GPU Snapshot Timeline Explained | Generated by AI
Here’s the timeline from the forensic evidence:
March 16, 2026 — AMD built the snapshot image:
rocm:latestDocker image built with buildkit (26GBsetup.sh= ROCm install layer)droplet-agentpurged (standard image-building cleanup)
May 20, 2026 04:39 — Your Droplet was created from that snapshot:
- Cloud-init ran, re-installed
droplet-agent - Host already had ROCm 7.2.0 at
/opt/rocm-7.2.0/(22G, baked into the image)
May 20, 2026 04:40 — Container rocm started:
- Ran Jupyter Lab with ROCm GPU access
- Exited ~May 21 (11 days ago)
Answer to your question: When you created the GPU Droplet, you chose an image like Ubuntu 7.2-gpu-mi300x1-192gb-devcloud-atl1. That image had both:
- ROCm 7.2.0 installed on the host (
/opt/rocm-7.2.0/) — 22G rocm:latestDocker image pre-loaded (another 36G with ROCm inside) — with Jupyter Lab pre-configured
You didn’t install anything manually. AMD Dev Cloud baked it all into the snapshot. The Docker image is just a containerized duplicate of what’s already on the host — meant for running Jupyter Lab easily.
Bottom line: The 59G in containerd is entirely from the AMD Dev Cloud pre-loaded snapshot. The rocm container exited 11 days ago and you’re not using it. The host ROCm at /opt/rocm-7.2.0/ (22G) is what actually powers GPU workloads — the Docker image is redundant. Safe to nuke the Docker artifacts. Want me to clean them up?