[DLMED] fix broken link
@@ -387,7 +387,7 @@ Wentao Zhu, Can Zhao, Wenqi Li, Holger Roth, Ziyue Xu, and Daguang Xu (2020) "LA
![LAMP UNet](../images/unet-pipe.png)
## Performance optimization and GPU acceleration
-Typically, model training is a time-consuming step during deep learning development, especially in medical imaging applications. Volumetric medical images are usually large (as multi-dimensional arrays) and the model training process can be complex. Even with powerful hardware (e.g. CPU/GPU with large RAM), it is not easy to fully leverage them to achieve high performance. MONAI provides a [fast training guide](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_model_training_guide.md) to achieve the best performance.
+Typically, model training is a time-consuming step during deep learning development, especially in medical imaging applications. Volumetric medical images are usually large (as multi-dimensional arrays) and the model training process can be complex. Even with powerful hardware (e.g. CPU/GPU with large RAM), it is not easy to fully leverage them to achieve high performance. MONAI provides a fast training guide to achieve the best performance: https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_model_training_guide.md.
NVIDIA GPUs have been widely applied in many areas of deep learning training and evaluation, and the CUDA parallel computation shows obvious acceleration when comparing to traditional computation methods. To fully leverage GPU features, many popular mechanisms raised, like automatic mixed precision (AMP), distributed data parallel, etc. MONAI can support these features and provides rich examples.
