A Component-by-Component Introduction to TensorFlow Extended (TFX)
View on TensorFlow.org
Run in Google Colab
View source on GitHubThis Colab-based tutorial will interactively walk through each built-in component of TensorFlow Extended (TFX).
It covers every step in an end-to-end machine learning pipeline, from data ingestion to pushing a model to serving.
When you're done, the contents of this notebook can be automatically exported as TFX pipeline source code, which you can orchestrate with Apache Airflow and Apache Beam.
Background
This notebook demonstrates how to use TFX in a Jupyter/Colab environment. Here, we walk through the Chicago Taxi example in an interactive notebook.
Working in an interactive notebook is a useful way to become familiar with the structure of a TFX pipeline. It's also useful when doing development of your own pipelines as a lightweight development environment, but you should be aware that there are differences in the way interactive notebooks are orchestrated, and how they access metadata artifacts.
Orchestration
In a production deployment of TFX, you will use an orchestrator such as Apache Airflow, Kubeflow Pipelines, or Apache Beam to orchestrate a pre-defined pipeline graph of TFX components. In an interactive notebook, the notebook itself is the orchestrator, running each TFX component as you execute the notebook cells.
Metadata
In a production deployment of TFX, you will access metadata through the ML Metadata (MLMD) API. MLMD stores metadata properties in a database such as MySQL or SQLite, and stores the metadata payloads in a persistent store such as on your filesystem. In an interactive notebook, both properties and payloads are stored in an ephemeral SQLite database in the /tmp directory on the Jupyter notebook or Colab server.
Setup
First, we install and import the necessary packages, set up paths, and download data.
Upgrade Pip
To avoid upgrading Pip in a system when running locally, check to make sure that we're running in Colab. Local systems can of course be upgraded separately.
try:
import colab
!pip install -q --upgrade pip
except:
pass
Install TFX
pip install -q tensorflow==2.2.0 tfx==0.22.0
ERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts. We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default. tensorflow-serving-api 2.3.0 requires tensorflow<3,>=2.3, but you'll have tensorflow 2.2.0 which is incompatible. tfx-bsl 0.22.1 requires absl-py<0.9,>=0.7, but you'll have absl-py 0.10.0 which is incompatible. tensorflow-transform 0.22.0 requires absl-py<0.9,>=0.7, but you'll have absl-py 0.10.0 which is incompatible. tensorflow-data-validation 0.22.2 requires absl-py<0.9,>=0.7, but you'll have absl-py 0.10.0 which is incompatible. tensorflow-model-analysis 0.22.2 requires absl-py<0.9,>=0.7, but you'll have absl-py 0.10.0 which is incompatible. tfx 0.22.0 requires absl-py<0.9,>=0.1.6, but you'll have absl-py 0.10.0 which is incompatible. WARNING: You are using pip version 20.2.2; however, version 20.2.3 is available. You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
Did you restart the runtime?
If you are using Google Colab, the first time that you run the cell above, you must restart the runtime (Runtime > Restart runtime ...). This is because of the way that Colab loads packages.
Import packages
We import necessary packages, including standard TFX component classes.
import os
import pprint
import tempfile
import urllib
import absl
import tensorflow as tf
import tensorflow_model_analysis as tfma
tf.get_logger().propagate = False
pp = pprint.PrettyPrinter()
import tfx
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import ResolverNode
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import Transform
from tfx.components.base import executor_spec
from tfx.components.trainer.executor import GenericExecutor
from tfx.dsl.experimental import latest_blessed_model_resolver
from tfx.orchestration import metadata
from tfx.orchestration import pipeline
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.proto import pusher_pb2
from tfx.proto import trainer_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.utils.dsl_utils import external_input
%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tfx/orchestration/data_types.py:191: UserWarning: RuntimeParameter is only supported on KubeflowDagRunner currently.
warnings.warn('RuntimeParameter is only supported on KubeflowDagRunner '
Let's check the library versions.
print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.2.0 TFX version: 0.22.0
Set up pipeline paths
# This is the root directory for your TFX pip package installation.
_tfx_root = tfx.__path__[0]
# This is the directory containing the TFX Chicago Taxi Pipeline example.
_taxi_root = os.path.join(_tfx_root, 'examples/chicago_taxi_pipeline')
# This is the path where your model will be pushed for serving.
_serving_model_dir = os.path.join(
tempfile.mkdtemp(), 'serving_model/taxi_simple')
# Set up logging.
absl.logging.set_verbosity(absl.logging.INFO)
Download example data
We download the example dataset for use in our TFX pipeline.
The dataset we're using is the Taxi Trips dataset released by the City of Chicago. The columns in this dataset are:
| pickup_community_area | fare | trip_start_month |
| trip_start_hour | trip_start_day | trip_start_timestamp |
| pickup_latitude | pickup_longitude | dropoff_latitude |
| dropoff_longitude | trip_miles | pickup_census_tract |
| dropoff_census_tract | payment_type | company |
| trip_seconds | dropoff_community_area | tips |
With this dataset, we will build a model that predicts the tips of a trip.
_data_root = tempfile.mkdtemp(prefix='tfx-data')
DATA_PATH = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv'
_data_filepath = os.path.join(_data_root, "data.csv")
urllib.request.urlretrieve(DATA_PATH, _data_filepath)
('/tmp/tfx-datahyvvtoz_/data.csv', <http.client.HTTPMessage at 0x7fdf08cabfd0>)
Take a quick look at the CSV file.
%%skip_for_export
!head {_data_filepath}
pickup_community_area,fare,trip_start_month,trip_start_hour,trip_start_day,trip_start_timestamp,pickup_latitude,pickup_longitude,dropoff_latitude,dropoff_longitude,trip_miles,pickup_census_tract,dropoff_census_tract,payment_type,company,trip_seconds,dropoff_community_area,tips ,12.45,5,19,6,1400269500,,,,,0.0,,,Credit Card,Chicago Elite Cab Corp. (Chicago Carriag,0,,0.0 ,0,3,19,5,1362683700,,,,,0,,,Unknown,Chicago Elite Cab Corp.,300,,0 60,27.05,10,2,3,1380593700,41.836150155,-87.648787952,,,12.6,,,Cash,Taxi Affiliation Services,1380,,0.0 10,5.85,10,1,2,1382319000,41.985015101,-87.804532006,,,0.0,,,Cash,Taxi Affiliation Services,180,,0.0 14,16.65,5,7,5,1369897200,41.968069,-87.721559063,,,0.0,,,Cash,Dispatch Taxi Affiliation,1080,,0.0 13,16.45,11,12,3,1446554700,41.983636307,-87.723583185,,,6.9,,,Cash,,780,,0.0 16,32.05,12,1,1,1417916700,41.953582125,-87.72345239,,,15.4,,,Cash,,1200,,0.0 30,38.45,10,10,5,1444301100,41.839086906,-87.714003807,,,14.6,,,Cash,,2580,,0.0 11,14.65,1,1,3,1358213400,41.978829526,-87.771166703,,,5.81,,,Cash,,1080,,0.0 This cell will be skipped during export to pipeline.
Disclaimer: This site provides applications using data that has been modified for use from its original source, www.cityofchicago.org, the official website of the City of Chicago. The City of Chicago makes no claims as to the content, accuracy, timeliness, or completeness of any of the data provided at this site. The data provided at this site is subject to change at any time. It is understood that the data provided at this site is being used at one’s own risk.
Create the InteractiveContext
Last, we create an InteractiveContext, which will allow us to run TFX components interactively in this notebook.
# Here, we create an InteractiveContext using default parameters. This will
# use a temporary directory with an ephemeral ML Metadata database instance.
# To use your own pipeline root or database, the optional properties
# `pipeline_root` and `metadata_connection_config` may be passed to
# InteractiveContext. Calls to InteractiveContext are no-ops outside of the
# notebook.
context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm as root for pipeline outputs. WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/metadata.sqlite.
Run TFX components interactively
In the cells that follow, we create TFX components one-by-one, run each of them, and visualize their output artifacts.
ExampleGen
The ExampleGen component is usually at the start of a TFX pipeline. It will:
- Split data into training and evaluation sets (by default, 2/3 training + 1/3 eval)
- Convert data into the
tf.Exampleformat - Copy data into the
_tfx_rootdirectory for other components to access
ExampleGen takes as input the path to your data source. In our case, this is the _data_root path that contains the downloaded CSV.
example_gen = CsvExampleGen(input=external_input(_data_root))
context.run(example_gen)
INFO:absl:Running driver for CsvExampleGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:select span = None INFO:absl:Running executor for CsvExampleGen INFO:absl:Generating examples. INFO:absl:Using 1 process(es) for Beam pipeline execution. WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features. INFO:absl:Processing input csv data /tmp/tfx-datahyvvtoz_/* to TFExample. WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be. INFO:absl:Examples generated. INFO:absl:Running publisher for CsvExampleGen INFO:absl:MetadataStore with DB connection initialized
Let's examine the output artifacts of ExampleGen. This component produces two artifacts, training examples and evaluation examples:
%%skip_for_export
artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/CsvExampleGen/examples/1 This cell will be skipped during export to pipeline.
We can also take a look at the first three training examples:
%%skip_for_export
# Get the URI of the output artifact representing the training examples, which is a directory
train_uri = os.path.join(example_gen.outputs['examples'].get()[0].uri, 'train')
# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
for name in os.listdir(train_uri)]
# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
serialized_example = tfrecord.numpy()
example = tf.train.Example()
example.ParseFromString(serialized_example)
pp.pprint(example)
features {
feature {
key: "company"
value {
bytes_list {
value: "Chicago Elite Cab Corp. (Chicago Carriag"
}
}
}
feature {
key: "dropoff_census_tract"
value {
}
}
feature {
key: "dropoff_community_area"
value {
}
}
feature {
key: "dropoff_latitude"
value {
}
}
feature {
key: "dropoff_longitude"
value {
}
}
feature {
key: "fare"
value {
float_list {
value: 12.449999809265137
}
}
}
feature {
key: "payment_type"
value {
bytes_list {
value: "Credit Card"
}
}
}
feature {
key: "pickup_census_tract"
value {
}
}
feature {
key: "pickup_community_area"
value {
}
}
feature {
key: "pickup_latitude"
value {
}
}
feature {
key: "pickup_longitude"
value {
}
}
feature {
key: "tips"
value {
float_list {
value: 0.0
}
}
}
feature {
key: "trip_miles"
value {
float_list {
value: 0.0
}
}
}
feature {
key: "trip_seconds"
value {
int64_list {
value: 0
}
}
}
feature {
key: "trip_start_day"
value {
int64_list {
value: 6
}
}
}
feature {
key: "trip_start_hour"
value {
int64_list {
value: 19
}
}
}
feature {
key: "trip_start_month"
value {
int64_list {
value: 5
}
}
}
feature {
key: "trip_start_timestamp"
value {
int64_list {
value: 1400269500
}
}
}
}
features {
feature {
key: "company"
value {
bytes_list {
value: "Chicago Elite Cab Corp."
}
}
}
feature {
key: "dropoff_census_tract"
value {
}
}
feature {
key: "dropoff_community_area"
value {
}
}
feature {
key: "dropoff_latitude"
value {
}
}
feature {
key: "dropoff_longitude"
value {
}
}
feature {
key: "fare"
value {
float_list {
value: 0.0
}
}
}
feature {
key: "payment_type"
value {
bytes_list {
value: "Unknown"
}
}
}
feature {
key: "pickup_census_tract"
value {
}
}
feature {
key: "pickup_community_area"
value {
}
}
feature {
key: "pickup_latitude"
value {
}
}
feature {
key: "pickup_longitude"
value {
}
}
feature {
key: "tips"
value {
float_list {
value: 0.0
}
}
}
feature {
key: "trip_miles"
value {
float_list {
value: 0.0
}
}
}
feature {
key: "trip_seconds"
value {
int64_list {
value: 300
}
}
}
feature {
key: "trip_start_day"
value {
int64_list {
value: 5
}
}
}
feature {
key: "trip_start_hour"
value {
int64_list {
value: 19
}
}
}
feature {
key: "trip_start_month"
value {
int64_list {
value: 3
}
}
}
feature {
key: "trip_start_timestamp"
value {
int64_list {
value: 1362683700
}
}
}
}
features {
feature {
key: "company"
value {
bytes_list {
value: "Taxi Affiliation Services"
}
}
}
feature {
key: "dropoff_census_tract"
value {
}
}
feature {
key: "dropoff_community_area"
value {
}
}
feature {
key: "dropoff_latitude"
value {
}
}
feature {
key: "dropoff_longitude"
value {
}
}
feature {
key: "fare"
value {
float_list {
value: 27.049999237060547
}
}
}
feature {
key: "payment_type"
value {
bytes_list {
value: "Cash"
}
}
}
feature {
key: "pickup_census_tract"
value {
}
}
feature {
key: "pickup_community_area"
value {
int64_list {
value: 60
}
}
}
feature {
key: "pickup_latitude"
value {
float_list {
value: 41.836151123046875
}
}
}
feature {
key: "pickup_longitude"
value {
float_list {
value: -87.64878845214844
}
}
}
feature {
key: "tips"
value {
float_list {
value: 0.0
}
}
}
feature {
key: "trip_miles"
value {
float_list {
value: 12.600000381469727
}
}
}
feature {
key: "trip_seconds"
value {
int64_list {
value: 1380
}
}
}
feature {
key: "trip_start_day"
value {
int64_list {
value: 3
}
}
}
feature {
key: "trip_start_hour"
value {
int64_list {
value: 2
}
}
}
feature {
key: "trip_start_month"
value {
int64_list {
value: 10
}
}
}
feature {
key: "trip_start_timestamp"
value {
int64_list {
value: 1380593700
}
}
}
}
This cell will be skipped during export to pipeline.
Now that ExampleGen has finished ingesting the data, the next step is data analysis.
StatisticsGen
The StatisticsGen component computes statistics over your dataset for data analysis, as well as for use in downstream components. It uses the TensorFlow Data Validation library.
StatisticsGen takes as input the dataset we just ingested using ExampleGen.
statistics_gen = StatisticsGen(
examples=example_gen.outputs['examples'])
context.run(statistics_gen)
INFO:absl:Running driver for StatisticsGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for StatisticsGen INFO:absl:Using 1 process(es) for Beam pipeline execution. INFO:absl:Generating statistics for split train INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/StatisticsGen/statistics/2/train. INFO:absl:Generating statistics for split eval INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/StatisticsGen/statistics/2/eval. INFO:absl:Running publisher for StatisticsGen INFO:absl:MetadataStore with DB connection initialized
After StatisticsGen finishes running, we can visualize the outputted statistics. Try playing with the different plots!
%%skip_for_export
context.show(statistics_gen.outputs['statistics'])
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_data_validation/utils/stats_util.py:229: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version. Instructions for updating: Use eager execution and: `tf.data.TFRecordDataset(path)`
This cell will be skipped during export to pipeline.
SchemaGen
The SchemaGen component generates a schema based on your data statistics. (A schema defines the expected bounds, types, and properties of the features in your dataset.) It also uses the TensorFlow Data Validation library.
SchemaGen will take as input the statistics that we generated with StatisticsGen, looking at the training split by default.
schema_gen = SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Running driver for SchemaGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for SchemaGen INFO:absl:Infering schema from statistics. INFO:absl:Schema written to /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/SchemaGen/schema/3/schema.pbtxt. INFO:absl:Running publisher for SchemaGen INFO:absl:MetadataStore with DB connection initialized
After SchemaGen finishes running, we can visualize the generated schema as a table.
%%skip_for_export
context.show(schema_gen.outputs['schema'])
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_data_validation/utils/display_util.py:138: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
pd.set_option('max_colwidth', -1)
This cell will be skipped during export to pipeline.
Each feature in your dataset shows up as a row in the schema table, alongside its properties. The schema also captures all the values that a categorical feature takes on, denoted as its domain.
To learn more about schemas, see the SchemaGen documentation.
ExampleValidator
The ExampleValidator component detects anomalies in your data, based on the expectations defined by the schema. It also uses the TensorFlow Data Validation library.
ExampleValidator will take as input the statistics from StatisticsGen, and the schema from SchemaGen.
By default, it compares the statistics from the evaluation split to the schema from the training split.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
context.run(example_validator)
INFO:absl:Running driver for ExampleValidator INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for ExampleValidator INFO:absl:Validating schema against the computed statistics. INFO:absl:Validation complete. Anomalies written to /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/ExampleValidator/anomalies/4. INFO:absl:Running publisher for ExampleValidator INFO:absl:MetadataStore with DB connection initialized
After ExampleValidator finishes running, we can visualize the anomalies as a table.
%%skip_for_export
context.show(example_validator.outputs['anomalies'])
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_data_validation/utils/display_util.py:173: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
pd.set_option('max_colwidth', -1)
This cell will be skipped during export to pipeline.
In the anomalies table, we can see that the company feature takes on new values that were not in the training split. This information can be used to debug model performance, understand how your data evolves over time, and identify data errors.
In our case, this company anomaly is innocuous, but the payment_type could be fixed. For now we move on to the next step of transforming the data.
Transform
The Transform component performs feature engineering for both training and serving. It uses the TensorFlow Transform library.
Transform will take as input the data from ExampleGen, the schema from SchemaGen, as well as a module that contains user-defined Transform code.
Let's see an example of user-defined Transform code below (for an introduction to the TensorFlow Transform APIs, see the tutorial). First, we define a few constants for feature engineering:
_taxi_constants_module_file = 'taxi_constants.py'
%%skip_for_export
%%writefile {_taxi_constants_module_file}
# Categorical features are assumed to each have a maximum value in the dataset.
MAX_CATEGORICAL_FEATURE_VALUES = [24, 31, 12]
CATEGORICAL_FEATURE_KEYS = [
'trip_start_hour', 'trip_start_day', 'trip_start_month',
'pickup_census_tract', 'dropoff_census_tract', 'pickup_community_area',
'dropoff_community_area'
]
DENSE_FLOAT_FEATURE_KEYS = ['trip_miles', 'fare', 'trip_seconds']
# Number of buckets used by tf.transform for encoding each feature.
FEATURE_BUCKET_COUNT = 10
BUCKET_FEATURE_KEYS = [
'pickup_latitude', 'pickup_longitude', 'dropoff_latitude',
'dropoff_longitude'
]
# Number of vocabulary terms used for encoding VOCAB_FEATURES by tf.transform
VOCAB_SIZE = 1000
# Count of out-of-vocab buckets in which unrecognized VOCAB_FEATURES are hashed.
OOV_SIZE = 10
VOCAB_FEATURE_KEYS = [
'payment_type',
'company',
]
# Keys
LABEL_KEY = 'tips'
FARE_KEY = 'fare'
def transformed_name(key):
return key + '_xf'
Writing taxi_constants.py This cell will be skipped during export to pipeline.
Next, we write a preprocessing_fn that takes in raw data as input, and returns transformed features that our model can train on:
_taxi_transform_module_file = 'taxi_transform.py'
%%skip_for_export
%%writefile {_taxi_transform_module_file}
import tensorflow as tf
import tensorflow_transform as tft
import taxi_constants
_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_FARE_KEY = taxi_constants.FARE_KEY
_LABEL_KEY = taxi_constants.LABEL_KEY
_transformed_name = taxi_constants.transformed_name
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in _DENSE_FLOAT_FEATURE_KEYS:
# Preserve this feature as a dense float, setting nan's to the mean.
outputs[_transformed_name(key)] = tft.scale_to_z_score(
_fill_in_missing(inputs[key]))
for key in _VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(
_fill_in_missing(inputs[key]),
top_k=_VOCAB_SIZE,
num_oov_buckets=_OOV_SIZE)
for key in _BUCKET_FEATURE_KEYS:
outputs[_transformed_name(key)] = tft.bucketize(
_fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT)
for key in _CATEGORICAL_FEATURE_KEYS:
outputs[_transformed_name(key)] = _fill_in_missing(inputs[key])
# Was this passenger a big tipper?
taxi_fare = _fill_in_missing(inputs[_FARE_KEY])
tips = _fill_in_missing(inputs[_LABEL_KEY])
outputs[_transformed_name(_LABEL_KEY)] = tf.where(
tf.math.is_nan(taxi_fare),
tf.cast(tf.zeros_like(taxi_fare), tf.int64),
# Test if the tip was > 20% of the fare.
tf.cast(
tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))
return outputs
def _fill_in_missing(x):
"""Replace missing values in a SparseTensor.
Fills in missing values of `x` with '' or 0, and converts to a dense tensor.
Args:
x: A `SparseTensor` of rank 2. Its dense shape should have size at most 1
in the second dimension.
Returns:
A rank 1 tensor where missing values of `x` have been filled in.
"""
default_value = '' if x.dtype == tf.string else 0
return tf.squeeze(
tf.sparse.to_dense(
tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),
default_value),
axis=1)
Writing taxi_transform.py This cell will be skipped during export to pipeline.
Now, we pass in this feature engineering code to the Transform component and run it to transform your data.
transform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=os.path.abspath(_taxi_transform_module_file))
context.run(transform)
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Transform
Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tfx/components/transform/executor.py:511: Schema (from tensorflow_transform.tf_metadata.dataset_schema) is deprecated and will be removed in a future version.
Instructions for updating:
Schema is a deprecated, use schema_utils.schema_from_feature_spec to create a `Schema`
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:220: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
INFO:absl:Using 1 process(es) for Beam pipeline execution.
Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
Warning:apache_beam.utils.interactive_utils:Failed to alter the label of a transform with the ipython prompt metadata. Cannot figure out the pipeline that the given pvalueish ({DatasetKey(key='tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm-CsvExampleGen-examples-1-train-STAR'): <PCollection[Decode[AnalysisIndex0]/ApplyDecodeFn.None] at 0x7fded017e0f0>}, None, {'_schema': feature {
name: "company"
value_count {
min: 1
max: 1
}
type: BYTES
domain: "company"
presence {
min_count: 1
}
}
feature {
name: "payment_type"
value_count {
min: 1
max: 1
}
type: BYTES
domain: "payment_type"
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "dropoff_census_tract"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "dropoff_community_area"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "dropoff_latitude"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_count: 1
}
}
feature {
name: "dropoff_longitude"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_count: 1
}
}
feature {
name: "fare"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "pickup_census_tract"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "pickup_community_area"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "pickup_latitude"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_count: 1
}
}
feature {
name: "pickup_longitude"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_count: 1
}
}
feature {
name: "tips"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_miles"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_seconds"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "trip_start_day"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_start_hour"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_start_month"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_start_timestamp"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_fraction: 1.0
min_count: 1
}
}
string_domain {
name: "company"
value: "0118 - 42111 Godfrey S.Awir"
value: "0694 - 59280 Chinesco Trans Inc"
value: "1085 - 72312 N and W Cab Co"
value: "2092 - 61288 Sbeih company"
value: "2192 - 73487 Zeymane Corp"
value: "2192 - Zeymane Corp"
value: "2733 - 74600 Benny Jona"
value: "2809 - 95474 C & D Cab Co Inc."
value: "2823 - 73307 Seung Lee"
value: "3011 - 66308 JBL Cab Inc."
value: "3152 - 97284 Crystal Abernathy"
value: "3201 - C&D Cab Co Inc"
value: "3201 - CID Cab Co Inc"
value: "3253 - 91138 Gaither Cab Co."
value: "3385 - 23210 Eman Cab"
value: "3385 - Eman Cab"
value: "3623 - 72222 Arrington Enterprises"
value: "3897 - 57856 Ilie Malec"
value: "3897 - Ilie Malec"
value: "4053 - Adwar H. Nikola"
value: "4197 - 41842 Royal Star"
value: "4615 - 83503 Tyrone Henderson"
value: "4615 - Tyrone Henderson"
value: "4623 - Jay Kim"
value: "5006 - 39261 Salifu Bawa"
value: "5074 - 54002 Ahzmi Inc"
value: "5074 - Ahzmi Inc"
value: "5129 - 87128"
value: "5129 - 98755 Mengisti Taxi"
value: "5129 - Mengisti Taxi"
value: "585 - Valley Cab Co"
value: "5864 - 73614 Thomas Owusu"
value: "5864 - Thomas Owusu"
value: "5874 - 73628 Sergey Cab Corp."
value: "5874 - Sergey Cab Corp."
value: "5997 - 65283 AW Services Inc."
value: "5997 - AW Services Inc."
value: "6057 - 24657 Richard Addo"
value: "6488 - 83287 Zuha Taxi"
value: "6574 - Babylon Express Inc."
value: "6742 - 83735 Tasha ride inc"
value: "Blue Ribbon Taxi Association Inc."
value: "C & D Cab Co Inc"
value: "Chicago Elite Cab Corp."
value: "Chicago Elite Cab Corp. (Chicago Carriag"
value: "Chicago Medallion Leasing INC"
value: "Chicago Medallion Management"
value: "Choice Taxi Association"
value: "Dispatch Taxi Affiliation"
value: "KOAM Taxi Association"
value: "Northwest Management LLC"
value: "Taxi Affiliation Services"
value: "Top Cab Affiliation"
}
string_domain {
name: "payment_type"
value: "Cash"
value: "Credit Card"
value: "Dispute"
value: "No Charge"
value: "Pcard"
value: "Unknown"
}
}) belongs to. Thus noop.
Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Transform/transform_graph/5/.temp_path/tftransform_tmp/c9d5204d048e403491ef1e461e201be0/saved_model.pb
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Transform/transform_graph/5/.temp_path/tftransform_tmp/414ce502736848e3a7cc51dc74cf8cfd/saved_model.pb
Warning:apache_beam.utils.interactive_utils:Failed to alter the label of a transform with the ipython prompt metadata. Cannot figure out the pipeline that the given pvalueish {'_schema': feature {
name: "company"
value_count {
min: 1
max: 1
}
type: BYTES
domain: "company"
presence {
min_count: 1
}
}
feature {
name: "payment_type"
value_count {
min: 1
max: 1
}
type: BYTES
domain: "payment_type"
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "dropoff_census_tract"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "dropoff_community_area"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "dropoff_latitude"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_count: 1
}
}
feature {
name: "dropoff_longitude"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_count: 1
}
}
feature {
name: "fare"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "pickup_census_tract"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "pickup_community_area"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "pickup_latitude"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_count: 1
}
}
feature {
name: "pickup_longitude"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_count: 1
}
}
feature {
name: "tips"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_miles"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_seconds"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_count: 1
}
}
feature {
name: "trip_start_day"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_start_hour"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_start_month"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_fraction: 1.0
min_count: 1
}
}
feature {
name: "trip_start_timestamp"
value_count {
min: 1
max: 1
}
type: INT
presence {
min_fraction: 1.0
min_count: 1
}
}
string_domain {
name: "company"
value: "0118 - 42111 Godfrey S.Awir"
value: "0694 - 59280 Chinesco Trans Inc"
value: "1085 - 72312 N and W Cab Co"
value: "2092 - 61288 Sbeih company"
value: "2192 - 73487 Zeymane Corp"
value: "2192 - Zeymane Corp"
value: "2733 - 74600 Benny Jona"
value: "2809 - 95474 C & D Cab Co Inc."
value: "2823 - 73307 Seung Lee"
value: "3011 - 66308 JBL Cab Inc."
value: "3152 - 97284 Crystal Abernathy"
value: "3201 - C&D Cab Co Inc"
value: "3201 - CID Cab Co Inc"
value: "3253 - 91138 Gaither Cab Co."
value: "3385 - 23210 Eman Cab"
value: "3385 - Eman Cab"
value: "3623 - 72222 Arrington Enterprises"
value: "3897 - 57856 Ilie Malec"
value: "3897 - Ilie Malec"
value: "4053 - Adwar H. Nikola"
value: "4197 - 41842 Royal Star"
value: "4615 - 83503 Tyrone Henderson"
value: "4615 - Tyrone Henderson"
value: "4623 - Jay Kim"
value: "5006 - 39261 Salifu Bawa"
value: "5074 - 54002 Ahzmi Inc"
value: "5074 - Ahzmi Inc"
value: "5129 - 87128"
value: "5129 - 98755 Mengisti Taxi"
value: "5129 - Mengisti Taxi"
value: "585 - Valley Cab Co"
value: "5864 - 73614 Thomas Owusu"
value: "5864 - Thomas Owusu"
value: "5874 - 73628 Sergey Cab Corp."
value: "5874 - Sergey Cab Corp."
value: "5997 - 65283 AW Services Inc."
value: "5997 - AW Services Inc."
value: "6057 - 24657 Richard Addo"
value: "6488 - 83287 Zuha Taxi"
value: "6574 - Babylon Express Inc."
value: "6742 - 83735 Tasha ride inc"
value: "Blue Ribbon Taxi Association Inc."
value: "C & D Cab Co Inc"
value: "Chicago Elite Cab Corp."
value: "Chicago Elite Cab Corp. (Chicago Carriag"
value: "Chicago Medallion Leasing INC"
value: "Chicago Medallion Management"
value: "Choice Taxi Association"
value: "Dispatch Taxi Affiliation"
value: "KOAM Taxi Association"
value: "Northwest Management LLC"
value: "Taxi Affiliation Services"
value: "Top Cab Affiliation"
}
string_domain {
name: "payment_type"
value: "Cash"
value: "Credit Card"
value: "Dispute"
value: "No Charge"
value: "Pcard"
value: "Unknown"
}
} belongs to. Thus noop.
Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
Warning:apache_beam.utils.interactive_utils:Failed to alter the label of a transform with the ipython prompt metadata. Cannot figure out the pipeline that the given pvalueish ((<PCollection[Decode[TransformIndex0]/ApplyDecodeFn.None] at 0x7fdee801fa90>, {'_schema': feature {
name: "company"
type: BYTES
}
feature {
name: "dropoff_census_tract"
type: INT
}
feature {
name: "dropoff_community_area"
type: INT
}
feature {
name: "dropoff_latitude"
type: FLOAT
}
feature {
name: "dropoff_longitude"
type: FLOAT
}
feature {
name: "fare"
type: FLOAT
}
feature {
name: "payment_type"
type: BYTES
}
feature {
name: "pickup_census_tract"
type: INT
}
feature {
name: "pickup_community_area"
type: INT
}
feature {
name: "pickup_latitude"
type: FLOAT
}
feature {
name: "pickup_longitude"
type: FLOAT
}
feature {
name: "tips"
type: FLOAT
}
feature {
name: "trip_miles"
type: FLOAT
}
feature {
name: "trip_seconds"
type: INT
}
feature {
name: "trip_start_day"
type: INT
}
feature {
name: "trip_start_hour"
type: INT
}
feature {
name: "trip_start_month"
type: INT
}
}), (<PCollection[Analyze/CreateSavedModel/BindTensors/ReplaceWithConstants.None] at 0x7fde9031be80>, BeamDatasetMetadata(dataset_metadata={'_schema': feature {
name: "company_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "dropoff_census_tract_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "dropoff_community_area_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "dropoff_latitude_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "dropoff_longitude_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "fare_xf"
type: FLOAT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "payment_type_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "pickup_census_tract_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "pickup_community_area_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "pickup_latitude_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "pickup_longitude_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "tips_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_miles_xf"
type: FLOAT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_seconds_xf"
type: FLOAT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_start_day_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_start_hour_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_start_month_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
}, deferred_metadata=<PCollection[Analyze/ComputeDeferredMetadata.None] at 0x7fde90373518>))) belongs to. Thus noop.
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
Warning:apache_beam.utils.interactive_utils:Failed to alter the label of a transform with the ipython prompt metadata. Cannot figure out the pipeline that the given pvalueish ((<PCollection[Decode[TransformIndex1]/ApplyDecodeFn.None] at 0x7fde903095f8>, {'_schema': feature {
name: "company"
type: BYTES
}
feature {
name: "dropoff_census_tract"
type: INT
}
feature {
name: "dropoff_community_area"
type: INT
}
feature {
name: "dropoff_latitude"
type: FLOAT
}
feature {
name: "dropoff_longitude"
type: FLOAT
}
feature {
name: "fare"
type: FLOAT
}
feature {
name: "payment_type"
type: BYTES
}
feature {
name: "pickup_census_tract"
type: INT
}
feature {
name: "pickup_community_area"
type: INT
}
feature {
name: "pickup_latitude"
type: FLOAT
}
feature {
name: "pickup_longitude"
type: FLOAT
}
feature {
name: "tips"
type: FLOAT
}
feature {
name: "trip_miles"
type: FLOAT
}
feature {
name: "trip_seconds"
type: INT
}
feature {
name: "trip_start_day"
type: INT
}
feature {
name: "trip_start_hour"
type: INT
}
feature {
name: "trip_start_month"
type: INT
}
}), (<PCollection[Analyze/CreateSavedModel/BindTensors/ReplaceWithConstants.None] at 0x7fde9031be80>, BeamDatasetMetadata(dataset_metadata={'_schema': feature {
name: "company_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "dropoff_census_tract_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "dropoff_community_area_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "dropoff_latitude_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "dropoff_longitude_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "fare_xf"
type: FLOAT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "payment_type_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "pickup_census_tract_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "pickup_community_area_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "pickup_latitude_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "pickup_longitude_xf"
type: INT
int_domain {
is_categorical: true
}
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "tips_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_miles_xf"
type: FLOAT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_seconds_xf"
type: FLOAT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_start_day_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_start_hour_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
feature {
name: "trip_start_month_xf"
type: INT
presence {
min_fraction: 1.0
}
shape {
}
}
}, deferred_metadata=<PCollection[Analyze/ComputeDeferredMetadata.None] at 0x7fde90373518>))) belongs to. Thus noop.
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Transform/transform_graph/5/.temp_path/tftransform_tmp/7d1338df10774db5b048ec11740dd44e/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Transform/transform_graph/5/.temp_path/tftransform_tmp/7d1338df10774db5b048ec11740dd44e/saved_model.pb
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"
Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"
Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"
Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized
Let's examine the output artifacts of Transform. This component produces two types of outputs:
transform_graphis the graph that can perform the preprocessing operations (this graph will be included in the serving and evaluation models).transformed_examplesrepresents the preprocessed training and evaluation data.
%%skip_for_export
transform.outputs
{'transform_graph': Channel(
type_name: TransformGraph
artifacts: [Artifact(type_name: TransformGraph, uri: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Transform/transform_graph/5, id: 6)]
), 'transformed_examples': Channel(
type_name: Examples
artifacts: [Artifact(type_name: Examples, uri: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Transform/transformed_examples/5, id: 7)]
)}
This cell will be skipped during export to pipeline.
Take a peek at the transform_graph artifact. It points to a directory containing three subdirectories.
%%skip_for_export
train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata'] This cell will be skipped during export to pipeline.
The transformed_metadata subdirectory contains the schema of the preprocessed data. The transform_fn subdirectory contains the actual preprocessing graph. The metadata subdirectory contains the schema of the original data.
We can also take a look at the first three transformed examples:
%%skip_for_export
# Get the URI of the output artifact representing the transformed examples, which is a directory
train_uri = os.path.join(transform.outputs['transformed_examples'].get()[0].uri, 'train')
# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
for name in os.listdir(train_uri)]
# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
serialized_example = tfrecord.numpy()
example = tf.train.Example()
example.ParseFromString(serialized_example)
pp.pprint(example)
features {
feature {
key: "company_xf"
value {
int64_list {
value: 7
}
}
}
feature {
key: "dropoff_census_tract_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_community_area_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_latitude_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_longitude_xf"
value {
int64_list {
value: 9
}
}
}
feature {
key: "fare_xf"
value {
float_list {
value: 0.07793104648590088
}
}
}
feature {
key: "payment_type_xf"
value {
int64_list {
value: 1
}
}
}
feature {
key: "pickup_census_tract_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "pickup_community_area_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "pickup_latitude_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "pickup_longitude_xf"
value {
int64_list {
value: 9
}
}
}
feature {
key: "tips_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "trip_miles_xf"
value {
float_list {
value: -0.4496246874332428
}
}
}
feature {
key: "trip_seconds_xf"
value {
float_list {
value: -0.9353184103965759
}
}
}
feature {
key: "trip_start_day_xf"
value {
int64_list {
value: 6
}
}
}
feature {
key: "trip_start_hour_xf"
value {
int64_list {
value: 19
}
}
}
feature {
key: "trip_start_month_xf"
value {
int64_list {
value: 5
}
}
}
}
features {
feature {
key: "company_xf"
value {
int64_list {
value: 10
}
}
}
feature {
key: "dropoff_census_tract_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_community_area_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_latitude_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_longitude_xf"
value {
int64_list {
value: 9
}
}
}
feature {
key: "fare_xf"
value {
float_list {
value: -1.1497559547424316
}
}
}
feature {
key: "payment_type_xf"
value {
int64_list {
value: 3
}
}
}
feature {
key: "pickup_census_tract_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "pickup_community_area_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "pickup_latitude_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "pickup_longitude_xf"
value {
int64_list {
value: 9
}
}
}
feature {
key: "tips_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "trip_miles_xf"
value {
float_list {
value: -0.4496246874332428
}
}
}
feature {
key: "trip_seconds_xf"
value {
float_list {
value: -0.571632981300354
}
}
}
feature {
key: "trip_start_day_xf"
value {
int64_list {
value: 5
}
}
}
feature {
key: "trip_start_hour_xf"
value {
int64_list {
value: 19
}
}
}
feature {
key: "trip_start_month_xf"
value {
int64_list {
value: 3
}
}
}
}
features {
feature {
key: "company_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_census_tract_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_community_area_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_latitude_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "dropoff_longitude_xf"
value {
int64_list {
value: 9
}
}
}
feature {
key: "fare_xf"
value {
float_list {
value: 1.5176283121109009
}
}
}
feature {
key: "payment_type_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "pickup_census_tract_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "pickup_community_area_xf"
value {
int64_list {
value: 60
}
}
}
feature {
key: "pickup_latitude_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "pickup_longitude_xf"
value {
int64_list {
value: 3
}
}
}
feature {
key: "tips_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "trip_miles_xf"
value {
float_list {
value: 1.6442170143127441
}
}
}
feature {
key: "trip_seconds_xf"
value {
float_list {
value: 0.7376344799995422
}
}
}
feature {
key: "trip_start_day_xf"
value {
int64_list {
value: 3
}
}
}
feature {
key: "trip_start_hour_xf"
value {
int64_list {
value: 2
}
}
}
feature {
key: "trip_start_month_xf"
value {
int64_list {
value: 10
}
}
}
}
This cell will be skipped during export to pipeline.
After the Transform component has transformed your data into features, and the next step is to train a model.
Trainer
The Trainer component will train a model that you define in TensorFlow. Default Trainer support Estimator API, to use Keras API, you need to specify Generic Trainer by setup custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor) in Trainer's contructor.
Trainer takes as input the schema from SchemaGen, the transformed data and graph from Transform, training parameters, as well as a module that contains user-defined model code.
Let's see an example of user-defined model code below (for an introduction to the TensorFlow Keras APIs, see the tutorial):
_taxi_trainer_module_file = 'taxi_trainer.py'
%%skip_for_export
%%writefile {_taxi_trainer_module_file}
from typing import List, Text
import os
import absl
import datetime
import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.executor import TrainerFnArgs
import taxi_constants
_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_MAX_CATEGORICAL_FEATURE_VALUES = taxi_constants.MAX_CATEGORICAL_FEATURE_VALUES
_LABEL_KEY = taxi_constants.LABEL_KEY
_transformed_name = taxi_constants.transformed_name
def _transformed_names(keys):
return [_transformed_name(key) for key in keys]
def _gzip_reader_fn(filenames):
"""Small utility returning a record reader that can read gzip'ed files."""
return tf.data.TFRecordDataset(
filenames,
compression_type='GZIP')
def _get_serve_tf_examples_fn(model, tf_transform_output):
"""Returns a function that parses a serialized tf.Example and applies TFT."""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function
def serve_tf_examples_fn(serialized_tf_examples):
"""Returns the output to be used in the serving signature."""
feature_spec = tf_transform_output.raw_feature_spec()
feature_spec.pop(_LABEL_KEY)
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return model(transformed_features)
return serve_tf_examples_fn
def _input_fn(file_pattern: List[Text],
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
"""Generates features and label for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
transformed_feature_spec = (
tf_transform_output.transformed_feature_spec().copy())
dataset = tf.data.experimental.make_batched_features_dataset(
file_pattern=file_pattern,
batch_size=batch_size,
features=transformed_feature_spec,
reader=_gzip_reader_fn,
label_key=_transformed_name(_LABEL_KEY))
return dataset
def _build_keras_model(hidden_units: List[int] = None) -> tf.keras.Model:
"""Creates a DNN Keras model for classifying taxi data.
Args:
hidden_units: [int], the layer sizes of the DNN (input layer first).
Returns:
A keras Model.
"""
real_valued_columns = [
tf.feature_column.numeric_column(key, shape=())
for key in _transformed_names(_DENSE_FLOAT_FEATURE_KEYS)
]
categorical_columns = [
tf.feature_column.categorical_column_with_identity(
key, num_buckets=_VOCAB_SIZE + _OOV_SIZE, default_value=0)
for key in _transformed_names(_VOCAB_FEATURE_KEYS)
]
categorical_columns += [
tf.feature_column.categorical_column_with_identity(
key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
for key in _transformed_names(_BUCKET_FEATURE_KEYS)
]
categorical_columns += [
tf.feature_column.categorical_column_with_identity( # pylint: disable=g-complex-comprehension
key,
num_buckets=num_buckets,
default_value=0) for key, num_buckets in zip(
_transformed_names(_CATEGORICAL_FEATURE_KEYS),
_MAX_CATEGORICAL_FEATURE_VALUES)
]
indicator_column = [
tf.feature_column.indicator_column(categorical_column)
for categorical_column in categorical_columns
]
model = _wide_and_deep_classifier(
# TODO(b/139668410) replace with premade wide_and_deep keras model
wide_columns=indicator_column,
deep_columns=real_valued_columns,
dnn_hidden_units=hidden_units or [100, 70, 50, 25])
return model
def _wide_and_deep_classifier(wide_columns, deep_columns, dnn_hidden_units):
"""Build a simple keras wide and deep model.
Args:
wide_columns: Feature columns wrapped in indicator_column for wide (linear)
part of the model.
deep_columns: Feature columns for deep part of the model.
dnn_hidden_units: [int], the layer sizes of the hidden DNN.
Returns:
A Wide and Deep Keras model
"""
# Following values are hard coded for simplicity in this example,
# However prefarably they should be passsed in as hparams.
# Keras needs the feature definitions at compile time.
# TODO(b/139081439): Automate generation of input layers from FeatureColumn.
input_layers = {
colname: tf.keras.layers.Input(name=colname, shape=(), dtype=tf.float32)
for colname in _transformed_names(_DENSE_FLOAT_FEATURE_KEYS)
}
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _transformed_names(_VOCAB_FEATURE_KEYS)
})
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _transformed_names(_BUCKET_FEATURE_KEYS)
})
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _transformed_names(_CATEGORICAL_FEATURE_KEYS)
})
# TODO(b/161952382): Replace with Keras preprocessing layers.
deep = tf.keras.layers.DenseFeatures(deep_columns)(input_layers)
for numnodes in dnn_hidden_units:
deep = tf.keras.layers.Dense(numnodes)(deep)
wide = tf.keras.layers.DenseFeatures(wide_columns)(input_layers)
output = tf.keras.layers.Dense(
1, activation='sigmoid')(
tf.keras.layers.concatenate([deep, wide]))
model = tf.keras.Model(input_layers, output)
model.compile(
loss='binary_crossentropy',
optimizer=tf.keras.optimizers.Adam(lr=0.001),
metrics=[tf.keras.metrics.BinaryAccuracy()])
model.summary(print_fn=absl.logging.info)
return model
# TFX Trainer will call this function.
def run_fn(fn_args: TrainerFnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
# Number of nodes in the first layer of the DNN
first_dnn_layer_size = 100
num_dnn_layers = 4
dnn_decay_factor = 0.7
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, tf_transform_output, 40)
eval_dataset = _input_fn(fn_args.eval_files, tf_transform_output, 40)
model = _build_keras_model(
# Construct layers sizes with exponetial decay
hidden_units=[
max(2, int(first_dnn_layer_size * dnn_decay_factor**i))
for i in range(num_dnn_layers)
])
# TODO(b/158106209): use ModelRun instead of Model artifact for logging.
log_dir = os.path.join(os.path.dirname(fn_args.serving_model_dir), 'logs')
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_dir, update_freq='batch')
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
callbacks=[tensorboard_callback])
signatures = {
'serving_default':
_get_serve_tf_examples_fn(model,
tf_transform_output).get_concrete_function(
tf.TensorSpec(
shape=[None],
dtype=tf.string,
name='examples')),
}
model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
Writing taxi_trainer.py This cell will be skipped during export to pipeline.
Now, we pass in this model code to the Trainer component and run it to train the model.
trainer = Trainer(
module_file=os.path.abspath(_taxi_trainer_module_file),
custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
context.run(trainer)
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Trainer
INFO:absl:Training model.
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type) Output Shape Param # Connected to
INFO:absl:==================================================================================================
INFO:absl:company_xf (InputLayer) [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_census_tract_xf (InputL [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_community_area_xf (Inpu [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_latitude_xf (InputLayer [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_longitude_xf (InputLaye [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:fare_xf (InputLayer) [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:payment_type_xf (InputLayer) [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_census_tract_xf (InputLa [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_community_area_xf (Input [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_latitude_xf (InputLayer) [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_longitude_xf (InputLayer [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_miles_xf (InputLayer) [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_seconds_xf (InputLayer) [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_day_xf (InputLayer) [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_hour_xf (InputLayer) [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_month_xf (InputLayer [(None,)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features (DenseFeatures) (None, 3) 0 company_xf[0][0]
INFO:absl: dropoff_census_tract_xf[0][0]
INFO:absl: dropoff_community_area_xf[0][0]
INFO:absl: dropoff_latitude_xf[0][0]
INFO:absl: dropoff_longitude_xf[0][0]
INFO:absl: fare_xf[0][0]
INFO:absl: payment_type_xf[0][0]
INFO:absl: pickup_census_tract_xf[0][0]
INFO:absl: pickup_community_area_xf[0][0]
INFO:absl: pickup_latitude_xf[0][0]
INFO:absl: pickup_longitude_xf[0][0]
INFO:absl: trip_miles_xf[0][0]
INFO:absl: trip_seconds_xf[0][0]
INFO:absl: trip_start_day_xf[0][0]
INFO:absl: trip_start_hour_xf[0][0]
INFO:absl: trip_start_month_xf[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense) (None, 100) 400 dense_features[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense) (None, 70) 7070 dense[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense) (None, 48) 3408 dense_1[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_3 (Dense) (None, 34) 1666 dense_2[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features_1 (DenseFeatures (None, 2127) 0 company_xf[0][0]
INFO:absl: dropoff_census_tract_xf[0][0]
INFO:absl: dropoff_community_area_xf[0][0]
INFO:absl: dropoff_latitude_xf[0][0]
INFO:absl: dropoff_longitude_xf[0][0]
INFO:absl: fare_xf[0][0]
INFO:absl: payment_type_xf[0][0]
INFO:absl: pickup_census_tract_xf[0][0]
INFO:absl: pickup_community_area_xf[0][0]
INFO:absl: pickup_latitude_xf[0][0]
INFO:absl: pickup_longitude_xf[0][0]
INFO:absl: trip_miles_xf[0][0]
INFO:absl: trip_seconds_xf[0][0]
INFO:absl: trip_start_day_xf[0][0]
INFO:absl: trip_start_hour_xf[0][0]
INFO:absl: trip_start_month_xf[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate) (None, 2161) 0 dense_3[0][0]
INFO:absl: dense_features_1[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_4 (Dense) (None, 1) 2162 concatenate[0][0]
INFO:absl:==================================================================================================
INFO:absl:Total params: 14,706
INFO:absl:Trainable params: 14,706
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
2/10000 [..............................] - ETA: 17:44 - loss: 0.6847 - binary_accuracy: 0.6750WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.105510). Check your callbacks.
10000/10000 [==============================] - 100s 10ms/step - loss: 0.2449 - binary_accuracy: 0.8762 - val_loss: 0.2075 - val_binary_accuracy: 0.8971
WARNING:tensorflow:Tensorflow version (2.2.0) found. TransformFeaturesLayer may not work as intended if the SavedModel contains an initialization op.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Trainer/model/6/serving_model_dir/assets
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Trainer/model/6/serving_model_dir
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized
Analyze Training with TensorBoard
Take a peek at the trainer artifact. It points to a directory containing the model subdirectories.
%%skip_for_export
model_artifact_dir = trainer.outputs['model'].get()[0].uri
pp.pprint(os.listdir(model_artifact_dir))
model_dir = os.path.join(model_artifact_dir, 'serving_model_dir')
pp.pprint(os.listdir(model_dir))
['logs', 'serving_model_dir'] ['variables', 'assets', 'saved_model.pb'] This cell will be skipped during export to pipeline.
Optionally, we can connect TensorBoard to the Trainer to analyze our model's training curves.
%%skip_for_export
log_dir = os.path.join(model_artifact_dir, 'logs')
%load_ext tensorboard
%tensorboard --logdir {log_dir}
This cell will be skipped during export to pipeline.
Evaluator
The Evaluator component computes model performance metrics over the evaluation set. It uses the TensorFlow Model Analysis library. The Evaluator can also optionally validate that a newly trained model is better than the previous model. This is useful in a production pipeline setting where you may automatically train and validate a model every day. In this notebook, we only train one model, so the Evaluator automatically will label the model as "good".
Evaluator will take as input the data from ExampleGen, the trained model from Trainer, and slicing configuration. The slicing configuration allows you to slice your metrics on feature values (e.g. how does your model perform on taxi trips that start at 8am versus 8pm?). See an example of this configuration below:
eval_config = tfma.EvalConfig(
model_specs=[
# This assumes a serving model with signature 'serving_default'. If
# using estimator based EvalSavedModel, add signature_name: 'eval' and
# remove the label_key.
tfma.ModelSpec(label_key='tips')
],
metrics_specs=[
tfma.MetricsSpec(
# The metrics added here are in addition to those saved with the
# model (assuming either a keras model or EvalSavedModel is used).
# Any metrics added into the saved model (for example using
# model.compile(..., metrics=[...]), etc) will be computed
# automatically.
# To add validation thresholds for metrics saved with the model,
# add them keyed by metric name to the thresholds map.
metrics=[
tfma.MetricConfig(class_name='ExampleCount'),
tfma.MetricConfig(class_name='BinaryAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.5}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
]
)
],
slicing_specs=[
# An empty slice spec means the overall slice, i.e. the whole dataset.
tfma.SlicingSpec(),
# Data can be sliced along a feature column. In this case, data is
# sliced along feature column trip_start_hour.
tfma.SlicingSpec(feature_keys=['trip_start_hour'])
])
Next, we give this configuration to Evaluator and run it.
# Use TFMA to compute a evaluation statistics over features of a model and
# validate them against a baseline.
# The model resolver is only required if performing model validation in addition
# to evaluation. In this case we validate against the latest blessed model. If
# no model has been blessed before (as in this case) the evaluator will make our
# candidate the first blessed model.
model_resolver = ResolverNode(
instance_name='latest_blessed_model_resolver',
resolver_class=latest_blessed_model_resolver.LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing))
context.run(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
# Change threshold will be ignored if there is no baseline (first run).
eval_config=eval_config)
context.run(evaluator)
INFO:absl:Running driver for ResolverNode.latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running publisher for ResolverNode.latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running driver for Evaluator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Evaluator
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
label_key: "tips"
}
slicing_specs {
}
slicing_specs {
feature_keys: "trip_start_hour"
}
metrics_specs {
metrics {
class_name: "ExampleCount"
threshold {
}
}
metrics {
class_name: "BinaryAccuracy"
threshold {
value_threshold {
lower_bound {
value: 0.5
}
}
}
}
}
INFO:absl:Using /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Trainer/model/6/serving_model_dir as model.
INFO:absl:Evaluating model.
INFO:absl:Using 1 process(es) for Beam pipeline execution.
WARNING:absl:inputs do not match those expected by the model: input_names=['examples'], found in extracts={}
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Evaluator/evaluation/8.
INFO:absl:Checking validation results.
INFO:absl:Blessing result True written to /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Evaluator/blessing/8.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized
Now let's examine the output artifacts of Evaluator.
%%skip_for_export
evaluator.outputs
{'evaluation': Channel(
type_name: ModelEvaluation
artifacts: [Artifact(type_name: ModelEvaluation, uri: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Evaluator/evaluation/8, id: 9)]
), 'blessing': Channel(
type_name: ModelBlessing
artifacts: [Artifact(type_name: ModelBlessing, uri: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Evaluator/blessing/8, id: 10)]
)}
This cell will be skipped during export to pipeline.
Using the evaluation output we can show the default visualization of global metrics on the entire evaluation set.
%%skip_for_export
context.show(evaluator.outputs['evaluation'])
This cell will be skipped during export to pipeline.
To see the visualization for sliced evaluation metrics, we can directly call the TensorFlow Model Analysis library.
%%skip_for_export
import tensorflow_model_analysis as tfma
# Get the TFMA output result path and load the result.
PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
tfma_result = tfma.load_eval_result(PATH_TO_RESULT)
# Show data sliced along feature column trip_start_hour.
tfma.view.render_slicing_metrics(
tfma_result, slicing_column='trip_start_hour')
SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'trip_start_hour:1', …
This cell will be skipped during export to pipeline.
This visualization shows the same metrics, but computed at every feature value of trip_start_hour instead of on the entire evaluation set.
TensorFlow Model Analysis supports many other visualizations, such as Fairness Indicators and plotting a time series of model performance. To learn more, see the tutorial.
Since we added thresholds to our config, validation output is also available. The precence of a blessing artifact indicates that our model passed validation. Since this is the first validation being performed the candidate is automatically blessed.
%%skip_for_export
blessing_uri = evaluator.outputs.blessing.get()[0].uri
!ls -l {blessing_uri}
total 0 -rw-rw-r-- 1 kbuilder kbuilder 0 Sep 11 09:10 BLESSED This cell will be skipped during export to pipeline.
Now can also verify the success by loading the validation result record:
%%skip_for_export
PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
print(tfma.load_validation_result(PATH_TO_RESULT))
validation_ok: true This cell will be skipped during export to pipeline.
Pusher
The Pusher component is usually at the end of a TFX pipeline. It checks whether a model has passed validation, and if so, exports the model to _serving_model_dir.
pusher = Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
context.run(pusher)
INFO:absl:Running driver for Pusher INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for Pusher INFO:absl:Model version: 1599815432 INFO:absl:Model written to serving path /tmp/tmprifdzwdu/serving_model/taxi_simple/1599815432. INFO:absl:Model pushed to /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Pusher/pushed_model/9. INFO:absl:Running publisher for Pusher INFO:absl:MetadataStore with DB connection initialized
Let's examine the output artifacts of Pusher.
%%skip_for_export
pusher.outputs
{'pushed_model': Channel(
type_name: PushedModel
artifacts: [Artifact(type_name: PushedModel, uri: /tmp/tfx-interactive-2020-09-11T09_07_59.074881-ff5ikpwm/Pusher/pushed_model/9, id: 11)]
)}
This cell will be skipped during export to pipeline.
In particular, the Pusher will export your model in the SavedModel format, which looks like this:
%%skip_for_export
push_uri = pusher.outputs.model_push.get()[0].uri
model = tf.saved_model.load(push_uri)
for item in model.signatures.items():
pp.pprint(item)
('serving_default',
<tensorflow.python.saved_model.load._WrapperFunction object at 0x7fde0b4d4208>)
This cell will be skipped during export to pipeline.
We're finished our tour of built-in TFX components!
After you're happy with experimenting with TFX components and code in this notebook, you may want to export it as a pipeline to be orchestrated with Apache Airflow or Apache Beam. See the final section.
Export to pipeline
To export the contents of this notebook as a pipeline to be orchestrated with Airflow or Beam, follow the instructions below.
If you're using Colab, make sure to save this notebook to Google Drive (File → Save a Copy in Drive) before exporting.
1. Mount Google Drive (Colab-only)
If you're using Colab, this notebook needs to mount your Google Drive to be able to access its own .ipynb file.
%%skip_for_export
import sys
if 'google.colab' in sys.modules:
# Colab.
from google.colab import drive
drive.mount('/content/drive')
This cell will be skipped during export to pipeline.
2. Select an orchestrator
_runner_type = 'beam'
_pipeline_name = 'chicago_taxi_%s' % _runner_type
3. Set up paths for the pipeline
# For Colab notebooks only.
# TODO(USER): Fill out the path to this notebook.
_notebook_filepath = (
'/content/drive/My Drive/Colab Notebooks/Copy of components_keras.ipynb')
# For Jupyter notebooks only.
# _notebook_filepath = os.path.join(os.getcwd(),
# 'taxi_pipeline_interactive.ipynb')
# TODO(USER): Fill out the paths for the exported pipeline.
_tfx_root = os.path.join(os.environ['HOME'], 'tfx')
_taxi_root = os.path.join(os.environ['HOME'], 'taxi')
_serving_model_dir = os.path.join(_taxi_root, 'serving_model')
_data_root = os.path.join(_taxi_root, 'data', 'simple')
_pipeline_root = os.path.join(_tfx_root, 'pipelines', _pipeline_name)
_metadata_path = os.path.join(_tfx_root, 'metadata', _pipeline_name,
'metadata.db')
4. Choose components to include in the pipeline
# TODO(USER): Specify components to be included in the exported pipeline.
components = [
example_gen, statistics_gen, schema_gen, example_validator, transform,
trainer, evaluator, pusher
]
5. Generate pipeline files
%%skip_for_export
if get_ipython().magics_manager.auto_magic:
print('Warning: %automagic is ON. Line magics specified without the % prefix '
'will not be scrubbed during export to pipeline.')
_pipeline_export_filepath = 'export_%s.py' % _pipeline_name
context.export_to_pipeline(notebook_filepath=_notebook_filepath,
export_filepath=_pipeline_export_filepath,
runner_type=_runner_type)
INFO:absl:Exporting contents of /content/drive/My Drive/Colab Notebooks/Copy of components_keras.ipynb to export_chicago_taxi_beam.py with beam runner. Warning: %automagic is ON. Line magics specified without the % prefix will not be scrubbed during export to pipeline.
<span class="ansired">---------------------------------------------------------------------------</span>
<span class="ansired">FileNotFoundError</span> Traceback (most recent call last)
<span class="ansigreen"><ipython-input-46-5b6effae2df9></span> in <span class="ansicyan"><module></span><span class="ansiblue"></span>
<span class="ansigreen"> 10</span> context.export_to_pipeline(notebook_filepath=_notebook_filepath,
<span class="ansigreen"> 11</span> export_filepath<span class="ansiblue">=</span>_pipeline_export_filepath<span class="ansiblue">,</span><span class="ansiblue"></span><span class="ansiblue"></span>
<span class="ansigreen">---> 12</span><span class="ansired"> runner_type=_runner_type)
</span>
<span class="ansigreen">/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tfx/orchestration/experimental/interactive/interactive_context.py</span> in <span class="ansicyan">run_if_ipython</span><span class="ansiblue">(*args, **kwargs)</span>
<span class="ansigreen"> 64</span> <span class="ansired"># __IPYTHON__ variable is set by IPython, see</span><span class="ansiblue"></span><span class="ansiblue"></span><span class="ansiblue"></span>
<span class="ansigreen"> 65</span> <span class="ansired"># <a href="https://ipython.org/ipython-doc/rel-0.10.2/html/interactive/reference.html#embedding-ipython">https://ipython.org/ipython-doc/rel-0.10.2/html/interactive/reference.html#embedding-ipython</a></span><span class="ansiblue"></span><span class="ansiblue"></span><span class="ansiblue"></span>
<span class="ansigreen">---> 66</span><span class="ansired"> </span><span class="ansigreen">return</span> fn<span class="ansiblue">(</span><span class="ansiblue">*</span>args<span class="ansiblue">,</span> <span class="ansiblue">**</span>kwargs<span class="ansiblue">)</span><span class="ansiblue"></span><span class="ansiblue"></span>
<span class="ansigreen"> 67</span> <span class="ansigreen">else</span><span class="ansiblue">:</span><span class="ansiblue"></span><span class="ansiblue"></span>
<span class="ansigreen"> 68</span> absl.logging.warning(
<span class="ansigreen">/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tfx/orchestration/experimental/interactive/interactive_context.py</span> in <span class="ansicyan">export_to_pipeline</span><span class="ansiblue">(self, notebook_filepath, export_filepath, runner_type)</span>
<span class="ansigreen"> 189</span> notebook_filepath, export_filepath, runner_type)
<span class="ansigreen"> 190</span> <span class="ansiblue"></span>
<span class="ansigreen">--> 191</span><span class="ansired"> </span><span class="ansigreen">with</span> open<span class="ansiblue">(</span>notebook_filepath<span class="ansiblue">)</span> <span class="ansigreen">as</span> notebook_f<span class="ansiblue">,</span><span class="ansired">\</span><span class="ansiblue"></span><span class="ansiblue"></span>
<span class="ansigreen"> 192</span> open<span class="ansiblue">(</span>export_filepath<span class="ansiblue">,</span> <span class="ansiblue">'w'</span><span class="ansiblue">)</span> <span class="ansigreen">as</span> export_f<span class="ansiblue">:</span><span class="ansiblue"></span><span class="ansiblue"></span>
<span class="ansigreen"> 193</span> notebook <span class="ansiblue">=</span> nbformat<span class="ansiblue">.</span>read<span class="ansiblue">(</span>notebook_f<span class="ansiblue">,</span> nbformat<span class="ansiblue">.</span>NO_CONVERT<span class="ansiblue">)</span><span class="ansiblue"></span><span class="ansiblue"></span>
<span class="ansired">FileNotFoundError</span>: [Errno 2] No such file or directory: '/content/drive/My Drive/Colab Notebooks/Copy of components_keras.ipynb'
This cell will be skipped during export to pipeline.
6. Download pipeline files
%%skip_for_export
if 'google.colab' in sys.modules:
from google.colab import files
import zipfile
zip_export_path = os.path.join(
tempfile.mkdtemp(), 'export.zip')
with zipfile.ZipFile(zip_export_path, mode='w') as export_zip:
export_zip.write(_pipeline_export_filepath)
export_zip.write(_taxi_constants_module_file)
export_zip.write(_taxi_transform_module_file)
export_zip.write(_taxi_trainer_module_file)
files.download(zip_export_path)
This cell will be skipped during export to pipeline.
To learn how to run the orchestrated pipeline with Apache Airflow, please refer to the TFX Orchestration Tutorial.