Document caching in kfp v2

content/en/docs/components/pipelines/v2/caching.md

@@ -0,0 +1,59 @@
++++
+title = "Caching"
+description = "How to use caching in Kubeflow Pipelines."
+weight = 13
++++
+
+Kubeflow Pipelines support caching to eliminate redundant executions and improve
+the efficiency of your pipeline runs. This page provides an overview of caching
+in KFP and how to use it in your pipelines.
+
+## Overview
+
+Caching in KFP is a feature that allows you to cache the results of a component
+execution and reuse them in subsequent runs. When caching is enabled for a
+component, KFP will reuse the component's outputs if the component
+is executed again with the same inputs and parameters (and the output is still
+available).
+
+Caching is particularly useful when you have components that take a long time to
+execute or when you have components that are executed multiple times with the
+same inputs and parameters.
+
+If a task's results are retrieved from cache, its representation in the UI will
+be marked with a green "arrow from cloud" icon.
+
+## How to use caching
+
+Caching is enabled by default for all components in KFP. You can disable caching
+for a component by calling `.set_caching_options(False)` on a task object.
+
+```python
+from kfp import dsl
+
+@dsl.component
+def say_hello(name: str) -> str:
+    hello_text = f'Hello, {name}!'
+    print(hello_text)
+    return hello_text
+
+@dsl.pipeline
+def hello_pipeline(recipient: str = 'World!') -> str:
+    hello_task = say_hello(name=recipient)
+    hello_task.set_caching_options(False)
+    return hello_task.output
+```
+
+You can also enable or disable caching for all components in a pipeline by
+setting the argument `caching` when submitting a pipeline for execution.
+This will override the caching settings for all components in the pipeline.
+
+```python
+from kfp.client import Client
+
+client = Client()
+client.create_run_from_pipeline_func(
+    hello_pipeline,
+    enable_caching=True,  # overrides the above disableing of caching
+)
+```