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2 changes: 0 additions & 2 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -197,8 +197,6 @@ This notebook compares the performance of `Dataset`, `CacheDataset` and `Persist
##### [fast_training_tutorial](./acceleration/fast_training_tutorial.ipynb)
This tutorial compares the training performance of pure PyTorch program and optimized program in MONAI based on NVIDIA GPU device and latest CUDA library.
The optimization methods mainly include: `AMP`, `CacheDataset`, `GPU transforms`, `ThreadDataLoader`, `DiceCELoss` and `SGD`.
##### [multi_gpu_test](./acceleration/multi_gpu_test.ipynb)
This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.
##### [threadbuffer_performance](./acceleration/threadbuffer_performance.ipynb)
Demonstrates the use of the `ThreadBuffer` class used to generate data batches during training in a separate thread.
##### [transform_speed](./acceleration/transform_speed.ipynb)
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2 changes: 0 additions & 2 deletions acceleration/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,6 @@ This notebook compares the performance of `Dataset`, `CacheDataset` and `Persist
#### [fast_training_tutorial](./fast_training_tutorial.ipynb)
This tutorial compares the training performance of pure PyTorch program and optimized program in MONAI based on NVIDIA GPU device and latest CUDA library.
The optimization methods mainly include: `AMP`, `CacheDataset` and `Novograd`.
#### [multi_gpu_test](./multi_gpu_test.ipynb)
This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.
#### [threadbuffer_performance](./threadbuffer_performance.ipynb)
Demonstrates the use of the `ThreadBuffer` class used to generate data batches during training in a separate thread.
#### [transform_speed](./transform_speed.ipynb)
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316 changes: 0 additions & 316 deletions acceleration/multi_gpu_test.ipynb

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