From 20303e47362bbc9c8953545d58c759301bb9f13c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrian=20W=C3=A4lchli?= <aedu.waelchli@gmail.com>
Date: Mon, 24 Apr 2023 10:28:52 +0200
Subject: [PATCH] Add Fabric.launch to Fabric methods section (#17437)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 docs/source-fabric/api/fabric_methods.rst | 27 +++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/docs/source-fabric/api/fabric_methods.rst b/docs/source-fabric/api/fabric_methods.rst
index 1df5aa5391..2a1a8cca8f 100644
--- a/docs/source-fabric/api/fabric_methods.rst
+++ b/docs/source-fabric/api/fabric_methods.rst
@@ -2,6 +2,33 @@
 Fabric Methods
 ##############
 
+launch
+======
+
+With :meth:`~lightning.fabric.fabric.Fabric.launch` you can conveniently launch your script or a function
+into multiple processes for distributed training on a single machine.
+
+.. code-block:: python
+
+    # Launch the script on 2 devices and init distributed backend
+    fabric = Fabric(devices=2)
+    fabric.launch()
+
+The same can be done with code inside a function:
+
+.. code-block:: python
+
+    def run(fabric):
+        # Your distributed code here
+        ...
+
+
+    # Launch a function on 2 devices and init distributed backend
+    fabric = Fabric(devices=2)
+    fabric.launch(run)
+
+For example, you can use the latter for multi-GPU training inside a :doc:`Jupyter notebook <../fundamentals/notebooks>`.
+For launching distributed training with the CLI, multi-node cluster, or cloud, see :doc:`../fundamentals/launch`.
 
 setup
 =====