qaziashikin opened a new pull request, #62240:
URL: https://github.com/apache/airflow/pull/62240
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## Description
This pull request adds a new AWS operator that will allow for managing
notebook schedules with the ability to specify frequencies, compute environment
settings, and notebook parameters, and to be used within Amazon SageMaker
Unified Studio.
[SageMaker Unified Studio](https://aws.amazon.com/sagemaker/unified-studio/)
(SMUS) supports development of Airflow DAGs (called "workflows" within the
product) that are run on an MWAA cluster managed by the project. These
workflows have the ability to orchestrate the execution of Unified Studio
artifacts that can connect to data assets stored in a SMUS project.
API-based execution or event-triggered capabilities outside of SageMaker
Unified Studio are also possible with the documented DataZone APIs that this
operator executes notebooks with. [TODO: Add links to these APIs]
## Components
- `SageMakerUnifiedStudioNotebookOperator`: this operator allows users to
execute Unified Studio artifacts within the context of their project
- `SageMakerUnifiedStudioNotebookHook`: this hook provides a wrapper around
the notebook execution API calls
- `SageMakerUnifiedStudioNotebookSensor`: this sensor polls for the status
of the notebook execution until a terminal state
- `SageMakerUnifiedStudioNotebookJobTrigger`: this trigger activates when
the notebook execution completes and allows the execution to be deferrable
## Usage
```python
run_notebook = SageMakerUnifiedStudioNotebookOperator(
task_id="notebook-task",
notebook_id=notebook_id, # This should be the notebook asset identifier
from within the SageMaker Unified Studio domain
domain_id=domain_id,
project_id=project_id,
client_token="unique-idempotency-token", # optional
notebook_parameters={
"param1": "value1",
"param2": "value2",
}, # optional
compute_configuration={"instance_type": "ml.m5.large"}, # optional
timeout_configuration={"run_timeout_in_minutes": 1440}, # optional
wait_for_completion=True, # optional
waiter_delay=30, # optional
deferrable=False, # optional
)
```
## Testing
### Unit Tests
```
breeze testing core-tests -p 3.11 -b postgres
providers/amazon/tests/provider_tests/amazon/aws/*/test_sagemaker_unified_studio_notebook.py
```
### System Tests
Ensure a properly configured SageMaker Unified Domain and Project as
indicated in the `example_sagemaker_unified_studio_notebook.py` file. Ensure
that AWS credentials are populated and up to date, and then run:
```
breeze shell --forward-credentials -b postgres -p 3.11
```
Then, populate the `DOMAIN_ID`, `PROJECT_ID`, and `NOTEBOOK_ID`:
```
export DOMAIN_ID="dzd-4sse0ajynr9skn"
export PROJECT_ID="3ha87cu1y4urs7"
export NOTEBOOK_ID="b8ipdh41dp2skn"
```
Run the system test:
```
pytest --system -v --setup-timeout=3600 --execution-timeout=3600
--teardown-timeout=3600 \
providers/amazon/tests/system/amazon/aws/example_sagemaker_unified_studio_notebook.py
```
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