Using Custom models on SAP AI Core VIA ollama

In this tutorial we are deploying ollama an open-source project that serves as a powerful and user-friendly platform for running LLMs on on SAP AI core. which acts as a bridge between the complexities of LLM technology and the desire for an accessible and customizable AI experience.

We can pick any model from the above model hubs and connect it to AI core for the example we are going to deploy ollama on AI core and enable Gemma and inference the same.

Workflows for SAP AI Core are created using YAML or JSON files that are compatible with the SAP AI Core schema. Let’s start with adding a Argo Workflow file to manage: ollama.

In your GitHub Create a new repository, click Add file > Create new file.

Type LearningScenarios/ollama.yaml into the Name your file field. This will automatically create the folder workflows and a workflow named ollama.yaml inside it.

CAUTION Do not use the name of your workflow file (ollama.yaml) as any other identifier within SAP AI Core.

Now copy and paste the following snippet to the editor.

apiVersion: ai.sap.com/v1alpha1
kind: ServingTemplate
metadata:
  name: ollama
  annotations:
    scenarios.ai.sap.com/description: "Run a ollama server on SAP AI Core"
    scenarios.ai.sap.com/name: "ollama"
    executables.ai.sap.com/description: "ollama service"
    executables.ai.sap.com/name: "ollama"
  labels:
    scenarios.ai.sap.com/id: "ollama"
    ai.sap.com/version: "0.0.1"
spec:
  template:
    apiVersion: "serving.kserve.io/v1beta1"
    metadata:
      annotations: |
        autoscaling.knative.dev/metric: concurrency
        autoscaling.knative.dev/target: 1
        autoscaling.knative.dev/targetBurstCapacity: 0
      labels: |
        ai.sap.com/resourcePlan: infer.s
    spec: |
      predictor:
        imagePullSecrets:
        - name: <YOUR_DOCKER_SECRET>
        minReplicas: 1
        maxReplicas: 1
        containers:
        - name: kserve-container
          image: docker.io/<YOUR_DOCKER_USER>/ollama:ai-core
          ports:
            - containerPort: 8080
              protocol: TCP

Replace <YOUR_DOCKER_SECRET> with Default and replace <YOUR_DOCKER_USER> with your docker username.

NOTE - we’ll generate the docker image referred here in the following steps.

Sign Up for a Docker account.

Click on the profile button (your profile name) and then select Account Settings.

Select Security from the navigation bar and click New Access Token.

###Creating a Docker Image

Create a directory (folder) named custom-llm. Create a file Dockerfile. Paste the following snippet in the file.

# Specify the base layers (default dependencies) to use
ARG BASE_IMAGE=ubuntu:22.04
FROM ${BASE_IMAGE}

# Update and install dependencies
RUN apt-get update && \
    apt-get install -y \
    ca-certificates \
    nginx \
    curl && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

# Install ollama
RUN curl -fsSL https://ollama.com/install.sh | sh

# Expose port and set environment variables for ollama
ENV ollama_HOST=0.0.0.0
ENV PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility

# Configure nginx for reverse proxy
RUN echo "events { use epoll; worker_connections 128; } \
    http { \
        server { \
                    listen 8080; \
                        location ^~ /v1/api/ { \
                            proxy_pass http://localhost:11434/api/; \
                            proxy_set_header Host \$host; \
                            proxy_set_header X-Real-IP \$remote_addr; \
                            proxy_set_header X-Forwarded-For \$proxy_add_x_forwarded_for; \
                            proxy_set_header X-Forwarded-Proto \$scheme; \
                        } \
                        location ^~ /v1/chat/ { \
                            proxy_pass http://localhost:11434/v1/chat/; \
                            proxy_set_header Host \$host; \
                            proxy_set_header X-Real-IP \$remote_addr; \
                            proxy_set_header X-Forwarded-For \$proxy_add_x_forwarded_for; \
                            proxy_set_header X-Forwarded-Proto \$scheme; \
                        } \
                } \
        }" > /etc/nginx/nginx.conf && \
    chmod -R 777 /var/log/nginx /var/lib/nginx /run

EXPOSE 8080

# Create directory for user nobody SAP AI Core run-time
RUN mkdir -p /nonexistent/.ollama && \
    chown -R nobody:nogroup /nonexistent && \
    chmod -R 770 /nonexistent
#   chmod -R 777 /nonexistent/.ollama

# Start nginx and ollama service
CMD service nginx start && /usr/local/bin/ollama serve

in the same directory open terminal and run the following commands:

1.Login to docker hub

docker login -u <YOUR_DOCKER_USER> -p <YOUR_DOCKER_ACCESS_TOKEN>

2.Build the docker image

docker build --platform=linux/amd64 -t docker.io/<YOUR_DOCKER_USER>/ollama:ai-core .

3.Push the docker image to docker hub to be used by deployment in SAP AI Core

docker push docker.io/<YOUR_DOCKER_USER>/ollama:ai-core

This step is required once. Storing Docker credentials enables SAP AI Core to pull (download) your Docker images from a private Docker repository. Use of a private Docker image prevents others from seeing your content.

Select your SAP AI Core connection under the Workspaces app.

Click Docker Registry Secrets in the AI Core Administration app. Click Add.

A Pop up will appear on screen and add the following Json with the details to your Docker credentials.

{
    ".dockerconfigjson": "{\"auths\":{\"YOUR_DOCKER_REGISTRY_URL\":{\"username\":\"YOUR_DOCKER_USERNAME\",\"password\":\"YOUR_DOCKER_ACCESS_TOKEN\"}}}"
}

Select on your SAP AI Core connection under Workspaces app in the SAP AI Launchpad.

Under the Git Repositories section in AI Core Administration app, click Add.

WARNING If you don’t see the AI Core Administration app, check that you had selected your SAP AI Core connection from the Workspaces app. If it is still not visible then ask your SAP AI Launchpad administrator to assign roles to you so that you can access the app.

Enter your GitHub repository details (created in the previous step) in the dialog box that appears, and click Add.

Use the following information as reference:

• URL: Paste the URL of your GitHub repository and add the suffix /workflows.

• Username: Your GitHub username.

• Password: Paste your GitHub Personal Access Token, generated in the previous step.

Note: Password does not gets validated at time of Adding GitHub Repository its just meant to save GitHub credentials to AI core. Passwords gets validated at time of creating Application or when Application refreshes connection to AI core.

You will see your GitHub onboarding completed in a few seconds. As a next steps we will enable an application on AI core.

Go to your SAP AI Launchpad. In the AI Core Administration app, click Applications > Create.

Using the reference below as a guide, specify the details of your application. This form will create your application on your SAP AI Launchpad.

Use the following information for reference:

• Application Name: An identifier of your choice. learning-scenarios-app is used as an example of best practice in this tutorial because it is a descriptive name.

• Repository URL: Your GitHub account URL and repository suffix. This helps you select the credentials to access the repository.

• Path: The folder in your GitHub where your workflow is located. For this tutorial it is LearningScenarios.

• Revision: The is the unique ID of your GitHub commit. Set this to HEAD to have it automatically refer to the latest commit.

NOTE:

1. If creation of application fails, check the ollama.yaml file, and ensure that the names in lines #4, #7, #9, and #11 are unique, and haven’t been used previously.

2. Generate a fresh classic git token for authentication, with all necessary privileges provided during creation.

3. Ensure that you have put the correct url in YOUR_DOCKER_REGISTRY_URL while setting up docket secret.

4. Refresh the launchpad, and create a fresh application.

Go to ML Operations > Configurations. Click on the Create button.

Enter the model Name and choose the workflow with following parameters

"name": "ollama",
"scenario_id": "ollama",
"executable_id": "ollama",

Then click on next > review and create.

In the model click on create deployment. A screen will appear

Set duration as standard and click on the Review button.

Once you create the deployment, wait for the current status to be set to RUNNING.

Once the deployment is running, you can access the LLM’s using ollama.

Now we need to import Gemma to our ollama pod before we can inference the model so here we would be using SAP AI API to call pull model call in Ollama.

{
    "name": "gemma:2b"
}

import requests
import json
url = "https://api.ai.prasfodeuonly.aws.ml.hana.ondemand.com/v2/inference/deployments/d78749e2ab8c3/v1/api/pull"
payload = json.dumps({
"model": "gemma:2b"
})
headers = {
'AI-Resource-Group': <ai-resource-group>,
'Content-Type': 'application/json',
'Authorization': 'Bearer <TOKEN>'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)

# Check the model list 
endpoint = f"{inference_base_url}/api/tags"
print(endpoint)
response = requests.get(endpoint, headers=headers)
print('Result:', response.text)

completion_api_endpoint = f"{inference_base_url}/api/generate"
#test ollama's completion api
json_data = {
"model": model,
"prompt": "What color is the sky at different times of the day? Respond in JSON",
"format": "json", #JSON mode
"stream": False   #Streaming or not
}
response = requests.post(url=completion_api_endpoint, headers=headers, json=json_data)
print('Result:', response.text)