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
This commit is contained in:
Animesh Singh
Kubernetes Prow Robot
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119405deaf
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d15697bdb1
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FROM python:3.6-slim
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RUN pip install kubernetes==9.0.0 kfserving==0.1.1 requests==2.22.0
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ENV APP_HOME /app
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COPY src $APP_HOME
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WORKDIR $APP_HOME
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ENTRYPOINT ["python"]
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CMD ["kfservingdeployer.py"]
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name: Kubeflow - Serve Model using KFServing
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description: Serve Models using Kubeflow KFServing
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inputs:
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- {name: Action, type: String, default: 'create', description: 'Action to execute on KFServing'}
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- {name: Model Name, type: String, default: '', description: 'Name to give to the deployed model'}
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- {name: Default Model URI, type: String, description: 'Path of the S3 or GCS compatible directory containing default model.'}
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- {name: Canary Model URI, type: String, default: '', description: 'Optional Path of the S3 or GCS compatible directory containing canary model.'}
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- {name: Canary Model Traffic Percentage, type: String, default: '0', description: 'Optional Traffic to be sent to default model'}
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- {name: Namespace, type: String, default: 'kubeflow', description: 'Kubernetes namespace where the KFServing service is deployed.'}
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- {name: Framework, type: String, default: 'tensorflow', description: 'Machine Learning Framework for Model Serving.'}
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outputs:
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- {name: Endpoint URI, type: String, description: 'URI of the deployed prediction service..'}
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implementation:
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container:
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image: animeshsingh/kfserving-component
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command: ['python']
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args: [
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-u, kfservingdeployer.py,
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--action, {inputValue: Action},
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--model-name, {inputValue: Model Name},
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--default-model-uri, {inputValue: Default Model URI},
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--canary-model-uri, {inputValue: Canary Model URI},
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--canary-model-traffic, {inputValue: Canary Model Traffic Percentage},
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--namespace, {inputValue: Namespace},
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--framework, {inputValue: Framework},
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--output_path, {outputPath: Endpoint URI}
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]
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# Copyright 2019 IBM Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import kfp.dsl as dsl
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from kfp import components
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import json
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kfserving_op = components.load_component_from_file('component.yaml')
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@dsl.pipeline(
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name='kfserving pipeline',
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description='A pipeline for kfserving.'
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)
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def kfservingPipeline(
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action = 'create',
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model_name='tensorflow-sample',
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default_model_uri='gs://kfserving-samples/models/tensorflow/flowers',
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canary_model_uri='gs://kfserving-samples/models/tensorflow/flowers',
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canary_model_traffic_percentage='10',
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namespace='kubeflow',
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framework='tensorflow'
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):
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# define workflow
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kfserving = kfserving_op(action = action,
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model_name=model_name,
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default_model_uri=default_model_uri,
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canary_model_uri=canary_model_uri,
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canary_model_traffic_percentage=canary_model_traffic_percentage,
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namespace=namespace,
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framework=framework)
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if __name__ == '__main__':
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import kfp.compiler as compiler
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compiler.Compiler().compile(kfservingPipeline, __file__ + '.tar.gz')
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# Copyright 2019 IBM Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import argparse
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import os
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from kubernetes import client
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from kfserving import KFServingClient
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from kfserving import constants
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from kfserving import V1alpha1ModelSpec
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from kfserving import V1alpha1TensorflowSpec
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from kfserving import V1alpha1PyTorchSpec
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from kfserving import V1alpha1SKLearnSpec
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from kfserving import V1alpha1XGBoostSpec
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from kfserving import V1alpha1TensorRTSpec
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from kfserving import V1alpha1CustomSpec
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from kfserving import V1alpha1KFServiceSpec
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from kfserving import V1alpha1KFService
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def ModelSpec(framework, model_uri):
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if framework == 'tensorflow':
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return V1alpha1ModelSpec(tensorflow=V1alpha1TensorflowSpec(model_uri=model_uri))
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elif framework == 'pytorch':
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return V1alpha1ModelSpec(pytorch=V1alpha1PyTorchSpec(model_uri=model_uri))
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elif framework == 'sklearn':
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return V1alpha1ModelSpec(sklearn=V1alpha1SKLearnSpec(model_uri=model_uri))
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elif framework == 'xgboost':
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return V1alpha1ModelSpec(xgboost=V1alpha1XGBoostSpec(model_uri=model_uri))
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elif framework == 'tensorrt':
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return V1alpha1ModelSpec(tensorrt=V1alpha1TensorRTSpec(model_uri=model_uri))
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elif framework == 'custom':
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# TODO: implement custom container spec with more args.
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# return V1alpha1ModelSpec(custom=V1alpha1CustomSpec(container=containerSpec))
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raise("Custom framework is not yet implemented")
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else:
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raise("Error: No matching framework: " + framework)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--action', type=str, help='Action to execute on KFServing', default='create')
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parser.add_argument('--model-name', type=str, help='Name to give to the deployed model', default="")
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parser.add_argument('--default-model-uri', type=str, help='Path of the S3, GCS or PVC directory containing default model.')
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parser.add_argument('--canary-model-uri', type=str, help='Optional path of the S3, GCS or PVC directory containing canary model.', default="")
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parser.add_argument('--canary-model-traffic', type=str, help='Optional Traffic to be sent to the default model', default='0')
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parser.add_argument('--namespace', type=str, help='Kubernetes namespace where the KFServing service is deployed.', default='kubeflow')
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parser.add_argument('--framework', type=str, help='Model Serving Framework', default='tensorflow')
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parser.add_argument('--output_path', type=str, help='Path to store URI output')
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args = parser.parse_args()
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action = args.action.lower()
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model_name = args.model_name
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default_model_uri = args.default_model_uri
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canary_model_uri = args.canary_model_uri
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canary_model_traffic = args.canary_model_traffic
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namespace = args.namespace
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framework = args.framework.lower()
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output_path = args.output_path
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default_model_spec = ModelSpec(framework, default_model_uri)
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metadata = client.V1ObjectMeta(name=model_name, namespace=namespace)
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# Create Canary deployment if canary model uri is provided.
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if canary_model_uri:
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canary_model_spec = ModelSpec(framework, canary_model_uri)
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kfsvc = V1alpha1KFService(api_version=constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION,
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kind=constants.KFSERVING_KIND,
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metadata=metadata,
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spec=V1alpha1KFServiceSpec(default=default_model_spec,
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canary=canary_model_spec,
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canary_traffic_percent=int(canary_model_traffic)))
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else:
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kfsvc = V1alpha1KFService(api_version=constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION,
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kind=constants.KFSERVING_KIND,
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metadata=metadata,
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spec=V1alpha1KFServiceSpec(default=default_model_spec))
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KFServing = KFServingClient()
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if action == 'create':
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KFServing.create(kfsvc)
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elif action == 'update':
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KFServing.patch(model_name, kfsvc)
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elif action == 'delete':
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KFServing.delete(model_name, namespace=namespace)
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else:
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raise("Error: No matching action: " + action)
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model_status = KFServing.get(model_name, namespace=namespace)
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print(model_status)
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if not os.path.exists(os.path.dirname(output_path)):
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os.makedirs(os.path.dirname(output_path))
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with open(output_path, "w") as report:
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report.write(json.dumps(model_status))
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