Refactor to match new samples folder structure (#1741)

components/aws/sagemaker/ground_truth/README.md

@@ -53,7 +53,7 @@ active_learning_model_arn | ARN of the resulting active learning model
 
 # Samples
 ## Used in a pipeline with workteam creation and training
-Mini image classification demo: [Demo](https://github.com/kubeflow/pipelines/blob/master/samples/aws-samples/ground_truth_pipeline_demo/)
+Mini image classification demo: [Demo](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/aws-samples/ground_truth_pipeline_demo/)
 
 # References
 * [Ground Truth documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/sms.html)

components/aws/sagemaker/hyperparameter_tuning/README.md

@@ -64,9 +64,9 @@ training_image | The registry path of the Docker image that contains the trainin
 
 # Samples
 ## On its own
-K-Means algorithm tuning on MNIST dataset: [pipeline](https://github.com/kubeflow/pipelines/blob/master/samples/aws-samples/mnist-kmeans-sagemaker/kmeans-hpo-pipeline.py)
+K-Means algorithm tuning on MNIST dataset: [pipeline](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/aws-samples/mnist-kmeans-sagemaker/kmeans-hpo-pipeline.py)
 
-Follow the steps as in the [README](https://github.com/kubeflow/pipelines/blob/master/samples/aws-samples/mnist-kmeans-sagemaker/README.md) with some modification:
+Follow the steps as in the [README](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/aws-samples/mnist-kmeans-sagemaker/README.md) with some modification:
 1. Get and store data in S3 buckets
 2. Prepare an IAM roles with permissions to run SageMaker jobs
 3. Add 'aws-secret' to your kubeflow namespace
@@ -78,7 +78,7 @@ dsl-compile --py kmeans-hpo-pipeline.py --output kmeans-hpo-pipeline.tar.gz
 6. Once the pipeline completes, you can see the outputs under 'Output parameters' in the HPO component's Input/Output section.
 
 ## Integrated into a pipeline
-MNIST Classification using K-Means pipeline: [Pipeline](https://github.com/kubeflow/pipelines/blob/master/samples/aws-samples/mnist-kmeans-sagemaker/mnist-classification-pipeline.py) | [Steps](https://github.com/kubeflow/pipelines/blob/master/samples/aws-samples/mnist-kmeans-sagemaker/README.md)
+MNIST Classification using K-Means pipeline: [Pipeline](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/aws-samples/mnist-kmeans-sagemaker/mnist-classification-pipeline.py) | [Steps](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/aws-samples/mnist-kmeans-sagemaker/README.md)
 
 # Resources
 * [Using Amazon built-in algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)

components/aws/sagemaker/workteam/README.md

@@ -38,7 +38,7 @@ workteam_arn | ARN of the workteam
 
 # Samples
 ## In a pipeline with Ground Truth and training
-Mini image classification: [Demo](https://github.com/kubeflow/pipelines/blob/master/samples/aws-samples/ground_truth_pipeline_demo/)
+Mini image classification: [Demo](https://github.com/kubeflow/pipelines/tree/master/samples/contrib/aws-samples/ground_truth_pipeline_demo)
 
 # References
 * [Managing a private workforce](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-management-private.html)

samples/contrib/aws-samples/ground_truth_pipeline_demo/README.md

@@ -13,7 +13,7 @@ Run the following to download `openimgs-annotations.csv`:
 ```bash
 wget https://storage.googleapis.com/openimages/2018_04/test/test-annotations-human-imagelabels-boxable.csv -O openimgs-annotations.csv
 ```
-Create a s3 bucket and run [this python script](https://github.com/kubeflow/pipelines/tree/master/samples/aws-samples/ground_truth_pipeline_demo/prep_inputs.py) to get the images and generate `train.manifest`, `validation.manifest`, `class_labels.json`, and `instuctions.template`.
+Create a s3 bucket and run [this python script](https://github.com/kubeflow/pipelines/tree/master/samples/contrib/aws-samples/ground_truth_pipeline_demo/prep_inputs.py) to get the images and generate `train.manifest`, `validation.manifest`, `class_labels.json`, and `instuctions.template`.
 
 
 ## Amazon Cognito user groups

samples/contrib/aws-samples/ground_truth_pipeline_demo/mini-image-classification-pipeline.py

@@ -5,9 +5,9 @@
 from kfp import dsl
 from kfp.aws import use_aws_secret
 
-sagemaker_workteam_op = components.load_component_from_file('../../../components/aws/sagemaker/workteam/component.yaml')
-sagemaker_gt_op = components.load_component_from_file('../../../components/aws/sagemaker/ground_truth/component.yaml')
-sagemaker_train_op = components.load_component_from_file('../../../components/aws/sagemaker/train/component.yaml')
+sagemaker_workteam_op = components.load_component_from_file('../../../../components/aws/sagemaker/workteam/component.yaml')
+sagemaker_gt_op = components.load_component_from_file('../../../../components/aws/sagemaker/ground_truth/component.yaml')
+sagemaker_train_op = components.load_component_from_file('../../../../components/aws/sagemaker/train/component.yaml')
 
 @dsl.pipeline(
     name='Ground Truth image classification test pipeline',

samples/contrib/aws-samples/mnist-kmeans-sagemaker/kmeans-hpo-pipeline.py

@@ -5,7 +5,7 @@
 from kfp import dsl
 from kfp.aws import use_aws_secret
 
-sagemaker_hpo_op = components.load_component_from_file('../../../components/aws/sagemaker/hyperparameter_tuning/component.yaml')
+sagemaker_hpo_op = components.load_component_from_file('../../../../components/aws/sagemaker/hyperparameter_tuning/component.yaml')
 
 @dsl.pipeline(
     name='MNIST HPO test pipeline',

samples/contrib/aws-samples/mnist-kmeans-sagemaker/mnist-classification-pipeline.py

@@ -5,11 +5,11 @@
 from kfp import dsl
 from kfp.aws import use_aws_secret
 
-sagemaker_hpo_op = components.load_component_from_file('../../../components/aws/sagemaker/hyperparameter_tuning/component.yaml')
-sagemaker_train_op = components.load_component_from_file('../../../components/aws/sagemaker/train/component.yaml')
-sagemaker_model_op = components.load_component_from_file('../../../components/aws/sagemaker/model/component.yaml')
-sagemaker_deploy_op = components.load_component_from_file('../../../components/aws/sagemaker/deploy/component.yaml')
-sagemaker_batch_transform_op = components.load_component_from_file('../../../components/aws/sagemaker/batch_transform/component.yaml')
+sagemaker_hpo_op = components.load_component_from_file('../../../../components/aws/sagemaker/hyperparameter_tuning/component.yaml')
+sagemaker_train_op = components.load_component_from_file('../../../../components/aws/sagemaker/train/component.yaml')
+sagemaker_model_op = components.load_component_from_file('../../../../components/aws/sagemaker/model/component.yaml')
+sagemaker_deploy_op = components.load_component_from_file('../../../../components/aws/sagemaker/deploy/component.yaml')
+sagemaker_batch_transform_op = components.load_component_from_file('../../../../components/aws/sagemaker/batch_transform/component.yaml')
 
 @dsl.pipeline(
     name='MNIST Classification pipeline',