From 145216bfc1ead82d2b05e97f126edd1c52fdcb01 Mon Sep 17 00:00:00 2001
From: Mingzhi Hu <49382651+y199387@users.noreply.github.com>
Date: Fri, 24 Jun 2022 16:38:48 +0800
Subject: [PATCH] Update pytorch to 1.11 (#4854)

* update setup and action

* fix yml

* update yml

* test yml

* update docs

* update notebook requirements

* test notebooks unit test

* reset yml

* delete comments

* fix yml

* fix errors in setup.py

* fix setup.py

* specify the version of IPEX
---
 .../source/doc/Nano/QuickStart/pytorch_train.md            | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/docs/readthedocs/source/doc/Nano/QuickStart/pytorch_train.md b/docs/readthedocs/source/doc/Nano/QuickStart/pytorch_train.md
index b9a1968e..874dd4a4 100644
--- a/docs/readthedocs/source/doc/Nano/QuickStart/pytorch_train.md
+++ b/docs/readthedocs/source/doc/Nano/QuickStart/pytorch_train.md
@@ -47,13 +47,6 @@ from bigdl.nano.pytorch import Trainer
 trainer = Trainer(max_epoch=10, use_ipex=True)
 ```
 
-Note: BigDL-Nano does not install IPEX by default. You can install IPEX using the following command:
-
-```bash
-python -m pip install torch_ipex==1.9.0 -f https://software.intel.com/ipex-whl-stable
-python -m pip install torchvision==0.10.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
-```
-
 #### Multi-instance Training
 
 When training on a server with dozens of CPU cores, it is often beneficial to use multiple training instances in a data-parallel fashion to make full use of the CPU cores. However, using PyTorch's DDP API is a little cumbersome and error-prone, and if not configured correctly, it will make the training even slow.