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Enhance pipeline TFX taxi sample to support on-prem cluster
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samples/tfx/taxi-cab-classification-pipeline-on-prem.py
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| #!/usr/bin/env python3 | ||
| # Copyright 2018 Google LLC | ||
| # | ||
| # 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. | ||
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| import kfp.dsl as dsl | ||
| from kubernetes import client as k8s_client | ||
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| def dataflow_tf_data_validation_op(inference_data, validation_data, | ||
| column_names, key_columns, project, mode, | ||
| validation_output, step_name='validation'): | ||
| return dsl.ContainerOp( | ||
| name=step_name, | ||
| image='gcr.io/ml-pipeline/ml-pipeline-dataflow-tfdv:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f', | ||
| arguments=[ | ||
| '--csv-data-for-inference', inference_data, | ||
| '--csv-data-to-validate', validation_data, | ||
| '--column-names', column_names, | ||
| '--key-columns', key_columns, | ||
| '--project', project, | ||
| '--mode', mode, | ||
| '--output', '%s/{{workflow.name}}/validation' % validation_output, | ||
| ], | ||
| file_outputs={ | ||
| 'schema': '/schema.txt', | ||
| 'validation': '/output_validation_result.txt', | ||
| } | ||
| ) | ||
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| def dataflow_tf_transform_op(train_data, evaluation_data, schema, | ||
| project, preprocess_mode, preprocess_module, | ||
| transform_output, step_name='preprocess'): | ||
| return dsl.ContainerOp( | ||
| name=step_name, | ||
| image='gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f', | ||
| arguments=[ | ||
| '--train', train_data, | ||
| '--eval', evaluation_data, | ||
| '--schema', schema, | ||
| '--project', project, | ||
| '--mode', preprocess_mode, | ||
| '--preprocessing-module', preprocess_module, | ||
| '--output', '%s/{{workflow.name}}/transformed' % transform_output, | ||
| ], | ||
| file_outputs={'transformed': '/output.txt'} | ||
| ) | ||
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| def tf_train_op(transformed_data_dir, schema, learning_rate: float, hidden_layer_size: int, | ||
| steps: int, target: str, preprocess_module, | ||
| training_output, step_name='training'): | ||
| return dsl.ContainerOp( | ||
| name=step_name, | ||
| image='gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f', | ||
| arguments=[ | ||
| '--transformed-data-dir', transformed_data_dir, | ||
| '--schema', schema, | ||
| '--learning-rate', learning_rate, | ||
| '--hidden-layer-size', hidden_layer_size, | ||
| '--steps', steps, | ||
| '--target', target, | ||
| '--preprocessing-module', preprocess_module, | ||
| '--job-dir', '%s/{{workflow.name}}/train' % training_output, | ||
| ], | ||
| file_outputs={'train': '/output.txt'} | ||
| ) | ||
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| def dataflow_tf_model_analyze_op(model: 'TensorFlow model', evaluation_data, schema, | ||
| project, analyze_mode, analyze_slice_column, analysis_output, | ||
| step_name='analysis'): | ||
| return dsl.ContainerOp( | ||
| name=step_name, | ||
| image='gcr.io/ml-pipeline/ml-pipeline-dataflow-tfma:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f', | ||
| arguments=[ | ||
| '--model', model, | ||
| '--eval', evaluation_data, | ||
| '--schema', schema, | ||
| '--project', project, | ||
| '--mode', analyze_mode, | ||
| '--slice-columns', analyze_slice_column, | ||
| '--output', '%s/{{workflow.name}}/analysis' % analysis_output, | ||
| ], | ||
| file_outputs={'analysis': '/output.txt'} | ||
| ) | ||
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| def dataflow_tf_predict_op(evaluation_data, schema, target: str, | ||
| model: 'TensorFlow model', predict_mode, project, prediction_output, | ||
| step_name='prediction'): | ||
| return dsl.ContainerOp( | ||
| name=step_name, | ||
| image='gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f', | ||
| arguments=[ | ||
| '--data', evaluation_data, | ||
| '--schema', schema, | ||
| '--target', target, | ||
| '--model', model, | ||
| '--mode', predict_mode, | ||
| '--project', project, | ||
| '--output', '%s/{{workflow.name}}/predict' % prediction_output, | ||
| ], | ||
| file_outputs={'prediction': '/output.txt'} | ||
| ) | ||
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| def confusion_matrix_op(predictions, output, step_name='confusion_matrix'): | ||
| return dsl.ContainerOp( | ||
| name=step_name, | ||
| image='gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f', | ||
| arguments=[ | ||
| '--output', '%s/{{workflow.name}}/confusionmatrix' % output, | ||
| '--predictions', predictions, | ||
| '--target_lambda', """lambda x: (x['target'] > x['fare'] * 0.2)""", | ||
| ]) | ||
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| def roc_op(predictions, output, step_name='roc'): | ||
| return dsl.ContainerOp( | ||
| name=step_name, | ||
| image='gcr.io/ml-pipeline/ml-pipeline-local-roc:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f', | ||
| arguments=[ | ||
| '--output', '%s/{{workflow.name}}/roc' % output, | ||
| '--predictions', predictions, | ||
| '--target_lambda', """lambda x: 1 if (x['target'] > x['fare'] * 0.2) else 0""", | ||
| ]) | ||
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| def kubeflow_deploy_op(model: 'TensorFlow model', tf_server_name, pvc_name, step_name='deploy'): | ||
| return dsl.ContainerOp( | ||
| name=step_name, | ||
| image='gcr.io/ml-pipeline/ml-pipeline-kubeflow-deployer:5df2cdc1ed145320204e8bc73b59cdbd7b3da28f', | ||
| arguments=[ | ||
| '--cluster-name', 'tfx-taxi-pipeline-on-prem', | ||
| '--model-path', model, | ||
| '--server-name', tf_server_name, | ||
| '--pvc-name', pvc_name, | ||
| ] | ||
| ) | ||
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| @dsl.pipeline( | ||
| name='TFX Taxi Cab Classification Pipeline Example for on-prem cluster', | ||
| description='Example pipeline that does classification with model analysis based on a public BigQuery dataset for on-prem cluster.' | ||
| ) | ||
| def taxi_cab_classification( | ||
| pvc_name='pipeline-pvc', | ||
| project='tfx-taxi-pipeline-on-prem', | ||
| column_names='taxi-cab-classification/column-names.json', | ||
| key_columns='trip_start_timestamp', | ||
| train='taxi-cab-classification/train.csv', | ||
| evaluation='taxi-cab-classification/eval.csv', | ||
| mode='local', | ||
| preprocess_module='taxi-cab-classification/preprocessing.py', | ||
| learning_rate=0.1, | ||
| hidden_layer_size=1500, | ||
| steps=3000, | ||
| analyze_slice_column='trip_start_hour'): | ||
| tf_server_name = 'taxi-cab-classification-model-{{workflow.name}}' | ||
| validation = dataflow_tf_data_validation_op('/mnt/%s' % train, '/mnt/%s' % evaluation, '/mnt/%s' % column_names, | ||
| key_columns, project, mode, '/mnt').add_volume( | ||
| k8s_client.V1Volume(name='pipeline-nfs', persistent_volume_claim=k8s_client.V1PersistentVolumeClaimVolumeSource( | ||
| claim_name='pipeline-pvc'))).add_volume_mount(k8s_client.V1VolumeMount(mount_path='/mnt', name='pipeline-nfs')) | ||
| preprocess = dataflow_tf_transform_op('/mnt/%s' % train, '/mnt/%s' % evaluation, validation.outputs['schema'], | ||
| project, mode, '/mnt/%s' % preprocess_module, '/mnt').add_volume_mount( | ||
| k8s_client.V1VolumeMount(mount_path='/mnt', name='pipeline-nfs')) | ||
| training = tf_train_op(preprocess.output, validation.outputs['schema'], learning_rate, hidden_layer_size, steps, | ||
| 'tips', '/mnt/%s' % preprocess_module, '/mnt').add_volume_mount( | ||
| k8s_client.V1VolumeMount(mount_path='/mnt', name='pipeline-nfs')) | ||
| analysis = dataflow_tf_model_analyze_op(training.output, '/mnt/%s' % evaluation, validation.outputs['schema'], | ||
| project, mode, analyze_slice_column, '/mnt').add_volume_mount( | ||
| k8s_client.V1VolumeMount(mount_path='/mnt', name='pipeline-nfs')) | ||
| prediction = dataflow_tf_predict_op('/mnt/%s' % evaluation, validation.outputs['schema'], 'tips', training.output, | ||
| mode, project, '/mnt').add_volume_mount( | ||
| k8s_client.V1VolumeMount(mount_path='/mnt', name='pipeline-nfs')) | ||
| cm = confusion_matrix_op(prediction.output, '/mnt').add_volume_mount( | ||
| k8s_client.V1VolumeMount(mount_path='/mnt', name='pipeline-nfs')) | ||
| roc = roc_op(prediction.output, '/mnt').add_volume_mount( | ||
| k8s_client.V1VolumeMount(mount_path='/mnt', name='pipeline-nfs')) | ||
| deploy = kubeflow_deploy_op(training.output, tf_server_name, pvc_name).add_volume_mount( | ||
| k8s_client.V1VolumeMount(mount_path='/mnt', name='pipeline-nfs')) | ||
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| if __name__ == '__main__': | ||
| import kfp.compiler as compiler | ||
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| compiler.Compiler().compile(taxi_cab_classification, __file__ + '.tar.gz') |
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