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151
samples/contrib/aws-samples/debugger_component_demo/debugger-component-demo.py
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| #!/usr/bin/env python3 | ||
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| # Uncomment the apply(use_aws_secret()) below if you are not using OIDC | ||
| # more info : https://github.com/kubeflow/pipelines/tree/master/samples/contrib/aws-samples/README.md | ||
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| import kfp | ||
| import json | ||
| import os | ||
| import copy | ||
| from kfp import components | ||
| from kfp import dsl | ||
| from kfp.aws import use_aws_secret | ||
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| cur_file_dir = os.path.dirname(__file__) | ||
| components_dir = os.path.join(cur_file_dir, '../../../../components/aws/sagemaker/') | ||
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| sagemaker_train_op = components.load_component_from_file(components_dir + '/train/component.yaml') | ||
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| debug_hook = { | ||
| 'S3OutputPath':'s3://kubeflow-pipeline-data/mnist_kmeans_example/hook_config' | ||
| } | ||
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| debug_hook['CollectionConfigurations'] = [] | ||
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| collection_list = { | ||
| 'feature_importance' : { | ||
| 'save_interval': '5' | ||
| }, | ||
| 'losses' : { | ||
| 'save_interval': '10' | ||
| }, | ||
| 'average_shap': { | ||
| 'save_interval': '5' | ||
| }, | ||
| 'metrics': { | ||
| 'save_interval': '5' | ||
| } | ||
| } | ||
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| for key, val in collection_list.items(): | ||
| debug_hook['CollectionConfigurations'].append({'CollectionName': key, 'CollectionParameters': val}) | ||
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| loss_rule = { | ||
| 'RuleConfigurationName': 'LossNotDecreasing', | ||
| 'RuleEvaluatorImage': '503895931360.dkr.ecr.us-east-1.amazonaws.com/sagemaker-debugger-rules:latest', | ||
| 'RuleParameters': { | ||
| 'rule_to_invoke': 'LossNotDecreasing', | ||
| 'tensor_regex': '.*' | ||
| } | ||
| } | ||
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| overtraining_rule = { | ||
| 'RuleConfigurationName': 'Overtraining', | ||
| 'RuleEvaluatorImage': '503895931360.dkr.ecr.us-east-1.amazonaws.com/sagemaker-debugger-rules:latest', | ||
| 'RuleParameters': { | ||
| 'rule_to_invoke': 'Overtraining', | ||
| 'patience_train': '10', | ||
| 'patience_validation': '20' | ||
| } | ||
| } | ||
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| debug_rule_configurations=[loss_rule, overtraining_rule] | ||
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| bad_hyperparameters = { | ||
| 'objective': 'reg:squarederror', | ||
| 'max_depth': '5', | ||
| 'eta': '0', | ||
| 'gamma': '4', | ||
| 'min_child_weight': '6', | ||
| 'silent': '0', | ||
| 'subsample': '0.7', | ||
| 'num_round': '50' | ||
| } | ||
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| channelObjList = [] | ||
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| channelObj = { | ||
| 'ChannelName': '', | ||
| 'DataSource': { | ||
| 'S3DataSource': { | ||
| 'S3Uri': '', | ||
| 'S3DataType': 'S3Prefix', | ||
| 'S3DataDistributionType': 'FullyReplicated' | ||
| } | ||
| }, | ||
| 'ContentType': 'text/csv', | ||
| 'CompressionType': 'None', | ||
| 'RecordWrapperType': 'None', | ||
| 'InputMode': 'File' | ||
| } | ||
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| channelObj['ChannelName'] = 'train' | ||
| channelObj['DataSource']['S3DataSource']['S3Uri'] = 's3://kubeflow-pipeline-data/mnist_kmeans_example/input/valid_data.csv' | ||
| channelObjList.append(copy.deepcopy(channelObj)) | ||
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| @dsl.pipeline( | ||
| name='xgboost-mnist-debugger', | ||
| description='SageMaker training job test with debugger' | ||
| ) | ||
| def training( | ||
| region='us-east-1', | ||
| endpoint_url='', | ||
| image='683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:0.90-2-cpu-py3', | ||
| training_input_mode='File', | ||
| hyperparameters=bad_hyperparameters, | ||
| channels=channelObjList, | ||
| instance_type='ml.m5.2xlarge', | ||
| instance_count=1, | ||
| volume_size=50, | ||
| max_run_time=3600, | ||
| model_artifact_path='s3://kubeflow-pipeline-data/mnist_kmeans_example/output/model', | ||
| output_encryption_key='', | ||
| network_isolation=True, | ||
| traffic_encryption=False, | ||
| spot_instance=False, | ||
| max_wait_time=3600, | ||
| checkpoint_config={}, | ||
| debug_hook_config=debug_hook, | ||
| debug_rule_config=debug_rule_configurations, | ||
| tensorboard_output_config={}, | ||
| role='' | ||
| ): | ||
| training = sagemaker_train_op( | ||
| region=region, | ||
| endpoint_url=endpoint_url, | ||
| image=image, | ||
| training_input_mode=training_input_mode, | ||
| hyperparameters=hyperparameters, | ||
| channels=channels, | ||
| instance_type=instance_type, | ||
| instance_count=instance_count, | ||
| volume_size=volume_size, | ||
| max_run_time=max_run_time, | ||
| model_artifact_path=model_artifact_path, | ||
| output_encryption_key=output_encryption_key, | ||
| network_isolation=network_isolation, | ||
| traffic_encryption=traffic_encryption, | ||
| spot_instance=spot_instance, | ||
| max_wait_time=max_wait_time, | ||
| checkpoint_config=checkpoint_config, | ||
| debug_hook_config=debug_hook_config, | ||
| debug_rule_config=debug_rule_config, | ||
| tensorboard_output_config=tensorboard_output_config, | ||
| role=role, | ||
| )#.apply(use_aws_secret('aws-secret', 'AWS_ACCESS_KEY_ID', 'AWS_SECRET_ACCESS_KEY')) | ||
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| if __name__ == '__main__': | ||
| kfp.compiler.Compiler().compile(training, __file__ + '.zip') |
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