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
- Navigate to console.spheron.network
- Log in to your account
- If you’re new to Spheron, you should have $20 in free credits. If not, reach out on Discord
Create New Deployment
- Go to Marketplace tab
- 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
- 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
- Select the template VS Code with Pytorch 2.5.1
- Put any password in
PASSWORD
field that you want to set for the VS Code Server - If you want you can increase the GPU count to access multiple GPUs at once
- You can select the duration of the deployment.
- Click on Confirm button to start the deployment
- Deployment will be done in less than 60 seconds
Access the VS Code Server
- Once deployed, go to Overview tab.
- Click on vsc-pytorch service to open the VS Code Server service.
- Click on the connection url to open the VS Code Server.
- 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.
Last updated on