Managing GPU Rentals with Rsync: Workflow for Volatile Cloud Resources (svana.name)

As GPU availability and persistent storage across cloud regions remain unreliable and costly — especially for solo developers and small teams using services like Lambda.ai — the author proposes a simple Rsync-based workflow to avoid keeping expensive VMs running while preserving fast iteration. The approach boots a GPU VM in any region with capacity, uses Rsync to copy code and training data to the instance, creates a virtual environment, runs training/fine-tuning, then Rsyncs results back to the local machine. This saves money, sidesteps region-bound persistent drives, and enables quick edit–sync–rerun cycles because only changed files are transferred. Technically, the solution is a small bash wrapper that accepts the VM IP, runs rsync -avz to sync code (excluding .venv) and data from local→remote, and pulls results remote→local; it uses a standard ubuntu user and checks exit codes for robustness. Compared with scp, Rsync’s delta transfers make iterative development practical over typical home/office upload speeds (author works with 100s of MB–few GB datasets and ~500 Mbit/s upload). Major caveats are dataset size and network bandwidth — for very large datasets or slow connections you’ll need alternative strategies (remote storage, snapshotting, or dedicated long-lived volume solutions). The pattern is lightweight, easily scriptable, and immediately useful for cost-conscious ML practitioners who need intermittent access to GPUs.