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Why

Use your own hardware — a cloud GPU instance, a local workstation, or a beefy VM — while keeping Alph’s notebook editor, AI assistance, and collaboration features. No compute charges from Alph.

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Setup

1

Install the CLI

On your machine:
2

Authenticate

3

Start JupyterLab

This starts JupyterLab locally and connects it to your Alph project via a secure tunnel.
Your notebooks in Alph now execute on your machine’s hardware.

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Cloud GPU Examples

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Shadeform

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Lambda Labs / Vast.ai / RunPod

Same process — SSH in, install the CLI, and connect. Any machine with Python and internet access works.

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AWS

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Local Workstation

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Firewall & Port Configuration

The CLI uses a secure outbound tunnel — no inbound ports need to be open for the notebook connection. However, if you want to expose a web app through Alph, make sure:
  • The --app-port port (default 5000) is not blocked by your firewall
  • Your cloud provider’s security group or firewall rules allow the app to bind to 0.0.0.0 on that port
On most cloud GPU providers, outbound traffic is open by default. If you’re behind a restrictive firewall, ensure outbound HTTPS (port 443) is allowed for the tunnel.

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What You Get

  • Your hardware, Alph’s interface: Edit notebooks in Alph, execute on your GPU
  • AI assistance: Cell generation and chat work regardless of where compute runs
  • Team access: Collaborators see your notebooks and outputs in Alph
  • Web apps: Add --app-port 5000 to expose a web app through Alph’s tunnel
  • Custom domains: Attach domains through the web UI — no restart needed

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Tips

  • Use --token for headless authentication on remote servers
  • Use --port to change the JupyterLab server port, --app-port for the web app port
  • Use tmux or screen to keep the connection alive after disconnecting SSH
  • The tunnel auto-reconnects on network interruptions