From e5c5b7fee94b96e8bdfeea452cb44fd6f1284201 Mon Sep 17 00:00:00 2001
From: Nesqulck <88182556+Nesqulck@users.noreply.github.com>
Date: Thu, 28 Oct 2021 15:41:41 +0200
Subject: [PATCH 1/2] Added advise for num_workers=0

Advise for disabling warning message for dataloaders with intentional low workers
---
 docs/source/guides/speed.rst | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/docs/source/guides/speed.rst b/docs/source/guides/speed.rst
index 0f5a34e69930a..eadd3603b452e 100644
--- a/docs/source/guides/speed.rst
+++ b/docs/source/guides/speed.rst
@@ -145,6 +145,13 @@ some references, [`1 <https://discuss.pytorch.org/t/guidelines-for-assigning-num
 
 The best thing to do is to increase the ``num_workers`` slowly and stop once you see no more improvement in your training speed.
 
+For debugging purposes or for dataloaders that load very small datasets, it is desirable to set ``num_workers=0``. However, this will always log a warning for every dataloader with ``num_workers <= min(2, os.cpu_count())``. In such cases, you can specifically filter this warning by using:
+
+.. code-block:: python
+
+    import warnings
+    warnings.filterwarnings("ignore", ".*does not have many workers. Consider increasing the value of the `num_workers` argument*")
+
 Spawn
 """""
 When using ``strategy=ddp_spawn`` or training on TPUs, the way multiple GPUs/TPU cores are used is by calling ``.spawn()`` under the hood.

From b30a862c85f040fb316101f19e74a01e8b5c1f5d Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 28 Oct 2021 13:51:38 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 docs/source/guides/speed.rst | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/source/guides/speed.rst b/docs/source/guides/speed.rst
index eadd3603b452e..0b8edb43b7ec5 100644
--- a/docs/source/guides/speed.rst
+++ b/docs/source/guides/speed.rst
@@ -150,7 +150,10 @@ For debugging purposes or for dataloaders that load very small datasets, it is d
 .. code-block:: python
 
     import warnings
-    warnings.filterwarnings("ignore", ".*does not have many workers. Consider increasing the value of the `num_workers` argument*")
+
+    warnings.filterwarnings(
+        "ignore", ".*does not have many workers. Consider increasing the value of the `num_workers` argument*"
+    )
 
 Spawn
 """""