[AWS SageMaker] Specify component input types

components/aws/sagemaker/THIRD-PARTY-LICENSES.txt

@@ -1,4 +1,4 @@
-** Amazon SageMaker Components for Kubeflow Pipelines; version 0.3.0 --
+** Amazon SageMaker Components for Kubeflow Pipelines; version 0.3.1 --
 https://github.com/kubeflow/pipelines/tree/master/components/aws/sagemaker
 Copyright 2019-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 ** boto3; version 1.12.33 -- https://github.com/boto/boto3/

components/aws/sagemaker/batch_transform/component.yaml

@@ -4,78 +4,102 @@ description: |
 inputs:
   - name: region
     description: 'The region where the cluster launches.'
+    type: String
   - name: job_name
     description: 'The name of the batch transform job.'
     default: ''
+    type: String
   - name: model_name
     description: 'The name of the model that you want to use for the transform job.'
+    type: String
   - name: max_concurrent
     description: 'The maximum number of parallel requests that can be sent to each instance in a transform job.'
     default: '0'
+    type: Integer
   - name: max_payload
     description: 'The maximum allowed size of the payload, in MB.'
     default: '6'
+    type: Integer
   - name: batch_strategy
     description: 'The number of records to include in a mini-batch for an HTTP inference request.'
     default: ''
+    type: String
   - name: environment
     description: 'The environment variables to set in the Docker container. Up to 16 key-value entries in the map.'
     default: '{}'
+    type: JSON
   - name: input_location
     description: 'The S3 location of the data source that is associated with a channel.'
+    type: String
   - name: data_type
     description: 'Data type of the input. Can be ManifestFile, S3Prefix, or AugmentedManifestFile.'
     default: 'S3Prefix'
+    type: String
   - name: content_type
     description: 'The multipurpose internet mail extension (MIME) type of the data.'
     default: ''
+    type: String
   - name: split_type
     description: 'The method to use to split the transform job data files into smaller batches.'
     default: 'None'
+    type: String
   - name: compression_type
     description: 'If the transform data is compressed, the specification of the compression type.'
     default: 'None'
+    type: String
   - name: output_location
     description: 'The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job.'
+    type: String
   - name: accept
     description: 'The MIME type used to specify the output data.'
     default: ''
+    type: String
   - name: assemble_with
     description: 'Defines how to assemble the results of the transform job as a single S3 object. Either None or Line.'
     default: ''
+    type: String
   - name: output_encryption_key
     description: 'The AWS Key Management Service ID of the key used to encrypt the output data.'
     default: ''
+    type: String
   - name: input_filter
     description: 'A JSONPath expression used to select a portion of the input data to pass to the algorithm.'
     default: ''
+    type: String
   - name: output_filter
     description: 'A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job.'
     default: ''
+    type: String
   - name: join_source
     description: 'Specifies the source of the data to join with the transformed data.'
     default: 'None'
+    type: String
   - name: instance_type
     description: 'The ML compute instance type.'
     default: 'ml.m4.xlarge'
+    type: String
   - name: instance_count
     description: 'The number of ML compute instances to use in each training job.'
     default: '1'
+    type: Integer
   - 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: ''
+    type: String
   - name: endpoint_url
     description: 'The endpoint URL for the private link VPC endpoint.'
     default: ''
+    type: String
   - name: tags
     description: 'Key-value pairs to categorize AWS resources.'
     default: '{}'
+    type: JSON
 outputs:
   - {name: output_location,    description: 'S3 URI of the transform job results.'}
 implementation:
   container:
-    image: amazon/aws-sagemaker-kfp-components:0.3.0
-    command: ['python']
+    image: amazon/aws-sagemaker-kfp-components:0.3.1
+    command: ['python3']
     args: [
       batch_transform.py,
       --region, {inputValue: region},

components/aws/sagemaker/batch_transform/src/batch_transform.py

@@ -26,31 +26,31 @@ def create_parser():
   parser = argparse.ArgumentParser(description='SageMaker Batch Transformation Job')
   _utils.add_default_client_arguments(parser)
 
-  parser.add_argument('--job_name', type=str.strip, required=False, help='The name of the transform job.', default='')
-  parser.add_argument('--model_name', type=str.strip, required=True, help='The name of the model that you want to use for the transform job.')
-  parser.add_argument('--max_concurrent', type=_utils.str_to_int, required=False, help='The maximum number of parallel requests that can be sent to each instance in a transform job.', default='0')
-  parser.add_argument('--max_payload', type=_utils.str_to_int, required=False, help='The maximum allowed size of the payload, in MB.', default='6')
-  parser.add_argument('--batch_strategy', choices=['MultiRecord', 'SingleRecord', ''], type=str.strip, required=False, help='The number of records to include in a mini-batch for an HTTP inference request.', default='')
-  parser.add_argument('--environment', type=_utils.str_to_json_dict, required=False, help='The dictionary of the environment variables to set in the Docker container. Up to 16 key-value entries in the map.', default='{}')
-  parser.add_argument('--input_location', type=str.strip, required=True, help='The S3 location of the data source that is associated with a channel.')
-  parser.add_argument('--data_type', choices=['ManifestFile', 'S3Prefix', 'AugmentedManifestFile', ''], type=str.strip, required=False, help='Data type of the input. Can be ManifestFile, S3Prefix, or AugmentedManifestFile.', default='S3Prefix')
-  parser.add_argument('--content_type', type=str.strip, required=False, help='The multipurpose internet mail extension (MIME) type of the data.', default='')
-  parser.add_argument('--split_type', choices=['None', 'Line', 'RecordIO', 'TFRecord', ''], type=str.strip, required=False, help='The method to use to split the transform job data files into smaller batches.', default='None')
-  parser.add_argument('--compression_type', choices=['None', 'Gzip', ''], type=str.strip, required=False, help='If the transform data is compressed, the specification of the compression type.', default='None')
-  parser.add_argument('--output_location', type=str.strip, required=True, help='The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job.')
-  parser.add_argument('--accept', type=str.strip, required=False, help='The MIME type used to specify the output data.')
-  parser.add_argument('--assemble_with', choices=['None', 'Line', ''], type=str.strip, required=False, help='Defines how to assemble the results of the transform job as a single S3 object. Either None or Line.')
-  parser.add_argument('--output_encryption_key', type=str.strip, required=False, help='The AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts.', default='')
-  parser.add_argument('--input_filter', type=str.strip, required=False, help='A JSONPath expression used to select a portion of the input data to pass to the algorithm.', default='')
-  parser.add_argument('--output_filter', type=str.strip, required=False, help='A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job.', default='')
-  parser.add_argument('--join_source', choices=['None', 'Input', ''], type=str.strip, required=False, help='Specifies the source of the data to join with the transformed data.', default='None')
+  parser.add_argument('--job_name', type=str, required=False, help='The name of the transform job.', default='')
+  parser.add_argument('--model_name', type=str, required=True, help='The name of the model that you want to use for the transform job.')
+  parser.add_argument('--max_concurrent', type=int, required=False, help='The maximum number of parallel requests that can be sent to each instance in a transform job.', default='0')
+  parser.add_argument('--max_payload', type=int, required=False, help='The maximum allowed size of the payload, in MB.', default='6')
+  parser.add_argument('--batch_strategy', choices=['MultiRecord', 'SingleRecord', ''], type=str, required=False, help='The number of records to include in a mini-batch for an HTTP inference request.', default='')
+  parser.add_argument('--environment', type=_utils.yaml_or_json_str, required=False, help='The dictionary of the environment variables to set in the Docker container. Up to 16 key-value entries in the map.', default={})
+  parser.add_argument('--input_location', type=str, required=True, help='The S3 location of the data source that is associated with a channel.')
+  parser.add_argument('--data_type', choices=['ManifestFile', 'S3Prefix', 'AugmentedManifestFile', ''], type=str, required=False, help='Data type of the input. Can be ManifestFile, S3Prefix, or AugmentedManifestFile.', default='S3Prefix')
+  parser.add_argument('--content_type', type=str, required=False, help='The multipurpose internet mail extension (MIME) type of the data.', default='')
+  parser.add_argument('--split_type', choices=['None', 'Line', 'RecordIO', 'TFRecord', ''], type=str, required=False, help='The method to use to split the transform job data files into smaller batches.', default='None')
+  parser.add_argument('--compression_type', choices=['None', 'Gzip', ''], type=str, required=False, help='If the transform data is compressed, the specification of the compression type.', default='None')
+  parser.add_argument('--output_location', type=str, required=True, help='The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job.')
+  parser.add_argument('--accept', type=str, required=False, help='The MIME type used to specify the output data.')
+  parser.add_argument('--assemble_with', choices=['None', 'Line', ''], type=str, required=False, help='Defines how to assemble the results of the transform job as a single S3 object. Either None or Line.')
+  parser.add_argument('--output_encryption_key', type=str, required=False, help='The AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts.', default='')
+  parser.add_argument('--input_filter', type=str, required=False, help='A JSONPath expression used to select a portion of the input data to pass to the algorithm.', default='')
+  parser.add_argument('--output_filter', type=str, required=False, help='A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job.', default='')
+  parser.add_argument('--join_source', choices=['None', 'Input', ''], type=str, required=False, help='Specifies the source of the data to join with the transformed data.', default='None')
   parser.add_argument('--instance_type', choices=['ml.m4.xlarge', 'ml.m4.2xlarge', 'ml.m4.4xlarge', 'ml.m4.10xlarge', 'ml.m4.16xlarge', 'ml.m5.large', 'ml.m5.xlarge', 'ml.m5.2xlarge', 'ml.m5.4xlarge',
     'ml.m5.12xlarge', 'ml.m5.24xlarge', 'ml.c4.xlarge', 'ml.c4.2xlarge', 'ml.c4.4xlarge', 'ml.c4.8xlarge', 'ml.p2.xlarge', 'ml.p2.8xlarge', 'ml.p2.16xlarge', 'ml.p3.2xlarge', 'ml.p3.8xlarge', 'ml.p3.16xlarge',
-    'ml.c5.xlarge', 'ml.c5.2xlarge', 'ml.c5.4xlarge', 'ml.c5.9xlarge', 'ml.c5.18xlarge'], type=str.strip, required=True, help='The ML compute instance type for the transform job.', default='ml.m4.xlarge')
-  parser.add_argument('--instance_count', type=_utils.str_to_int, required=False, help='The number of ML compute instances to use in the transform job.')
-  parser.add_argument('--resource_encryption_key', type=str.strip, required=False, help='The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s).', default='')
-  parser.add_argument('--tags', type=_utils.str_to_json_dict, required=False, help='An array of key-value pairs, to categorize AWS resources.', default='{}')
-  parser.add_argument('--output_location_file', type=str.strip, required=True, help='File path where the program will write the Amazon S3 URI of the transform job results.')
+    'ml.c5.xlarge', 'ml.c5.2xlarge', 'ml.c5.4xlarge', 'ml.c5.9xlarge', 'ml.c5.18xlarge'], type=str, required=True, help='The ML compute instance type for the transform job.', default='ml.m4.xlarge')
+  parser.add_argument('--instance_count', type=int, required=False, help='The number of ML compute instances to use in the transform job.')
+  parser.add_argument('--resource_encryption_key', type=str, required=False, help='The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s).', default='')
+  parser.add_argument('--tags', type=_utils.yaml_or_json_str, required=False, help='An array of key-value pairs, to categorize AWS resources.', default={})
+  parser.add_argument('--output_location_file', type=str, required=True, help='File path where the program will write the Amazon S3 URI of the transform job results.')
 
   return parser
 

components/aws/sagemaker/codebuild/deploy.buildspec.yml

@@ -1,10 +1,10 @@
-version: 0.2      
+version: 0.2
+
 phases:
   pre_build:
     commands:
       # Log in to Dockerhub
-      - mkdir -p ~/.docker
-      - echo $DOCKER_CONFIG > ~/.docker/config.json
+      - docker login -u $DOCKER_CONFIG_USERNAME -p $DOCKER_CONFIG_PASSWORD
 
   build:
     commands:

components/aws/sagemaker/codebuild/scripts/deploy.sh

@@ -5,6 +5,7 @@ set -e
 REMOTE_REPOSITORY="amazon/aws-sagemaker-kfp-components"
 DRYRUN="true"
 FULL_VERSION_TAG=""
+DOCKER_CONFIG_PATH=${DOCKER_CONFIG_PATH:-"/root/.docker"}
 
 while getopts ":d:v:" opt; do
 	case ${opt} in
@@ -64,13 +65,13 @@ echo "Tagged image with ${MAJOR_VERSION_IMAGE}"
 
 # Push to the remote repository
 if [ "${DRYRUN}" == "false" ]; then
-  docker push "${FULL_VERSION_IMAGE}"
+  docker --config "$DOCKER_CONFIG_PATH" push "${FULL_VERSION_IMAGE}"
   echo "Successfully pushed tag ${FULL_VERSION_IMAGE} to Docker Hub"
 
-	docker push "${MINOR_VERSION_IMAGE}"
+	docker --config "$DOCKER_CONFIG_PATH" push "${MINOR_VERSION_IMAGE}"
   echo "Successfully pushed tag ${MINOR_VERSION_IMAGE} to Docker Hub"
 
-	docker push "${MAJOR_VERSION_IMAGE}"
+	docker --config "$DOCKER_CONFIG_PATH" push "${MAJOR_VERSION_IMAGE}"
   echo "Successfully pushed tag ${MAJOR_VERSION_IMAGE} to Docker Hub"
 else
   echo "Dry run detected. Not pushing images."

components/aws/sagemaker/common/_utils.py

@@ -13,6 +13,7 @@
 import os
 import argparse
 from time import gmtime, strftime
+from distutils.util import strtobool
 import time
 import string
 import random
@@ -63,15 +64,15 @@ def nullable_string_argument(value):
 
 
 def add_default_client_arguments(parser):
-    parser.add_argument('--region', type=str.strip, required=True, help='The region where the training job launches.')
+    parser.add_argument('--region', type=str, required=True, help='The region where the training job launches.')
     parser.add_argument('--endpoint_url', type=nullable_string_argument, required=False, help='The URL to use when communicating with the Sagemaker service.')
 
 
 def get_component_version():
     """Get component version from the first line of License file"""
     component_version = 'NULL'
 
-    with open('/THIRD-PARTY-LICENSES.txt', 'r') as license_file:
+    with open('THIRD-PARTY-LICENSES.txt', 'r') as license_file:
         version_match = re.search('Amazon SageMaker Components for Kubeflow Pipelines; version (([0-9]+[.])+[0-9]+)',
                         license_file.readline())
         if version_match is not None:
@@ -858,35 +859,15 @@ def enable_spot_instance_support(training_job_config, args):
 def id_generator(size=4, chars=string.ascii_uppercase + string.digits):
   return ''.join(random.choice(chars) for _ in range(size))
 
-
-def str_to_bool(s):
-  if s.lower().strip() == 'true':
-    return True
-  elif s.lower().strip() == 'false':
-    return False
-  else:
-    raise argparse.ArgumentTypeError('"True" or "False" expected.')
-
-def str_to_int(s):
-  if s:
-    return int(s)
-  else:
-    return 0
-
-def str_to_float(s):
-  if s:
-    return float(s)
-  else:
-    return 0.0
-
-def str_to_json_dict(s):
-  if s != '':
-      return json.loads(s)
-  else:
-      return {}
-
-def str_to_json_list(s):
-  if s != '':
-      return json.loads(s)
-  else:
-      return []
+def yaml_or_json_str(str):
+  if str == "" or str == None:
+    return None
+  try:
+    return json.loads(str)
+  except:
+    return yaml.safe_load(str)
+
+def str_to_bool(str):
+    # This distutils function returns an integer representation of the boolean
+    # rather than a True/False value. This simply hard casts it.
+    return bool(strtobool(str))