API How-Tos

The Cloud API lets you manage Cloud GPU VMs programmatically using conventional HTTP requests. You can use the API to create, delete, and retrieve information about your Cloud GPU VMs.

Furthermore, you need templates to provision Cloud GPU VMs, but templates are not compatible with servers that support full flex configuration.

Cloud GPU VM workflow

1

Request access

To begin using the Cloud GPU VM, send a request to IONOS Cloud Support. For more information about the process, see Cloud GPU VM Access.

2

Discovery and selection

• List all Cloud GPU VM templates.

• Review Cloud GPU VM template specifications to select the appropriate template for your needs. For more information about a specific template, use the Retrieve a Template API.

• Use this template UUID to make an API request call to create a Cloud GPU VM.

3

Create a Cloud GPU VM

Initiate a Cloud GPU VM creation request through API with the following:

• The selected GPU ID

• Linux-based operating system

Note: The product supports only Linux-based operating systems during the launch.

• Network settings

• SSH keys, Cloud-init script, or other authentication methods

Result: The VM with the attached GPU is provisioned.

4

Access and setup

• Connect to VM using one of the following options:

  • Through the SSH.

  • Through the remote console.

• Install NVIDIA drivers. For more information, refer to the NVIDIA documentation.

• Run the following command in your terminal or command prompt to verify if the Cloud GPU VM is accessible: nvidia-smi

Result: A successful output should display the driver version, the GPU model(s), the GPU temperature, and the current memory usage. If the command is not found or returns an error, your drivers may be missing or the GPU is not properly installed.

• Install required framework dependencies. Use a package manager like pip or conda to install your chosen framework, ensuring you select the GPU-enabled version.

For PyTorch

Install the version that matches your CUDA toolkit. For example, CUDA 12.1.

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

For TensorFlow

• Use the server for your use case

5

Usage

• Run GPU workloads, such as model training (finetuning), inference, or graphics rendering

• Monitor GPU utilization and performance metrics

6

Management

• Start, restart, or delete the Cloud GPU VM as needed.

• Monitor costs and usage. For more information, see Cost Alert and Cost & Usage.

7

Cleanup

• Back up your results and data. For more information, see Install Acronis Backup Agent on Linux.

• Delete the Cloud GPU VM from the VDC.

Quick Links

Retrieve Template list

Learn how to retrieve a list of available templates.

Retrieve Template details

Learn how to retrieve details of a specific template.

Create a Cloud GPU VM

Learn how to create a Cloud GPU VM.

Delete a Cloud GPU VM

Learn how to delete a Cloud GPU VM.