Pre-requisites for Deploying NAI

In this part of the lab we will prepare pre-requisites for LLM application on GPU nodes.

The following is the flow of the applications lab:

stateDiagram-v2
    direction LR

    state PreRequisites {
        [*] --> CreateFilesShare  
        CreateFilesShare --> PrepareHuggingFace
        PrepareHuggingFace --> [*]
    }
    state CreateOfflineHelmContainers {
        [*] --> PrepareNAIHelmCharts
        PrepareNAIHelmCharts --> PrepareNAIContainerImages
        PrepareNAIContainerImages --> [*]
    }

    [*] --> PreRequisites
    PreRequisites --> CreateOfflineHelmContainers
    CreateOfflineHelmContainers --> DeployNAI : next section
    DeployNAI --> TestNAI
    TestNAI --> [*]

Prepare the following pre-requisites needed to deploy NAI on target kubernetes cluster.

We will create Nutanix Files storage class which will be used to create a pvc that will store the LLama-3-8B model files.

In Prism Central, choose Files from the menu
Choose the file server (e.g. labFS)
Click on Shares & Exports
Click on +New Share or Export
Fill the details of the Share
- Name - model_share
- Description - for NAI model store
- Share path - leave blank
- Max Size - 10 GiB (adjust to the model file size)
- Primary Protocol Access - NFS
Click Next and make sure Enable compression in checked
Click Next
In NFS Protocol Access, choose the following:
- Authentication - System
- Default Access (for all clients) - Read-Write
- Squash - Root Squash
Note

Consider changing access options for Production environment
Click Next
Confirm the share details and click on Create
This is where all the pvc will be created for the models eventually
Create another share using the same steps for initial model download that will be mounted on the jumphost
- Name - model_download
- Description - for NAI model store
- Share path - leave blank
- Max Size - 10 GiB (adjust to the model file size)
- Primary Protocol Access - NFS
- Authentication - System
- Default Access (for all clients) - Read-Write
- Squash - Root Squash

Create the Files Storage Class

Run the following command to check K8S status of the nkpdarksite cluster

CommandCommand output

export KUBECONFIG=nkpdarksite.conf
kubectl get nodes

$ kubectl get nodes

NAME                                  STATUS   ROLES           AGE     VERSION
nkpdarksite-md-0-x948v-hvxtj-9r698           Ready    <none>          4h49m   v1.29.6
nkpdarksite-md-0-x948v-hvxtj-fb75c           Ready    <none>          4h50m   v1.29.6
nkpdarksite-md-0-x948v-hvxtj-mdckn           Ready    <none>          4h49m   v1.29.6
nkpdarksite-md-0-x948v-hvxtj-shxc8           Ready    <none>          4h49m   v1.29.6
nkpdarksite-r4fwl-8q4ch                      Ready    control-plane   4h50m   v1.29.6
nkpdarksite-r4fwl-jf2s8                      Ready    control-plane   4h51m   v1.29.6
nkpdarksite-r4fwl-q888c                      Ready    control-plane   4h49m   v1.29.6

In VSC Explorer, click on New File and create a config file with the following name:

nai-nfs-storage.yaml

Add the following content and replace the nfsServerName with the name of the Nutanix Files server name .

Finding nfsServerName and nfsServer fqdn

Template YAMLSample YAML

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: nai-nfs-storage
parameters:
  nfsPath: <nfs-path>
  nfsServer: <nfs-server>
  storageType: NutanixFiles
provisioner: csi.nutanix.com
reclaimPolicy: Delete
volumeBindingMode: Immediate

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: nai-nfs-storage
parameters:
  nfsPath: /model_share
  nfsServer: labFS.ntnxlab.local
  storageType: NutanixFiles
provisioner: csi.nutanix.com
reclaimPolicy: Delete
volumeBindingMode: Immediate

Create the storage class
```
kubectl apply -f nai-nfs-storage.yaml
```

Check storage classes in the cluster for the Nutanix Files storage class

CommandCommand output

kubectl get storageclass

kubectl get storageclass

NAME                       PROVISIONER                     RECLAIMPOLICY   VOLUMEBINDINGMODE      ALLOWVOLUMEEXPANSION   AGE
dkp-object-store           kommander.ceph.rook.io/bucket   Delete          Immediate              false                  28h
nai-nfs-storage            csi.nutanix.com                 Delete          Immediate              true                   24h
nutanix-volume (default)   csi.nutanix.com                 Delete          WaitForFirstConsumer   false                  28h

Request Access to Model on Hugging Face

Gated models will require a Hugging Face token to be able to download.

In this section we will create a token as Meta-Llama is a gated model.

Info

Non-gated models can be directly downloaded using Hugging Face CLI.

Follow these steps to request access to the meta-llama/Meta-Llama-3.1-8B-Instruct model:

LLM Recommendation

From testing google/gemma-2-2b-it model is quicker to download and obtain download rights, than meta-llama/Meta-Llama-3.1-8B-Instruct model.

Feel free to use the google/gemma-2-2b-it model if necessary. The procedure to request access to the model is the same.

Sign in to your Hugging Face account:
- Visit Hugging Face and log in to your account.
Navigate to the model page:
- Go to the Meta-Llama-3.1-8B-Instruct model page.
Request access:
- On the model page, you will see a section or button labeled Request Access (this is usually near the top of the page or near the "Files and versions" section).
- Click Request Access.
Complete the form:
- You may be prompted to fill out a form or provide additional details about your intended use of the model.
- Complete the required fields and submit the request.
Wait for approval:
- After submitting your request, you will receive a notification or email once your access is granted.
- This process can take some time depending on the approval workflow.

Once access is granted, there will be an email notification.

Note

Email from Hugging Face can take a few minutes or hours before it arrives.

Create a Hugging Face Token with Read Permissions

Follow these steps to create a Hugging Face token with read permissions:

Sign in to your Hugging Face account:
- Visit Hugging Face and log in to your account.
Access your account settings:
- Click on your profile picture in the top-right corner.
- From the dropdown, select Settings.
Navigate to the "Access Tokens" section:
- In the sidebar, click on Access Tokens.
- You will see a page where you can create and manage tokens.
Create a new token:
- Click the New token button.
- Enter a name for your token (i.e., read-only-token).
Set token permissions:
- Under the permissions dropdown, select Read. For Example:
Create and copy the token:
- After selecting the permissions, click Create.
- Your token will be generated and displayed only once, so make sure to copy it and store it securely.

Use this token for accessing Hugging Face resources with read-only permissions.

Download the model

We have two options in downloading a model to be accessed later by NAI.

Nutanix Files and Nutanix Objects (bucket) can be mounted as a file share on the jumphost with secure mount options.

File share - create a file share on Nutanix Files and mount it on the jumphost
Object storage - create a bucket on Nutanix Objects and mount it as a file share on the jumphost

File Share Design

For production environments, consider creating two file shares:

For model initial download location - /model_download for example
For NAI to store models - /model_share

This will ensure that concurrency and latency requirements are taken care of.

In Nutanix Files, create the /model_download share by following instructions here

Mount the /model_download file share created here on the jumphost using the following script

Script Script output

#!/bin/bash
set -e

# ==============================================================================
# CONFIGURATION
# ==============================================================================
# 1. Hugging Face Access Token (Must have accepted license terms on the web repo)
HF_TOKEN="hf_your_actual_token_here"

# 2. File share mounting details
SHARE_SOURCE="labFS.ntnxlab.local:/model_download"
SHARE_TYPE="nfs"
SHARE_OPTIONS="rw,sync,hard,intr"

# Target Directories
MOUNT_POINT="/model_share"
TARGET_DIR="${MOUNT_POINT}/Llama-3.1-8B-Instruct"
MODEL_REPO="meta-llama/Llama-3.1-8B-Instruct"

# Binary Location from pipx
HF_BIN="/root/.local/bin/hf"

# ==============================================================================
# 1. MOUNT FILE SHARE
# ==============================================================================
echo "Creating mount point folder at ${MOUNT_POINT}..."
mkdir -p "${MOUNT_POINT}"

echo "Mounting ${SHARE_SOURCE} to ${MOUNT_POINT}..."
if mountpoint -q "${MOUNT_POINT}"; then
    echo "Directory is already mounted."
else
    sudo mount -t "${SHARE_TYPE}" -o "${SHARE_OPTIONS}" "${SHARE_SOURCE}" "${MOUNT_POINT}"
    echo "Mount successful."
fi

# Create the final model directory inside the share 
# (This side-steps root-squashing permissions on the parent directory)
echo "Preparing local target folder..."
mkdir -p "${TARGET_DIR}"

# ==============================================================================
# 2. DOWNLOAD VIA MODERN HF CLIENT
# ==============================================================================
echo "Starting download for ${MODEL_REPO} using modern hf CLI..."

# Run the download command referencing the direct absolute path
# Using the updated 'hf download' structure and mapping parameters
${HF_BIN} download \
    "${MODEL_REPO}" \
    --local-dir "${TARGET_DIR}" \
    --token "${HF_TOKEN}"

echo "Download completed successfully!"
echo "Model files are located at: ${TARGET_DIR}"

Creating mount point at /model_share...
Mounting labFS.ntnxlab.local:/model_share/ to /model_download...
Directory is already mounted.
'huggingface-hub' already seems to be installed. Not modifying existing installation in
'/root/.local/share/pipx/venvs/huggingface-hub'. Pass '--force' to force installation.
injected package hf-transfer into venv huggingface-hub
done! ✨ 🌟 ✨
Starting download for meta-llama/Llama-3.1-8B-Instruct...
/root/.local/share/pipx/venvs/huggingface-hub/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.
warnings.warn(
Downloading bytes:                                                                      |  0.00B            Still waiting to acquire lock on /model_share/Llama-3.1-8B-Instruct/.cache/huggingface/.gitignore.lock (elapsed: 0.1 seconds)es:   0%|                                                             | 0/17 [00:00<?, ?it/s]
Fetching 17 files: 100%|████████████████████████████████████████████████████| 17/17 [01:07<00:00,  3.98s/it]
Download complete: : ███████████████████████████████████████████████████████████████████| 27.6GB, 37.5MB/s  ✓ Downloadedion complete: 100%|█████████████████████████████████████████████████| 32.1GB / 32.1GB,  303MB/s  
path: /model_share/Llama-3.1-8B-Instruct
Download complete: : ███████████████████████████████████████████████████████████████████| 27.6GB, 37.5MB/s  
Reconstruction complete: 100%|█████████████████████████████████████████████████| 32.1GB / 32.1GB,  303MB/s  
Download completed successfully! 
Model located at: /model_share/Llama-3.1-8B-Instruct

Confirm model files in the directory on the File share

Command Command output

ls -l /model_share/Llama-3.1-8B-Instruct/

$ ls -l /model_share/Llama-3.1-8B-Instruct/
#
total 15714428
drwxr-xr-x  4 4294967294 4294967294         18 Jul 20 01:34 ./
drwxrwxrwt 12 root       root               12 Jul 20 01:38 ../
drwxr-xr-x  3 4294967294 4294967294          3 Jul 20 01:33 .cache/
-rw-r--r--  1 4294967294 4294967294       1519 Jul 20 01:33 .gitattributes
-rw-r--r--  1 4294967294 4294967294       7627 Jul 20 01:33 LICENSE
-rw-r--r--  1 4294967294 4294967294      44044 Jul 20 01:33 README.md
-rw-r--r--  1 4294967294 4294967294       4691 Jul 20 01:33 USE_POLICY.md
-rw-r--r--  1 4294967294 4294967294        855 Jul 20 01:33 config.json
-rw-r--r--  1 4294967294 4294967294        184 Jul 20 01:33 generation_config.json
-rw-r--r--  1 4294967294 4294967294 4976698672 Jul 20 01:34 model-00001-of-00004.safetensors
-rw-r--r--  1 4294967294 4294967294 4999802720 Jul 20 01:34 model-00002-of-00004.safetensors
-rw-r--r--  1 4294967294 4294967294 4915916176 Jul 20 01:34 model-00003-of-00004.safetensors
-rw-r--r--  1 4294967294 4294967294 1168138808 Jul 20 01:33 model-00004-of-00004.safetensors
-rw-r--r--  1 4294967294 4294967294      23950 Jul 20 01:33 model.safetensors.index.json
drwxr-xr-x  2 4294967294 4294967294          5 Jul 20 01:34 original/
-rw-r--r--  1 4294967294 4294967294        296 Jul 20 01:33 special_tokens_map.json
-rw-r--r--  1 4294967294 4294967294    9085657 Jul 20 01:33 tokenizer.json
-rw-r--r--  1 4294967294 4294967294      55351 Jul 20 01:33 tokenizer_config.json

Buckets Store

Follow the same procedure to mount the bucket on the jump host and download the model.

Create a bucket
Create access and secret keys
Enable NFS mount options on the buckets
Mount the share on jumphost VM
Download the model to mounted file share using the script in the previous section

Prepare Container Images and Helm Charts

The Jumphost VM will be used as a medium to download the NAI container images and upload them to the internal Harbor container registry.

stateDiagram-v2
    direction LR

    state LoginToNutanixPortal {
        [*] --> CreateDockerIDandAccessToken
        CreateDockerIDandAccessToken --> LoginToDockerCLI
        LoginToDockerCLI --> [*]
    }

    state PrepareNAIDockerImages {
        [*] --> DownloadUploadImagesToHarbor
        DownloadUploadImagesToHarbor --> [*]
    }

    [*] --> LoginToNutanixPortal
    LoginToNutanixPortal --> PrepareNAIDockerImages
    PrepareNAIDockerImages --> CreateNKPCatalogCollection
    CreateNKPCatalogCollection --> [*]

Prepare Environment

In this section we will use NKP product catalog extensions to prepare NAI container images.

High Level Steps

Login to docker.io using ntnxsvcgpt account (obtained from Portal)
Create NAI Airgap bundle
Push the bundle to local (internal) Harbor registry
Create NAI Catalog collection and use this to install all pre-requisites and NAI

We will prepare the helm charts necessary for NAI and pre-requisite applications install:

NAI
Envoy Gateway
Kserve
OpenTelemetry Operator

The procedure will be done on the jumphost VM.

Login to Nutanix Portal using your credentials
Go to Downloads > Nutanix Eneterprise AI > Manage Access Token
Copy the download ntnxsvcgpt accounts credentials
Open new VSCode window on your jumphost VM
In VSCode Explorer pane, click on existing $HOME folder
Click on New Folder name it: airgap-nai
On VSCode Explorer plane, click the $HOME/airgap-nai folder
On VSCode menu, select Terminal > New Terminal
Browse to airgap-nai directory
```
cd $HOME/airgap-nai
```
In VSC, under the newly created airgap-nai folder, click on New File and create file with the following name:
```
.env
```

Add (append) the following environment variables and save it

Template $HOME/airgap-nai/.env Sample $HOME/airgap-nai/.env

export DOCKER_NAI_USERNAME=ntnxsvcgpt
export DOCKER_NAI_PAT=_XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
export HARBOR_REGISTRY=_your_harbor_registry_url
export HARBOR_REGISTRY_USERNAME=admin
export HARBOR_REGISTRY_PASSWORD=_xxxxxxx
export HARBOR_PROJECT=_your_harbor_project_name      # (1)!
export HARBOR_REGISTRY_CACERT=_path_to_ca_cert_of_registry  # (2)!

The Harbor project must exist for uploads to work
File must contain private CA server and Harbor server's public certificate in one file

export DOCKER_NAI_USERNAME=ntnxsvcgpt
export DOCKER_NAI_PAT=_XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
export HARBOR_REGISTRY=harbor.10.x.x.134.nip.io
export HARBOR_REGISTRY_USERNAME=admin
export HARBOR_REGISTRY_PASSWORD=_xxxxxxx
export HARBOR_PROJECT=nutanix                             # (1)!
export HARBOR_REGISTRY_CACERT=$HOME/harbor/certs/full_chain.pem  # (2)!

The Harbor project must exist for uploads to work - we will create the project in the next step
File must contain privatre CA server and Harbor server's public certificate in one file

Source the $HOME/airgap-nai/.env file to import environment variables
Command
source $HOME/airgap-nai/.env

Create a project called nutanix in the Harbor registry using the following curl command or simply use the Harbor GUI

API Call API Sample Call

curl -X POST \
  -u "${HARBOR_REGISTRY_USERNAME}:${HARBOR_REGISTRY_PASSWORD}" \
  -H "Content-Type: application/json" \
  "https://${HARBOR_REGISTRY}" \
  -d '{
  "project_name": "${HARBOR_PROJECT}",
  "metadata": {
      "public": "false"
  }
  }'

curl -X POST \
  -u "admin:_XXXXXXXXX" \
  -H "Content-Type: application/json" \
  "https://harbor.10.x.x.134.nip.io" \
  -d '{
  "project_name": "nutanix",
  "metadata": {
      "public": "false"
  }
  }'

If your Harbour registry is certified by a private CA, make sure to copy the CA's public key ${HARBOR_REGISTRY_CACERT} to your jumphost's Docker certificate store

Command Sample command

sudo mkdir -p /etc/docker/certs.d/${HARBOR_REGISTRY}/
sudo cp ${HARBOR_REGISTRY_CACERT} /etc/docker/certs.d/${HARBOR_REGISTRY}/

sudo mkdir -p /etc/docker/certs.d/harbor.10.x.x.134.nip.io/
sudo cp /home/ubuntu/harbor/certs/full_chain.pem /etc/docker/certs.d/harbor.10.x.x.134.nip.io/

Clone NKP product catalog git repository

Command

git clone https://github.com/nutanix-cloud-native/nkp-nutanix-product-catalog
cd nkp-nutanix-product-catalog

Login to docker.io with ntnxsvcgpt credentials to be able to download NAI container images and artifacts

Command Command output

docker login -u ${DOCKER_NAI_USERNAME} -p ${DOCKER_NAI_PAT}

$ docker login -u ntnxsvcgpt -p _XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
#
i Info → A Personal Access Token (PAT) can be used instead.
        To create a PAT, visit https://app.docker.com/settings


Password: 

WARNING! Your credentials are stored unencrypted in '/home/ubuntu/.docker/config.json'.
Configure a credential helper to remove this warning. See
https://docs.docker.com/go/credential-store/

Create NAI Airgap Bundle and NKP Catalog Applications

In this section we will create the NKP catalog applications components that can be used to deploy pre-requistes applications.

Run the following command to check K8S status of the nkpdarksite cluster and set context for NKP cluster

CommandCommand output

export KUBECONFIG=nkpdarksite.conf
kubectl get nodes

$ kubectl get nodes

NAME                                  STATUS   ROLES           AGE     VERSION
nkpdarksite-md-0-x948v-hvxtj-9r698           Ready    <none>          4h49m   v1.29.6
nkpdarksite-md-0-x948v-hvxtj-fb75c           Ready    <none>          4h50m   v1.29.6
nkpdarksite-md-0-x948v-hvxtj-mdckn           Ready    <none>          4h49m   v1.29.6
nkpdarksite-md-0-x948v-hvxtj-shxc8           Ready    <none>          4h49m   v1.29.6
nkpdarksite-r4fwl-8q4ch                      Ready    control-plane   4h50m   v1.29.6
nkpdarksite-r4fwl-jf2s8                      Ready    control-plane   4h51m   v1.29.6
nkpdarksite-r4fwl-q888c                      Ready    control-plane   4h49m   v1.29.6

Create the Airgap bundle for NAI

Command Command output

nkp create catalog-bundle --airgapped \
  --collection-tag 2.17 \
  --apps=nutanix-ai=2.7.0,envoy-gateway-nai=1.7.0,kserve=0.15.0,opentelemetry-operator=0.102.0

nkp create catalog-bundle --airgapped --collection-tag 2.17  --apps=nutanix-ai=2.7.0,envoy-gateway-nai=1.7.0,kserve=0.15.0,opentelemetry-operator=0.102.0
Bundling 4 application(s) (airgapped : true)
✓ Building OCI artifact nkp-nutanix-product-catalog/collection:2.17
✓ Building OCI artifact nkp-nutanix-product-catalog/nutanix-ai:2.7.0
✓ Building OCI artifact nkp-nutanix-product-catalog/envoy-gateway-nai:1.7.0
✓ Building OCI artifact nkp-nutanix-product-catalog/kserve:0.15.0
✓ Building OCI artifact nkp-nutanix-product-catalog/opentelemetry-operator:0.102.0
Processing application nutanix-ai/2.7.0
✓ K8s [1.34.0 1.35.0 1.36.0]: Parsing resources 
✓ K8s v1.34.0: Validating 
✓ K8s v1.35.0: Validating 
✓ K8s v1.36.0: Validating 
Processing application envoy-gateway-nai/1.7.0
✓ K8s [1.34.0 1.35.0 1.36.0]: Parsing resources 
✓ K8s v1.34.0: Validating 
✓ K8s v1.35.0: Validating 
✓ K8s v1.36.0: Validating 
Processing application kserve/0.15.0
✓ K8s [1.33.0 1.34.0 1.35.0 1.36.0]: Parsing resources 
✓ K8s v1.33.0: Validating 
✓ K8s v1.34.0: Validating 
✓ K8s v1.35.0: Validating 
✓ K8s v1.36.0: Validating 
Processing application opentelemetry-operator/0.102.0
✓ K8s [1.33.0 1.34.0 1.35.0 1.36.0]: Parsing resources 
✓ K8s v1.33.0: Validating 
✓ K8s v1.34.0: Validating 
✓ K8s v1.35.0: Validating 
✓ K8s v1.36.0: Validating 
✓ Pulling requested images [==================================>57/57] (time elapsed 9m59s) 
✓ Saving application bundle to /home/ubuntu/temp/nkp-nutanix-product-catalog/nkp-nutanix-product-catalog-airgapped.tar 
Run the following to push the artifact to your registry:

nkp push bundle --bundle /home/ubuntu/temp/nkp-nutanix-product-catalog/nkp-nutanix-product-catalog-airgapped.tar --to-registry <your-registry-url>


Run the following command to create catalog artifact(s) after pushing them:

nkp create catalog-collection --url oci://<registry-url>/nkp-nutanix-product-catalog/collection --tag 2.17 --workspace kommander-workspace

Login to your local Harbor registry with credentials to be able to upload NAI container images and artifacts

Command Command output

docker login ${HARBOR_REGISTRY} -u admin # (1)!

Make sure that the private CA's public certificate is in Docker certificate store - documented above

$ docker login harbor.10.x.x.134.nip.io -u admin
#
Password: 

WARNING! Your credentials are stored unencrypted in '/home/ubuntu/.docker/config.json'.
Configure a credential helper to remove this warning. See
https://docs.docker.com/go/credential-store/

Login Succeeded

Push the created bundle to the registry

Command Sample command Command output

nkp push bundle \
  --bundle nkp-nutanix-product-catalog-airgapped.tar \ 
  --to-registry ${HARBOR_REGISTRY}/${HARBOR_PROJECT}

nkp push bundle \
  --bundle nkp-nutanix-product-catalog-airgapped.tar \
  --to-registry harbor.10.x.x.134.nip.io/nutanix

nkp push bundle --bundle /home/ubuntu/temp/nkp-nutanix-product-catalog/nkp-nutanix-product-catalog-airgapped.tar --to-registry harbor.10.x.x.134.nip.io/nutanix
✓ Creating temporary directory
✓ Extracting bundle configs from "/home/ubuntu/temp/nkp-nutanix-product-catalog/nkp-nutanix-product-catalog-airgapped.tar"
✓ Parsing image bundle config
✓ Starting temporary Docker registry
✓ Pushing bundled images [==================================>57/57] (time elapsed 1m29s)

Deploy catalog collection

Command Sample command Command output

nkp create catalog-collection \
  --url oci://${HARBOR_REGISTRY}/${HARBOR_PROJECT}/nkp-nutanix-product-catalog/collection \
  --tag 2.17 \
  --workspace kommander-workspace

nkp create catalog-collection \
  --url oci://harbor.10.x.x.134.nip.io/nutanix/nkp-nutanix-product-catalog/collection \
  --tag 2.17 \
  --workspace kommander-workspace

Catalog collection nkp-nutanix-product-catalog-collection created. 
Use 'nkp edit ocirepository -n kommander nkp-nutanix-product-catalog-collection'to change its configuration if needed.

We can now proceed to deploy NAI.

Pre-requisites for Deploying NAI

Create Nutanix Files Share

Create the Files Storage Class

Request Access to Model on Hugging Face

Create a Hugging Face Token with Read Permissions

Download the model

File Share

Buckets Store

Prepare Container Images and Helm Charts

Prepare Environment

Create NAI Airgap Bundle and NKP Catalog Applications