Initial kfserving pipeline component (#1838)

* initial kfserving pipeline component

* changing some descriptions

* adding action as an input, and pinning versions

* moving Dockerfile in correct location

* adding namespace to delete

components/kubeflow/kfserving/Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.6-slim
+
+RUN pip install kubernetes==9.0.0 kfserving==0.1.1 requests==2.22.0
+
+ENV APP_HOME /app
+COPY src $APP_HOME
+WORKDIR $APP_HOME
+
+ENTRYPOINT ["python"]
+CMD ["kfservingdeployer.py"]

components/kubeflow/kfserving/component.yaml

@@ -0,0 +1,27 @@
+name: Kubeflow - Serve Model using KFServing
+description: Serve Models using Kubeflow KFServing
+inputs:
+  - {name: Action,                          type: String, default: 'create',   description: 'Action to execute on KFServing'}
+  - {name: Model Name,                      type: String, default: '',         description: 'Name to give to the deployed model'}
+  - {name: Default Model URI,               type: String,                      description: 'Path of the S3 or GCS compatible directory containing default model.'}
+  - {name: Canary Model URI,                type: String, default: '',         description: 'Optional Path of the S3 or GCS compatible directory containing canary model.'}
+  - {name: Canary Model Traffic Percentage, type: String, default: '0',        description: 'Optional Traffic to be sent to default model'}
+  - {name: Namespace,                       type: String, default: 'kubeflow', description: 'Kubernetes namespace where the KFServing service is deployed.'}
+  - {name: Framework,                       type: String, default: 'tensorflow', description: 'Machine Learning Framework for Model Serving.'}
+outputs:
+  - {name: Endpoint URI, type: String,                      description: 'URI of the deployed prediction service..'}
+implementation:
+  container:
+    image: animeshsingh/kfserving-component
+    command: ['python']
+    args: [
+      -u, kfservingdeployer.py,
+      --action,                {inputValue: Action},
+      --model-name,            {inputValue: Model Name},
+      --default-model-uri,     {inputValue: Default Model URI},
+      --canary-model-uri,      {inputValue: Canary Model URI},
+      --canary-model-traffic,  {inputValue: Canary Model Traffic Percentage},
+      --namespace,             {inputValue: Namespace},
+      --framework,             {inputValue: Framework},
+      --output_path,           {outputPath: Endpoint URI}
+    ]

components/kubeflow/kfserving/sample-pipeline.py

@@ -0,0 +1,45 @@
+# Copyright 2019 IBM Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import kfp.dsl as dsl
+from kfp import components
+import json
+
+kfserving_op = components.load_component_from_file('component.yaml')
+
+@dsl.pipeline(
+  name='kfserving pipeline',
+  description='A pipeline for kfserving.'
+)
+def kfservingPipeline(
+    action = 'create',
+    model_name='tensorflow-sample',
+    default_model_uri='gs://kfserving-samples/models/tensorflow/flowers',
+    canary_model_uri='gs://kfserving-samples/models/tensorflow/flowers',
+    canary_model_traffic_percentage='10',
+    namespace='kubeflow',
+    framework='tensorflow'
+):
+
+    # define workflow
+    kfserving = kfserving_op(action = action,
+                             model_name=model_name,
+                             default_model_uri=default_model_uri,
+                             canary_model_uri=canary_model_uri,
+                             canary_model_traffic_percentage=canary_model_traffic_percentage,
+                             namespace=namespace,
+                             framework=framework)
+
+if __name__ == '__main__':
+    import kfp.compiler as compiler
+    compiler.Compiler().compile(kfservingPipeline, __file__ + '.tar.gz')

components/kubeflow/kfserving/src/kfservingdeployer.py

@@ -0,0 +1,106 @@
+# Copyright 2019 IBM Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import json
+import argparse
+import os
+
+from kubernetes import client
+
+from kfserving import KFServingClient
+from kfserving import constants
+from kfserving import V1alpha1ModelSpec
+from kfserving import V1alpha1TensorflowSpec
+from kfserving import V1alpha1PyTorchSpec
+from kfserving import V1alpha1SKLearnSpec
+from kfserving import V1alpha1XGBoostSpec
+from kfserving import V1alpha1TensorRTSpec
+from kfserving import V1alpha1CustomSpec
+from kfserving import V1alpha1KFServiceSpec
+from kfserving import V1alpha1KFService
+
+
+def ModelSpec(framework, model_uri):
+    if framework == 'tensorflow':
+        return V1alpha1ModelSpec(tensorflow=V1alpha1TensorflowSpec(model_uri=model_uri))
+    elif framework == 'pytorch':
+        return V1alpha1ModelSpec(pytorch=V1alpha1PyTorchSpec(model_uri=model_uri))
+    elif framework == 'sklearn':
+        return V1alpha1ModelSpec(sklearn=V1alpha1SKLearnSpec(model_uri=model_uri))
+    elif framework == 'xgboost':
+        return V1alpha1ModelSpec(xgboost=V1alpha1XGBoostSpec(model_uri=model_uri))
+    elif framework == 'tensorrt':
+        return V1alpha1ModelSpec(tensorrt=V1alpha1TensorRTSpec(model_uri=model_uri))
+    elif framework == 'custom':
+        # TODO: implement custom container spec with more args.
+        # return V1alpha1ModelSpec(custom=V1alpha1CustomSpec(container=containerSpec))
+        raise("Custom framework is not yet implemented")
+    else:
+        raise("Error: No matching framework: " + framework)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--action', type=str, help='Action to execute on KFServing', default='create')
+    parser.add_argument('--model-name', type=str, help='Name to give to the deployed model', default="")
+    parser.add_argument('--default-model-uri', type=str, help='Path of the S3, GCS or PVC directory containing default model.')
+    parser.add_argument('--canary-model-uri', type=str, help='Optional path of the S3, GCS or PVC directory containing canary model.', default="")
+    parser.add_argument('--canary-model-traffic', type=str, help='Optional Traffic to be sent to the default model', default='0')
+    parser.add_argument('--namespace', type=str, help='Kubernetes namespace where the KFServing service is deployed.', default='kubeflow')
+    parser.add_argument('--framework', type=str, help='Model Serving Framework', default='tensorflow')
+    parser.add_argument('--output_path', type=str, help='Path to store URI output')
+    args = parser.parse_args()
+
+    action = args.action.lower()
+    model_name = args.model_name
+    default_model_uri = args.default_model_uri
+    canary_model_uri = args.canary_model_uri
+    canary_model_traffic = args.canary_model_traffic
+    namespace = args.namespace
+    framework = args.framework.lower()
+    output_path = args.output_path
+
+    default_model_spec = ModelSpec(framework, default_model_uri)
+    metadata = client.V1ObjectMeta(name=model_name, namespace=namespace)
+
+    # Create Canary deployment if canary model uri is provided.
+    if canary_model_uri:
+        canary_model_spec = ModelSpec(framework, canary_model_uri)
+        kfsvc = V1alpha1KFService(api_version=constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION,
+                                  kind=constants.KFSERVING_KIND,
+                                  metadata=metadata,
+                                  spec=V1alpha1KFServiceSpec(default=default_model_spec,
+                                                             canary=canary_model_spec,
+                                                             canary_traffic_percent=int(canary_model_traffic)))
+    else:
+        kfsvc = V1alpha1KFService(api_version=constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION,
+                                  kind=constants.KFSERVING_KIND,
+                                  metadata=metadata,
+                                  spec=V1alpha1KFServiceSpec(default=default_model_spec))
+    KFServing = KFServingClient()
+
+    if action == 'create':
+        KFServing.create(kfsvc)
+    elif action  == 'update':
+        KFServing.patch(model_name, kfsvc)
+    elif action  == 'delete':
+        KFServing.delete(model_name, namespace=namespace)
+    else:
+        raise("Error: No matching action: " + action)
+
+    model_status = KFServing.get(model_name, namespace=namespace)
+    print(model_status)
+
+    if not os.path.exists(os.path.dirname(output_path)):
+        os.makedirs(os.path.dirname(output_path))
+    with open(output_path, "w") as report:
+        report.write(json.dumps(model_status))