Integrate TFX with KFP Tutorials

This tutorial will guide you through the process of integrating TFX (TensorFlow Extended) with KFP (Kubeflow Pipelines). TFX is a set of libraries and tools for defining, running, and managing end-to-end machine learning workflows. KFP is a platform for building, training, and deploying machine learning pipelines on Kubernetes.

Prerequisites

Before you start, make sure you have the following prerequisites:

• A Kubernetes cluster
• Python 3.7 or later
• Docker installed
• JupyterLab installed
• TFX and KFP installed and configured

Overview

In this tutorial, we will:

1. Create a TFX pipeline
2. Create a KFP pipeline
3. Integrate the two pipelines

Step 1: Create a TFX Pipeline

First, let's create a TFX pipeline. TFX pipelines are defined using Python code. We will create a simple pipeline that loads data, preprocesses it, trains a model, and evaluates the model.

# pipeline.py
import tensorflow as tf
from tfx import v1 as tfx

# Define the pipeline
pipeline_config = tfx.pipeline_config.PipelineConfig(
    pipeline_name="my_pipeline",
    # ... other configurations ...
)

# Create the pipeline
pipeline = tfx.pipeline.Pipeline(
    pipeline_config=pipeline_config,
    # ... other configurations ...
)

Step 2: Create a KFP Pipeline

Next, let's create a KFP pipeline. KFP pipelines are defined using YAML files. We will create a simple pipeline that loads data, preprocesses it, trains a model, and evaluates the model.

# kfp_pipeline.yaml
apiVersion: v1
kind: Pipeline
metadata:
  name: my_pipeline
spec:
  # ... other configurations ...
  templates:
    - name: LoadData
      # ... configurations for loading data ...
    - name: PreprocessData
      # ... configurations for preprocessing data ...
    - name: TrainModel
      # ... configurations for training model ...
    - name: EvaluateModel
      # ... configurations for evaluating model ...

Step 3: Integrate the Two Pipelines

To integrate the TFX and KFP pipelines, we can use the TFX KFP Executor. The executor allows us to run TFX pipelines using KFP.

# integrate.py
import tfx
from tfx import v1 as tfx
from tfx.kfp.v1 import executor as tfx_kfp_executor

# Define the pipeline
pipeline_config = tfx.pipeline_config.PipelineConfig(
    pipeline_name="my_pipeline",
    # ... other configurations ...
)

# Create the pipeline
pipeline = tfx.pipeline.Pipeline(
    pipeline_config=pipeline_config,
    # ... other configurations ...
)

# Run the pipeline using the KFP Executor
tfx_kfp_executor.execute(pipeline_config, executor=tfx_kfp_executor.KfpExecutor())

For more information on integrating TFX with KFP, please refer to the official documentation.

TensorFlow

Kubernetes

Machine Learning

Kubeflow

TensorFlow Extended