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wyli merged 6 commits into from
Oct 24, 2021
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3177 Update brats distributed training chart in docs #3179

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Merge pull request #19 from Project-MONAI/master
Nic-Ma Feb 1, 2021
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Merge pull request #32 from Project-MONAI/master
Nic-Ma Feb 24, 2021
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Merge pull request #180 from Project-MONAI/dev
Nic-Ma Jul 22, 2021
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Merge pull request #214 from Project-MONAI/dev
Nic-Ma Sep 8, 2021
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Merge pull request #257 from Project-MONAI/dev
Nic-Ma Oct 24, 2021
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[DLMED] add link and update chart
Nic-Ma Oct 24, 2021
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4 changes: 2 additions & 2 deletions docs/source/highlights.md
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Expand Up @@ -409,9 +409,9 @@ We also tried to combine `AMP` with `CacheDataset`, `GPU cache`, `GPU transforms
More details is available at [Fast training tutorial](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_training_tutorial.ipynb).

### 3. Distributed data parallel
Distributed data parallel is an important feature of PyTorch to connect multiple GPU devices on single or multiple nodes to train or evaluate models. The distributed data parallel APIs of MONAI are compatible with native PyTorch distributed module, pytorch-ignite distributed module, Horovod, XLA, and the SLURM platform. MONAI provides demos for reference: train/evaluate with PyTorch DDP, train/evaluate with Horovod, train/evaluate with Ignite DDP, partition dataset and train with SmartCacheDataset, as well as a real world training example based on Decathlon challenge Task01 - Brain Tumor segmentation. The demo contains distributed caching, training, and validation. We obtained performance benchmarks for reference (based on PyTorch 1.6, CUDA 11, NVIDIA V100 GPUs):
Distributed data parallel is an important feature of PyTorch to connect multiple GPU devices on single or multiple nodes to train or evaluate models. The distributed data parallel APIs of MONAI are compatible with native PyTorch distributed module, pytorch-ignite distributed module, Horovod, XLA, and the SLURM platform. MONAI provides demos for reference: train/evaluate with PyTorch DDP, train/evaluate with Horovod, train/evaluate with Ignite DDP, partition dataset and train with SmartCacheDataset, as well as a real world training example based on Decathlon challenge Task01 - Brain Tumor segmentation. The [tutorial](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/distributed_training/brats_training_ddp.py) contains distributed caching, training, and validation. We obtained performance benchmarks for reference (based on PyTorch 1.9.1, CUDA 11.4, NVIDIA V100 GPUs. The `optimization` means that with more GPU resources, we can split the data and cache into GPU memory and execute GPU transforms directly):

![distributed training results](../images/distributed_training.png)
![distributed training results](../images/brats_distributed.png)

### 4. C++/CUDA optimized modules
To further accelerate the domain-specific routines in the workflows, MONAI C++/CUDA implementation are introduced as extensions of the PyTorch native implementations.
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