diff --git a/backend/src/apiserver/config/sample_config.json b/backend/src/apiserver/config/sample_config.json
index 0d25e697841..59390f49c94 100644
--- a/backend/src/apiserver/config/sample_config.json
+++ b/backend/src/apiserver/config/sample_config.json
@@ -4,6 +4,11 @@
     "description": "[GCP Permission requirements](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost_training_cm#requirements) A trainer that does end-to-end distributed training for XGBoost models. [source code](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost_training_cm)",
     "file": "/samples/core/xgboost_training_cm/xgboost_training_cm.py.tar.gz"
   },
+  {
+    "name": "[Sample] Unified DSL - Taxi Tip Prediction Model Trainer",
+    "description": "[GCP Permission requirements](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/parameterized_tfx_oss#permission). Example pipeline that does classification with model analysis based on a public tax cab BigQuery dataset. [source code](https://github.com/kubeflow/pipelines/tree/master/samples/contrib/parameterized_tfx_oss)",
+    "file": "/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.tar.gz"
+  },
   {
     "name": "[Sample] Basic - Sequential execution",
     "description": "A pipeline with two sequential steps. [source code](https://github.com/kubeflow/pipelines/blob/master/samples/core/sequential/sequential.py)",
@@ -23,10 +28,5 @@
     "name": "[Sample] Basic - Exit Handler",
     "description": "A pipeline that downloads a message and prints it out. Exit Handler will run at the end. [source code](https://github.com/kubeflow/pipelines/blob/master/samples/core/exit_handler/exit_handler.py)",
     "file": "/samples/core/exit_handler/exit_handler.py.tar.gz"
-  },
-  {
-    "name": "[Sample] ML - TFX - Taxi Tip Prediction Model Trainer",
-    "description": "[GCP Permission requirements](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/parameterized_tfx_oss#permission). Example pipeline that does classification with model analysis based on a public tax cab BigQuery dataset. [source code](https://github.com/kubeflow/pipelines/tree/master/samples/contrib/parameterized_tfx_oss)",
-    "file": "/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.tar.gz"
   }
 ]
diff --git a/samples/contrib/parameterized_tfx_oss/README.md b/samples/contrib/parameterized_tfx_oss/README.md
index 21c38f5b1fb..f53718382d6 100644
--- a/samples/contrib/parameterized_tfx_oss/README.md
+++ b/samples/contrib/parameterized_tfx_oss/README.md
@@ -9,11 +9,13 @@ This sample demonstrates how to author a ML pipeline in TFX and run it on a KFP
 Please refer to inline comments for the purpose of each step.
 
 In order to successfully compile this sample, you'll need to have a TFX installation at HEAD.
-First, you can clone their repo and
-then point the version tag in `tfx/version.py` to TFX's latest nightly build image of version 
-`0.15.0dev` (e.g., `0.15.0dev20191007`, list of image available can be found [here](https://hub.docker.com/r/tensorflow/tfx/tags)).
-Finally, run `python setup.py install` from `tfx/tfx`. After that, running 
-`chicago_taxi_pipeline_simple.py` compiles the TFX pipeline into KFP pipeline package.
+First, you can clone their repo and run `python setup.py install` from `tfx/tfx`. 
+The image used in the pipeline is specified as `tfx_image` in the 
+`KubeflowDagRunnerConfig`. Currently we're using our own patched version of TFX image containing visualization support.
+List of officially released nightly build image available can be found [here](https://hub.docker.com/r/tensorflow/tfx/tags)).
+
+After that, running 
+`python3 chicago_taxi_pipeline_simple.py` compiles the TFX pipeline into KFP pipeline package.
 This pipeline requires google storage permission to run. 
 
 
diff --git a/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.py b/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.py
index 5942a0b0abe..dcca8be540f 100644
--- a/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.py
+++ b/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.py
@@ -141,7 +141,8 @@ def _get_kubeflow_metadata_config() -> kubeflow_pb2.KubeflowMetadataConfig:
       enable_cache=enable_cache)
 
   config = kubeflow_dag_runner.KubeflowDagRunnerConfig(
-      kubeflow_metadata_config=_get_kubeflow_metadata_config())
+      kubeflow_metadata_config=_get_kubeflow_metadata_config(),
+      tfx_image='gcr.io/ml-pipeline/patched-tfx:0.1.32')
 
   kfp_runner = kubeflow_dag_runner.KubeflowDagRunner(config=config)
   # Make sure kfp_runner recognizes those parameters.
diff --git a/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.tar.gz b/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.tar.gz
index 02c52d443e4..9d639a25584 100644
Binary files a/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.tar.gz and b/samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.tar.gz differ