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Merge pull request #6914 from smokestacklightnin/ci/docs/followup
Documentation Followup
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.github/workflows/cd-docs.yml

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mkdocs-material-
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- name: Install Dependencies
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run: pip install mkdocs mkdocs-material mkdocstrings[python] griffe-inherited-docstrings mkdocs-autorefs mkdocs-jupyter mkdocs-caption
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run: pip install mkdocs mkdocs-material mkdocstrings[python] griffe-inherited-docstrings mkdocs-autorefs mkdocs-jupyter mkdocs-caption markdown-grid-tables
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- name: Deploy to GitHub Pages
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run: mkdocs gh-deploy --force

docs/api/v1/extensions.md

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# Extension
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::: tfx.v1.extensions
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options:
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show_if_no_docstring: true

docs/api/v1/index.md

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# Modules
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[components][tfx.v1.components] module: TFX components module.
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[dsl][tfx.v1.dsl] module: TFX DSL module.
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[extensions][tfx.v1.extensions] module: TFX extensions module.
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[orchestration][tfx.v1.orchestration] module: TFX orchestration module.
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[proto][tfx.v1.proto] module: TFX proto module.
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[testing][tfx.v1.testing] module: Public testing modules for TFX.
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[types][tfx.v1.types] module: TFX types module.
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[utils][tfx.v1.utils] module: TFX utils module.

docs/api/v1/orchestration.md

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# Orchestration
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::: tfx.v1.orchestration
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docs/api/v1/root.md

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docs/api/v1/testing.md

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# Testing
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::: tfx.v1.testing
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options:
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docs/api/v1/utils.md

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# Utils
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::: tfx.v1.utils
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docs/guide/build_local_pipeline.md

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implement a pipeline for tabular data using the TFX standard components. If
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you are moving an existing ML workflow into a pipeline, you may need to
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revise your code to make full use of
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[TFX standard components](index.md#tfx_standard_components). You may also need
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[TFX standard components](index.md#tfx-standard-components). You may also need
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to create [custom components](understanding_custom_components.md) that
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implement features which are unique to your workflow or that are not yet
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supported by TFX standard components.
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features such as data augmentation.
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* Learn more about
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[standard TFX components](index.md#tfx_standard_components).
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[standard TFX components](index.md#tfx-standard-components).
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* Learn more about [custom components](understanding_custom_components.md).
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1. Create a script file to define your pipeline using the following example.

docs/guide/build_tfx_pipeline.md

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# Building TFX pipelines
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Note: For a conceptual view of TFX Pipelines, see
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[Understanding TFX Pipelines](understanding_tfx_pipelines.md).
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!!! Note
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For a conceptual view of TFX Pipelines, see
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[Understanding TFX Pipelines](understanding_tfx_pipelines.md).
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Note: Want to build your first pipeline before you dive into the details? Get
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started
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[building a pipeline using a template](build_local_pipeline.md#build-a-pipeline-using-a-template).
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!!!Note
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Want to build your first pipeline before you dive into the details? Get
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started
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[building a pipeline using a template](build_local_pipeline.md#build-a-pipeline-using-a-template).
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## Using the `Pipeline` class
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### Task-based dependencies
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Note: Using task-based dependencies is typically not recommended. Defining the
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execution graph with artifact dependencies lets you take advantage of the
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automatic artifact lineage tracking and caching features of TFX.
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!!! Note
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Using task-based dependencies is typically not recommended. Defining the
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execution graph with artifact dependencies lets you take advantage of the
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automatic artifact lineage tracking and caching features of TFX.
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You can also define task-based dependencies using your component's
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[`add_upstream_node` and `add_downstream_node`](https://github.com/tensorflow/tfx/blob/master/tfx/components/base/base_node.py){: .external }
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The easiest way to get a pipeline set up quickly, and to see how all the pieces
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TFX Pipeline Locally](build_local_pipeline).
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TFX Pipeline Locally](../build_local_pipeline).
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## Caching
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