diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md index 63feee5c56..b1edd168c4 100644 --- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md +++ b/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: diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/firewall-setup.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/firewall-setup.md index 27dc74d1f4..291467f36b 100644 --- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/firewall-setup.md +++ b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/firewall-setup.md @@ -16,7 +16,7 @@ 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. @@ -24,7 +24,7 @@ Set the **Name** of the new rule to "allow-ray-ports". Select your network that 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. diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/instance.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/instance.md index c6785ae89e..ae24434e63 100644 --- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/instance.md +++ b/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. diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/setup_and_cluster.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/setup_and_cluster.md index ed7e34ecfd..1cd68fae18 100644 --- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/setup_and_cluster.md +++ b/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: diff --git a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/tuning_serving_benchmark.md b/content/learning-paths/servers-and-cloud-computing/ray-on-axion/tuning_serving_benchmark.md index db438d5027..28137d9c0f 100644 --- a/content/learning-paths/servers-and-cloud-computing/ray-on-axion/tuning_serving_benchmark.md +++ b/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