[WIP] Add support for kserve

docs/ramalama-serve.1.md

@@ -60,8 +60,9 @@ Generate specified configuration format for running the AI Model as a service
 
 | Key          | Description                                                              |
 | ------------ | -------------------------------------------------------------------------|
-| quadlet      | Podman supported container definition for running AI Model under systemd |
+| kserve       | Kserve YAML definition for running the AI Model as a kserve service in Kubernetes        |
 | kube         | Kubernetes YAML definition for running the AI Model as a service         |
+| quadlet      | Podman supported container definition for running AI Model under systemd |
 | quadlet/kube | Kubernetes YAML definition for running the AI Model as a service and Podman supported container definition for running the Kube YAML specified pod under systemd|
 
 #### **--help**, **-h**
@@ -119,7 +120,7 @@ llama.cpp explains this as:
 
     The higher the number is the more creative the response is, but more likely to hallucinate when set too high.
 
-        Usage: Lower numbers are good for virtual assistants where we need deterministic responses. Higher numbers are good for roleplay or creative tasks like editing stories
+	Usage: Lower numbers are good for virtual assistants where we need deterministic responses. Higher numbers are good for roleplay or creative tasks like editing stories
 
 #### **--tls-verify**=*true*
 require HTTPS and verify certificates when contacting OCI registries
@@ -140,6 +141,73 @@ CONTAINER ID  IMAGE                             COMMAND               CREATED
 3f64927f11a5  quay.io/ramalama/ramalama:latest  /usr/bin/ramalama...  17 seconds ago  Up 17 seconds  0.0.0.0:8082->8082/tcp  ramalama_YMPQvJxN97
 ```
 
+### Generate kserve service off of OCI Model car quay.io/ramalama/granite:1.0
+```
+./bin/ramalama serve --port 8081 --generate kserve oci://quay.io/ramalama/granite:1.0
+Generating kserve runtime file: granite-1.0-kserve-runtime.yaml
+Generating kserve file: granite-1.0-kserve.yaml
+
+$  cat granite-1.0-kserve-runtime.yaml 
+apiVersion: serving.kserve.io/v1alpha1
+kind: ServingRuntime
+metadata:
+  name: llama.cpp-runtime
+  annotations:
+    openshift.io/display-name: KServe ServingRuntime for quay.io/ramalama/granite:1.0
+    opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]'
+  labels:
+    opendatahub.io/dashboard: 'true'
+spec:
+  annotations:
+    prometheus.io/port: '8081'
+    prometheus.io/path: '/metrics'
+  multiModel: false
+  supportedModelFormats:
+    - autoSelect: true
+      name: vLLM
+  containers:
+    - name: kserve-container
+      image: quay.io/ramalama/ramalama:latest
+      command:
+        - python
+        - -m
+        - vllm.entrypoints.openai.api_server
+      args:
+        - "--port=8081"
+        - "--model=/mnt/models"
+        - "--served-model-name={.Name}"
+      env:
+        - name: HF_HOME
+          value: /tmp/hf_home
+      ports:
+        - containerPort: 8081
+          protocol: TCP
+
+$  cat granite-1.0-kserve.yaml
+# RamaLama quay.io/ramalama/granite:1.0 AI Model Service
+# kubectl create -f to import this kserve file into Kubernetes.
+#
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+  name: huggingface-quay.io/ramalama/granite:1.0
+spec:
+  predictor:
+    model:
+      modelFormat:
+        name: vLLM
+      storageUri: "oci://quay.io/ramalama/granite:1.0"
+      resources:
+        limits:
+          cpu: "6"
+          memory: 24Gi
+          nvidia.com/gpu: "1"
+        requests:
+          cpu: "6"
+          memory: 24Gi
+          nvidia.com/gpu: "1"
+```
+
 ### Generate quadlet service off of HuggingFace granite Model
 ```
 $ ramalama serve --name MyGraniteServer --generate=quadlet granite

ramalama/cli.py

@@ -859,7 +859,7 @@ def serve_parser(subparsers):
     parser.add_argument("--host", default=config.get('host', "0.0.0.0"), help="IP address to listen")
     parser.add_argument(
         "--generate",
-        choices=["quadlet", "kube", "quadlet/kube"],
+        choices=["kserve", "kube", "quadlet", "quadlet/kube", ],
         help="generate specified configuration format for running the AI Model as a service",
     )
     parser.add_argument(

ramalama/kserve.py

@@ -0,0 +1,105 @@
+import os
+
+from ramalama.common import get_env_vars
+
+
+class Kserve:
+    def __init__(self, model, image, args, exec_args):
+        self.ai_image = model
+        if hasattr(args, "MODEL"):
+            self.ai_image = args.MODEL
+        self.ai_image = self.ai_image.removeprefix("oci://")
+        if args.name:
+            self.name = args.name
+        else:
+            self.name = os.path.basename(self.ai_image)
+
+        self.model = model.removeprefix("oci://")
+        self.args = args
+        self.exec_args = exec_args
+        self.image = image
+        self.runtime = args.runtime
+
+    def generate(self):
+        env_var_string = ""
+        for k, v in get_env_vars().items():
+            env_var_string += f"Environment={k}={v}\n"
+
+        _gpu = ""
+        if os.getenv("CUDA_VISIBLE_DEVICES") != "":
+            _gpu = 'nvidia.com/gpu'
+        elif os.getenv("HIP_VISIBLE_DEVICES") != "":
+            _gpu = 'amd.com/gpu'
+        if _gpu != "":
+            gpu = f'\n          {_gpu}: "1"'
+
+        outfile = self.name + "-kserve-runtime.yaml"
+        outfile = outfile.replace(":", "-")
+        print(f"Generating kserve runtime file: {outfile}")
+        with open(outfile, 'w') as c:
+            c.write(
+                f"""\
+apiVersion: serving.kserve.io/v1alpha1
+kind: ServingRuntime
+metadata:
+  name: {self.runtime}-runtime
+  annotations:
+    openshift.io/display-name: KServe ServingRuntime for {self.model}
+    opendatahub.io/recommended-accelerators: '["{_gpu}"]'
+  labels:
+    opendatahub.io/dashboard: 'true'
+spec:
+  annotations:
+    prometheus.io/port: '{self.args.port}'
+    prometheus.io/path: '/metrics'
+  multiModel: false
+  supportedModelFormats:
+    - autoSelect: true
+      name: vLLM
+  containers:
+    - name: kserve-container
+      image: {self.image}
+      command:
+        - python
+        - -m
+        - vllm.entrypoints.openai.api_server
+      args:
+        - "--port={self.args.port}"
+        - "--model=/mnt/models"
+        - "--served-model-name={{.Name}}"
+      env:
+        - name: HF_HOME
+          value: /tmp/hf_home
+      ports:
+        - containerPort: {self.args.port}
+          protocol: TCP
+""")
+
+        outfile = self.name + "-kserve.yaml"
+        outfile = outfile.replace(":", "-")
+        print(f"Generating kserve file: {outfile}")
+        with open(outfile, 'w') as c:
+            c.write(
+                f"""\
+# RamaLama {self.model} AI Model Service
+# kubectl create -f to import this kserve file into Kubernetes.
+#
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+  name: huggingface-{self.model}
+spec:
+  predictor:
+    model:
+      modelFormat:
+        name: vLLM
+      storageUri: "oci://{self.model}"
+      resources:
+        limits:
+          cpu: "6"
+          memory: 24Gi{gpu}
+        requests:
+          cpu: "6"
+          memory: 24Gi{gpu}
+"""
+            )

ramalama/model.py

@@ -17,6 +17,7 @@
 from ramalama.kube import Kube
 from ramalama.model_inspect import GGUFModelInfo, ModelInfoBase
 from ramalama.quadlet import Quadlet
+from ramalama.kserve import Kserve
 from ramalama.version import version
 
 MODEL_TYPES = ["file", "https", "http", "oci", "huggingface", "hf", "ollama"]
@@ -360,7 +361,6 @@ def get_model_path(self, args):
 
         if args.dryrun:
             return "/path/to/model"
-
         model_path = self.pull(args)
 
         return model_path
@@ -486,16 +486,15 @@ def handle_runtime(self, args, exec_args, exec_model_path):
 
     def generate_container_config(self, model_path, args, exec_args):
         self.image = self._image(args)
+        if args.generate == "kserve":
+            return self.kserve(model_path, args, exec_args)
         if args.generate == "quadlet":
-            self.quadlet(model_path, args, exec_args)
-        elif args.generate == "kube":
-            self.kube(model_path, args, exec_args)
-        elif args.generate == "quadlet/kube":
-            self.quadlet_kube(model_path, args, exec_args)
-        else:
-            return False
-
-        return True
+            return self.quadlet(model_path, args, exec_args)
+        if args.generate == "kube":
+            return self.kube(model_path, args, exec_args)
+        if args.generate == "quadlet/kube":
+            return self.quadlet_kube(model_path, args, exec_args)
+        return False
 
     def execute_command(self, model_path, exec_args, args):
         try:
@@ -526,19 +525,27 @@ def serve(self, args):
 
         self.execute_command(model_path, exec_args, args)
 
+    def kserve(self, model, args, exec_args):
+        kserve = Kserve(model, self.image, args, exec_args)
+        kserve.generate()
+        return True
+
     def quadlet(self, model, args, exec_args):
         quadlet = Quadlet(model, self.image, args, exec_args)
         quadlet.generate()
+        return True
 
     def quadlet_kube(self, model, args, exec_args):
         kube = Kube(model, self.image, args, exec_args)
         kube.generate()
         quadlet = Quadlet(model, self.image, args, exec_args)
         quadlet.kube()
+        return True
 
     def kube(self, model, args, exec_args):
         kube = Kube(model, self.image, args, exec_args)
         kube.generate()
+        return True
 
     def path(self, args):
         return self.model_path(args)