68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
# Copyright 2019 The Kubeflow Authors
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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 kfp.dsl as dsl
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from string import Template
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@dsl.pipeline(
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name="Deploy example SKLearn Iris",
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description="SKLearn Iris simple deployment example"
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)
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def iris_storagebucket(bucket='gs://seldon-models/sklearn/iris'):
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#simple serving of an iris sklearn model based on https://docs.seldon.io/projects/seldon-core/en/latest/servers/overview.html
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#requires seldon 0.3.2 or higher
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sklearnjson_template = Template("""
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{
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"apiVersion": "machinelearning.seldon.io/v1alpha2",
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"kind": "SeldonDeployment",
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"metadata": {
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"name": "sklearn"
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},
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"spec": {
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"name": "iris",
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"predictors": [
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{
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"graph": {
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"children": [],
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"implementation": "SKLEARN_SERVER",
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"modelUri": "$bucket",
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"name": "classifier"
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},
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"name": "default",
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"replicas": 1
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}
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]
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}
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}
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""")
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sklearnjson = sklearnjson_template.substitute({ 'bucket': str(bucket)})
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sklearndeployment = json.loads(sklearnjson)
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deploy = dsl.ResourceOp(
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name="deploy",
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k8s_resource=sklearndeployment,
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action="apply",
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success_condition='status.state == Available'
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)
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if __name__ == "__main__":
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import kfp.compiler as compiler
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compiler.Compiler().compile(iris_storagebucket, __file__ + ".tar.gz")
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