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Code Block
helm upgrade --install gv-platform charts/gv-platform-$VERSION.tgz --wait \
--timeout=10m0s --kubeconfig /etc/rancher/k3s/k3s.yaml \
--set-string clusterLabels.environment=prod \
--set-string clusterLabels.cluster_reseller=$RESELLER \
--set-string clusterLabels.cluster_name=mycluster \
--set-string clusterLabels.product=$PRODUCT

Install

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custom artifact bundles

Models and other artifacts, like custom agent versions or custom consul configuration can be shipped inside auto deployable bundles. These bundles are docker images that contain the artifacts to be deployed alongside scripts to deploy them. To create a new bundle or modify an existing one follow this guide first: https://getvisibility.atlassian.net/wiki/spaces/GS/pages/65372391/Model+deployment+guide#1.-Create-a-new-model-bundle-or-modify-an-existing-one . The list of all the available bundles is inside the bundles/ directory on the models-ci project on github.

After the model bundle is published, for example images.master.k3s.getvisibility.com/models:company-1.0.1 You’ll have to generate a public link to this image by running the k3s-air-gap Publish ML models GitHub CI task. The task will ask you for the docker image URL.

Info

We are still using the images.master.k3s.getvisibility.com/models repo because the bundles were only used to deploy custom models at first.

Once the task is complete you’ll get a public URL to download the artifact on the summary of the task. After that you have to execute the following commands.

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  • $URL with the URL to the model bundle provided by the task

  • $MODEL_BUNDLE $BUNDLE with the name of the artifact, in this case models_sec company-1.0.1

Code Block
mkdir modelscustom
wget -O modelscustom/$MODEL_BUNDLE$BUNDLE.tar.gz $URL
gunzip modelscustom/$MODEL_BUNDLE$BUNDLE.tar.gz
ctr -n=k8s.io images import models/$MODEL_BUNDLE$BUNDLE.tar

Now you’ll need to execute the model artifact deployment job. This job will unpack models the artifacts from a the docker image into a MinIO bucket and then it will restart the classifiers so that they can get the modelsinside the on premise cluster and restart any services that use them.

Replace the following variables:

  • $MODEL$GV_DEPLOYER_VERSION with the version of the model deployer available under charts/

  • $MODEL_BUNDLE$BUNDLE_VERSION with the version of the artifact, in this case sec company-1.0.1

Code Block
 helm upgrade \
 --install gv-model-deployer charts/gv-model-deployer-$MODEL$GV_DEPLOYER_VERSION.tgz \
 --wait --timeout=10m0s --kubeconfig /etc/rancher/k3s/k3s.yaml \
 --set models.version="$MODEL_BUNDLE$BUNDLE_VERSION"

You should be able to verify that everything went alright by locating the ml-model job that was launched. The logs should look like this:

Code Block
root@ip-172-31-9-140:~# kubectl logs -f ml-model-0jvaycku9prx-84nbf
Uploading models
Added `myminio` successfully.
`/models/AIP-1.0.0.zip` -> `myminio/models-data/AIP-1.0.0.zip`
`/models/Commercial-1.0.0.zip` -> `myminio/models-data/Commercial-1.0.0.zip`
`/models/Default-1.0.0.zip` -> `myminio/models-data/Default-1.0.0.zip`
`/models/classifier-6.1.2.zip` -> `myminio/models-data/classifier-6.1.2.zip`
`/models/lm-full-en-2.1.2.zip` -> `myminio/models-data/lm-full-en-2.1.2.zip`
`/models/sec-mapped-1.0.0.zip` -> `myminio/models-data/sec-mapped-1.0.0.zip`
Total: 0 B, Transferred: 297.38 MiB, Speed: 684.36 MiB/s
Restart classifier
deployment.apps/classifier-focus restarted
root@ip-172-31-9-140:~#

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~#

In addition you can enter the different services that consume these artifacts to check if they have been correctly deployed. For example for the models you can open a shell inside the classifier containers and check the /models directory or check the models-data bucket inside MinIO. Both should contain the expected models.

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  1. Install gv-kube-fledged helm chart.
    Replace $VERSION with the version that is present in the bundle that has been downloaded.
    To check all the charts that have been download run ls charts.

    Code Block
    $ helm upgrade --install gv-kube-fledged charts/gv-kube-fledged-$VERSION.tgz -n kube-fledged \
--timeout=10m0s \
--kubeconfig /etc/rancher/k3s/k3s.yaml \
--create-namespace

  2. Create and deploy imagecache.yaml

    Code Block
    $ sh scripts/create-imagecache-file.sh
$ kubectl apply -f scripts/imagecache.yaml

Install

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custom artifacts

Models and other artifacts, like custom agent versions or custom consul configuration can be shipped inside auto deployable bundles. The procedure to install models custom artifact bundles on an HA cluster is the same as in the single node cluster case. Take a look at the guide for single-node clusters above.

Upgrade

Focus/Synergy/Enterprise Helm Chart

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Info

  1. Import Docker images only to the Master1 node

  2. In the case of HA deployment, Recreate and redeploy the imagecache.yaml file
    https://getvisibility.atlassian.net/wiki/spaces/GS/pages/148242433/Air Gap Installation | Install Kube fledged (Reach out to Support)+Gap+Installation#Install-Kube-fledged: Perform the 2nd Step

GetVisibility Essentials Helm Chart

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Info

  1. Import Docker images only to the Master1 node

  2. In the case of HA deployment, Recreate and redeploy the imagecache.yaml file
    https://getvisibility.atlassian.net/wiki/spaces/GS/pages/148242433/Air Gap Installation | Install Kube fledged (Reach out to Support)+Gap+Installation#Install-Kube-fledged: Perform the 2nd Step

Models

Install custom artifacts

Models and other artifacts, like custom agent versions or custom consul configuration can be shipped inside auto deployable bundles. The procedure to upgrade models custom artifact bundles is the same as the installation one, take a look at the guides above for single-node and multi-node installations.

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