Building a TFX Pipeline Locally¶

TFX makes it easier to orchestrate your machine learning (ML) workflow as a pipeline, in order to:

Automate your ML process, which lets you regularly retrain, evaluate, and deploy your model.
Create ML pipelines which include deep analysis of model performance and validation of newly trained models to ensure performance and reliability.
Monitor training data for anomalies and eliminate training-serving skew
Increase the velocity of experimentation by running a pipeline with different sets of hyperparameters.

A typical pipeline development process begins on a local machine, with data analysis and component setup, before being deployed into production. This guide describes two ways to build a pipeline locally.

Customize a TFX pipeline template to fit the needs of your ML workflow. TFX pipeline templates are prebuilt workflows that demonstrate best practices using the TFX standard components.
Build a pipeline using TFX. In this use case, you define a pipeline without starting from a template.

As you are developing your pipeline, you can run it with LocalDagRunner. Then, once the pipeline components have been well defined and tested, you would use a production-grade orchestrator such as Kubeflow or Airflow.

Before you begin¶

TFX is a Python package, so you will need to set up a Python development environment, such as a virtual environment or a Docker container. Then:

pip install tfx

If you are new to TFX pipelines, learn more about the core concepts for TFX pipelines before continuing.

Build a pipeline using a template¶

TFX Pipeline Templates make it easier to get started with pipeline development by providing a prebuilt set of pipeline definitions that you can customize for your use case.

The following sections describe how to create a copy of a template and customize it to meet your needs.

Create a copy of the pipeline template¶

See list of the available TFX pipeline templates:
```
tfx template list
```
Select a template from the list
```
tfx template copy --model=template --pipeline_name=pipeline-name \
--destination_path=destination-path
```
Replace the following:
- template: The name of the template you want to copy.
- pipeline-name: The name of the pipeline to create.
- destination-path: The path to copy the template into.
Learn more about the tfx template copy command.
A copy of the pipeline template has been created at the path you specified.

Note: The rest of this guide assumes you selected the penguin template.

Explore the pipeline template¶

This section provides an overview of the scaffolding created by a template.

Explore the directories and files that were copied to your pipeline's root directory
- A pipeline directory with
  - pipeline.py - defines the pipeline, and lists which components are being used
  - configs.py - hold configuration details such as where the data is coming from or which orchestrator is being used
- A data directory
  - This typically contains a data.csv file, which is the default source for ExampleGen. You can change the data source in configs.py.
- A models directory with preprocessing code and model implementations
- The template copies DAG runners for local environment and Kubeflow.
- Some templates also include Python Notebooks so that you can explore your data and artifacts with Machine Learning MetaData.
Run the following commands in your pipeline directory:
```
tfx pipeline create --pipeline_path local_runner.py
```
```
tfx run create --pipeline_name <var>pipeline_name</var>
```
The command creates a pipeline run using LocalDagRunner, which adds the following directories to your pipeline:
- A tfx_metadata directory which contains the ML Metadata store used locally.
- A tfx_pipeline_output directory which contains the pipeline's file outputs.
Note: LocalDagRunner is one several orchestrators which are supported in TFX. It is specially suitable for running pipelines locally for faster iterations, possibly with smaller datasets. LocalDagRunner may not be suitable for production use since it runs on a single machine, which is more vulnerable to work being lost if the system becomes unavailable. TFX also supports orchestrators such as Apache Beam, Apache Airflow, and Kubeflow Pipeline. If you're using TFX with a different orchestrator, use the appropriate DAG runner for that orchestrator.

Note: As of this writing, LocalDagRunner is used in the penguin template, while the taxi template uses Apache Beam. The config files for the taxi template are set up to use Beam, and the CLI command is the same.
Open your pipeline's pipeline/configs.py file and review the contents. This script defines the configuration options used by the pipeline and the component functions. This is where you would specify things like the location of the datasource or the number of training steps in a run.
Open your pipeline's pipeline/pipeline.py file and review the contents. This script creates the TFX pipeline. Initially, the pipeline contains only an ExampleGen component.
- Follow the instructions in the TODO comments in pipeline.py to add more steps to the pipeline.
Open local_runner.py file and review the contents. This script creates a pipeline run and specifies the run's parameters, such as the data_path and preprocessing_fn.
You have reviewed the scaffolding created by the template and created a pipeline run using LocalDagRunner. Next, customize the template to fit your requirements.

Customize your pipeline¶

This section provides an overview of how to get started customizing your template.

Design your pipeline. The scaffolding that a template provides helps you implement a pipeline for tabular data using the TFX standard components. If you are moving an existing ML workflow into a pipeline, you may need to revise your code to make full use of TFX standard components. You may also need to create custom components that implement features which are unique to your workflow or that are not yet supported by TFX standard components.
Once you have designed your pipeline, iteratively customize the pipeline using the following process. Start from the component that ingests data into your pipeline, which is usually the ExampleGen component.
1. Customize the pipeline or a component to fit your use case. These customizations may include changes like:
  - Changing pipeline parameters.
  - Adding components to the pipeline or removing them.
  - Replacing the data input source. This data source can either be a file or queries into services such as BigQuery.
  - Changing a component's configuration in the pipeline.
  - Changing a component's customization function.
2. Run the component locally using the local_runner.py script, or another appropriate DAG runner if you are using a different orchestrator. If the script fails, debug the failure and retry running the script.
3. Once this customization is working, move on to the next customization.
Working iteratively, you can customize each step in the template workflow to meet your needs.

Build a custom pipeline¶

Use the following instructions to learn more about building a custom pipeline without using a template.

Design your pipeline. The TFX standard components provide proven functionality to help you implement a complete ML workflow. If you are moving an existing ML workflow into a pipeline, you may need to revise your code to make full use of TFX standard components. You may also need to create custom components that implement features such as data augmentation.
- Learn more about standard TFX components.
- Learn more about custom components.

Create a script file to define your pipeline using the following example. This guide refers to this file as my_pipeline.py.

import os
from typing import Optional, Text, List
from absl import logging
from ml_metadata.proto import metadata_store_pb2
import tfx.v1 as tfx

PIPELINE_NAME = 'my_pipeline'
PIPELINE_ROOT = os.path.join('.', 'my_pipeline_output')
METADATA_PATH = os.path.join('.', 'tfx_metadata', PIPELINE_NAME, 'metadata.db')
ENABLE_CACHE = True

def create_pipeline(
  pipeline_name: Text,
  pipeline_root:Text,
  enable_cache: bool,
  metadata_connection_config: Optional[
    metadata_store_pb2.ConnectionConfig] = None,
  beam_pipeline_args: Optional[List[Text]] = None
):
  components = []

  return tfx.dsl.Pipeline(
        pipeline_name=pipeline_name,
        pipeline_root=pipeline_root,
        components=components,
        enable_cache=enable_cache,
        metadata_connection_config=metadata_connection_config,
        beam_pipeline_args=beam_pipeline_args, <!-- needed? -->
    )

def run_pipeline():
  my_pipeline = create_pipeline(
      pipeline_name=PIPELINE_NAME,
      pipeline_root=PIPELINE_ROOT,
      enable_cache=ENABLE_CACHE,
      metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(METADATA_PATH)
      )

  tfx.orchestration.LocalDagRunner().run(my_pipeline)

if __name__ == '__main__':
  logging.set_verbosity(logging.INFO)
  run_pipeline()

In the coming steps, you define your pipeline in create_pipeline and run your pipeline locally using the local runner.

Iteratively build your pipeline using the following process.

Customize the pipeline or a component to fit your use case. These customizations may include changes like:
- Changing pipeline parameters.
- Adding components to the pipeline or removing them.
- Replacing a data input file.
- Changing a component's configuration in the pipeline.
- Changing a component's customization function.
Run the component locally using the local runner or by running the script directly. If the script fails, debug the failure and retry running the script.
Once this customization is working, move on to the next customization.

Start from the first node in your pipeline's workflow, typically the first node ingests data into your pipeline.

Add the first node in your workflow to your pipeline. In this example, the pipeline uses the ExampleGen standard component to load a CSV from a directory at ./data.

from tfx.components import CsvExampleGen

DATA_PATH = os.path.join('.', 'data')

def create_pipeline(
  pipeline_name: Text,
  pipeline_root:Text,
  data_path: Text,
  enable_cache: bool,
  metadata_connection_config: Optional[
    metadata_store_pb2.ConnectionConfig] = None,
  beam_pipeline_args: Optional[List[Text]] = None
):
  components = []

  example_gen = tfx.components.CsvExampleGen(input_base=data_path)
  components.append(example_gen)

  return tfx.dsl.Pipeline(
        pipeline_name=pipeline_name,
        pipeline_root=pipeline_root,
        components=components,
        enable_cache=enable_cache,
        metadata_connection_config=metadata_connection_config,
        beam_pipeline_args=beam_pipeline_args, <!-- needed? -->
    )

def run_pipeline():
  my_pipeline = create_pipeline(
    pipeline_name=PIPELINE_NAME,
    pipeline_root=PIPELINE_ROOT,
    data_path=DATA_PATH,
    enable_cache=ENABLE_CACHE,
    metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(METADATA_PATH)
    )

  tfx.orchestration.LocalDagRunner().run(my_pipeline)

CsvExampleGen creates serialized example records using the data in the CSV at the specified data path. By setting the CsvExampleGen component's input_base parameter with the data root.

Create a data directory in the same directory as my_pipeline.py. Add a small CSV file to the data directory.

Use the following command to run your my_pipeline.py script.

python my_pipeline.py

The result should be something like the following:

INFO:absl:Component CsvExampleGen depends on [].
INFO:absl:Component CsvExampleGen is scheduled.
INFO:absl:Component CsvExampleGen is running.
INFO:absl:Running driver for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for CsvExampleGen
INFO:absl:Generating examples.
INFO:absl:Using 1 process(es) for Local pipeline execution.
INFO:absl:Processing input csv data ./data/* to TFExample.
WARNING:root: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
INFO:absl:Component CsvExampleGen is finished.

Continue to iteratively add components to your pipeline.