Tech review of the ray on axion lp

content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md

@@ -1,5 +1,6 @@
 ---
 title: Scale AI workloads with Ray on Google Cloud C4A Axion VM
+description: Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based VMs, covering parallel tasks, hyperparameter tuning, and model serving with Ray Core, Train, Tune, and Serve.
 
 draft: true
 cascade:

content/learning-paths/servers-and-cloud-computing/ray-on-axion/firewall-setup.md

@@ -16,15 +16,15 @@ For help with GCP setup, see the Learning Path [Getting started with Google Clou
 
 Navigate to the [Google Cloud Console](https://console.cloud.google.com/), go to **VPC Network > Firewall**, and select **Create firewall rule**.
 
-![Google Cloud Console VPC Network Firewall page showing the Create firewall rule button in the top menu bar alt-txt#center](images/firewall-rule.png "Create a firewall rule in Google Cloud Console")
+![Google Cloud Console VPC Network Firewall page showing the Create firewall rule button in the top menu bar#center](images/firewall-rule.png "Create a firewall rule in Google Cloud Console")
 
 Next, create the firewall rule that exposes required ports for Ray.
 
 Set the **Name** of the new rule to "allow-ray-ports". Select your network that you intend to bind to your VM.
 
 Set **Direction of traffic** to "Ingress". Set **Allow on match** to "Allow" and **Targets** to "Specified target tags". Enter "allow-ray-ports" in the **Target tags** text field. Set **Source IPv4 ranges** to "0.0.0.0/0".
 
-![Google Cloud Console Create firewall rule form with Name set to allow-ray-ports and Direction of traffic set to Ingress alt-txt#center](images/network-rule.png "Configuring the allow-ray-ports firewall rule")
+![Google Cloud Console Create firewall rule form with Name set to allow-ray-ports and Direction of traffic set to Ingress#center](images/network-rule.png "Configuring the allow-ray-ports firewall rule")
 
 Finally, select **Specified protocols and ports** under the **Protocols and ports** section. Select the **TCP** checkbox and enter:
 
@@ -47,4 +47,4 @@ In this section, you:
 * Created a firewall rule to expose Ray Dashboard and Serve API
 * Enabled external access to monitor jobs and access deployed services
 
-Next, you'll deploy and run Ray workloads on your ARM-based virtual machine.
+Next, you'll deploy and run Ray workloads on your Arm-based virtual machine.

content/learning-paths/servers-and-cloud-computing/ray-on-axion/instance.md

@@ -32,11 +32,12 @@ To create a virtual machine based on the C4A instance type:
   - For the license type, choose **Pay as you go**.
   - Increase **Size (GB)** from **10** to **100** to allocate sufficient disk space.
   - Select **Choose** to apply the changes.
+- Expand the **Networking** section and enter `allow-ray-ports` in the **Network tags** field. This tag links the VM to the firewall rule you created earlier, enabling external access to the Ray Dashboard and Serve API ports.
 - 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.
 
-![Google Cloud Console VM instances page displaying running instance with green checkmark and SSH button in the Connect column alt-txt#center](images/gcp-pubip-ssh.png "Connecting to a running C4A VM using SSH")
+![Google Cloud Console VM instances page showing the running C4A instance with a green status checkmark and the SSH button highlighted in the Connect column#center](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.
 

content/learning-paths/servers-and-cloud-computing/ray-on-axion/setup_and_cluster.md

@@ -73,7 +73,7 @@ Install common ML libraries:
 pip install torch torchvision pandas scikit-learn
 ```
 
-## Verify installation:
+## Verify the installation
 
 Check that Ray is installed correctly:
 

content/learning-paths/servers-and-cloud-computing/ray-on-axion/tuning_serving_benchmark.md

@@ -1,5 +1,5 @@
 ---
-title: Ray Tune, Serve and Benchmarking
+title: Ray Tune, Serve, and Benchmarking
 weight: 7
 
 ### FIXED, DO NOT MODIFY
@@ -193,7 +193,7 @@ ray start --head --num-cpus=4
 python3 ray_benchmark.py
 ```
 
-Output:
+The output is similar to:
 
 ```output
 Execution Time: 5.171869277954102