Restructure samples (#1710)

* restructure samples
* update apiserver and sample test for the new location

backend/Dockerfile

@@ -29,7 +29,7 @@ COPY ./samples .
 #I think it's better to just use a shell loop though.
 #RUN  for pipeline in $(find . -maxdepth 2 -name '*.py' -type f); do dsl-compile --py "$pipeline" --output "$pipeline.tar.gz"; done
 #The "for" loop breaks on all whitespace, so we either need to override IFS or use the "read" command instead.
-RUN  find . -maxdepth 2 -name '*.py' -type f | while read pipeline; do dsl-compile --py "$pipeline" --output "$pipeline.tar.gz"; done
+RUN  find . -maxdepth 3 -name '*.py' -type f | while read pipeline; do dsl-compile --py "$pipeline" --output "$pipeline.tar.gz"; done
 
 FROM debian:stretch
 

backend/src/apiserver/config/sample_config.json

@@ -1,32 +1,32 @@
 [
   {
     "name":"[Sample] ML - XGBoost - Training with Confusion Matrix",
-    "description":"A trainer that does end-to-end distributed training for XGBoost models. For source code, refer to https://github.com/kubeflow/pipelines/tree/master/samples/xgboost-spark",
-    "file":"/samples/xgboost-spark/xgboost-training-cm.py.tar.gz"
+    "description":"A trainer that does end-to-end distributed training for XGBoost models. For source code, refer to https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost-spark",
+    "file":"/samples/core/xgboost-spark/xgboost-training-cm.py.tar.gz"
   },
   {
     "name":"[Sample] ML - TFX - Taxi Tip Prediction Model Trainer",
-    "description":"Example pipeline that does classification with model analysis based on a public tax cab BigQuery dataset. For source code, refer to https://github.com/kubeflow/pipelines/tree/master/samples/tfx",
-    "file":"/samples/tfx/taxi-cab-classification-pipeline.py.tar.gz"
+    "description":"Example pipeline that does classification with model analysis based on a public tax cab BigQuery dataset. For source code, refer to https://github.com/kubeflow/pipelines/tree/master/samples/core/tfx",
+    "file":"/samples/core/tfx/taxi-cab-classification-pipeline.py.tar.gz"
   },
   {
     "name":"[Sample] Basic - Sequential execution",
-    "description":"A pipeline with two sequential steps. For source code, refer to https://github.com/kubeflow/pipelines/blob/master/samples/basic/sequential.py",
-    "file":"/samples/basic/sequential.py.tar.gz"
+    "description":"A pipeline with two sequential steps. For source code, refer to https://github.com/kubeflow/pipelines/blob/master/samples/core/sequential/sequential.py",
+    "file":"/samples/core/sequential/sequential.py.tar.gz"
   },
   {
     "name":"[Sample] Basic - Parallel execution",
-    "description":"A pipeline that downloads two messages in parallel and prints the concatenated result. For source code, refer to https://github.com/kubeflow/pipelines/blob/master/samples/basic/parallel_join.py",
-    "file":"/samples/basic/parallel_join.py.tar.gz"
+    "description":"A pipeline that downloads two messages in parallel and prints the concatenated result. For source code, refer to https://github.com/kubeflow/pipelines/blob/master/samples/core/parallel_join/parallel_join.py",
+    "file":"/samples/core/parallel_join/parallel_join.py.tar.gz"
   },
   {
     "name":"[Sample] Basic - Conditional execution",
-    "description":"A pipeline shows how to use dsl.Condition. For source code, refer to https://github.com/kubeflow/pipelines/blob/master/samples/basic/condition.py",
-    "file":"/samples/basic/condition.py.tar.gz"
+    "description":"A pipeline shows how to use dsl.Condition. For source code, refer to https://github.com/kubeflow/pipelines/blob/master/samples/core/condition/condition.py",
+    "file":"/samples/core/condition/condition.py.tar.gz"
   },
   {
     "name":"[Sample] Basic - Exit Handler",
-    "description":"A pipeline that downloads a message and prints it out. Exit Handler will run at the end. For source code, refer to https://github.com/kubeflow/pipelines/blob/master/samples/basic/exit_handler.py",
-    "file":"/samples/basic/exit_handler.py.tar.gz"
+    "description":"A pipeline that downloads a message and prints it out. Exit Handler will run at the end. For source code, refer to https://github.com/kubeflow/pipelines/blob/master/samples/core/exit_handler/exit_handler.py",
+    "file":"/samples/core/exit_handler/exit_handler.py.tar.gz"
   }
 ]

samples/README.md

@@ -1,3 +1,35 @@
 The sample pipelines give you a quick start to building and deploying machine learning pipelines with Kubeflow.
 * Follow the guide to [deploy the Kubeflow pipelines service](https://www.kubeflow.org/docs/guides/pipelines/deploy-pipelines-service/).
 * Build and deploy your pipeline [using the provided samples](https://www.kubeflow.org/docs/guides/pipelines/pipelines-samples/).
+
+
+
+
+This page tells you how to use the _basic_ sample pipelines contained in the repo.
+
+## Compile the pipeline specification
+
+Follow the guide to [building a pipeline](https://www.kubeflow.org/docs/guides/pipelines/build-pipeline/) to install the Kubeflow Pipelines SDK and compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file. 
+
+For convenience, you can download a pre-compiled, compressed YAML file containing the
+specification of the `core/sequential.py` pipeline. This saves you the steps required
+to compile and compress the pipeline specification:
+[sequential.tar.gz](https://storage.googleapis.com/sample-package/sequential.tar.gz)
+
+## Deploy
+
+Open the Kubeflow pipelines UI, and follow the prompts to create a new pipeline and upload the generated workflow
+specification, `my-pipeline.tar.gz` (example: `sequential.tar.gz`).
+
+## Run
+
+Follow the pipeline UI to create pipeline runs. 
+
+Useful parameter values:
+
+* For the "exit_handler" and "sequential" samples: `gs://ml-pipeline-playground/shakespeare1.txt`
+* For the "parallel_join" sample: `gs://ml-pipeline-playground/shakespeare1.txt` and `gs://ml-pipeline-playground/shakespeare2.txt`
+
+## Components source
+
+All samples use pre-built components. The command to run for each container is built into the pipeline file.

samples/basic/README.md

@@ -1,30 +0,0 @@
-
-
-This page tells you how to use the _basic_ sample pipelines contained in the repo.
-
-## Compile the pipeline specification
-
-Follow the guide to [building a pipeline](https://www.kubeflow.org/docs/guides/pipelines/build-pipeline/) to install the Kubeflow Pipelines SDK and compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file. 
-
-For convenience, you can download a pre-compiled, compressed YAML file containing the
-specification of the `sequential.py` pipeline. This saves you the steps required
-to compile and compress the pipeline specification:
-[sequential.tar.gz](https://storage.googleapis.com/sample-package/sequential.tar.gz)
-
-## Deploy
-
-Open the Kubeflow pipelines UI, and follow the prompts to create a new pipeline and upload the generated workflow
-specification, `my-pipeline.tar.gz` (example: `sequential.tar.gz`).
-
-## Run
-
-Follow the pipeline UI to create pipeline runs. 
-
-Useful parameter values:
-
-* For the "exit_handler" and "sequential" samples: `gs://ml-pipeline-playground/shakespeare1.txt`
-* For the "parallel_join" sample: `gs://ml-pipeline-playground/shakespeare1.txt` and `gs://ml-pipeline-playground/shakespeare2.txt`
-
-## Components source
-
-All samples use pre-built components. The command to run for each container is built into the pipeline file.