Train AI Models on GPU Nodes with SSH Access

Theta EdgeCloud offers GPU nodes with SSH access for you to run AI model training, and any other computational tasks.

Launching a GPU Node in EdgeCloud

First navigate to the "GPU node" page under the "Training" category, which can be assessed by simply clicking on the "AI" icon on the left bar, and then click on the "GPU Node" tab.

Next, click on "New GPU Node". You should see the a modal like below popping up.

Most fields on the modal are self-explanatory. In particular:

• GPU Node ID: this is a read-only field generated automatically.
• GPU Node Name(Optional): you can specify a name for the node in the "GPU Node Name" field. You can also leave it blank update the name later after the node is launched.
• GPU Node Image(Required): You need to specify the container image for the GPU node in the "GPU Node Image" field. The image needs to be a container with sshd running in the background. Otherwise you will not be able to SSH to the node. You can either type in the container URL (e.g. thetalabsorg/ubuntu-sshd:latest), or select an image image we prepared from the drop-down list.
• SSH port (Required): This is the port the sshd process in the container listens to. By default it is 22. If your sshd process is using another port, please update this field accordingly.
• SSH Public Key(Required): Please paste your SSH public key here, which is a long string starting with ssh-rsa . The RSA public is typically stored in your ~/.ssh/id_isa.pub file. Please checkout this link on how to generate your SSH public key.
• HTTP Port(Optional): This field is empty by default. However, if you have an HTTP server running in your container (e.g. Jupyter notebook, TensorBoard server), you can specify the port it listens to. We will map it to an HTTPs endpoint for you to interact with the server (see Note 2 in the "Tips" section).
• VM Type: Please choose the GPU machine type you want to use from the dropdown menu.

After filling in the above fields, please click on the "Create GPU Node" button to launch the GPU node, which should also redirect you to a page similar to the following:

SSH to the GPU Node

Depending on the size of the container image, it may take a few minutes to fire up the GPU node. Once it is up and running, you can connect to the node via SSH. Simply click the green "Show" button in the above screenshot to see the SSH command:

Now, you can copy the ssh command to your local terminal to connect to the GPU node. If your SSH private key is stored in another file other than ~/.ssh/id_rsa, please specify the correct path:

Enjoy the high performance GPU nodes provided by Theta EdgeCloud! You can run model training or any other computational tasks there freely.

Tips

Note 1: You can also connect to the GPU node from IDEs such as Microsoft VSCode. Please learn more in this link. The SSH server URL should have this format user@IP:port. In the above example, the server URL would be [email protected]:30017.

Note 2: If you specified the HTTP port, the "Open" button in the following screenshot will also turn green once the HTTP server is ready. Click on the button will open the HTTP endpoint in a new browser tab. For example, in the screenshot below, the GPU node named gpu node with jupyter notebook runs the Jupyter notebook server in the container. Once the server is up, you can click on "Open" to access the Jupyter notebook.

Note 3: You can click into the GPU node to see view more details, the GPU node status, and the container logs (if it prints anything).