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FineTuning_Kaito.md

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Fine-Tuning with Kaito

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster.

Kaito has the following key differentiations compared to most of the mainstream model deployment methodologies built on top of virtual machine infrastructures:

  • Manage model files using container images. A http server is provided to perform inference calls using the model library.
  • Avoid tuning deployment parameters to fit GPU hardware by providing preset configurations.
  • Auto-provision GPU nodes based on model requirements.
  • Host large model images in the public Microsoft Container Registry (MCR) if the license allows.

Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

Architecture

Kaito follows the classic Kubernetes Custom Resource Definition(CRD)/controller design pattern. User manages a workspace custom resource which describes the GPU requirements and the inference specification. Kaito controllers will automate the deployment by reconciling the workspace custom resource.

Kaito architecture

The above figure presents the Kaito architecture overview. Its major components consist of:

  • Workspace controller: It reconciles the workspace custom resource, creates machine (explained below) custom resources to trigger node auto provisioning, and creates the inference workload (deployment or statefulset) based on the model preset configurations.
  • Node provisioner controller: The controller's name is gpu-provisioner in gpu-provisioner helm chart. It uses the machine CRD originated from Karpenter to interact with the workspace controller. It integrates with Azure Kubernetes Service(AKS) APIs to add new GPU nodes to the AKS cluster.

Note: The gpu-provisioner is an open sourced component. It can be replaced by other controllers if they support Karpenter-core APIs.

Installation

Please check the installation guidance here.

Quick start

After installing Kaito, one can try following commands to start a fine-tuning service.

apiVersion: kaito.sh/v1alpha1
kind: Workspace
metadata:
  name: workspace-tuning-phi-3
resource:
  instanceType: "Standard_NC6s_v3"
  labelSelector:
    matchLabels:
      app: tuning-phi-3
tuning:
  preset:
    name: phi-3-mini-128k-instruct
  method: qlora
  input:
    urls:
      - "https://huggingface.co/datasets/philschmid/dolly-15k-oai-style/resolve/main/data/train-00000-of-00001-54e3756291ca09c6.parquet?download=true"
  output:
    image: "ACR_REPO_HERE.azurecr.io/IMAGE_NAME_HERE:0.0.1" # Tuning Output ACR Path
    imagePushSecret: ACR_REGISTRY_SECRET_HERE

$ cat examples/fine-tuning/kaito_workspace_tuning_phi_3.yaml

apiVersion: kaito.sh/v1alpha1
kind: Workspace
metadata:
  name: workspace-tuning-phi-3
resource:
  instanceType: "Standard_NC6s_v3"
  labelSelector:
    matchLabels:
      app: tuning-phi-3
tuning:
  preset:
    name: phi-3-mini-128k-instruct
  method: qlora
  input:
    urls:
      - "https://huggingface.co/datasets/philschmid/dolly-15k-oai-style/resolve/main/data/train-00000-of-00001-54e3756291ca09c6.parquet?download=true"
  output:
    image: "ACR_REPO_HERE.azurecr.io/IMAGE_NAME_HERE:0.0.1" # Tuning Output ACR Path
    imagePushSecret: ACR_REGISTRY_SECRET_HERE
    

$ kubectl apply -f examples/fine-tuning/kaito_workspace_tuning_phi_3.yaml

The workspace status can be tracked by running the following command. When the WORKSPACEREADY column becomes True, the model has been deployed successfully.

$ kubectl get workspace kaito_workspace_tuning_phi_3.yaml
NAME                  INSTANCE            RESOURCEREADY   INFERENCEREADY   WORKSPACEREADY   AGE
workspace-tuning-phi-3   Standard_NC6s_v3   True            True             True             10m

Next, one can find the inference service's cluster ip and use a temporal curl pod to test the service endpoint in the cluster.

$ kubectl get svc workspace_tuning
NAME                  TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)            AGE
workspace-tuning-phi-3   ClusterIP   <CLUSTERIP>  <none>        80/TCP,29500/TCP   10m

export CLUSTERIP=$(kubectl get svc workspace-tuning-phi-3 -o jsonpath="{.spec.clusterIPs[0]}") 
$ kubectl run -it --rm --restart=Never curl --image=curlimages/curl -- curl -X POST http://$CLUSTERIP/chat -H "accept: application/json" -H "Content-Type: application/json" -d "{\"prompt\":\"YOUR QUESTION HERE\"}"