Fix sample (#2526)

samples/contrib/parameterized_tfx_oss/README.md

@@ -11,7 +11,7 @@ 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 run `python setup.py install` from `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.
+`KubeflowDagRunnerConfig`. Please make sure you use the latest TFX build so that visualization support is included.
 List of officially released nightly build image available can be found [here](https://hub.docker.com/r/tensorflow/tfx/tags)).
 
 After that, running 

samples/contrib/parameterized_tfx_oss/parameterized_tfx_oss.py

@@ -131,7 +131,7 @@ def _create_test_pipeline(pipeline_root: Text, csv_input_location: Text,
     enable_cache=enable_cache)
   config = kubeflow_dag_runner.KubeflowDagRunnerConfig(
     kubeflow_metadata_config=kubeflow_dag_runner.get_default_kubeflow_metadata_config(),
-    tfx_image='gcr.io/ml-pipeline/patched-tfx:0.1.32')
+    tfx_image='tensorflow/tfx:0.16.0.dev20191101')
   kfp_runner = kubeflow_dag_runner.KubeflowDagRunner(config=config)
   # Make sure kfp_runner recognizes those parameters.
   kfp_runner._params.extend([_data_root_param, _taxi_module_file_param])