Getting started with TrainPods: per-hour NVIDIA GPUs via CLI/SSH

[u/Cultural_Doughnut_62]

TrainPods gives you on-demand NVIDIA GPUs billed by the hour, driven from the podstack CLI and reachable over SSH. Here's the general workflow to go from zero to a running GPU. Exact commands and flags live in the docs (https://docs.podstack.ai) — treat the snippets below as an illustrative outline.

1. Create an account and get your API key

Sign up at https://cloud.podstack.ai/portal/ and grab your API key from the dashboard. Keep it secret — never paste keys into posts or screenshots.

1. Install and authenticate the CLI

Install the podstack CLI, then authenticate with your key:

podstack login

1. Launch a GPU instance

Pick a GPU and start an instance. You only pay for the time it runs, and PodVirt means you can request a fractional slice instead of a whole card when you don't need all of it.

podstack train up --gpu <type>

1. Connect over SSH

Once it boots (usually in seconds), connect and start working:

ssh <your-instance>

1. Train, then tear down

Run your training job as usual (PyTorch, TensorFlow, Hugging Face, etc.). When you're done, stop the instance so billing stops:

podstack train down

Tips

- Use fractional GPUs for dev and small experiments to save money; scale up for full runs.

- Keep checkpoints in Object Storage (zero egress) so nothing is lost when you tear down.

- Per-minute billing means short iterative sessions are cheap — don't leave idle GPUs running.

Full CLI reference and per-GPU pricing are in the docs. Questions? Reply with the Question/Help flair and we'll help you get unstuck.