[Docs] Fix typo (#1385)

open-mmlab · Oct 10, 2023 · 3b639da · 3b639da
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diff --git a/docs/en/common_usage/large_model_training.md b/docs/en/common_usage/large_model_training.md
@@ -1,4 +1,4 @@
-# Traning Big Models
+# Training Big Models
 
 When training large models, significant resources are required. A single GPU memory is often insufficient to meet the training needs. As a result, techniques for training large models have been developed, and one typical approach is [DeepSpeed ZeRO](https://www.deepspeed.ai/tutorials/zero/#zero-overview). DeepSpeed ZeRO supports optimizer, gradient, and parameter sharding.
 
@@ -85,7 +85,7 @@ torchrun --nproc-per-node 2 examples/distributed_training_with_flexible_runner.p
 ```
 
 <details>
-<summary>traning log</summary>
+<summary>training log</summary>
 
 ```
 07/03 13:04:17 - mmengine - INFO - Epoch(train)  [1][ 10/196]  lr: 3.3333e-04  eta: 0:13:14  time: 0.4073  data_time: 0.0335  memory: 970  loss: 6.1887
@@ -157,7 +157,7 @@ torchrun --nproc-per-node 2 examples/distributed_training_with_flexible_runner.p
 ```
 
 <details>
-<summary>traning log</summary>
+<summary>training log</summary>
 
 ```
 07/03 13:05:37 - mmengine - INFO - Epoch(train)  [1][ 10/196]  lr: 3.3333e-04  eta: 0:08:28  time: 0.2606  data_time: 0.0330  memory: 954  loss: 6.1265

diff --git a/docs/en/common_usage/save_gpu_memory.md b/docs/en/common_usage/save_gpu_memory.md
@@ -100,7 +100,7 @@ runner.train()
 ## Large Model Training
 
 ```{warning}
-If you have the requirement to train large models, we recommend reading [Traning Big Models](./large_model_training.md).
+If you have the requirement to train large models, we recommend reading [Training Big Models](./large_model_training.md).
 ```
 
 `FSDP` is officially supported from PyTorch 1.11. The config can be written in this way: