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.github/workflows/pep8.yml

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# quick tests for every pull request
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push:
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branches:
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- master
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- main
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pull_request:
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jobs:

3d_segmentation/challenge_baseline/README.md

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- For MONAI technical documentation, please visit [docs.monai.io](https://docs.monai.io/).
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- Please visit [`Project-MONAI/tutorials`](https://github.com/Project-MONAI/tutorials) for more examples, including:
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- [`3D segmentation pipelines`](https://github.com/Project-MONAI/tutorials/tree/master/3d_segmentation),
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- [`3D segmentation pipelines`](https://github.com/Project-MONAI/tutorials/tree/main/3d_segmentation),
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- [`Dynamic UNet`](https://github.com/Project-MONAI/tutorials/blob/main/modules/dynunet_tutorial.ipynb),
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- [`Training acceleration`](https://github.com/Project-MONAI/tutorials/tree/master/acceleration).
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- [`Training acceleration`](https://github.com/Project-MONAI/tutorials/tree/main/acceleration).
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## Submitting to the leaderboard
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acceleration/threadbuffer_performance.ipynb

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"source": [
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"## Setup Environment\n",
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"\n",
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"The current MONAI master branch must be installed for this feature (as of release 0.3.0), skip this step if already installed:"
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"The current MONAI main branch must be installed for this feature (as of release 0.3.0), skip this step if already installed:"
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]
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},
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{

federated_learning/substra/README.md

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# MONAI on the Substra platform
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This examples demonstrates how to run the [3D segmentation tutorial](https://github.com/Project-MONAI/tutorials/tree/master/3d_segmentation) on the [Substra](https://github.com/substrafoundation/substra) platform.
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This examples demonstrates how to run the [3D segmentation tutorial](https://github.com/Project-MONAI/tutorials/tree/main/3d_segmentation) on the [Substra](https://github.com/substrafoundation/substra) platform.
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In order to run the example, you'll need to have an instance of the substra platform running. Please refer to the [Substra documentation](https://doc.substra.ai/) for more information.
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full_gpu_inference_pipeline/README.md

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## Prepare the model repository
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The full pipeline is as below:
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<img src="https://github.com/Project-MONAI/tutorials/raw/master/full_gpu_inference_pipeline/pics/Picture3.png">
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<img src="https://github.com/Project-MONAI/tutorials/raw/main/full_gpu_inference_pipeline/pics/Picture3.png">
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### Prepare the model repository file directories
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The Triton model repository of the experiment can be fast set up by: 
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Note that all the processes (pre/post and AI inference) are on GPU.
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From the result, we can come to a conclusion that using shared memory will greatly reduce the latency when data transfer is huge.
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![](https://github.com/Project-MONAI/tutorials/raw/master/full_gpu_inference_pipeline/pics/Picture2.png)
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![](https://github.com/Project-MONAI/tutorials/raw/main/full_gpu_inference_pipeline/pics/Picture2.png)
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### Pre/Post-processing on GPU vs. CPU 
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After doing pre and post-processing on GPU, we can get a 12x speedup for the full pipeline.
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![](https://github.com/Project-MONAI/tutorials/raw/master/full_gpu_inference_pipeline/pics/Picture1.png)
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![](https://github.com/Project-MONAI/tutorials/raw/main/full_gpu_inference_pipeline/pics/Picture1.png)

modules/cross_validation_models_ensemble.ipynb

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"## Define a training process based on workflows\n",
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"\n",
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"More usage examples of MONAI workflows are available at: [workflow examples](https://github.com/Project-MONAI/tutorials/tree/master/modules/engines)."
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"More usage examples of MONAI workflows are available at: [workflow examples](https://github.com/Project-MONAI/tutorials/tree/main/modules/engines)."
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]
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},
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{

modules/interpretability/cats_and_dogs.ipynb

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