Run Watson NLP for Embed on an IBM Cloud Kubernetes cluster

This example project has two objectives.

• Create an IBM Cloud Kubernetes cluster in a Virtual Private Cloud (VPC) environment with Terraform
• Deploy Watson NLP for embed` to the created cluster with Helm

The example project reuses code from project Use Terraform to create a VPC and a Kubernetes Cluster on IBM Cloud.

Visit the related blog post Run Watson NLP for Embed on an IBM Cloud Kubernetes cluster in a Virtual Private Cloud environment.

Simplified IBM Cloud architecture diagram

Terraform will create and configure on IBM Cloud:

• 1 x VPC

  • 3 x Security Groups

    • 1 x Default
    • 2 x Related to the Kubernetes Cluster (created by the Kubernetes Service creation)

  • 1 x Access control list

  • 1 x Routing table

  • 1 x Public gateway

  • 1 x Virtual Private Endpoint Gateway (created by the Kubernetes Service creation)

  • 1 x Public load balancer (created by the Kubernetes Service creation)

• 1 x Kubernetes Cluster

  • Including 3 fully IBM managed master nodes
  • Configured 2 Worker nodes (managed by IBM) (see responsibilities)
  • Enabled Block Storage for VPC
  • Enabled service endpoint for public and private communication

This is a simplified diagram of the created infrastructure with terraform.

Prerequisites

To use the bash automation you need to have following tools to be installed on your local computer:

• IBM Cloud CLI
• Plugin VPC infrastructure
• Plugin Container-service
• Terraform
• Helm
• kubectl

Example setup

The example setup contains two bash automations:

• One for Terraform
• One for Helm

Step 1: Clone the repo

git clone https://github.com/thomassuedbroecker/terraform-vpc-kubernetes-watson-nlp.git
cd terraform-vpc-kubernetes-watson-nlp

Create the Kubernetes cluster and VPC

Step 1: Navigate to the terraform_setup

cd code/terraform_setup

Step 2: Create a .env file

cat .env_template > .env

Step 3: Add an IBM Cloud access key to your local .env file

nano .env

Content of the file:

export IC_API_KEY=YOUR_IBM_CLOUD_ACCESS_KEY
export REGION="us-east"
export GROUP="tsuedbro"

Step 4: Verify the global variables in the bash script automation

Inspect the bash automation create_vpc_kubernetes_cluster_with_terraform.sh and adjust the values to your need.

nano create_vpc_kubernetes_cluster_with_terraform.sh

#export TF_LOG=debug
export TF_VAR_flavor="bx2.4x16"
export TF_VAR_worker_count="2"
export TF_VAR_kubernetes_pricing="tiered-pricing"
export TF_VAR_resource_group=$GROUP
export TF_VAR_vpc_name="watson-nlp-tsuedbro"
export TF_VAR_region=$REGION
export TF_VAR_kube_version="1.25.5"
export TF_VAR_cluster_name="watson-nlp-tsuedbro"

Step 5: Execute the bash automation

The creation can take up to 1 hour, depending on the region you use.

sh create_vpc_kubernetes_cluster_with_terraform.sh

• Example output:

...
Apply complete! Resources: 5 added, 0 changed, 0 destroyed.
*********************************

Deploy Watson NLP embed with Helm

Step 1: Navigate to the helm_setup

cd code/helm_setup

Step 2: Create a .env file

cat .env_template > .env

Step 3: Add an IBM Cloud access key to your local .env file

export IC_API_KEY=YOUR_IBM_CLOUD_ACCESS_KEY
export IBM_ENTITLEMENT_KEY="YOUR_KEY"
export IBM_ENTITLEMENT_EMAIL="YOUR_EMAIL"
export CLUSTER_ID="YOUR_CLUSTER"
export REGION="us-east"
export GROUP="tsuedbro"

Step 4: Execute the bash automation

sh deploy-watson-nlp-to-kubernetes.sh

The script does following steps and the links are pointing to the relevant function in the bash automation:

1. Log on to IBM Cloud with an IBM Cloud API key.
2. It ensures that is connected to the cluster.
3. It creates a Docker Config File which will be used to create a pull secret.
4. It installs the Helm chart for Watson NLP embed configured for REST API usage.
5. It verifies that the container is running and invokes a REST API call inside the runtime-container of Watson NLP emded.
6. It verifies that the exposed Kubernetes URL with a load balancer service is working and invokes a the same REST API call as before from the local machine.

• Example output:

*********************
loginIBMCloud
*********************

...

*********************
connectToCluster
*********************

OK
...

*********************
createDockerCustomConfigFile
*********************

IBM_ENTITLEMENT_SECRET: 
...

*********************
installHelmChart
*********************

...

*********************
verifyDeploment
*********************


------------------------------------------------------------------------
Check watson-nlp-container
Status: watson-nlp-container
2023-01-12 09:43:32 Status: watson-nlp-container is created
------------------------------------------------------------------------

*********************
verifyPod could take 10 min
*********************


------------------------------------------------------------------------
Check watson-nlp-container
Status: 0/1
2023-01-12 09:43:32 Status: watson-nlp-container(0/1)
------------------------------------------------------------------------
Status: 0/1
2023-01-12 09:44:33 Status: watson-nlp-container(0/1)
------------------------------------------------------------------------
Status: 1/1
2023-01-12 09:45:34 Status: watson-nlp-container is created
------------------------------------------------------------------------

*********************
verifyWatsonNLPContainer
*********************

Pod: watson-nlp-container-557d9fcf68-wm4vp

Result of the Watson NLP API request:
http://localhost:8080/v1/watson.runtime.nlp.v1/NlpService/SyntaxPredict

{"text":"This is a test sentence.", "producerId":{"name":"Izumo Text Processing", "version":"0.0.1"}, "tokens":[{"span":{"begin":0, "end":4, "text":"This"}, "lemma":"", "partOfSpeech":"POS_UNSET", "dependency":null, "features":[]}, {"span":{"begin":5, "end":7, "text":"is"}, "lemma":"", "partOfSpeech":"POS_UNSET", "dependency":null, "features":[]}, {"span":{"begin":8, "end":9, 
...

The image below shows the running container on the Kubernetes cluster.