remove tfx-taxi sample in favor of the tfx oss sample

backend/src/apiserver/config/sample_config.json

@@ -4,11 +4,6 @@
     "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_training_cm/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/core/tfx",
-    "file":"/samples/core/tfx_cab_classification/tfx_cab_classification.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/core/sequential/sequential.py",

samples/README.md

@@ -92,7 +92,7 @@ Only the core samples are expected to be added to the sample test infrastructure
 1. Make sure the sample follows the [sample conventions](#sample-conventions).
 2. If the sample requires argument inputs, they can be specified in a config yaml file
 placed under `test/sample-test/configs`. See 
-[`tfx_cab_classification.config.yaml`](https://github.com/kubeflow/pipelines/blob/master/test/sample-test/configs/tfx_cab_classification.config.yaml) 
+[`xgboost_training_cm.config.yaml`](https://github.com/kubeflow/pipelines/blob/master/test/sample-test/configs/xgboost_training_cm.config.yaml) 
 as an example. 
 The config yaml file will be validated according to `schema.config.yaml`. 
 If no config yaml is provided, pipeline parameters will be substituted by their default values.