docs: update for multi-runtime support

README.md

@@ -14,8 +14,9 @@ Kaito is an operator that automates the AI/ML model inference or tuning workload
 The target models are popular open-sourced large models such as [falcon](https://huggingface.co/tiiuae) and [phi-3](https://huggingface.co/docs/transformers/main/en/model_doc/phi3).
 Kaito has the following key differentiations compared to most of the mainstream model deployment methodologies built on top of virtual machine infrastructures:
 
-- Manage large model files using container images. A http server is provided to perform inference calls using the model library.
+- Manage large model files using container images. An OpenAI-compatible server is provided to perform inference calls.
 - Provide preset configurations to avoid adjusting workload parameters based on GPU hardware.
+- Provide support for popular open-sourced inference runtimes: [vLLM](https://github.com/vllm-project/vllm) and [transformers](https://github.com/huggingface/transformers).
 - Auto-provision GPU nodes based on model requirements.
 - Host large model images in the public Microsoft Container Registry (MCR) if the license allows.
 
@@ -40,43 +41,69 @@ Please check the installation guidance [here](./docs/installation.md) for deploy
 
 ## Quick start
 
-After installing Kaito, one can try following commands to start a falcon-7b inference service.
+After installing Kaito, one can try following commands to start a phi-3.5-mini-instruct inference service.
 
 ```sh
-$ cat examples/inference/kaito_workspace_falcon_7b.yaml
+$ cat examples/inference/kaito_workspace_phi_3.5-instruct.yaml
 apiVersion: kaito.sh/v1alpha1
 kind: Workspace
 metadata:
-  name: workspace-falcon-7b
+  name: workspace-phi-3-5-mini
 resource:
   instanceType: "Standard_NC12s_v3"
   labelSelector:
     matchLabels:
-      apps: falcon-7b
+      apps: phi-3-5
 inference:
   preset:
-    name: "falcon-7b"
+    name: phi-3.5-mini-instruct
 
-$ kubectl apply -f examples/inference/kaito_workspace_falcon_7b.yaml
+$ kubectl apply -f examples/inference/kaito_workspace_phi_3.5-instruct.yaml
 ```
 
 The workspace status can be tracked by running the following command. When the WORKSPACEREADY column becomes `True`, the model has been deployed successfully.
 
 ```sh
-$ kubectl get workspace workspace-falcon-7b
-NAME                  INSTANCE            RESOURCEREADY   INFERENCEREADY   JOBSTARTED  WORKSPACESUCCEEDED  AGE
-workspace-falcon-7b   Standard_NC12s_v3   True            True             True        True                10m
+$ kubectl get workspace workspace-phi-3-5-mini
+NAME                     INSTANCE           RESOURCEREADY   INFERENCEREADY   JOBSTARTED   WORKSPACESUCCEEDED   AGE
+workspace-phi-3-5-mini   Standard_NC12s_v3  True            True                          True                 4h15m
 ```
 
 Next, one can find the inference service's cluster ip and use a temporal `curl` pod to test the service endpoint in the cluster.
 
 ```sh
-$ kubectl get svc workspace-falcon-7b
-NAME                  TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)            AGE
-workspace-falcon-7b   ClusterIP   <CLUSTERIP>  <none>        80/TCP,29500/TCP   10m
-
-export CLUSTERIP=$(kubectl get svc workspace-falcon-7b -o jsonpath="{.spec.clusterIPs[0]}") 
-$ kubectl run -it --rm --restart=Never curl --image=curlimages/curl -- curl -X POST http://$CLUSTERIP/chat -H "accept: application/json" -H "Content-Type: application/json" -d "{\"prompt\":\"YOUR QUESTION HERE\"}"
+# find service endpoint
+$ kubectl get svc workspace-phi-3-5-mini
+NAME                     TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)            AGE
+workspace-phi-3-5-mini   ClusterIP   <CLUSTERIP>  <none>        80/TCP,29500/TCP   10m
+$ export CLUSTERIP=$(kubectl get svc workspace-phi-3-5-mini -o jsonpath="{.spec.clusterIPs[0]}")
+
+# find availalbe models
+$ kubectl run -it --rm --restart=Never curl --image=curlimages/curl -- curl -s  http://$CLUSTERIP/v1/models | jq
+{
+  "object": "list",
+  "data": [
+    {
+      "id": "phi-3.5-mini-instruct",
+      "object": "model",
+      "created": 1733370094,
+      "owned_by": "vllm",
+      "root": "/workspace/vllm/weights",
+      "parent": null,
+      "max_model_len": 16384
+    }
+  ]
+}
+
+# make an inference call using the model id (phi-3.5-mini-instruct) from previous step
+$ kubectl run -it --rm --restart=Never curl --image=curlimages/curl -- curl -X POST http://$CLUSTERIP/v1/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "phi-3.5-mini-instruct",
+    "prompt": "What is kubernetes?",
+    "max_tokens": 7,
+    "temperature": 0
+  }'
 ```
 
 ## Usage

charts/kaito/workspace/values.yaml

@@ -17,6 +17,7 @@ securityContext:
       - "ALL"
 featureGates:
   Karpenter: "false"
+  vLLM: "true"
 webhook:
   port: 9443
 presetRegistryName: mcr.microsoft.com/aks/kaito

docs/inference/README.md

@@ -12,7 +12,7 @@ kind: Workspace
 metadata:
   name: workspace-falcon-7b
 resource:
-  instanceType: "Standard_NC12s_v3"
+  instanceType: "Standard_NC6s_v3"
   labelSelector:
     matchLabels:
       apps: falcon-7b
@@ -40,6 +40,29 @@ Next, the user needs to add the node names in the `preferredNodes` field in the
 > [!IMPORTANT]
 > The node objects of the preferred nodes need to contain the same matching labels as specified in the `resource` spec. Otherwise, the Kaito controller would not recognize them.
 
+### Inference runtime selection
+
+KAITO now supports both [vLLM](https://github.com/vllm-project/vllm) and [transformers](https://github.com/huggingface/transformers) runtime. `vLLM` provides better serving latency and throughput. `transformers` provides more compatibility with models in the Huggingface model hub.
+
+From KAITO v0.4.0, the default runtime is switched to `vLLM`. If you want to use `transformers` runtime, you can specify the runtime in the `inference` spec using an annotation. For example,
+
+```yaml
+apiVersion: kaito.sh/v1alpha1
+kind: Workspace
+metadata:
+  name: workspace-falcon-7b
+  annotations:
+    kaito.sh/runtime: "transformers"
+resource:
+  instanceType: "Standard_NC12s_v3"
+  labelSelector:
+    matchLabels:
+      apps: falcon-7b
+inference:
+  preset:
+    name: "falcon-7b"
+```
+
 ### Inference with LoRA adapters 
 
 Kaito also supports running the inference workload with LoRA adapters produced by [model fine-tuning jobs](../tuning/README.md). Users can specify one or more adapters in the `adapters` field of the `inference` spec. For example,
@@ -69,6 +92,9 @@ Currently, only images are supported as adapter sources. The `strength` field sp
 
 For detailed `InferenceSpec` API definitions, refer to the [documentation](https://github.com/kaito-project/kaito/blob/2ccc93daf9d5385649f3f219ff131ee7c9c47f3e/api/v1alpha1/workspace_types.go#L75).
 
+### Inference API
+
+The OpenAPI specification for the inference API is available at [vLLM API](../../presets/workspace/inference/vllm/api_spec.json), [transformers API](../../presets/workspace/inference/text-generation/api_spec.json).
 
 # Inference workload
 

examples/inference/kaito_workspace_phi_3.5-instruct.yaml

@@ -0,0 +1,12 @@
+apiVersion: kaito.sh/v1alpha1
+kind: Workspace
+metadata:
+  name: workspace-phi-3-5-mini
+resource:
+  instanceType: "Standard_NC12s_v3"
+  labelSelector:
+    matchLabels:
+      apps: phi-3-5
+inference:
+  preset:
+    name: phi-3.5-mini-instruct