Support for TFX 1.4, TF 2.6, Beam 2.33.0 (#58)

* ermoved used _float_feature

* Updated dependencies

* updated tfdv notebook

* updated apache beam example

* updated components

* updated interactive notebook, working with tfx 1.4

* airflow updates

* updated beam pipeline example

* kubeflow updates

* updated beam_arg

* added vertex example

* renamed to vertex

* Updated readme

README.md

@@ -2,6 +2,10 @@
 
 Code repository for the O'Reilly publication ["Building Machine Learning Pipelines"](http://www.buildingmlpipelines.com) by Hannes Hapke & Catherine Nelson
 
+## Update
+
+* The example code has been updated to work with TFX 1.4.0, TensorFlow 2.6.1, and Apache Beam 2.33.0. A GCP Vertex example (training and serving) was added.
+
 ## Set up the demo project
 
 Download the initial dataset. From the root of this repository, execute
@@ -63,7 +67,9 @@ Chapter 14. Code for training a differentially private version of the demo proje
 
 The code was written and tested for version 0.22.
 
+- As of 11/23/21, the examples have been updated to support TFX 1.4.0, TensorFlow 2.6.1, and Apache Beam 2.33.0. A GCP Vertex example (training and serving) was added.
+
 - As of 9/22/20, the interactive pipeline runs on TFX version 0.24.0rc1.
 Due to tiny TFX bugs, the pipelines currently don't work on the releases 0.23 and 0.24-rc0. Github issues have been filed with the TFX team specifically for the book pipelines ([Issue 2500](https://github.com/tensorflow/tfx/issues/2500#issuecomment-695363847)). We will update the repository once the issue is resolved.
 
-- As of 9/14/20, TFX only supports Python 3.8 with version >0.24.0rc0. 
+- As of 9/14/20, TFX only supports Python 3.8 with version >0.24.0rc0.

chapters/data_ingestion/convert_data_to_tfrecords.py

@@ -8,13 +8,7 @@
 
 
 def _bytes_feature(value):
-    return tf.train.Feature(
-        bytes_list=tf.train.BytesList(value=[value.encode()])
-    )
-
-
-def _float_feature(value):
-    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
+    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value.encode()]))
 
 
 def _int64_feature(value):
@@ -26,12 +20,13 @@ def clean_rows(row):
         row["zip_code"] = "99999"
     return row
 
+
 def convert_zipcode_to_int(zipcode):
     if isinstance(zipcode, str) and "XX" in zipcode:
         zipcode = zipcode.replace("XX", "00")
     int_zipcode = int(zipcode)
     return int_zipcode
-        
+
 
 original_data_file = "../../data/consumer_complaints_with_narrative.csv"
 tfrecords_filename = "consumer-complaints.tfrecords"
@@ -53,9 +48,7 @@ def convert_zipcode_to_int(zipcode):
                     "company": _bytes_feature(row["company"]),
                     "company_response": _bytes_feature(row["company_response"]),
                     "timely_response": _bytes_feature(row["timely_response"]),
-                    "consumer_disputed": _bytes_feature(
-                        row["consumer_disputed"]
-                    ),
+                    "consumer_disputed": _bytes_feature(row["consumer_disputed"]),
                 }
             )
         )