34 lines
1.8 KiB
YAML
34 lines
1.8 KiB
YAML
name: 'Sagemaker - Training Job'
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description: |
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Train Machine Learning and Deep Learning Models using SageMaker
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inputs:
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- {name: region, description: 'The region where the training job launches.'}
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- {name: image, description: 'The registry path of the Docker image that contains the training algorithm.'}
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- {name: instance_type, description: 'The ML compute instance type.'}
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- {name: instance_count, description: 'The registry path of the Docker image that contains the training algorithm.'}
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- {name: volume_size, description: 'The size of the ML storage volume that you want to provision.'}
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- {name: dataset_path, description: 'The S3 location of the data source that is associated with a channel.'}
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- {name: model_artifact_path, description: 'Identifies the S3 path where you want Amazon SageMaker to store the model artifacts.'}
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- {name: role, description: 'The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf.'}
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outputs:
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- {name: model_artifact_url, description: 'Model artifacts url'}
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- {name: job_name, description: 'Training job name'}
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implementation:
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container:
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image: seedjeffwan/kubeflow-pipeline-aws-sm:20190501-05
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command: ['python']
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args: [
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train.py,
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--region, {inputValue: region},
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--image, {inputValue: image},
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--instance_type, {inputValue: instance_type},
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--instance_count, {inputValue: instance_count},
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--volume_size, {inputValue: volume_size},
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--dataset_path, {inputValue: dataset_path},
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--model_artifact_path, {inputValue: model_artifact_path},
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--role, {inputValue: role}
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]
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fileOutputs:
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model_artifact_url: /tmp/model_artifact_url.txt
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job_name: /tmp/job_name.txt
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