From 42a4fe06b04455d543b7de305af715422a5544c9 Mon Sep 17 00:00:00 2001
From: William Falcon <waf2107@columbia.edu>
Date: Mon, 12 Oct 2020 12:15:33 -0400
Subject: [PATCH] docs (#4096)

---
 docs/source/cloud_training.rst  | 29 +++++++++++++++++++++++++++++
 docs/source/hyperparameters.rst | 31 -------------------------------
 docs/source/index.rst           |  1 +
 3 files changed, 30 insertions(+), 31 deletions(-)
 create mode 100644 docs/source/cloud_training.rst

diff --git a/docs/source/cloud_training.rst b/docs/source/cloud_training.rst
new file mode 100644
index 0000000000..9fef417da7
--- /dev/null
+++ b/docs/source/cloud_training.rst
@@ -0,0 +1,29 @@
+################
+AWS/GCP training
+################
+Lightning has a native solution for training on AWS/GCP at scale (Lightning-Grid).
+Grid is in private early-access now but you can request access at `grid.ai <https://www.grid.ai/>`_.
+
+We've designed Grid to work for Lightning users without needing to make ANY changes to their code.
+
+To use grid, take your regular command:
+
+.. code-block:: bash
+
+    python my_model.py --learning_rate 1e-6 --layers 2 --gpus 4
+
+And change it to use the grid train command:
+
+.. code-block:: bash
+
+    grid train --grid_gpus 4 my_model.py --learning_rate 'uniform(1e-6, 1e-1, 20)' --layers '[2, 4, 8, 16]'
+
+The above command will launch (20 * 4) experiments each running on 4 GPUs (320 GPUs!) - by making ZERO changes to
+your code.
+
+The `uniform` command is part of our new expressive syntax which lets you construct hyperparameter combinations
+using over 20+ distributions, lists, etc. Of course, you can also configure all of this using yamls which
+can be dynamically assembled at runtime.
+
+
+.. hint:: Grid supports the search strategy of your choice! (and much more than just sweeps)
\ No newline at end of file
diff --git a/docs/source/hyperparameters.rst b/docs/source/hyperparameters.rst
index e85a64e58d..4d1d7ee455 100644
--- a/docs/source/hyperparameters.rst
+++ b/docs/source/hyperparameters.rst
@@ -12,37 +12,6 @@ Hyperparameters
 Lightning has utilities to interact seamlessly with the command line ``ArgumentParser``
 and plays well with the hyperparameter optimization framework of your choice.
 
-------------
-
-Lightning-Grid
---------------
-Lightning has a native solution for doing sweeps and training models at scale called Lightning-Grid.
-Grid lets you launch sweeps from your laptop on the cloud provider of your choice. We've designed Grid to
-work for Lightning users without needing to make ANY changes to their code.
-
-To use grid, take your regular command:
-
-.. code-block:: bash
-
-    python my_model.py --learning_rate 1e-6 --layers 2 --gpus 4
-
-And change it to use the grid train command:
-
-.. code-block:: bash
-
-    grid train --grid_gpus 4 my_model.py --learning_rate 'uniform(1e-6, 1e-1, 20)' --layers '[2, 4, 8, 16]'
-
-The above command will launch (20 * 4) experiments each running on 4 GPUs (320 GPUs!) - by making ZERO changes to
-your code.
-
-The `uniform` command is part of our new expressive syntax which lets you construct hyperparameter combinations
-using over 20+ distributions, lists, etc. Of course, you can also configure all of this using yamls which
-can be dynamically assembled at runtime.
-
-Grid is in private early-access now but you can request access at `grid.ai <https://www.grid.ai/>`_.
-
-.. hint:: Grid supports the search strategy of your choice! (and much more than just sweeps)
-
 ----------
 
 ArgumentParser
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 9f74884d4d..071f5ea04d 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -82,6 +82,7 @@ PyTorch Lightning Documentation
    :name: Common Use Cases
    :caption: Common Use Cases
 
+   cloud_training
    amp
    slurm
    child_modules