Connect to CPU Instance

SSH access

1. Save your SSH public key

If you haven't already, go to Account Settings → Security and paste your SSH public key. This key is shared across all your instances (GPU and CPU).

2. Get the connection details

Click on your running CPU instance in the My Instances tab. The details panel shows the SSH host and port under Connection.

3. Connect via terminal

ssh root@<host> -p <port> -i ~/.ssh/your_key

Replace <host> and <port> with the values from the connection panel. You log in as root.

File transfer (SSH)

You can transfer files to and from your instance securely using standard SSH tools like scp or rsync.

Using SCP

To upload a file from your local machine to the instance:

scp -P <port> -i ~/.ssh/your_key ./local-file.tar.gz root@<host>:/workspace/

To download a file from the instance to your local machine:

scp -P <port> -i ~/.ssh/your_key root@<host>:/workspace/data.tar.gz ./local-backup/

Using rsync

For large directories or incremental syncing, rsync is recommended. The following command uploads a directory, skipping files that haven't changed:

rsync -avz -e "ssh -p <port> -i ~/.ssh/your_key" ./local-directory/ root@<host>:/workspace/remote-directory/

Troubleshooting

• Connection refused: Ensure your instance is RUNNING and you have correctly saved your SSH public key in Account Settings before deploying the instance.
• Permission denied: Verify that the path to your private key following the -i flag is correct, and that your private key has the proper file permissions (chmod 600 ~/.ssh/your_key).

JupyterLab access

CPU instances with JupyterLab enabled run a browser-based IDE accessible from the connection URL in your instance details panel. Click the link to open JupyterLab in a new tab.

The auto-generated Jupyter password is displayed in the instance details panel. JupyterLab opens in the /workspace directory (or root if no Network Volume is attached).

Differences from GPU instances

The connection experience is identical — same SSH command format, same JupyterLab interface, same root access. The only difference is that CPU instances don't have CUDA or GPU libraries available, so GPU-dependent tools (like nvidia-smi) won't be present.