# Using serverless visual workflows
With Amazon SageMaker Unified Studio serverless visual workflows, you can create and orchestrate tasks using an intuitive drag-and-drop interface without writing code. Visual workflows supports over 80 tasks to help interact with multiple AWS services and automate use-cases across analytics, compute, catalog and storage. For the full list of supported tasks, see [Supported operators](https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/operators.html).
+ [Create a serverless visual workflow](#serverless-create-visual-workflow)
+ [Add Python and Bash operators to a workflow](#serverless-python-bash-operators)
+ [View visual workflows code](#serverless-view-visual-workflow-code)
+ [Monitor your workflow](#serverless-monitor-visual-workflow)
+ [Converting existing Airflow DAGs](#serverless-convert-airflow-dags)
+ [Import SageMaker Pipelines](#serverless-import-sagemaker-pipelines)
## Create a serverless visual workflow
Use visual workflows to orchestrate tasks in your project. With visual workflows, you can define a collection of tasks organized as a directed acyclic graph (DAG) that can run on a user-defined schedule.
**To create a visual workflow**
1. Log in to Amazon SageMaker Unified Studio.
1. In the left navigation pane, choose **Workflows**.
1. Choose **Create new workflow** to open the Visual Workflows editor.
1. Provide a name to your workflow and choose **Save**.
1. In the Find tasks search window under Add tasks, choose a task to add to your workflow. The selected task appears in the canvas.
1. Configure the task by giving it a name and editing the prepopulated fields.
1. Choose the **\+** symbol to add more tasks. You can drag the tasks to fit your workflow.
1. Complete the workflow by connecting the tasks. To connect the tasks, choose the **\+** symbol of one task to the **\+** symbol of another task. The arrows represent the execution order and data flow.
1. After you've created your workflow, you can configure its settings. Choose the settings gear.
1. In the Workflow settings tab you can:
+ Edit the Workflow name if the workflow has never been saved to a project.
+ Provide an optional description to the workflow.
+ Toggle the Run on schedule button and set the Schedule status to Active or Paused.
+ Choose an option from the Schedule dropdown menu to set a schedule for your workflow or specify a CRON expression in the Start date and time in UTC and End date and time in UTC fields below.
1. After the settings are set, choose **Apply** to save them.
1. In the Default parameters tab, choose **Add parameter** and provide a name and a default value to the parameter and choose **Apply** to save them.
1. In the Tags tab, choose **Add tag** to create an airflow tag to your workflow and provide a name to the tag, then choose **Apply** to save it. Airflow tags help in filtering the workflows. This step is optional.
1. Choose **Save** to save the current workflow. If there are any validation errors, the notifications symbol next to the settings gear will show a number next to it which indicates the number of errors. You must fix them before you can successfully run the workflow.
## Add Python and Bash operators to a workflow
In addition to operators that integrate with AWS services, you can use two general purpose operators to run your own code as a task:
+ **Python Operator** – Runs a Python callable that you provide.
+ **Bash Operator** – Runs a Bash script, command, or set of commands that you provide.
To find these operators, open the task search window and expand the **General Purpose** category.
Before you begin, prepare the Python or Bash files that your operators reference. You upload these files in the Storage settings, as described in [Upload Python and Bash operator files](#serverless-upload-operator-files).
### Add a Bash Operator task
**To add a Bash Operator task**
1. In the task search window under **Add tasks**, expand **General Purpose** and choose **Bash Operator**. The task appears on the canvas.
1. Provide a **Task name**.
1. In the **Bash command** field, enter the command, set of commands, or a reference to a Bash script to run. A referenced script must have a `.sh` extension and must be uploaded in the Storage settings. This field supports templated values, so you can reference other task outputs or workflow variables in the command.
1. (Optional) Expand **Optional configurations** to set additional operator parameters.
To use the output of a Bash Operator task in another task, reference it using the syntax shown under **Task output** (for example, `{{Bash-task.output}}`).
### Add a Python Operator task
**To add a Python Operator task**
1. In the task search window under **Add tasks**, expand **General Purpose** and choose **Python Operator**. The task appears on the canvas.
1. Provide a **Task name**.
1. Under **Python Callable**, specify the callable to run in the format `module_name.function_name` using the following fields:
1. For **File**, choose the Python file that contains your callable. This file must be uploaded in the Storage settings.
1. For **Function**, enter the name of the function to call.
1. (Optional) Expand **Optional configurations** to pass arguments to your callable:
+ **Op arguments** – A list of positional arguments that are unpacked when calling your callable (for example, `[us-east-1, gold, 100]`).
+ **Op kwargs** – A dictionary of keyword arguments that are unpacked when calling your callable.
To use the output of a Python Operator task in another task, reference it using the syntax shown under **Task output** (for example, `{{Python-task.output}}`).
### Upload Python and Bash operator files
You must upload the Python (`.py`) and Bash (`.sh`) files that your operators reference so that they are available when the workflow runs. If you upload a single file, you can upload it as is without zipping. If you upload multiple files, they are packaged into a `.zip` archive, uploaded to Amazon S3, and extracted during workflow execution.
**To upload operator files**
1. In the visual workflows editor, choose the settings gear to open **Settings**, then expand **Storage**.
1. Under **Python/Bash Operator Files**, choose **Choose file**, or drag and drop your files, to upload the Python, Bash, or `.zip` files that your operators need.
1. Choose **Save**. Your uploaded files are zipped, stored in the operator files S3 location, and referenced from your workflow definition.
**Note**
The following considerations apply to operator files:
Only `.py`, `.sh`, and `.zip` files are supported. The total size of the resulting `.zip` archive must be less than 250 MB.
To use third-party Python dependencies, run `pip install --target ./package your-dependency` locally, then upload the contents of the package folder alongside your scripts.
## View visual workflows code
To view a visual workflow code, navigate to the workflow details page by selecting a workflow from the Workflows page list. Then choose the Actions dropdown menu and choose **View code**.
## Monitor your workflow
**To monitor your workflow**
1. From the Workflows view, choose the vertical dots to the far right of your workflow's name and select **View runs**.
1. In the subsequent Runs view you will see your workflow runs.
1. Choose the run to show the tasks.
1. Choose the task ID to show the task output and associated logs.
## Converting existing Airflow DAGs
You can convert existing Airflow workflows to YAML through a Python library. For more information, see [Introducing Amazon MWAA Serverless](https://aws.amazon.com/blogs/big-data/introducing-amazon-mwaa-serverless/) on the AWS Big Data Blog.
## Import SageMaker Pipelines
You can import existing SageMaker pipelines into Amazon SageMaker Unified Studio and automatically convert them to Serverless Apache Airflow workflows. This is particularly beneficial for teams looking to migrate existing ML pipelines to Serverless Apache Airflow without rebuilding from scratch.
**Important**
This feature is only available for IAM domains.
**To import a SageMaker pipeline**
1. On the Workflows listing page, choose **Create Workflow**.
1. Select **Import SageMaker Pipeline** from the dropdown.
1. In the side panel, select the pipeline you want to import from the list of SageMaker pipelines in your account.
1. Choose **Import**.
Amazon SageMaker Unified Studio converts the SageMaker pipeline definition into a Serverless Apache Airflow workflow YAML and creates an interactive visual workflow DAG. The resulting workflow opens in the visual workflows editor, where you can continue to edit, rearrange, and add tasks using the drag-and-drop interface. This means you can use the imported pipeline as a starting point and customize it further to fit your needs.
**Note**
The following limitations apply when importing SageMaker pipelines:
A model evaluation step followed by a condition step is not currently supported.
Only the latest version of the pipeline is imported. Older versions are not included.
Associated schedules are not imported.
The import creates a copy of the pipeline definition. Changes to the original SageMaker pipeline are not reflected in the imported workflow.