Using cuda servers for remote development

When your laptop needs more muscle, you get a personal box on the cuda-dev hosts — your own container with the GPU, where you can edit, build, and run Claude. This page covers working remotely on it; for first-time setup (your key, ssh config, tmux, Claude) start with getting started.

Connecting

When your box was provisioned you were sent a Bitwarden Send link with your SSH key and a ready-to-paste ~/.ssh/config snippet (the full walkthrough is in getting started). Once that’s in place:

ssh cuda-dev2-kvdb

You log in to your own container as the dev user — never the host. You need to be on the office network or the VPN.

VS Code

Install the Remote - SSH extension and connect to your cuda-dev2-kvdb host — the same alias from your ssh config, key-based, no password. Open your project folder and the editor and its integrated terminal run on the container.

Using the GPU

The GPU is yours directly — no setup, no Docker:

nvidia-smi

CUDA, PyTorch, TensorFlow and friends use it natively. Install your project’s dependencies in your container (a venv or uv) and run as normal.

Docker

Docker is available for containerized workflows; the daemon runs as root in your container, so use sudo:

sudo docker run --rm hello-world

GPU passthrough into Docker (docker --gpus) is not set up on the dev boxes, so use the GPU directly as above. If you genuinely need the GPU inside a Docker container, ask infra.

Long-running jobs

Your session lives on the box, so run inside tmux (or mosh) and your work survives a disconnect — reattach later from anywhere. See the remote session management guide.

Per-project sessions

To work on a specific project with scoped, isolated credentials, use work <project> — it gives that project its own throwaway container. That is a separate, more advanced topic.