gpu-custom-setup

/gpu-custom-setup — onboard a user with their own GPU setup

Rockie's default GPU layer is the deidentified GPU router (gpu.py). But some users have their own setup they prefer to drive themselves — their own AWS account, on-prem cluster, university HPC quota, custom SSH tunnel, etc. They opt out by setting ROCKIE_GPU_MODE=custom, and the agent reaches for THIS skill on first GPU need to learn how THEY provision compute.

Pre-flight checks

Run these BEFORE invoking the skill. If any fail, do not proceed.

# 1. Are we in custom mode?
test "$(echo "${ROCKIE_GPU_MODE:-router}")" = "custom" || exit 0

# 2. Has setup already been done?
test -s .codex/gpu-custom.md && {
  echo "[gpu-custom-setup] .codex/gpu-custom.md already populated — skipping"
  exit 0
}

If both checks pass, continue.

What to do

This is a Q&A skill — the agent prompts the user, the user pastes commands or describes their flow, the agent structures the answers into .codex/gpu-custom.md and saves.

Sections to elicit, in this order:

1. Authentication

Ask: "How do you authenticate to your GPU provider? Paste any commands or env vars you set up. (e.g. aws configure, gcloud auth login, ssh-add ~/.ssh/key, custom token in env, etc.)"

Probe: if they say "AWS", ask whether they use IAM role, access keys, or SSO. If "on-prem", ask whether SSH key auth or password.

2. Provision (start a GPU)

Ask: "How do you spin up a GPU? Paste the command, script, or describe the steps. (e.g. aws ec2 run-instances ..., sbatch train.slurm, ssh worker && ./start_pod.sh, terraform apply, etc.)"

Probe for:

• Default instance type / GPU model
• How long it usually takes from "go" to "SSH-ready"
• Any pre-flight checks they run (capacity, quota, billing)
• How they pass training scripts to the pod (rsync, S3, git clone, etc.)

3. Connect (SSH to running GPU)

Ask: "Once the GPU is provisioned, how do you connect? Paste the SSH template (e.g. ssh user@host -i key.pem)."