# Create a Google Axion C4A virtual machine for DeepSpeed

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/)
- [Understand DeepSpeed and Google Axion C4A for AI training](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/background/)
- [Create a Google Axion C4A virtual machine for DeepSpeed](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/instance/)
- [Set up PyTorch and DeepSpeed on a Google Axion C4A virtual machine](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/install-deepspeed-arm/)
- [Train and benchmark AI workloads on an Arm-based Google Axion virtual machine](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/train-benchmark-deepspeed-arm/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/_next-steps/)

## Set up the virtual machine
Create a Google Axion C4A Arm-based virtual machine (VM) on Google Cloud Platform. You’ll use the `c4a-standard-4` machine type with 4 vCPUs and 16 GB of memory. This VM will host PyTorch and DeepSpeed training and benchmarking workloads.

> **Note**  
> For help with Google Cloud Platform setup, see the Learning Path [Getting started with Google Cloud Platform](https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/google/).

To create a C4A virtual machine in the Google Cloud console:

1. Navigate to the [Google Cloud console](https://console.cloud.google.com/).
2. Go to **Compute Engine** > **VM Instances** and select **Create Instance**.
3. Under **Machine configuration**, populate fields such as **Instance name**, **Region**, and **Zone**.
4. Set **Series** to `C4A`, then select `c4a-standard-4` for **Machine type**.

![Configuring machine type to C4A in Google Cloud Console](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/images/gcp-vm.png)  
*Configuring machine type to C4A in Google Cloud Console*

5. Under **OS and storage**, select **Change** and then choose an Arm64-based operating system image. For this Learning Path, select **SUSE Linux Enterprise Server**.
6. For the license type, choose **Pay as you go**.
7. Increase **Size (GB)** from **10** to **100** to allocate sufficient disk space, and then select **Choose**.
8. Select **Create** to launch the virtual machine.

After the instance starts, select **SSH** next to the VM in the instance list to open a browser-based terminal session.

![Connecting to a running C4A VM using SSH](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/images/gcp-pubip-ssh.png)  
*Connecting to a running C4A VM using SSH*

A new browser window opens with a terminal connected to your VM.

![Terminal session connected to the VM](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/images/gcp-shell.png)  
*Terminal session connected to the VM*

## What you’ve accomplished and what’s next
You’ve now provisioned a Google Axion C4A Arm VM and connected to it using SSH.

Next, you’ll install PyTorch and DeepSpeed and configure the Python environment for AI training.