From 8568bf1bcff53962de1d5083b2736cccd5d3360d Mon Sep 17 00:00:00 2001
From: Fanli Lin <fanli0116@gmail.com>
Date: Tue, 19 Nov 2024 01:58:26 +0800
Subject: [PATCH] [docs] make `empty_cache` device-agnostic (#34774)

make device-agnostic
---
 docs/source/en/training.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/source/en/training.md b/docs/source/en/training.md
index aacf174fbd..6fcf680920 100644
--- a/docs/source/en/training.md
+++ b/docs/source/en/training.md
@@ -287,9 +287,10 @@ model.fit(tf_dataset)
 At this point, you may need to restart your notebook or execute the following code to free some memory:
 
 ```py
+from accelerate.utils.memory import clear_device_cache
 del model
 del trainer
-torch.cuda.empty_cache()
+clear_device_cache()
 ```
 
 Next, manually postprocess `tokenized_dataset` to prepare it for training.
@@ -364,8 +365,9 @@ Lastly, specify `device` to use a GPU if you have access to one. Otherwise, trai
 
 ```py
 >>> import torch
+>>> from accelerate.test_utils.testing import get_backend
 
->>> device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+>>> device, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
 >>> model.to(device)
 ```