connection_name\nworkers = spark_conn.data.number_of_workers\nglue_version = spark_conn.data.glue_version\n```\n\n#### Catalogs\n\nIf your `Connection` is of the `LAKEHOUSE` or `IAM` type, you can retrieve a list of catalogs, or a single catalog by providing its id.\n\n```python\nconn_catalogs: List[Catalog] = proj.connection(\"project.iam\").catalogs\nmy_catalog: Catalog = proj.connection(\"project.iam\").catalog(\"1234567890:catalog1/sub_catalog\")\nproj.connection(\"project.default_lakehouse\").catalogs\n```\n\nEach `Catalog` object has several properties that can provide information about the catalog.\n\n```python\nmy_catalog.name\nmy_catalog.id\nmy_catalog.type\nmy_catalog.spark_catalog_name\nmy_catalog.resource_arn\n```\n\n#### Secrets\nRetrieve the secret (username, password, other connection related metadata) for the connection using this property.\n\n```python\nsnowflake_connection: Connection = proj.connection(\"project.snowflake\")\nsecret = snowflake_connection.secret\n```\nSecret can be a dictionary containing credentials, or a single string depending on the connection type.\n\n#### Databases and Tables\n\n##### Databases\n\nWithin a catalog, you can retrieve a list of databases, or a single database by providing its name.\n\n```python\nmy_catalog: Catalog\n\ncatalog_dbs: List[Database] = my_catalog.databases\nmy_db: Database = my_catalog.database(\"my_db\")\n```\n\nEach `Database` object has several properties that can provide information about the database.\n\n```python\nmy_db.name\nmy_db.catalog_id\nmy_db.location_uri\nmy_db.project_id\nmy_db.domain_id\n```\n\n##### Tables\n\nYou can also retrieve either a list of tables or a specific table within a `Database`.\n\n```python\nmy_db_tables: List[Table] = my_db.tables\nmy_table: Table = my_db.table(\"my_table\")\n```\n\nEach `Table` object has several properties that can provide information about the table.\n\n```python\nmy_table.name\nmy_table.database_name\nmy_table.catalog_id\nmy_table.location\n```\n\nYou can also retrieve a list of the columns within a table. `Column` contains the column name and the data type of the column.\n\n```python\nmy_table_columns: List[Column] = my_table.columns\ncol_0: Column = my_table_columns[0]\ncol_0.name\ncol_0.type\n```\n\n### Execution APIs\nExecution APIs provide you the ability to start an execution to run a notebook headlessly either within the same user space or on remote compute.\n\n#### Local Execution APIs\nUse these APIs to start/stop/get/list executions within the user's space.\n\n##### StartExecution\nYou can start a notebook execution headlessly within the same user space.\n\n```python\nfrom sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI\nfrom sagemaker_studio._openapi.models import(\n StartExecutionRequest,\n)\nfrom sagemaker_studio import ClientConfig\n\nconfig = ClientConfig(overrides={\n \"execution_api\": {\n \"local\": True,\n }\n })\nsagemaker_studio_api = SageMakerStudioAPI(config)\n\nresult = sagemaker_studio_api.execution_client.start_execution(\n StartExecutionRequest(\n execution_name=\"my-execution\",\n input_config={\"notebook_config\": {\n \"input_path\": \"src/folder2/test.ipynb\"}},\n execution_type=\"NOTEBOOK\",\n output_config={\"notebook_config\": {\n \"output_formats\": [\"NOTEBOOK\", \"HTML\"]\n }}\n )\n)\nprint(result)\n```\n\n##### GetExecution\nYou can retrieve details about a local execution using the `GetExecution` API.\n\n```python\nfrom sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI\nfrom sagemaker_studio._openapi.models import(\n GetExecutionRequest,\n)\nfrom sagemaker_studio import ClientConfig\n\nconfig = ClientConfig(region=\"us-west-2\", overrides={\n \"execution_api\": {\n \"local\": True,\n }\n })\nsagemaker_studio_api = SageMakerStudioAPI(config)\n\nget_response = sagemaker_studio_api.execution_client.get_execution(GetExecutionRequest(execution_id=\"asdf-3b998be2-02dd-42af-8802-593d48d04daa\"))\nprint(get_response)\n```\n\n##### ListExecutions\nYou can use `ListExecutions` API to list all the executions that ran in the user's space.\n\n```python\nfrom sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI\nfrom sagemaker_studio._openapi.models import(\n ListExecutionsRequest,\n)\nfrom sagemaker_studio import ClientConfig\n\nconfig = ClientConfig(region=\"us-west-2\", overrides={\n \"execution_api\": {\n \"local\": True,\n }\n })\nsagemaker_studio_api = SageMakerStudioAPI(config)\n\nlist_executions_response = sagemaker_studio_api.execution_client.list_executions(ListExecutionsRequest(status=\"COMPLETED\"))\nprint(list_executions_response)\n```\n\n##### StopExecution\nYou can use `StopExecution` API to stop an execution that's running in the user space.\n\n```python\nfrom sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI\nfrom sagemaker_studio._openapi.models import(\n StopExecutionRequest,\n)\nfrom sagemaker_studio import ClientConfig\n\nconfig = ClientConfig(region=\"us-west-2\", overrides={\n \"execution_api\": {\n \"local\": True,\n }\n })\nsagemaker_studio_api = SageMakerStudioAPI(config)\n\nstop_response = sagemaker_studio_api.execution_client.stop_execution(StopExecutionRequest(execution_id=\"asdf-3b998be2-02dd-42af-8802-593d48d04daa\"))\nprint(stop_response)\n\n```\n\n\n#### Remote Execution APIs\nUse these APIs to start/stop/get/list executions running on remote compute.\n\n##### StartExecution\nYou can start a notebook execution headlessly on a remote compute specified in the StartExecution request.\n\n```python\nfrom sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI\nfrom sagemaker_studio._openapi.models import(\n StartExecutionRequest,\n)\nfrom sagemaker_studio import ClientConfig\n\nconfig = ClientConfig(region=\"us-west-2\")\nsagemaker_studio_api = SageMakerStudioAPI(config)\n\nresult = sagemaker_studio_api.execution_client.start_execution(\n StartExecutionRequest(\n execution_name=\"my-execution\",\n execution_type=\"NOTEBOOK\",\n input_config={\"notebook_config\": {\"input_path\": \"src/folder2/test.ipynb\"}},\n output_config={\"notebook_config\": {\"output_formats\": [\"NOTEBOOK\", \"HTML\"]}},\n termination_condition={\"max_runtime_in_seconds\": 9000},\n compute={\n \"instance_type\": \"ml.c5.xlarge\",\n \"image_details\": {\n # provide either ecr_uri or (image_name and image_version)\n \"image_name\": \"sagemaker-distribution-embargoed-loadtest\",\n \"image_version\": \"2.2\",\n \"ecr_uri\": \"123456123456.dkr.ecr.us-west-2.amazonaws.com/sagemaker-distribution-embargoed-prod:3.0.0-reinvent2024-cpu\"\n }\n }\n )\n)\nprint(result)\n```\n\n##### GetExecution\nYou can retrieve details about an execution running on remote compute using the `GetExecution` API.\n\n```python\nfrom sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI\nfrom sagemaker_studio._openapi.models import(\n GetExecutionRequest,\n)\nfrom sagemaker_studio import ClientConfig\n\nconfig = ClientConfig(region=\"us-west-2\")\nsagemaker_studio_api = SageMakerStudioAPI(config)\n\nget_response = sagemaker_studio_api.execution_client.get_execution(GetExecutionRequest(execution_id=\"asdf-3b998be2-02dd-42af-8802-593d48d04daa\"))\nprint(get_response)\n```\n\n##### ListExecutions\nYou can use `ListExecutions` API to list all the headless executions that ran on remote compute.\n\n```python\nfrom sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI\nfrom sagemaker_studio._openapi.models import(\n ListExecutionsRequest,\n)\nfrom sagemaker_studio import ClientConfig\n\nconfig = ClientConfig(region=\"us-west-2\")\nsagemaker_studio_api = SageMakerStudioAPI(config)\n\nlist_executions_response = sagemaker_studio_api.execution_client.list_executions(ListExecutionsRequest(status=\"COMPLETED\"))\nprint(list_executions_response)\n```\n\n##### StopExecution\nYou can use `StopExecution` API to stop an execution that's running on remote compute.\n\n```python\nfrom sagemaker_studio.sagemaker_studio_api import SageMakerStudioAPI\nfrom sagemaker_studio._openapi.models import(\n StopExecutionRequest,\n)\nfrom sagemaker_studio import ClientConfig\n\nconfig = ClientConfig(region=\"us-west-2\")\nsagemaker_studio_api = SageMakerStudioAPI(config)\n\nstop_response = sagemaker_studio_api.execution_client.stop_execution(StopExecutionRequest(execution_id=\"asdf-3b998be2-02dd-42af-8802-593d48d04daa\"))\nprint(stop_response)\n```\n",
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