How does the vrun command work on Velda?

Updated

vrun is Velda's single entry point for running a command on cloud or cluster compute. You prefix any command with vrun, and Velda executes it remotely instead of on your local machine — for example, vrun -P h200-1 python train.py runs that training script on an H200 GPU. The point is that you keep your local development workflow (your editor, your files, your dependencies) while the actual compute happens elsewhere. There are no image builds, no manifests, and no dependency drift to manage.

What vrun actually does

Velda describes vrun as "all you need" — the entire interface is a prefix. When you type:

vrun -P h200-1 python train.py

you are telling Velda to take the command that follows (python train.py) and run it on a resource described by the flags (-P h200-1, i.e. an H200 GPU instance). The command itself is unchanged; only where it runs changes.

This matters because it removes the usual steps between writing code and using remote hardware:

  • No image builds. You don't package your environment into a container first.
  • No manifests. You don't write YAML or cluster config to describe the job.
  • No dependency drift. Because you run from your dev environment, the dependencies you developed against are the ones that execute.

How to use it

  1. Develop locally as usual. Write and test your script in your normal environment — Velda's templates include options like a PyTorch ML template with torch, pandas, and matplotlib preinstalled, or you can tailor your own.
  2. Prefix the command with vrun. Take the exact command you'd run locally and put vrun in front of it.
  3. Specify the compute with flags. -P h200-1 selects the target resource. The example in Velda's docs uses an H200 GPU; the platform also shows CPU head nodes and worker nodes for scaling.
  4. Run it. The command executes on Velda's compute rather than your machine, and you get the result back in your dev loop.

The expected result is that a workload which would be too heavy for your local machine — training, inference, data pipelines — runs on provisioned GPU or cluster resources without you leaving your editor.

Why the prefix model matters

The design choice is deliberate: Velda provisions compute while jobs are running, not while you're coding. The site contrasts this with a traditional dev machine, where a high-end workstation sits idle during breaks and coding, and with Kubernetes/cluster setups that add container and manifest overhead. With vrun, the same local workflow scales from a single command to distributed work, and you pay for compute when jobs run rather than during development or between workloads.

Practical notes

  • IDE flexibility. You can launch VS Code in the browser or connect your preferred IDE, so vrun fits into whichever environment you already use.
  • Templates as a starting point. Prebuilt templates (e.g. the PyTorch ML template) let you start instantly, then customize.
  • Scope of workloads. Velda positions vrun for training, serving, and data pipelines — anything you'd otherwise run on a GPU box or cluster.
  • Pricing and access. Velda lists a Pricing page and mentions a free monthly credit for its managed cloud tier, plus self-hosted/enterprise options. Check the pricing page for current terms rather than assuming a free tier covers your usage.

If your goal is to run a heavy command without rebuilding your environment or writing cluster config, vrun is the mechanism: prefix, target, run.