Creating a Custom TFX Executor

TensorFlow Extended (TFX) is a platform for creating production-ready ML pipelines. TFX was created by Google and provides the backbone of Google’s ML services and applications, and we’ve been open sourcing TFX for everyone who needs to create production ML pipelines.

TFX components consist of three main pieces:

The splits are defined in the output Channel for the component that you’re overriding the executor for, typically in the constructor:

So far we’ve been assuming that your pipeline is running locally, using code available in your $PYTHONPATH. An upcoming blog post will explain how to execute custom executors packaged in containers, or as PyPI packages.

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