A Component-by-Component Introduction to TensorFlow Extended (TFX)
View on TensorFlow.org
Run in Google Colab
View source on GitHub
Download notebookThis 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 --upgrade pip
except:
pass
Install TFX
pip install -U tfx
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()
from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip
2021-07-27 09:07:32.686219: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
Let's check the library versions.
print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.5.0 TFX version: 1.0.0
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-datazpmc_2k0/data.csv', <http.client.HTTPMessage at 0x7f29e4316290>)
Take a quick look at the CSV file.
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
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-2021-07-27T09_07_38.527065-m86gazca as root for pipeline outputs. WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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 (learn more here) - 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 = tfx.components.CsvExampleGen(input_base=_data_root)
context.run(example_gen)
INFO:absl:Running driver for CsvExampleGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:select span and version = (0, None) INFO:absl:latest span and version = (0, None) INFO:absl:Running executor for CsvExampleGen INFO:absl:Generating examples. 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-datazpmc_2k0/* to TFExample. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. 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:
artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/CsvExampleGen/examples/1
We can also take a look at the first three training examples:
# 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, 'Split-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 {
int64_list {
}
}
}
feature {
key: "dropoff_community_area"
value {
int64_list {
}
}
}
feature {
key: "dropoff_latitude"
value {
float_list {
}
}
}
feature {
key: "dropoff_longitude"
value {
float_list {
}
}
}
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 {
int64_list {
}
}
}
feature {
key: "pickup_community_area"
value {
int64_list {
}
}
}
feature {
key: "pickup_latitude"
value {
float_list {
}
}
}
feature {
key: "pickup_longitude"
value {
float_list {
}
}
}
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: "Taxi Affiliation Services"
}
}
}
feature {
key: "dropoff_census_tract"
value {
int64_list {
}
}
}
feature {
key: "dropoff_community_area"
value {
int64_list {
}
}
}
feature {
key: "dropoff_latitude"
value {
float_list {
}
}
}
feature {
key: "dropoff_longitude"
value {
float_list {
}
}
}
feature {
key: "fare"
value {
float_list {
value: 27.049999237060547
}
}
}
feature {
key: "payment_type"
value {
bytes_list {
value: "Cash"
}
}
}
feature {
key: "pickup_census_tract"
value {
int64_list {
}
}
}
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
}
}
}
}
features {
feature {
key: "company"
value {
bytes_list {
}
}
}
feature {
key: "dropoff_census_tract"
value {
int64_list {
}
}
}
feature {
key: "dropoff_community_area"
value {
int64_list {
}
}
}
feature {
key: "dropoff_latitude"
value {
float_list {
}
}
}
feature {
key: "dropoff_longitude"
value {
float_list {
}
}
}
feature {
key: "fare"
value {
float_list {
value: 16.450000762939453
}
}
}
feature {
key: "payment_type"
value {
bytes_list {
value: "Cash"
}
}
}
feature {
key: "pickup_census_tract"
value {
int64_list {
}
}
}
feature {
key: "pickup_community_area"
value {
int64_list {
value: 13
}
}
}
feature {
key: "pickup_latitude"
value {
float_list {
value: 41.98363494873047
}
}
}
feature {
key: "pickup_longitude"
value {
float_list {
value: -87.72357940673828
}
}
}
feature {
key: "tips"
value {
float_list {
value: 0.0
}
}
}
feature {
key: "trip_miles"
value {
float_list {
value: 6.900000095367432
}
}
}
feature {
key: "trip_seconds"
value {
int64_list {
value: 780
}
}
}
feature {
key: "trip_start_day"
value {
int64_list {
value: 3
}
}
}
feature {
key: "trip_start_hour"
value {
int64_list {
value: 12
}
}
}
feature {
key: "trip_start_month"
value {
int64_list {
value: 11
}
}
}
feature {
key: "trip_start_timestamp"
value {
int64_list {
value: 1446554700
}
}
}
}
2021-07-27 09:07:45.320714: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-27 09:07:46.182366: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.183343: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-27 09:07:46.183380: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-27 09:07:46.186392: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2021-07-27 09:07:46.186495: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2021-07-27 09:07:46.187463: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10
2021-07-27 09:07:46.187792: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10
2021-07-27 09:07:46.188571: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11
2021-07-27 09:07:46.189318: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11
2021-07-27 09:07:46.189509: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8
2021-07-27 09:07:46.189639: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.190658: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.191561: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-27 09:07:46.192211: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-07-27 09:07:46.192702: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.193636: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-27 09:07:46.193733: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.194748: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.195660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-27 09:07:46.195706: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-27 09:07:46.821426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-27 09:07:46.821464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0
2021-07-27 09:07:46.821473: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N
2021-07-27 09:07:46.821754: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.822812: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.823824: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.824737: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
2021-07-27 09:07:46.854033: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2021-07-27 09:07:46.854452: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2000185000 Hz
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 = tfx.components.StatisticsGen(
examples=example_gen.outputs['examples'])
context.run(statistics_gen)
INFO:absl:Excluding no splits because exclude_splits is not set. INFO:absl:Running driver for StatisticsGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for StatisticsGen INFO:absl:Generating statistics for split train. INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/StatisticsGen/statistics/2/Split-train. INFO:absl:Generating statistics for split eval. INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/StatisticsGen/statistics/2/Split-eval. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. 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!
context.show(statistics_gen.outputs['statistics'])
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 = tfx.components.SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Excluding no splits because exclude_splits is not set. INFO:absl:Running driver for SchemaGen INFO:absl:MetadataStore with DB connection initialized 2021-07-27 09:07:50.046440: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type INFO:absl:Running executor for SchemaGen INFO:absl:Processing schema from statistics for split train. INFO:absl:Processing schema from statistics for split eval. INFO:absl:Schema written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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.
context.show(schema_gen.outputs['schema'])
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_data_validation/utils/display_util.py:180: 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)
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.
example_validator = tfx.components.ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
context.run(example_validator)
INFO:absl:Excluding no splits because exclude_splits is not set. 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 for split train. INFO:absl:Validation complete for split train. Anomalies written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/ExampleValidator/anomalies/4/Split-train. INFO:absl:Validating schema against the computed statistics for split eval. INFO:absl:Validation complete for split eval. Anomalies written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/ExampleValidator/anomalies/4/Split-eval. 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.
context.show(example_validator.outputs['anomalies'])
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_data_validation/utils/display_util.py:217: 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)
In the anomalies table, we can see that there are no anomalies. This is what we'd expect, since this the first dataset that we've analyzed and the schema is tailored to it. You should review this schema -- anything unexpected means an anomaly in the data. Once reviewed, the schema can be used to guard future data, and anomalies produced here can be used to debug model performance, understand how your data evolves over time, and identify data errors.
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'
%%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
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'
%%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.
"""
if not isinstance(x, tf.sparse.SparseTensor):
return x
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
Now, we pass in this feature engineering code to the Transform component and run it to transform your data.
transform = tfx.components.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:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py' (including modules: ['taxi_constants', 'taxi_transform']).
INFO:absl:User module package has hash fingerprint version ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpoqmg142s/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpanjcjub4', '--dist-dir', '/tmp/tmpdsj16ttp']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'; target user module is 'taxi_transform'.
INFO:absl:Full user module path is 'taxi_transform@/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
2021-07-27 09:07:50.744965: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Transform
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_constants.py -> build/lib
copying taxi_transform.py -> build/lib
installing to /tmp/tmpanjcjub4
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmpanjcjub4
copying build/lib/taxi_transform.py -> /tmp/tmpanjcjub4
running install_egg_info
running egg_info
creating tfx_user_code_Transform.egg-info
writing tfx_user_code_Transform.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
Copying tfx_user_code_Transform.egg-info to /tmp/tmpanjcjub4/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3.7.egg-info
running install_scripts
creating /tmp/tmpanjcjub4/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/WHEEL
creating '/tmp/tmpdsj16ttp/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' and adding '/tmp/tmpanjcjub4' to it
adding 'taxi_constants.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/RECORD'
removing /tmp/tmpanjcjub4
2021-07-27 09:07:50.749018: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpgm2vzpks', '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl']
Processing /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpgvos8jkh', '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f
Processing /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:266: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Installing '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpfzcjc_89', '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl']
Processing /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
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: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:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
2021-07-27 09:08:04.817364: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/transform_graph/5/.temp_path/tftransform_tmp/da700272a3d54e20a2b7ccdc18a4fecc/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/transform_graph/5/.temp_path/tftransform_tmp/688d8c4944014a3cb6680dd2e0499418/assets
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.
transform.outputs
{'transform_graph': Channel(
type_name: TransformGraph
artifacts: [Artifact(artifact: id: 5
type_id: 13
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/transform_graph/5"
custom_properties {
key: "name"
value {
string_value: "transform_graph"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 13
name: "TransformGraph"
)]
additional_properties: {}
additional_custom_properties: {}
),
'transformed_examples': Channel(
type_name: Examples
artifacts: [Artifact(artifact: id: 6
type_id: 5
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/transformed_examples/5"
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "name"
value {
string_value: "transformed_examples"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
)]
additional_properties: {}
additional_custom_properties: {}
),
'updated_analyzer_cache': Channel(
type_name: TransformCache
artifacts: [Artifact(artifact: id: 7
type_id: 14
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/updated_analyzer_cache/5"
custom_properties {
key: "name"
value {
string_value: "updated_analyzer_cache"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 14
name: "TransformCache"
)]
additional_properties: {}
additional_custom_properties: {}
),
'pre_transform_schema': Channel(
type_name: Schema
artifacts: [Artifact(artifact: id: 8
type_id: 9
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/pre_transform_schema/5"
custom_properties {
key: "name"
value {
string_value: "pre_transform_schema"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 9
name: "Schema"
)]
additional_properties: {}
additional_custom_properties: {}
),
'pre_transform_stats': Channel(
type_name: ExampleStatistics
artifacts: [Artifact(artifact: id: 9
type_id: 7
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/pre_transform_stats/5"
custom_properties {
key: "name"
value {
string_value: "pre_transform_stats"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 7
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]
additional_properties: {}
additional_custom_properties: {}
),
'post_transform_schema': Channel(
type_name: Schema
artifacts: [Artifact(artifact: id: 10
type_id: 9
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/post_transform_schema/5"
custom_properties {
key: "name"
value {
string_value: "post_transform_schema"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 9
name: "Schema"
)]
additional_properties: {}
additional_custom_properties: {}
),
'post_transform_stats': Channel(
type_name: ExampleStatistics
artifacts: [Artifact(artifact: id: 11
type_id: 7
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/post_transform_stats/5"
custom_properties {
key: "name"
value {
string_value: "post_transform_stats"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 7
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]
additional_properties: {}
additional_custom_properties: {}
),
'post_transform_anomalies': Channel(
type_name: ExampleAnomalies
artifacts: [Artifact(artifact: id: 12
type_id: 11
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/post_transform_anomalies/5"
custom_properties {
key: "name"
value {
string_value: "post_transform_anomalies"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Transform"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 11
name: "ExampleAnomalies"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
)]
additional_properties: {}
additional_custom_properties: {}
)}
Take a peek at the transform_graph artifact. It points to a directory containing three subdirectories.
train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata']
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:
# 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, 'Split-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: 8
}
}
}
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: 0.061060599982738495
}
}
}
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: -0.15886740386486053
}
}
}
feature {
key: "trip_seconds_xf"
value {
float_list {
value: -0.7118487358093262
}
}
}
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: 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: 1.2521240711212158
}
}
}
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: 0.532160758972168
}
}
}
feature {
key: "trip_seconds_xf"
value {
float_list {
value: 0.5509493350982666
}
}
}
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
}
}
}
}
features {
feature {
key: "company_xf"
value {
int64_list {
value: 48
}
}
}
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: 0.3873794376850128
}
}
}
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: 13
}
}
}
feature {
key: "pickup_latitude_xf"
value {
int64_list {
value: 9
}
}
}
feature {
key: "pickup_longitude_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "tips_xf"
value {
int64_list {
value: 0
}
}
}
feature {
key: "trip_miles_xf"
value {
float_list {
value: 0.21955278515815735
}
}
}
feature {
key: "trip_seconds_xf"
value {
float_list {
value: 0.0019067146349698305
}
}
}
feature {
key: "trip_start_day_xf"
value {
int64_list {
value: 3
}
}
}
feature {
key: "trip_start_hour_xf"
value {
int64_list {
value: 12
}
}
}
feature {
key: "trip_start_month_xf"
value {
int64_list {
value: 11
}
}
}
}
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'
%%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 import v1 as tfx
from tfx_bsl.public import tfxio
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 _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],
data_accessor: tfx.components.DataAccessor,
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.
data_accessor: DataAccessor for converting input to RecordBatch.
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.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
tfxio.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_transformed_name(_LABEL_KEY)),
tf_transform_output.transformed_metadata.schema)
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)(
tf.keras.layers.concatenate([deep, wide]))
model = tf.keras.Model(input_layers, output)
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
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: tfx.components.FnArgs):
"""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, fn_args.data_accessor,
tf_transform_output, 40)
eval_dataset = _input_fn(fn_args.eval_files, fn_args.data_accessor,
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)
])
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=fn_args.model_run_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
Now, we pass in this model code to the Trainer component and run it to train the model.
trainer = tfx.components.Trainer(
module_file=os.path.abspath(_taxi_trainer_module_file),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=tfx.proto.TrainArgs(num_steps=10000),
eval_args=tfx.proto.EvalArgs(num_steps=5000))
context.run(trainer)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py' (including modules: ['taxi_constants', 'taxi_trainer', 'taxi_transform']).
INFO:absl:User module package has hash fingerprint version 3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp_lnvffb5/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpc26kvw4n', '--dist-dir', '/tmp/tmpjsv1y_4u']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl'; target user module is 'taxi_trainer'.
INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl'
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
2021-07-27 09:08:19.212010: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Trainer
2021-07-27 09:08:19.215726: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
INFO:absl:udf_utils.get_fn {'train_args': '{\n "num_steps": 10000\n}', 'eval_args': '{\n "num_steps": 5000\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'taxi_trainer@/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl'} 'run_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp7xh_pre7', '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_constants.py -> build/lib
copying taxi_trainer.py -> build/lib
copying taxi_transform.py -> build/lib
installing to /tmp/tmpc26kvw4n
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmpc26kvw4n
copying build/lib/taxi_transform.py -> /tmp/tmpc26kvw4n
copying build/lib/taxi_trainer.py -> /tmp/tmpc26kvw4n
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmpc26kvw4n/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3.7.egg-info
running install_scripts
creating /tmp/tmpc26kvw4n/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/WHEEL
creating '/tmp/tmpjsv1y_4u/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl' and adding '/tmp/tmpc26kvw4n' to it
adding 'taxi_constants.py'
adding 'taxi_trainer.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/RECORD'
removing /tmp/tmpc26kvw4n
Processing /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
"The `lr` argument is deprecated, use `learning_rate` instead.")
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:__________________________________________________________________________________________________
2021-07-27 09:08:21.580710: I tensorflow/core/profiler/lib/profiler_session.cc:126] Profiler session initializing.
2021-07-27 09:08:21.580762: I tensorflow/core/profiler/lib/profiler_session.cc:141] Profiler session started.
2021-07-27 09:08:21.580858: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1611] Profiler found 1 GPUs
2021-07-27 09:08:21.643763: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcupti.so.11.2
2021-07-27 09:08:21.847952: I tensorflow/core/profiler/lib/profiler_session.cc:159] Profiler session tear down.
2021-07-27 09:08:21.853003: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1743] CUPTI activity buffer flushed
2021-07-27 09:08:23.104739: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
1/10000 [..............................] - ETA: 4:30:47 - loss: 0.6922 - binary_accuracy: 0.7750
2021-07-27 09:08:23.544669: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2021-07-27 09:08:23.585375: I tensorflow/core/profiler/lib/profiler_session.cc:126] Profiler session initializing.
2021-07-27 09:08:23.585425: I tensorflow/core/profiler/lib/profiler_session.cc:141] Profiler session started.
21/10000 [..............................] - ETA: 3:42 - loss: 0.6493 - binary_accuracy: 0.7821
2021-07-27 09:08:23.797730: I tensorflow/core/profiler/lib/profiler_session.cc:66] Profiler session collecting data.
2021-07-27 09:08:23.800405: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1743] CUPTI activity buffer flushed
2021-07-27 09:08:23.833723: I tensorflow/core/profiler/internal/gpu/cupti_collector.cc:673] GpuTracer has collected 301 callback api events and 298 activity events.
2021-07-27 09:08:23.841253: I tensorflow/core/profiler/lib/profiler_session.cc:159] Profiler session tear down.
2021-07-27 09:08:23.850367: I tensorflow/core/profiler/rpc/client/save_profile.cc:137] Creating directory: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23
2021-07-27 09:08:23.857266: I tensorflow/core/profiler/rpc/client/save_profile.cc:143] Dumped gzipped tool data for trace.json.gz to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.trace.json.gz
2021-07-27 09:08:23.876579: I tensorflow/core/profiler/rpc/client/save_profile.cc:137] Creating directory: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23
2021-07-27 09:08:23.879755: I tensorflow/core/profiler/rpc/client/save_profile.cc:143] Dumped gzipped tool data for memory_profile.json.gz to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.memory_profile.json.gz
2021-07-27 09:08:23.880489: I tensorflow/core/profiler/rpc/client/capture_profile.cc:251] Creating directory: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23Dumped tool data for xplane.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.xplane.pb
Dumped tool data for overview_page.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.overview_page.pb
Dumped tool data for input_pipeline.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.input_pipeline.pb
Dumped tool data for tensorflow_stats.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.tensorflow_stats.pb
Dumped tool data for kernel_stats.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.kernel_stats.pb
10000/10000 [==============================] - 83s 8ms/step - loss: 0.2376 - binary_accuracy: 0.8600 - val_loss: 0.2218 - val_binary_accuracy: 0.8729
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model/6/Format-Serving/assets
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6
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.
model_artifact_dir = trainer.outputs['model'].get()[0].uri
pp.pprint(os.listdir(model_artifact_dir))
model_dir = os.path.join(model_artifact_dir, 'Format-Serving')
pp.pprint(os.listdir(model_dir))
['Format-Serving'] ['variables', 'assets', 'keras_metadata.pb', 'saved_model.pb']
Optionally, we can connect TensorBoard to the Trainer to analyze our model's training curves.
model_run_artifact_dir = trainer.outputs['model_run'].get()[0].uri
%load_ext tensorboard
%tensorboard --logdir {model_run_artifact_dir}
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 will be ignored if there is no
# baseline model resolved from MLMD (first run).
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 = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
model_blessing=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
'latest_blessed_model_resolver')
context.run(model_resolver)
evaluator = tfx.components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
context.run(evaluator)
INFO:absl:Running driver for latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running publisher for latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running driver for Evaluator
INFO:absl:MetadataStore with DB connection initialized
2021-07-27 09:09:55.670102: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Evaluator
2021-07-27 09:09:55.673778: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Nonempty beam arg extra_packages already includes dependency
INFO:absl:udf_utils.get_fn {'eval_config': '{\n "metrics_specs": [\n {\n "metrics": [\n {\n "class_name": "ExampleCount"\n },\n {\n "class_name": "BinaryAccuracy",\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.5\n }\n }\n }\n ]\n }\n ],\n "model_specs": [\n {\n "label_key": "tips"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "trip_start_hour"\n ]\n }\n ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': None, 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model'
ERROR:absl:There are change thresholds, but the baseline is missing. This is allowed only when rubber stamping (first run).
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"
}
metrics {
class_name: "BinaryAccuracy"
threshold {
value_threshold {
lower_bound {
value: 0.5
}
}
}
}
}
INFO:absl:Using /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model/6/Format-Serving as model.
Exception ignored in: <function CapturableResource.__del__ at 0x7f2a1f16d9e0>
Traceback (most recent call last):
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
self._destroy_resource()
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
result = self._call(*args, **kwds)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
results = self._stateful_fn(*args, **kwds)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
filtered_flat_args) = self._maybe_define_function(args, kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f2a1f16d9e0>
Traceback (most recent call last):
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
self._destroy_resource()
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
result = self._call(*args, **kwds)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
results = self._stateful_fn(*args, **kwds)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
filtered_flat_args) = self._maybe_define_function(args, kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f2a1f16d9e0>
Traceback (most recent call last):
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
self._destroy_resource()
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
result = self._call(*args, **kwds)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
results = self._stateful_fn(*args, **kwds)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
filtered_flat_args) = self._maybe_define_function(args, kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f2a1f16d9e0>
Traceback (most recent call last):
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
self._destroy_resource()
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
result = self._call(*args, **kwds)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
results = self._stateful_fn(*args, **kwds)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
filtered_flat_args) = self._maybe_define_function(args, kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.
Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f2a90368b90> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f29e04590d0>).
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:udf_utils.get_fn {'eval_config': '{\n "metrics_specs": [\n {\n "metrics": [\n {\n "class_name": "ExampleCount"\n },\n {\n "class_name": "BinaryAccuracy",\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.5\n }\n }\n }\n ]\n }\n ],\n "model_specs": [\n {\n "label_key": "tips"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "trip_start_hour"\n ]\n }\n ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': None, 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors'
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"
}
metrics {
class_name: "BinaryAccuracy"
threshold {
value_threshold {
lower_bound {
value: 0.5
}
}
}
}
model_names: ""
}
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"
}
metrics {
class_name: "BinaryAccuracy"
threshold {
value_threshold {
lower_bound {
value: 0.5
}
}
}
}
model_names: ""
}
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"
}
metrics {
class_name: "BinaryAccuracy"
threshold {
value_threshold {
lower_bound {
value: 0.5
}
}
}
}
model_names: ""
}
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.
Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f29e3987050> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f29e011d590>).
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.
Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f29c83c43d0> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f29c8161ed0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.
Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f295867efd0> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f295868bd10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.
Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f2526140410> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f2526309cd0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.
Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f29587df150> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f2a902f1490>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.
Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f2960342a10> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f2a63a7af10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.
Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f250d20c090> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f250d230fd0>).
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Evaluator/evaluation/8.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:113: 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)`
INFO:absl:Blessing result True written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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.
evaluator.outputs
{'evaluation': Channel(
type_name: ModelEvaluation
artifacts: [Artifact(artifact: id: 15
type_id: 20
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Evaluator/evaluation/8"
custom_properties {
key: "name"
value {
string_value: "evaluation"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Evaluator"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 20
name: "ModelEvaluation"
)]
additional_properties: {}
additional_custom_properties: {}
),
'blessing': Channel(
type_name: ModelBlessing
artifacts: [Artifact(artifact: id: 16
type_id: 21
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Evaluator/blessing/8"
custom_properties {
key: "blessed"
value {
int_value: 1
}
}
custom_properties {
key: "current_model"
value {
string_value: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model/6"
}
}
custom_properties {
key: "current_model_id"
value {
int_value: 13
}
}
custom_properties {
key: "name"
value {
string_value: "blessing"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Evaluator"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 21
name: "ModelBlessing"
)]
additional_properties: {}
additional_custom_properties: {}
)}
Using the evaluation output we can show the default visualization of global metrics on the entire evaluation set.
context.show(evaluator.outputs['evaluation'])
To see the visualization for sliced evaluation metrics, we can directly call the TensorFlow Model Analysis library.
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:19',…
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.
blessing_uri = evaluator.outputs['blessing'].get()[0].uri
!ls -l {blessing_uri}
total 0 -rw-rw-r-- 1 kbuilder kbuilder 0 Jul 27 09:10 BLESSED
Now can also verify the success by loading the validation result record:
PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
print(tfma.load_validation_result(PATH_TO_RESULT))
validation_ok: true
validation_details {
slicing_details {
slicing_spec {
}
num_matching_slices: 25
}
}
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 = tfx.components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
context.run(pusher)
INFO:absl:Running driver for Pusher INFO:absl:MetadataStore with DB connection initialized 2021-07-27 09:10:20.780757: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type INFO:absl:Running executor for Pusher INFO:absl:Model version: 1627377020 INFO:absl:Model written to serving path /tmp/tmpf2y8jc9r/serving_model/taxi_simple/1627377020. INFO:absl:Model pushed to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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.
pusher.outputs
{'pushed_model': Channel(
type_name: PushedModel
artifacts: [Artifact(artifact: id: 17
type_id: 23
uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Pusher/pushed_model/9"
custom_properties {
key: "name"
value {
string_value: "pushed_model"
}
}
custom_properties {
key: "producer_component"
value {
string_value: "Pusher"
}
}
custom_properties {
key: "pushed"
value {
int_value: 1
}
}
custom_properties {
key: "pushed_destination"
value {
string_value: "/tmp/tmpf2y8jc9r/serving_model/taxi_simple/1627377020"
}
}
custom_properties {
key: "pushed_version"
value {
string_value: "1627377020"
}
}
custom_properties {
key: "state"
value {
string_value: "published"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.0.0"
}
}
state: LIVE
, artifact_type: id: 23
name: "PushedModel"
)]
additional_properties: {}
additional_custom_properties: {}
)}
In particular, the Pusher will export your model in the SavedModel format, which looks like this:
push_uri = pusher.outputs['pushed_model'].get()[0].uri
model = tf.saved_model.load(push_uri)
for item in model.signatures.items():
pp.pprint(item)
('serving_default',
<ConcreteFunction signature_wrapper(*, examples) at 0x7F250C448490>)
We're finished our tour of built-in TFX components!