Skip to content

1017 add fast_training tutorial #22

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Sep 16, 2020
Merged

1017 add fast_training tutorial #22

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
affe3fc
[DLMED] add fast_training tutorial
Nic-Ma Sep 15, 2020
44c817e
[DLMED] update README
Nic-Ma Sep 16, 2020
8de0d50
[DLMED] complete fast training tutorial
Nic-Ma Sep 16, 2020
a9928ee
[DLMED] update according to comments
Nic-Ma Sep 16, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 5 additions & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,9 @@ And compares the training speed and memory usage with/without AMP.
This tutorial shows how to construct a training workflow of multi-labels segmentation task based on [MSD Brain Tumor dataset](http://medicaldecathlon.com).
#### [dataset_type_performance](./dataset_type_performance.ipynb)
This notebook compares the performance of `Dataset`, `CacheDataset` and `PersistentDataset`. These classes differ in how data is stored (in memory or on disk), and at which moment transforms are applied.
#### [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`.
#### [integrate_3rd_party_transforms](./integrate_3rd_party_transforms.ipynb)
This tutorial shows how to integrate 3rd party transforms into MONAI program.
Mainly shows transforms from BatchGenerator, TorchIO, Rising and ITK.
Expand All @@ -48,6 +51,8 @@ This tutorial shows how to leverage `EnsembleEvaluator`, `MeanEnsemble` and `Vot
This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.
#### [nifti_read_example](./nifti_read_example.ipynb)
Illustrate reading NIfTI files and iterating over image patches of the volumes loaded from them.
#### [nnunet_tutorial](./nnunet_tutorial.ipynb)
This tutorial shows how to train 3D segmentation tasks on all the 10 decathlon datasets with the reimplementation of NNUnet in MONAI.
#### [post_transforms](./post_transforms.ipynb)
This notebook shows the usage of several post transforms based on the model output of spleen segmentation task.
#### [public_datasets](./public_datasets.ipynb)
Expand Down
Loading