From 90c5b7652f257413f2ea2268eb967cc15cfed62f Mon Sep 17 00:00:00 2001 From: Xinyue Ruan Date: Tue, 21 Mar 2023 16:09:02 +0800 Subject: [PATCH] update --- website/docs/features/simple_deep_learning/about.md | 2 +- .../version-0.11.0/features/simple_deep_learning/about.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/website/docs/features/simple_deep_learning/about.md b/website/docs/features/simple_deep_learning/about.md index 679747d46d..4ede10d040 100644 --- a/website/docs/features/simple_deep_learning/about.md +++ b/website/docs/features/simple_deep_learning/about.md @@ -38,7 +38,7 @@ make it an excellent choice for SynapseML's Simple Deep Learning: - Flexibility: PyTorch Lightning retains the flexibility and expressiveness of PyTorch while adding useful abstractions to simplify the training loop and other boilerplate code. - Built-in Best Practices: PyTorch Lightning incorporates many best practices for deep learning, such as automatic optimization, gradient clipping, and learning rate scheduling, making it easier for users to achieve optimal performance. - Compatibility: PyTorch Lightning is compatible with a wide range of popular tools and frameworks, including Horovod, which allows users to easily use distributed training capabilities. -- Rapid Development: With PyTorch Lightning, users can quickly prototype and experiment with different model architectures, even training strategies without worrying about low-level implementation details. +- Rapid Development: With PyTorch Lightning, users can quickly experiment with different model architectures and training strategies without worrying about low-level implementation details. ### Sample usage with DeepVisionClassifier DeepVisionClassifier incorporates all models supported by [torchvision](https://github.com/pytorch/vision). diff --git a/website/versioned_docs/version-0.11.0/features/simple_deep_learning/about.md b/website/versioned_docs/version-0.11.0/features/simple_deep_learning/about.md index 679747d46d..4ede10d040 100644 --- a/website/versioned_docs/version-0.11.0/features/simple_deep_learning/about.md +++ b/website/versioned_docs/version-0.11.0/features/simple_deep_learning/about.md @@ -38,7 +38,7 @@ make it an excellent choice for SynapseML's Simple Deep Learning: - Flexibility: PyTorch Lightning retains the flexibility and expressiveness of PyTorch while adding useful abstractions to simplify the training loop and other boilerplate code. - Built-in Best Practices: PyTorch Lightning incorporates many best practices for deep learning, such as automatic optimization, gradient clipping, and learning rate scheduling, making it easier for users to achieve optimal performance. - Compatibility: PyTorch Lightning is compatible with a wide range of popular tools and frameworks, including Horovod, which allows users to easily use distributed training capabilities. -- Rapid Development: With PyTorch Lightning, users can quickly prototype and experiment with different model architectures, even training strategies without worrying about low-level implementation details. +- Rapid Development: With PyTorch Lightning, users can quickly experiment with different model architectures and training strategies without worrying about low-level implementation details. ### Sample usage with DeepVisionClassifier DeepVisionClassifier incorporates all models supported by [torchvision](https://github.com/pytorch/vision).