How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?
Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
Explanations:
AWS Systems Manager Agent is used for managing EC2 instances and automating tasks, but it is not specifically designed for managing package installations on Amazon SageMaker notebook instances. SageMaker lifecycle configurations are a more direct way to manage package installation on notebook instances.
Placing a Jupyter notebook with installation commands in the /etc/init directory is not a valid approach. The /etc/init directory is used for system services and not for automating package installations on SageMaker notebook instances.
While conda can be used within the Jupyter notebook console to install packages, this option would require manual intervention each time the notebook instance is started, and does not automate the package installation process.
Amazon SageMaker lifecycle configurations are specifically designed to automate tasks such as package installations when the notebook instance starts. By creating a lifecycle configuration with package installation commands, it ensures that the required packages are automatically available whenever the notebook instance is launched.