Copyright 2021 The TensorFlow Authors.¶
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Simple TFX Pipeline Tutorial using Penguin dataset¶
A Short tutorial to run a simple TFX pipeline.
Note: We recommend running this tutorial in a Colab notebook, with no setup required! Just click "Run in Google Colab".
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In this notebook-based tutorial, we will create and run a TFX pipeline for a simple classification model. The pipeline will consist of three essential TFX components: ExampleGen, Trainer and Pusher. The pipeline includes the most minimal ML workflow like importing data, training a model and exporting the trained model.
Please see Understanding TFX Pipelines to learn more about various concepts in TFX.
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 by clicking above "RESTART RUNTIME" button or using "Runtime > Restart runtime ..." menu. This is because of the way that Colab loads packages.
Check the TensorFlow and TFX versions.
import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__))
Set up variables¶
There are some variables used to define a pipeline. You can customize these variables as you want. By default all output from the pipeline will be generated under the current directory.
import os
PIPELINE_NAME = "penguin-simple"
# Output directory to store artifacts generated from the pipeline.
PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)
# Path to a SQLite DB file to use as an MLMD storage.
METADATA_PATH = os.path.join('metadata', PIPELINE_NAME, 'metadata.db')
# Output directory where created models from the pipeline will be exported.
SERVING_MODEL_DIR = os.path.join('serving_model', PIPELINE_NAME)
from absl import logging
logging.set_verbosity(logging.INFO) # Set default logging level.
Prepare example data¶
We will download the example dataset for use in our TFX pipeline. The dataset we are using is Palmer Penguins dataset which is also used in other TFX examples.
There are four numeric features in this dataset:
- culmen_length_mm
- culmen_depth_mm
- flipper_length_mm
- body_mass_g
All features were already normalized to have range [0,1]. We will build a
classification model which predicts the species of penguins.
Because TFX ExampleGen reads inputs from a directory, we need to create a directory and copy dataset to it.
import urllib.request
import tempfile
DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data') # Create a temporary directory.
_data_url = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/data/labelled/penguins_processed.csv'
_data_filepath = os.path.join(DATA_ROOT, "data.csv")
urllib.request.urlretrieve(_data_url, _data_filepath)
Take a quick look at the CSV file.
!head {_data_filepath}
You should be able to see five values. species is one of 0, 1 or 2, and all
other features should have values between 0 and 1.
Create a pipeline¶
TFX pipelines are defined using Python APIs. We will define a pipeline which consists of following three components.
- CsvExampleGen: Reads in data files and convert them to TFX internal format for further processing. There are multiple ExampleGens for various formats. In this tutorial, we will use CsvExampleGen which takes CSV file input.
- Trainer: Trains an ML model. Trainer component requires a model definition code from users. You can use TensorFlow APIs to specify how to train a model and save it in a saved_model format.
- Pusher: Copies the trained model outside of the TFX pipeline. Pusher component can be thought of as a deployment process of the trained ML model.
Before actually define the pipeline, we need to write a model code for the Trainer component first.
Write model training code¶
We will create a simple DNN model for classification using TensorFlow Keras API. This model training code will be saved to a separate file.
In this tutorial we will use
Generic Trainer
of TFX which support Keras-based models. You need to write a Python file
containing run_fn function, which is the entrypoint for the Trainer
component.
_trainer_module_file = 'penguin_trainer.py'
%%writefile {_trainer_module_file}
from typing import List
from absl import logging
import tensorflow as tf
from tensorflow import keras
from tensorflow_transform.tf_metadata import schema_utils
from tfx import v1 as tfx
from tfx_bsl.public import tfxio
from tensorflow_metadata.proto.v0 import schema_pb2
_FEATURE_KEYS = [
'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g'
]
_LABEL_KEY = 'species'
_TRAIN_BATCH_SIZE = 20
_EVAL_BATCH_SIZE = 10
# Since we're not generating or creating a schema, we will instead create
# a feature spec. Since there are a fairly small number of features this is
# manageable for this dataset.
_FEATURE_SPEC = {
**{
feature: tf.io.FixedLenFeature(shape=[1], dtype=tf.float32)
for feature in _FEATURE_KEYS
},
_LABEL_KEY: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)
}
def _input_fn(file_pattern: List[str],
data_accessor: tfx.components.DataAccessor,
schema: schema_pb2.Schema,
batch_size: int = 200) -> tf.data.Dataset:
"""Generates features and label for training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
schema: schema of the input data.
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=_LABEL_KEY),
schema=schema).repeat()
def _build_keras_model() -> tf.keras.Model:
"""Creates a DNN Keras model for classifying penguin data.
Returns:
A Keras Model.
"""
# The model below is built with Functional API, please refer to
# https://www.tensorflow.org/guide/keras/overview for all API options.
inputs = [keras.layers.Input(shape=(1,), name=f) for f in _FEATURE_KEYS]
d = keras.layers.concatenate(inputs)
for _ in range(2):
d = keras.layers.Dense(8, activation='relu')(d)
outputs = keras.layers.Dense(3)(d)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(
optimizer=keras.optimizers.Adam(1e-2),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.summary(print_fn=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.
"""
# This schema is usually either an output of SchemaGen or a manually-curated
# version provided by pipeline author. A schema can also derived from TFT
# graph if a Transform component is used. In the case when either is missing,
# `schema_from_feature_spec` could be used to generate schema from very simple
# feature_spec, but the schema returned would be very primitive.
schema = schema_utils.schema_from_feature_spec(_FEATURE_SPEC)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
schema,
batch_size=_TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
schema,
batch_size=_EVAL_BATCH_SIZE)
model = _build_keras_model()
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps)
# The result of the training should be saved in `fn_args.serving_model_dir`
# directory.
model.save(fn_args.serving_model_dir, save_format='tf')
Now you have completed all preparation steps to build a TFX pipeline.
Write a pipeline definition¶
We define a function to create a TFX pipeline. A Pipeline object
represents a TFX pipeline which can be run using one of the pipeline
orchestration systems that TFX supports.
def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
module_file: str, serving_model_dir: str,
metadata_path: str) -> tfx.dsl.Pipeline:
"""Creates a three component penguin pipeline with TFX."""
# Brings data into the pipeline.
example_gen = tfx.components.CsvExampleGen(input_base=data_root)
# Uses user-provided Python function that trains a model.
trainer = tfx.components.Trainer(
module_file=module_file,
examples=example_gen.outputs['examples'],
train_args=tfx.proto.TrainArgs(num_steps=100),
eval_args=tfx.proto.EvalArgs(num_steps=5))
# Pushes the model to a filesystem destination.
pusher = tfx.components.Pusher(
model=trainer.outputs['model'],
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir)))
# Following three components will be included in the pipeline.
components = [
example_gen,
trainer,
pusher,
]
return tfx.dsl.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
metadata_connection_config=tfx.orchestration.metadata
.sqlite_metadata_connection_config(metadata_path),
components=components)
Run the pipeline¶
TFX supports multiple orchestrators to run pipelines.
In this tutorial we will use LocalDagRunner which is included in the TFX
Python package and runs pipelines on local environment.
We often call TFX pipelines "DAGs" which stands for directed acyclic graph.
LocalDagRunner provides fast iterations for development and debugging.
TFX also supports other orchestrators including Kubeflow Pipelines and Apache
Airflow which are suitable for production use cases.
See TFX on Cloud AI Platform Pipelines or TFX Airflow Tutorial to learn more about other orchestration systems.
Now we create a LocalDagRunner and pass a Pipeline object created from the
function we already defined.
The pipeline runs directly and you can see logs for the progress of the pipeline including ML model training.
tfx.orchestration.LocalDagRunner().run(
_create_pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_root=DATA_ROOT,
module_file=_trainer_module_file,
serving_model_dir=SERVING_MODEL_DIR,
metadata_path=METADATA_PATH))
You should see "INFO:absl:Component Pusher is finished." at the end of the
logs if the pipeline finished successfully. Because Pusher component is the
last component of the pipeline.
The pusher component pushes the trained model to the SERVING_MODEL_DIR which
is the serving_model/penguin-simple directory if you did not change the
variables in the previous steps. You can see the result from the file browser
in the left-side panel in Colab, or using the following command:
# List files in created model directory.
!find {SERVING_MODEL_DIR}
Next steps¶
You can find more resources on https://www.tensorflow.org/tfx/tutorials.
Please see Understanding TFX Pipelines to learn more about various concepts in TFX.