Seamless Migration Options from JupyterLab 3x to JupyterLab 4x on AWS SageMaker

Discover integrated strategies to migrate and preserve your project files smoothly.

Key Highlights

Utilize AWS Integrated Tools – Migrate using built-in migration scripts and SageMaker Studio features.
Automate with AWS CLI & S3 – Leverage S3 for file backups and automated transfer using AWS CLI for a seamless migration.
Custom Lifecycle Configurations – Use tailored automation scripts that run on instance start-up to ensure your files transfer safely.

Comprehensive Migration Plan

As the open-source graph-notebook project evolves and transitions from JupyterLab 3x to JupyterLab 4x on AWS SageMaker, it is critical to ensure that users do not lose their valuable files during this migration. With SageMaker deprecating JupyterLab 3x, we need to provide robust and accessible migration options that cater to varying user expertise levels. Below, we outline three options with detailed steps, tools needed, and considerations for a close-to-automated migration process.

Option 1: Migrate via SageMaker Studio Migration Framework

Overview

AWS has introduced new migration pathways with SageMaker Studio for users transitioning from Studio Classic. This method leverages a built-in migration process that moves workloads from JupyterLab 3x to the enhanced environment based on JupyterLab 4x. This approach minimizes manual intervention while ensuring that all working files and configurations are preserved.

Implementation Steps

Step 1: Identify Affected Resources – Utilize the AWS Health Dashboard to review your SageMaker domains running JupyterLab 3x so you can track which instances require migration.
Step 2: Review IAM Policies – Ensure that your current IAM policies allow creation and management of resources in the new SageMaker Studio and Studio Classic environments. Permissions should cover adding tags and setting up new domains.
Step 3: Initiate the Migration Process – Follow the official migration guide provided in AWS documentation. It typically involves creating a new SageMaker Studio domain configured for JupyterLab 4x and transferring notebooks and configurations automatically from your old environment.
Step 4: Validate and Test – Once migration is complete, open the new JupyterLab 4x instance to ensure that all notebooks, scripts, and dependencies are intact. Test key functionalities to verify performance improvements and integration with existing workflows.

This method takes advantage of AWS’s migration scripts and the enhanced interface of SageMaker Studio, offering a balance between automation and guided migration.

Option 2: Automated Migration Using AWS S3 and CLI

Overview

This approach uses AWS CLI tools and S3 integration to automate both the backup and restoration of your files. By storing your Jupyter notebooks and project files in an S3 bucket, you can ensure a secure backup while providing a straightforward way to launch a new JupyterLab 4x environment with minimal manual download/upload steps.

Implementation Steps

Step 1: Backup Notebooks to S3 – From your JupyterLab 3x environment, utilize either the inbuilt file browser features or AWS CLI to upload your notebooks and essential files into a dedicated S3 bucket.
Step 2: Create a New Notebook Instance – Launch a new SageMaker Notebook instance that is configured to run JupyterLab 4x, ensuring that the instance has access to the S3 bucket containing your backups.
Step 3: Restore Files from S3 – Within the new instance, use AWS CLI commands or integrated S3 browser functionalities to download the files from the S3 bucket into your active workspace.
Step 4: Automate with Scripts – To minimize manual steps, consider writing a simple AWS CLI script that orchestrates the backup and restore process. This script can list files in the bucket, verify file integrity post-upload, and trigger notifications once the migration is successfully completed.

This option guarantees a relatively seamless migration process by interlinking S3 for data integrity and AWS CLI for automation, thereby reducing the risk of file loss and manual errors.

Option 3: Use Custom Lifecycle Configuration Scripts

Overview

For users who prefer a more integrated, automated experience, implementing custom lifecycle configuration scripts can offer a powerful solution. These scripts execute during the startup of new JupyterLab 4x notebook instances and automatically copy over files from the legacy JupyterLab 3x environment. This approach is particularly effective for environments managed at scale.

Implementation Steps

Step 1: Develop a Migration Script – Craft a script using AWS CLI or shell commands that automates the file transfer. The script should be designed to copy all relevant notebooks and resource files from the old system path to the new one.
Step 2: Configure Lifecycle Settings – Use the SageMaker console to set this script as a lifecycle configuration for new JupyterLab 4x instances. This configuration ensures that every time an instance is launched, it automatically checks for and migrates any pending files.
Step 3: Testing the Automation – Deploy a pilot instance with the lifecycle script enabled. Verify that the file copy process runs as expected by confirming that all project files from the JupyterLab 3x workspace are present in the new environment.
Step 4: Rollout and Monitor – Once validated, roll out this configuration for production-level instances. Monitor the process using AWS CloudWatch to ensure that migration errors or discrepancies are quickly addressed.

Lifecycle configuration scripts not only streamline the migration but also encapsulate custom logic which can be updated as the project evolves. This setup minimizes direct user intervention and provides a robust fallback system should migration errors occur.

Comparison Table of Migration Options

Option	Pros	Cons	Tools Required
SageMaker Studio Migration	Highly integrated with AWS ecosystem Minimal manual intervention Seamless migration process with direct AWS support	May require updates to IAM policies Dependent on timely availability of migration scripts	AWS Health Dashboard, Studio migration scripts
AWS CLI and S3 Backup	Automated backup and restoration Secure file storage in S3 Easy to script and customize	Requires basic scripting and AWS CLI knowledge Involves temporary use of an additional AWS service (S3) which may incur costs	AWS CLI, S3 buckets, SageMaker Notebook Instance
Custom Lifecycle Configuration Scripts	Fully automated file transfer during startup Customizable to project-specific needs Reduces manual errors	Requires advanced scripting knowledge Complexity in handling different file paths and permissions	Lifecycle configuration, AWS CLI, and custom scripts

Implementation Considerations and Best Practices

When planning the migration of files from a JupyterLab 3x environment to the new JupyterLab 4x setup on AWS SageMaker, consider the following best practices:

Data Backup and Verification

Before initiating any migration, it is critical to create a full backup of your files. Utilize S3 or EFS snapshots to ensure that you have a recovery point in case of any discrepancies during the migration process. Verification steps like comparing file checksums between the old and new environments can help guarantee data integrity.

Permission and Access Control

Review and update your IAM policies to ensure that all migration-related actions are permitted. This step is especially important for automated processes that use the AWS CLI or lifecycle scripts. Ensure that both the source and destination environments are properly authenticated and permitted to perform file transfers.

Monitoring and Logging

Enable logging via AWS CloudWatch to monitor the automated migration process. This level of scrutiny will help you quickly identify any issues or transfer failures, making it easier to troubleshoot and rerun failed steps without affecting other operations.

User Communication and Support

Communicate these migration options clearly in your user documentation or migration guide. Providing detailed, step-by-step instructions along with troubleshooting tips can help users have a smooth experience. Also, include references to official AWS documentation and blog posts where applicable.