Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix word spelling mistake #1424

Merged
merged 1 commit into from
Mar 28, 2022
Merged

fix word spelling mistake #1424

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion docs/zh_cn/train.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@ evaluation = dict(interval=4000) # 每4000 iterations 评估一次模型的性
**\*重要提示\***: 在配置文件里的默认学习率是针对4卡 GPU 和2张图/GPU (此时 batchsize = 4x2 = 8)来设置的。
同样，您也可以使用8卡 GPU 和 1张图/GPU 的设置，因为所有的模型均使用 cross-GPU 的 SyncBN 模式。

我们可以在训练速度和 GPU 显存之间做平衡。当模型或者 Batch Size 比较大的时，可以传递`--cfg-options model.backbone.with_cp=True` ，使用 `with_cp` 来节省显存，但是速度会更慢，因为原先使用 `ith_cp` 时，是逐层反向传播(Back Propagation, BP)，不会保存所有的梯度。
我们可以在训练速度和 GPU 显存之间做平衡。当模型或者 Batch Size 比较大的时，可以传递`--cfg-options model.backbone.with_cp=True` ，使用 `with_cp` 来节省显存，但是速度会更慢，因为原先使用 `with_cp` 时，是逐层反向传播(Back Propagation, BP)，不会保存所有的梯度。

### 使用单卡 GPU 训练

Expand Down