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Chapter 5. Installing RHEL AI on Google Cloud Platform (GCP) (Technology preview)


For installing and deploying Red Hat Enterprise Linux AI on Google Cloud Platform, you must first convert the RHEL AI image into an GCP image. You can then launch an instance using the GCP image and deploy RHEL AI on a Google Cloud Platform machine.

Important

Installing Red Hat Enterprise Linux AI on GCP is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

5.1. Converting the RHEL AI image into a Google Cloud Platform image.

To create a bootable image in Google Cloud Platform you must configure your Google Cloud Platform account, create an Google Cloud Storage bucket, and create an Google Cloud Platform image using the RHEL AI raw image.

Prerequisites

  • You installed the Google Cloud Platform CLI on your specific machine. For more information about installing the GCP CLI, see Install the Google Cloud Platform CLI on Linux.
  • You must be on a Red Hat Enterprise Linux version 9.2 - 9.4 system
  • Your machine must have an additional 100 GB of disk space.

Procedure

  1. Log in to Google Cloud Platform with the following command:

    $ gcloud auth login

    Example output of the login.

    $ gcloud auth login
    Your browser has been opened to visit:
    
        https://accounts.google.com/o/oauth2/auth?XXXXXXXXXXXXXXXXXXXX
    
    
    You are now logged in as [user@example.com].
    Your current project is [your-project].  You can change this setting by running:
      $ gcloud config set project PROJECT_ID

  2. You need to set up some Google Cloud Platform configurations and create your GCP Storage Container before creating the GCP image.

    1. Configure Google Cloud Platform CLI to use your project.

      $ gcloud_project=your-gcloud-project
      $ gcloud config set project $gcloud_project
    2. Create an environment variable defining the region where you want to operate.

      $ gcloud_region=us-central1
    3. Create a Google Cloud Platform Storage Container.

      $ gcloud_bucket=name-for-your-bucket
      $ gsutil mb -l $gcloud_region gs://$gcloud_bucket
  3. Red Hat currently does not provide RHEL AI Google Cloud Platform images. You need to create a GCP disk image using RHEL AI bootc image as base.

    1. Create this Containerfile file, using the appropriate version of RHEL AI in the FROM line.

      FROM registry.redhat.io/rhelai1/bootc-nvidia-rhel9:1.2
      
      RUN eval $(grep VERSION_ID /etc/os-release) \
        && echo -e "[google-compute-engine]\nname=Google Compute Engine\nbaseurl=https://packages.cloud.google.com/yum/repos/google-compute-engine-el${VERSION_ID/.*}-x86_64-stable\nenabled=1\ngpgcheck=1\nrepo_gpgcheck=0\ngpgkey=https://packages.cloud.google.com/yum/doc/yum-key.gpg\n      https://packages.cloud.google.com/yum/doc/rpm-package-key.gpg" > /etc/yum.repos.d/google-cloud.repo \
        && dnf install -y --nobest \
           acpid \
           cloud-init \
           google-compute-engine \
           google-osconfig-agent \
           langpacks-en \
           rng-tools \
           timedatex \
           tuned \
           vim \
        && curl -sSo /tmp/add-google-cloud-ops-agent-repo.sh https://dl.google.com/cloudagents/add-google-cloud-ops-agent-repo.sh \
        && bash /tmp/add-google-cloud-ops-agent-repo.sh --also-install --remove-repo \
        && rm /tmp/add-google-cloud-ops-agent-repo.sh \
        && mkdir -p /var/lib/rpm-state \
        && dnf remove -y irqbalance microcode_ctl \
        && rmdir /var/lib/rpm-state \
        && rm -f /etc/yum.repos.d/google-cloud.repo \
        && sed -i -e '/^pool /c\server metadata.google.internal iburst' /etc/chrony.conf \
        && echo -e 'PermitRootLogin no\nPasswordAuthentication no\nClientAliveInterval 420' >> /etc/ssh/sshd_config \
        && echo -e '[InstanceSetup]\nset_boto_config = false' > /etc/default/instance_configs.cfg \
        && echo 'blacklist floppy' > /etc/modprobe.d/blacklist_floppy.conf \
        && echo -e '[install]\nkargs = ["net.ifnames=0", "biosdevname=0", "scsi_mod.use_blk_mq=Y", "console=ttyS0,38400n8d", "cloud-init=disabled"]' > /usr/lib/bootc/install/05-cloud-kargs.toml
    2. Build the bootc image, in the same directory that holds the Containerfile, by running the following commands:

      $ GCP_BOOTC_IMAGE=quay.io/yourquayusername/bootc-nvidia-rhel9-gcp
      $ podman build --file Containerfile --tag ${GCP_BOOTC_IMAGE} .
Note

Ensure you are running the podman build command from a RHEL enabled system. If you are not on a RHEL system, building the Containerfile file will fail.

  1. Create a config.toml file that will be used in disk image generation.

    [customizations.kernel]
    name = "gcp"
    append = "net.ifnames=0 biosdevname=0 scsi_mod.use_blk_mq=Y console=ttyS0,38400n8d cloud-init=disabled"
  2. Build the disk image using bootc-image-builder by running the following commands:

    $ mkdir -p build/store build/output
    $ podman run --rm -ti --privileged --pull newer \
      -v /var/lib/containers/storage:/var/lib/containers/storage \
      -v ./build/store:/store -v ./build/output:/output \
      -v ./config.toml:/config.toml \
      quay.io/centos-bootc/bootc-image-builder \
        --config /config.toml \
        --chown 0:0 \
        --local \
        --type raw \
        --target-arch x86_64 \
        ${GCP_BOOTC_IMAGE}
    1. Set the name you want to use as the RHEL AI Google Cloud Platform image.

      $ image_name=rhel-ai-1-2
    2. Create a tar.gz file containing the RAW file you created.

      $ raw_file=<path-to-raw-file>
      $ tar cf rhelai_gcp.tar.gz --transform "s|$raw_file|disk.raw|" --use-compress-program=pigz "$raw_file"
      Note

      You can use gzip instead of pigz.

    3. Upload the tar.gz file to the Google Cloud Platform Storage Container by running the following command:

      $ gsutil cp rhelai_gcp.tar.gz "gs://${gcloud_bucket}/$image_name.tar.gz"
    4. Create an Google Cloud Platform image from the tar.gz file you just uploaded with the following command:

      $ gcloud compute images create \
          "$image_name" \
          --source-uri="gs://${gcloud_bucket}/$image_name.tar.gz" \
          --family "rhel-ai" \
          --guest-os-features=GVNIC

5.2. Deploying your instance on Google Cloud Platform using the CLI

You can launch an instance with your new RHEL AI Google Cloud Platform image from the Google Cloud Platform web console or the CLI. You can use whichever method of deployment you want to launch your instance. The following procedure displays how you can use the CLI to launch an Google Cloud Platform instance with the custom Google Cloud Platform image

If you choose to use the CLI as a deployment option, there are several configurations you have to create, as shown in "Prerequisites".

Prerequisites

  • You created your RHEL AI Google Cloud Platform image. For more information, see "Converting the RHEL AI image to a Google Cloud Platform image".
  • You installed the Google Cloud Platform CLI on your specific machine, see Install the Google Cloud Platform CLI on Linux.

Procedure

  1. Log in to your Google Cloud Platform account by running the following command:

    $ gcloud auth login
  2. Before launching your Google Cloud Platform instance on the CLI, you need to create several configuration variables for your instance.
  3. You need to select the instance profile that you want to use for the deployment. List all the profiles in the desired region by running the following command:

    $ gcloud compute machine-types list --zones=<zone>

    Make a note of your preferred machine type, you will need it for your instance deployment.

  4. You can now start creating your Google Cloud Platform instance. Populate environment variables for when you create the instance.

    name=my-rhelai-instance
    zone=us-central1-a
    machine_type=a3-highgpu-8g
    accelerator="type=nvidia-h100-80gb,count=8"
    image=my-custom-rhelai-image
    disk_size=1024
    subnet=default
  5. Configure the zone to be used.

    $ gcloud config set compute/zone $zone
  6. You can now launch your instance, by running the following command:

    $ gcloud compute instances create \
        ${name} \
        --machine-type ${machine_type} \
        --image $image \
        --zone $zone \
        --subnet $subnet \
        --boot-disk-size ${disk_size} \
        --boot-disk-device-name ${name} \
        --accelerator=$accelerator

Verification

  • To verify that your Red Hat Enterprise Linux AI tools are installed correctly, run the ilab command:

    $ ilab

    Example output

    $ ilab
    Usage: ilab [OPTIONS] COMMAND [ARGS]...
    
      CLI for interacting with InstructLab.
    
      If this is your first time running ilab, it's best to start with `ilab
      config init` to create the environment.
    
    Options:
      --config PATH  Path to a configuration file.  [default:
                     /home/auser/.config/instructlab/config.yaml]
      -v, --verbose  Enable debug logging (repeat for even more verbosity)
      --version      Show the version and exit.
      --help         Show this message and exit.
    
    Commands:
      config    Command Group for Interacting with the Config of InstructLab.
      data      Command Group for Interacting with the Data generated by...
      model     Command Group for Interacting with the Models in InstructLab.
      system    Command group for all system-related command calls
      taxonomy  Command Group for Interacting with the Taxonomy of InstructLab.
    
    Aliases:
      chat      model chat
      convert   model convert
      diff      taxonomy diff
      download  model download
      evaluate  model evaluate
      generate  data generate
      init      config init
      list      model list
      serve     model serve
      sysinfo   system info
      test      model test
      train     model train

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