name: 'Sagemaker - Training Job'
description: |
  Train Machine Learning and Deep Learning Models using SageMaker
inputs:
  - name: region
    description: 'The region where the training job launches.'
  - name: job_name
    description: 'The name of the batch training job.'
    default: ''
  - name: role
    description: 'The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf.'
  - name: image
    description: 'The registry path of the Docker image that contains the training algorithm.'
    default: ''
  - name: algorithm_name
    description: 'The name of the algorithm resource to use for the training job. Do not specify a value for this if using training image.'
    default: ''
  - name: metric_definitions
    description: 'The dictionary of name-regex pairs specify the metrics that the algorithm emits.'
    default: '{}'
  - name: training_input_mode
    description: 'The input mode that the algorithm supports. File or Pipe.'
    default: 'File'
  - name: hyperparameters
    description: 'Dictionary of hyperparameters for the the algorithm.'
    default: '{}'
  - name: channels
    description: 'A list of dicts specifying the input channels. Must have at least one.'
  - name: data_location_1
    description: 'The S3 URI of the input data source for channel 1.'
    default: ''
  - name: data_location_2
    description: 'The S3 URI of the input data source for channel 2.'
    default: ''
  - name: data_location_3
    description: 'The S3 URI of the input data source for channel 3.'
    default: ''
  - name: data_location_4
    description: 'The S3 URI of the input data source for channel 4.'
    default: ''
  - name: data_location_5
    description: 'The S3 URI of the input data source for channel 5.'
    default: ''
  - name: data_location_6
    description: 'The S3 URI of the input data source for channel 6.'
    default: ''
  - name: data_location_7
    description: 'The S3 URI of the input data source for channel 7.'
    default: ''
  - name: data_location_8
    description: 'The S3 URI of the input data source for channel 8.'
    default: ''
  - name: instance_type
    description: 'The ML compute instance type.'
    default: 'ml.m4.xlarge'
  - name: instance_count
    description: 'The number of ML compute instances to use in each training job.'
    default: '1'
  - name: volume_size
    description: 'The size of the ML storage volume that you want to provision.'
    default: '1'
  - name: resource_encryption_key
    description: 'The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s).'
    default: ''
  - name: max_run_time
    description: 'The maximum run time in seconds for the training job.'
    default: '86400'
  - name: model_artifact_path
    description: 'Identifies the S3 path where you want Amazon SageMaker to store the model artifacts.'
  - name: output_encryption_key
    description: 'The AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts.'
    default: ''
  - name: vpc_security_group_ids
    description: 'The VPC security group IDs, in the form sg-xxxxxxxx.'
    default: ''
  - name: vpc_subnets
    description: 'The ID of the subnets in the VPC to which you want to connect your hpo job.'
    default: ''
  - name: network_isolation
    description: 'Isolates the training container.'
    default: 'True'
  - name: traffic_encryption
    description: 'Encrypts all communications between ML compute instances in distributed training.'
    default: 'False'
  - name: tags
    description: 'Key-value pairs, to categorize AWS resources.'
    default: '{}'
outputs:
  - {name: model_artifact_url,    description: 'Model artifacts url'}
  - {name: job_name,              description: 'Training job name'}
  - {name: training_image,         description: 'The registry path of the Docker image that contains the training algorithm'}
implementation:
  container:
    image: carowang/kubeflow-pipeline-aws-sm:20190809-02
    command: ['python']
    args: [
      train.py,
      --region, {inputValue: region},
      --job_name, {inputValue: job_name},
      --role, {inputValue: role},
      --image, {inputValue: image},
      --algorithm_name, {inputValue: algorithm_name},
      --metric_definitions, {inputValue: metric_definitions},
      --training_input_mode, {inputValue: training_input_mode},
      --hyperparameters, {inputValue: hyperparameters},
      --channels, {inputValue: channels},
      --data_location_1, {inputValue: data_location_1},
      --data_location_2, {inputValue: data_location_2},
      --data_location_3, {inputValue: data_location_3},
      --data_location_4, {inputValue: data_location_4},
      --data_location_5, {inputValue: data_location_5},
      --data_location_6, {inputValue: data_location_6},
      --data_location_7, {inputValue: data_location_7},
      --data_location_8, {inputValue: data_location_8},
      --instance_type, {inputValue: instance_type},
      --instance_count, {inputValue: instance_count},
      --volume_size, {inputValue: volume_size},
      --resource_encryption_key, {inputValue: resource_encryption_key},
      --max_run_time, {inputValue: max_run_time},
      --model_artifact_path, {inputValue: model_artifact_path},
      --output_encryption_key, {inputValue: output_encryption_key},
      --vpc_security_group_ids, {inputValue: vpc_security_group_ids},
      --vpc_subnets, {inputValue: vpc_subnets},
      --network_isolation, {inputValue: network_isolation},
      --traffic_encryption, {inputValue: traffic_encryption},
      --tags, {inputValue: tags}
    ]
    fileOutputs:
      model_artifact_url: /tmp/model_artifact_url.txt
      job_name: /tmp/job_name.txt
      training_image: /tmp/training_image.txt