# Configure the default parameters of an Autopilot experiment (for administrators)
<a name="autopilot-set-default-parameters-create-experiment"></a>

Autopilot supports setting default values to simplify the configuration of Amazon SageMaker Autopilot when you create an Autopilot experiment using the Studio Classic UI. Administrators can use Studio Classic [lifecycle configurations](studio-lcc.md) (LCC) to set infrastructure, networking, and security values in configuration files and pre-populate the [advanced settings](autopilot-automate-model-development-create-experiment-ui.md#advanced-settings) of `AutoML` jobs.

By doing so, they can fully control network connectivity and access permissions for the resources associated with Amazon SageMaker Studio Classic, including SageMaker AI instances, data sources, output data, and other related services. Specifically, administrators can configure a desired network architecture, such as Amazon VPC, subnets, and security groups, for a Studio Classic domain or individual user profiles. Data scientists can focus on data science specific parameters when creating their Autopilot experiments using the Studio Classic UI. Furthermore, administrators can manage the encryption of data on the instance in which Autopilot experiments run by setting default encryption keys.

**Note**  
This feature is currently not available in the Asia Pacific (Hong Kong) and Middle East (Bahrain) opt-in Regions.

In the following sections, you can find the full list of parameters supporting the setting of defaults when creating an Autopilot experiment using the Studio Classic UI, and learn how to set those default values.

**Topics**
+ [List of default parameters supported](#autopilot-list-default-parameters-create-experiment)
+ [Set default Autopilot experiment parameters](#autopilot-set-default-parameters-create-experiment-howto)

## List of default parameters supported
<a name="autopilot-list-default-parameters-create-experiment"></a>

The following parameters support setting default values with a configuration file for creating an Autopilot experiment using the Studio Classic UI. Once set, the values automatically fill in their corresponding field in the Autopilot' **Create Experiment** tab in the Studio Classic UI. See [Advanced settings (optional)](autopilot-automate-model-development-create-experiment-ui.md#advanced-settings) for a full description of each field.
+ **Security:** Amazon VPC, subnets, and security groups.
+ **Access:** AWS IAM role ARNs.
+ **Encryption:** AWS KMS key IDs.
+ **Tags:** Key-value pairs used to label and organize SageMaker AI resources.

## Set default Autopilot experiment parameters
<a name="autopilot-set-default-parameters-create-experiment-howto"></a>

Administrators can set default values in a configuration file, then manually place the file in a recommended location within the Studio Classic environment of specific users, or they can pass the file to a lifecycle configuration script (LCC) to automate the customization of the Studio Classic environment for a given domain or user profile.
+ To set up the configuration file, start by filling in its default parameters.

  To configure any or all default values listed in [List of default parameters supported](#autopilot-list-default-parameters-create-experiment), administrators can create a configuration file named `config.yaml`, the structure of which should adhere to this [sample configuration file](https://sagemaker.readthedocs.io/en/stable/overview.html#configuration-file-structure). The following snippet shows a sample configuration file with all the supported `AutoML` parameters. For more information on the format of this file, refer to the [full schema](https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/config/config_schema.py).

  ```
  SchemaVersion: '1.0'
  SageMaker:
    AutoMLJob:
      # https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html
      AutoMLJobConfig:
        SecurityConfig:
          EnableInterContainerTrafficEncryption: true
          VolumeKmsKeyId: '{{kms-key-id}}'
          VpcConfig:
            SecurityGroupIds:
              - '{{security-group-id-1}}'
              - '{{security-group-id-2}}'
            Subnets:
              - '{{subnet-1}}'
              - '{{subnet-2}}'
      OutputDataConfig:
        KmsKeyId: '{{kms-key-id}}'
      RoleArn: '{{arn:aws:iam::111222333444:role/Admin}}'
      Tags:
      - Key: '{{tag_key}}'
        Value: '{{tag_value}}'
  ```
+ Then, place the configuration file in the recommended location by either [manually copying the file](#autopilot-intelligent-defaults-manual-setup) to its recommended paths or using a [lifecycle configuration](#autopilot-intelligent-defaults-lcc-setup) (LCC).

  The configuration file needs to be present in at least one of the following locations in the user's Studio Classic environment. By default, SageMaker AI searches for a configuration file in two locations:
  + First, in `/etc/xdg/sagemaker/config.yaml`. We refer to this file as the *administrator configuration file*.
  + Then, in `/root/.config/sagemaker/config.yaml`. We refer to this file as the *user configuration file*.

  Using the *administrator* configuration file, administrators can define a set of default values. Optionally, they can use the *user* configuration file to override values set in the *administrator* configuration file, or set additional default parameter values.

  The following snippet shows a sample script which writes the default parameters configuration file to the *administrator* location in the user's Studio Classic environment. You can replace `/etc/xdg/sagemaker` with `/root/.config/sagemaker` to write the file to the *user* location.

  ```
  ## Sample script with AutoML intelligent defaults
  #!/bin/bash
  
  sudo mkdir -p /etc/xdg/sagemaker
  
  echo "SchemaVersion: '1.0'
  CustomParameters:
    AnyStringKey: 'AnyStringValue'
  SageMaker:
    AutoMLJob:
      # https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html
      AutoMLJobConfig:
        SecurityConfig:
          EnableInterContainerTrafficEncryption: true
          VolumeKmsKeyId: '{{kms-key-id}}'
          VpcConfig:
            SecurityGroupIds:
              - '{{security-group-id-1}}'
              - '{{security-group-id-2}}'
            Subnets:
              - '{{subnet-1}}'
              - '{{subnet-2}}'
      OutputDataConfig:
        KmsKeyId: '{{kms-key-id}}'
      RoleArn: '{{arn:aws:iam::111222333444:role/Admin}}'
      Tags:
      - Key: '{{tag_key}}'
        Value: '{{tag_value}}'
  " | sudo tee /etc/xdg/sagemaker/config.yaml
  ```
  + **Copy the files manually** – To copy the configuration files manually, run the [script](#autopilot-intelligent-defaults-manual-setup) created in the previous step from a Studio Classic terminal. In this case, the user profile that executed the script can create Autopilot experiments with the default values applicable only to them.
  +  **Create a SageMaker AI lifecycle configuration** – Alternatively, you can use a [lifecycle configuration](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lcc.html) (LCC) to automate the customization of your Studio Classic environment. LCC are shell scripts triggered by Amazon SageMaker Studio Classic lifecycle events such as starting a Studio Classic application. This customization includes installing custom packages, configuring notebook extensions, pre-loading datasets, setting up source code repositories, or, in our case, pre-populating default parameters. Administrators can attach the LCC to a Studio Classic domain to automate the configuration of default values for each user profile within that domain.

    The following sections detail how to create a lifecycle configuration so users can load Autopilot default parameters automatically when launching Studio Classic. You can choose to create an LCC using the SageMaker AI Console or the AWS CLI.

------
#### [ Create a LCC from the SageMaker AI Console ]

    Use the following steps to create an LCC containing your default parameters, attach the LCC to a domain or a user profile, then launch a Studio Classic application pre-populated with the default parameters set by the LCC using the SageMaker AI Console.
    + **To create a lifecycle configuration that runs the [script](#autopilot-intelligent-defaults-script) containing your default values using the SageMaker AI Console**
      + Open the SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).
      + On the left side, navigate to **Admin configurations**, then **Lifecycle configurations**.
      + From the **Lifecycle configurations** page, navigate to the Studio Classic tab, then choose **Create configuration**.
      + For **Name**, type a name using alphanumeric characters and "-", but no spaces. The name can have a maximum of 63 characters.
      + Paste your [script](#autopilot-intelligent-defaults-script) in the **Scripts** section.
      + Choose **Create configuration** to create the lifecycle configuration. This creates an LCC of type `Kernel gateway app`.
    +  **To attach the lifecycle configuration to a Studio Classic domain, a space, or a user profile**

      Follow the steps in [Attach the lifecycle configuration to Studio Classic domain or user profile ](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lcc-create-console.html#studio-lcc-create-console-step2) to attach your LCC to a Studio Classic domain or a specific user profile.
    +  **To launch your Studio Classic application with the lifecycle configuration**

      Once the LCC is attached to a domain or a user profile, impacted users can start a Studio Classic application from the landing page of Studio Classic in Studio to pick up the defaults set by the LCC automatically. This auto-populates the Studio Classic UI when creating an Autopilot experiment.

------
#### [ Create a LCC from the AWS CLI ]

    Use the following snippets to launch a Studio Classic application that runs your [script](#autopilot-intelligent-defaults-manual-setup) using the AWS CLI. Note that `lifecycle_config.sh` is the name given to your script in this example.

    Before getting started:
    + Ensure that you have updated and configured AWS CLI by completing the prerequisites described in [Create a lifecycle configuration from the AWS CLI](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lcc-create-cli.html).
    + Install [OpenSSL](https://www.openssl.org/source/) documentation. The AWS CLI command uses the open-source library *OpenSSL* to encode your script in Base64 format. This requirement prevents errors that occur from spacing and line break encoding.

    You can now follow these three steps:
    +  **Create a new lifecycle configuration referencing the configuration script `lifecycle_config.sh`**

      ```
      LCC_CONTENT=`openssl base64 -A -in {{lifecycle_config.sh}}`
      
      ## Create a new lifecycle config 
      aws sagemaker create-studio-lifecycle-config --region {{region}} \
      --studio-lifecycle-config-name {{lcc-name}} \
      --studio-lifecycle-config-content $LCC_CONTENT \
      --studio-lifecycle-config-app-type default
      ```

      Note the ARN of the newly created lifecycle configuration that is returned. This ARN is required to attach the lifecycle configuration to your application.
    +  **Attach the lifecycle configuration to your `JupyterServerApp`**

      The following example shows how to create a new user profile with a lifecycle configuration attached. To update an existing user profile, use the AWS CLI [update-user-profile](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/update-user-profile.html) command. To create or update a domain, see [create-domain](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/create-domain.html) and [update-domain](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/update-domain.html). Add the lifecycle configuration ARN from the previous step to the settings of the `JupyterServerAppSettings` application type. You can add multiple lifecycle configurations at the same time by using a list of lifecycle configurations.

      ```
      # Create a new UserProfile
      aws sagemaker create-user-profile --domain-id {{domain-id}} \
      --user-profile-name {{user-profile-name}} \
      --region {{region}} \
      --user-settings '{
      "JupyterServerAppSettings": {
        "LifecycleConfigArns":
          ["{{lifecycle-configuration-arn}}"]
        }
      }'
      ```

      Once the LCC is attached to a domain or a user profile, impacted users can shut down and update their existing Studio Classic application by following the steps in [Shut down and Update Amazon SageMaker Studio Classic](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-tasks-update-studio.html), or start a new Studio Classic application from the AWS Console to pick up the defaults set by the LCC automatically. This auto-populates the Studio Classic UI when creating an Autopilot experiment. Alternatively, they can launch a new Studio Classic application using the AWS CLI as follows.
    +  **Launch your Studio Classic application with the lifecycle configuration using the AWS CLI**

      ```
      # Create a Jupyter Server application
      aws sagemaker create-app --domain-id {{domain-id}} \
      --user-profile-name {{user-profile-name}} \
      --region {{region}} \
      --app-type JupyterServer \
      --resource-spec LifecycleConfigArn={{lifecycle-configuration-arn}} \
      --app-name default
      ```

      For more information on creating a lifecycle configuration using the AWS CLI, see [Create a Lifecycle Configuration from the AWS CLI](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lcc-create-cli.html).

------