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VS Code Server with GPU Support

Deploy your own VS Code Server instance with GPU support on Spheron.

Prerequisites

  • Spheron account
  • Basic VS Code familiarity
  • Sufficient credits for GPU usage

Deployment Steps

Access the Spheron Console

  1. Navigate to console.spheron.network
  2. Log in to your account
  3. If you’re new to Spheron, you should have $20 in free credits. If not, reach out on Discord

Create New Deployment

  1. Go to Marketplace tab
  2. You have 2 options to choose from:
    • Secure: For deploying on secure and data center grade provider. It is super reliable but costs more
    • Community: For deploying on community fizz nodes that are running on someones home machine. It might not be very reliable
  3. Now select any GPU you want to deploy on. You can also search the GPU name to find the exact GPU you want to deploy on

Configure Deployment

  1. Select the template VS Code with Pytorch 2.5.1
  2. Put any password in PASSWORD field that you want to set for the VS Code Server
  3. If you want you can increase the GPU count to access multiple GPUs at once
  4. You can select the duration of the deployment.
  5. Click on Confirm button to start the deployment
  6. Deployment will be done in less than 60 seconds

Access the VS Code Server

  1. Once deployed, go to Overview tab.
  2. Click on vsc-pytorch service to open the VS Code Server service.
  3. Click on the connection url to open the VS Code Server.
  4. Use the password you set in the previous step to log in.

Verification

Verify GPU access by running:

nvidia-smi

Additional Tip

  • Save your work regularly on Github.
  • Monitor your memory usage carefully - if your notebook uses more memory than available (Out Of Memory/OOM), the server will automatically terminate and restart your notebook session, causing you to lose any unsaved work. You can check memory usage by running nvidia-smi in a notebook cell.
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