The Project | About Us | Contribute | Donations | License

HOME

TensorFlow Extended (TFX) is an end-to-end platform for deploying production machine learning (ML) pipelines. It provides a set of components and libraries that help you build, manage, and scale ML workflows. In this section, we will cover the basics of TFX, its components, and how it integrates with TensorFlow.

What is TFX?

TFX is designed to help you manage the entire lifecycle of a machine learning model, from data ingestion and validation to model training, evaluation, and deployment. It ensures that your ML models are reproducible, scalable, and maintainable.

Key Features of TFX:

End-to-End ML Pipelines: TFX provides a comprehensive set of components to build and manage ML pipelines.
Scalability: TFX is designed to handle large-scale data and models.
Reproducibility: Ensures that your ML experiments are reproducible.
Integration with TensorFlow: Seamlessly integrates with TensorFlow for model training and serving.

TFX Components

TFX consists of several components, each designed to handle a specific part of the ML workflow. Here are the main components:

Component	Description
ExampleGen	Ingests and splits data into training and evaluation datasets.
StatisticsGen	Computes statistics over the dataset for data analysis.
SchemaGen	Generates a schema based on the computed statistics.
ExampleValidator	Detects anomalies in the dataset by comparing it against the schema.
Transform	Preprocesses and transforms the data for training.
Trainer	Trains the ML model using TensorFlow.
Evaluator	Evaluates the trained model and validates its performance.
ModelValidator	Validates the model to ensure it meets the required criteria.
Pusher	Deploys the validated model to a serving infrastructure.

Setting Up TFX

Before we dive into using TFX, let's set up the environment.

Prerequisites:

Python 3.6 or later
TensorFlow 2.x

Installation:

You can install TFX using pip:

pip install tfx

Building a Simple TFX Pipeline

Let's create a simple TFX pipeline to understand how the components work together. We'll use a sample dataset and build a pipeline that ingests data, computes statistics, and trains a simple model.

Step 1: Import Required Libraries

import tensorflow as tf
import tfx
from tfx.components import CsvExampleGen, StatisticsGen, SchemaGen, ExampleValidator, Transform, Trainer, Evaluator, ModelValidator, Pusher
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext

Step 2: Initialize the Interactive Context

context = InteractiveContext()

Step 3: Define the Pipeline Components

ExampleGen

example_gen = CsvExampleGen(input_base='path/to/csv/data')
context.run(example_gen)

StatisticsGen

statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
context.run(statistics_gen)

SchemaGen

schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'])
context.run(schema_gen)

ExampleValidator

example_validator = ExampleValidator(statistics=statistics_gen.outputs['statistics'], schema=schema_gen.outputs['schema'])
context.run(example_validator)

Transform

transform = Transform(examples=example_gen.outputs['examples'], schema=schema_gen.outputs['schema'], module_file='path/to/preprocessing.py')
context.run(transform)

Trainer

trainer = Trainer(module_file='path/to/trainer.py', transformed_examples=transform.outputs['transformed_examples'], schema=schema_gen.outputs['schema'], transform_graph=transform.outputs['transform_graph'])
context.run(trainer)

Evaluator

evaluator = Evaluator(examples=example_gen.outputs['examples'], model_exports=trainer.outputs['model'])
context.run(evaluator)

ModelValidator

model_validator = ModelValidator(examples=example_gen.outputs['examples'], model=trainer.outputs['model'])
context.run(model_validator)

Pusher

pusher = Pusher(model=trainer.outputs['model'], model_blessing=model_validator.outputs['blessing'], push_destination=tfx.proto.PushDestination(filesystem=tfx.proto.PushDestination.Filesystem(base_directory='path/to/serving/model')))
context.run(pusher)

Conclusion

In this section, we introduced TensorFlow Extended (TFX) and its key components. We also walked through the process of setting up a simple TFX pipeline. TFX provides a robust framework for managing the entire lifecycle of machine learning models, ensuring scalability, reproducibility, and maintainability. In the next sections, we will dive deeper into each component and explore advanced features of TFX.

Introduction to TFX

What is TFX?

Key Features of TFX:

TFX Components

Setting Up TFX

Prerequisites:

Installation:

Building a Simple TFX Pipeline

Step 1: Import Required Libraries

Step 2: Initialize the Interactive Context

Step 3: Define the Pipeline Components

ExampleGen

StatisticsGen

SchemaGen

ExampleValidator

Transform

Trainer

Evaluator

ModelValidator

Pusher

Conclusion

TensorFlow Course

Module 1: Introduction to TensorFlow

Module 2: TensorFlow Basics

Module 3: Data Handling in TensorFlow

Module 4: Building Neural Networks

Module 5: Convolutional Neural Networks (CNNs)

Module 6: Recurrent Neural Networks (RNNs)

Module 7: Advanced TensorFlow Techniques

Module 8: TensorFlow for Production

Module 9: TensorFlow Extended (TFX)

Module 10: Special Topics