# Create a notebook job in Studio
<a name="create-notebook-auto-run-studio"></a>

**Note**  
The notebook scheduler is built from the Amazon EventBridge, SageMaker Training, and Pipelines services. If your notebook jobs fail, you might see errors related to these services. The following provides information on how to create a notebook job in the Studio UI.

SageMaker Notebook Jobs gives you the tools to create and manage your noninteractive notebook jobs using the Notebook Jobs widget. You can create jobs, view the jobs you created, and pause, stop, or resume existing jobs. You can also modify notebook schedules.

When you create your scheduled notebook job with the widget, the scheduler tries to infer a selection of default options and automatically populates the form to help you get started quickly. If you are using Studio, at minimum you can submit an on-demand job without setting any options. You can also submit a (scheduled) notebook job definition supplying just the time-specific schedule information. However, you can customize other fields if your scheduled job requires specialized settings. If you are running a local Jupyter notebook, the scheduler extension provides a feature for you to specify your own defaults (for a subset of options) so you don't have to manually insert the same values every time.

When you create a notebook job, you can include additional files such as datasets, images, and local scripts. To do so, choose **Run job with input folder**. The Notebook Job will now have access to all files under the input file's folder. While the notebook job is running the file structure of directory remains unchanged.

To schedule a notebook job, complete the following steps.

1. Open the **Create Job** form.

   In local JupyterLab environments, choose the **Create a notebook job** icon (![\[Blue icon of a calendar with a checkmark, representing a scheduled task or event.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/icons/notebook-schedule.png)) in the taskbar. If you don't see the icon, follow the instructions in [Installation guide](scheduled-notebook-installation.md) to install it.

   In Studio, open the form in one of two ways:
   + Using the **File Browser**

     1. In the **File Browser** in the left panel, right-click on the notebook you want to run as a scheduled job.

     1. Choose **Create Notebook Job**.
   + Within the Studio notebook
     + Inside the Studio notebook you want to run as a scheduled job, choose the **Create a notebook job** icon (![\[Blue icon of a calendar with a checkmark, representing a scheduled task or event.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/icons/notebook-schedule.png)) in the Studio toolbar.

1. Complete the popup form. The form displays the following fields:
   + **Job name**: A descriptive name you specify for your job.
   + **Input file**: The name of the notebook which you are scheduling to run in noninteractive mode.
   + **Compute type**: The type of Amazon EC2 instance in which you want to run your notebook.
   + **Parameters**: Custom parameters you can optionally specify as inputs to your notebook. To use this feature, you might optionally want to tag a specific cell in your Jupyter notebook with the **parameters** tag to control where your parameters are applied. For more details, see [Parameterize your notebook](notebook-auto-run-troubleshoot-override.md).
   + (Optional)**Run job with input folder**: If selected the scheduled job will have access to all the files found in the same folder as the **Input file**.
   + **Additional Options**: You can specify additional customizations for your job. For example, you can specify an image or kernel, input and output folders, job retry and timeout options, encryption details, and custom initialization scripts. For the complete listing of customizations you can apply, see [Available options](create-notebook-auto-execution-advanced.md).

1. Schedule your job. You can run your notebook on demand or on a fixed schedule.
   + To run the notebook on demand, complete the following steps:
     + Select **Run Now**.
     + Choose **Create**.
     + The **Notebook Jobs** tab appears. Choose **Reload** to load your job into the dashboard.
   + To run the notebook on a fixed schedule, complete the following steps:
     + Choose **Run on a schedule**.
     + Choose the **Interval** dropdown list and select an interval. The intervals range from every minute to monthly. You can also select **Custom schedule**.
     + Based on the interval you choose, additional fields appear to help you further specify your desired run day and time. For example, if you select **Day** for a daily run, an additional field appears for you to specify the desired time. Note that any time you specify is in UTC format. Note also that if you choose a small interval, such as one minute, your jobs overlap if the previous job is not complete when the next job starts.

       If you select a custom schedule, you use cron syntax in the expression box to specify your exact run date and time. The cron syntax is a space-separated list of digits, each of which represent a unit of time from seconds to years. For help with cron syntax, you can choose **Get help with cron syntax** under the expression box.
     + Choose **Create**.
     + The **Notebook Job Definitions** tab appears. Choose **Reload** to load your job definition into the dashboard.

# Set up default options for local notebooks
<a name="create-notebook-auto-execution-advanced-default"></a>

**Important**  
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).  
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).

You can set up default options when you create a notebook job. This can save you time if you plan to create multiple notebook jobs with different options than the provided defaults. The following provides information on how to set up the default options for local notebooks.

If you have to manually type (or paste in) custom values in the **Create Job** form, you can store new default values and the scheduler extension inserts your new values every time you create a new job definition. This feature is available for the following options:
+ **Role ARN**
+ **S3 Input Folder**
+ **S3 Output Folder**
+ **Output encryption KMS key** (if you turn on **Configure Job Encryption**)
+ **Job instance volume encryption KMS key** (if you turn on **Configure Job Encryption**)

This feature saves you time if you insert different values than the provided defaults and continue to use those values for future job runs. Your chosen user settings are stored on the machine that runs your JupyterLab server and are retrieved with the help of native API. If you provide new default values for one or more but not all five options, the previous defaults are taken for the ones you don’t customize.

The following instructions show you how to preview the existing default values, set new default values, and reset your default values for your notebook jobs.

**To preview existing default values for your notebook jobs, complete the following steps:**

1. Open the Amazon SageMaker Studio Classic console by following the instructions in [Launch Amazon SageMaker Studio Classic](studio-launch.md).

1. In the **File Browser** in the left panel, right-click on the notebook you want to run as a scheduled job.

1. Choose **Create Notebook Job**.

1. Choose **Additional options** to expand the tab of notebook job settings. You can view the default settings here. 

**To set new default values for your future notebook jobs, complete the following steps:**

1. Open the Amazon SageMaker Studio Classic console by following the instructions in [Launch Amazon SageMaker Studio Classic](studio-launch.md).

1. From the top menu in Studio Classic, choose **Settings**, then choose **Advanced Settings Editor**.

1. Choose **Amazon SageMaker Scheduler** from the list below **Settings**. This may already be open by default.

1. You can update the default settings directly in this UI page or by using the JSON editor.
   + In the UI you can insert new values for **Role ARN**, **S3 Input Folder**, **S3 Output Folder**, **Output encryption KMS key**, or **Job instance volume encryption KMS key**. If you change these values, you will see the new defaults for these fields while you create your next notebook job under **Additional options**.
   + (Optional) To update the user defaults using the **JSON Settings Editor**, complete the following steps:

     1. In the top right corner, choose **JSON Settings Editor**.

     1. In the **Settings** left sidebar, choose **Amazon SageMaker AI Scheduler**. This may already be open by default.

        You can see your current default values in the **User Preferences** panel.

        You can see the system default values in the **System Defaults** panel.

     1. To update your default values, copy and paste the JSON snippet from the **System Defaults** panel to the **User Preferences** panel, and update the fields.

     1. If you updated the default values, choose the **Save User Settings** icon (![\[Icon of a cloud with an arrow pointing upward, representing cloud upload functionality.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/icons/Notebook_save.png)) in the top right corner. Closing the editor does not save the changes.

**If you previously changed and now want to reset the user-defined default values, complete following steps:**

1. From the top menu in Studio Classic, choose **Settings**, then choose **Advanced Settings Editor**.

1. Choose **Amazon SageMaker Scheduler** from the list below **Settings**. This may already be open by default.

1. You can restore the defaults by directly using this UI page or using the JSON editor.
   + In the UI you can choose **Restore to Defaults** in the top right corner. Your defaults are restored to empty strings. You only see this option if you previously changed your default values.
   + (Optional) To restart the default settings using the **JSON Settings Editor**, complete the following steps:

     1. In the top right corner, choose **JSON Settings Editor**.

     1. In the **Settings** left sidebar, choose **Amazon SageMaker AI Scheduler**. This may already be open by default.

        You can see your current default values in the **User Preferences** panel.

        You can see the system default values in the **System Defaults** panel.

     1. To restore your current default settings copy the content from the **System Defaults** panel to the **User Preferences** panel.

     1. Choose the **Save User Settings** icon (![\[Icon of a cloud with an arrow pointing upward, representing cloud upload functionality.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/icons/Notebook_save.png)) in the top right corner. Closing the editor does not save the changes.