# Migrating from cloud VMs

Move a workload off hand-managed instances without losing reproducibility.

Source: https://university.eugo.io/resources/migrating-from-cloud-vms-checklist

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- Kind: Checklist
- Topics: fundamentals, workspaces, data-io

## Inventory what you have

- **Write down the current environment** — Python version, every pinned dependency, and any system package your code relies on. Undocumented environments are what make migrations drag.
- **Locate every input and output path** — List where data is read from and written to today. Local filesystem paths need a replacement; object storage paths usually carry over unchanged.
- **Note anything that is not Python** — Shell steps, cron entries, and compiled binaries in your pipeline need explicit handling rather than an assumption they will come along.

## Move it

- **Reproduce the environment in a workspace** — Install the same dependencies and confirm your existing code runs unchanged on a small input before touching anything else.
- **Repoint paths at shared storage** — Replace local paths with object storage or workspace-persistent locations, so every worker can reach the same data.
- **Verify results match the old pipeline** — Run the same input through both and compare outputs. Do this before distributing anything, because debugging correctness and parallelism at once is twice the work.

## Then distribute

- **Distribute only after the serial version is correct** — Get it running correctly on one worker first. Only then wrap the per-unit work in a distributed function.
- **Decommission the old instances** — Once the migrated pipeline has run clean a few times, shut the VMs down. Parallel infrastructure kept "just in case" is a standing bill.