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setup a documentation website auto-generated from code docstrings #12
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Copying in the initial email for complete issue history; hope that's ok. Let me know if this is okay with you. Thanks, |
I don't have experience with python doc generation; do you have some go to tools for it? |
If I could put my 2 cents to this issue. In PyTorch, Torchvision (and Ignite) they are using Sphinx for docs generation. For example, for torchvision:
Documentation pages are written in reStructured Text formats. Docstrings are as mentioned in the first message are in Google style. HTH |
Yes we do. We have been doing that in Clara Train
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On Jan 14, 2020, at 11:59 AM, Ben Murray <[email protected]<mailto:[email protected]>> wrote:
I don't have experience with python doc generation; do you have some go to tools for it?
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We also use Sphinx in Clara Train
…Sent from my iPhone
On Jan 14, 2020, at 7:12 PM, vfdev <[email protected]<mailto:[email protected]>> wrote:
If I could put my 2 cents in this issue. In PyTorch, Torchvision (and Ignite) they are using Sphinx<https://www.sphinx-doc.org/en/master/usage/quickstart.html> for docs generation. For example, for torchvision:
* https://github.com/pytorch/vision/tree/master/docs
* Makefile can be left as is (replacing the project name) and conf.py<https://github.com/pytorch/vision/blob/master/docs/source/conf.py> should be adapted to your project.
* main page is https://github.com/pytorch/vision/blob/master/docs/source/index.rst
* make html build the documentation
Documentation pages are written in reStructured Text formats. Docstrings are as mentioned in the first message are in Google style.
HTH
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I forked this repo and created the document of MONAI API here. It was built by Sphinix, and github.io automatically pulls master branch /docs folder into repository's github pages. I tried to avoid pollute this repo, so once that page looks OK, I will create a PR. There are at least three sub-tasks in this tasks.
For current stage, we are doing most of them manually. In the future, we would like to automate them as part of CD. |
Isaac to investigate Readthedocs service. |
ReadTheDocs limits memory to 1GB. However, pytorch package is around 800MB, which exceeds the memory size. With some mocked packages, I managed to build all pages and pushed them to https://monai-readthedocstest.readthedocs.io/en/latest/. The process required manually creating mocked packages/classes/functions, and did not scale well. Please take a look at https://gitlab.com/project-monai/readthedocstest/-/tree/master for those mocked codes. |
@IsaacYangSLA maybe it is possible to use pytorch CPU only to build the docs which takes < 100mb |
fixed via #68. |
* adds network * adds basic training * update loading * working prototype * update validation set * [MONAI] Add author; paper info; PDDCA18 (#6) + Author + Early accept + PDDCA18 link * Update README.md * adds network * adds basic training * update loading * working prototype * update validation set * [MONAI] Update TRAIN_PATH, VAL_PATH (#8) + Update TRAIN_PATH, VAL_PATH * [MONAI] Add data link (#7) + Add data link https://drive.google.com/file/d/1A2zpVlR3CkvtkJPvtAF3-MH0nr1WZ2Mn/view?usp=sharing * fixes typos * tested new dataset * print more infor, checked new dataset * [MONAI] Add paper link (#9) Add paper link https://arxiv.org/abs/2006.12575 * [MONAI] Use dice loss + focal loss to train (#10) Use dice loss + focal loss to train * [MONAI] Support non-one-hot ground truth (#11) Support non-one-hot ground truth * fixes format and docstrings, adds argparser options * resume the focal_loss * adds tests * [MONAI] Support non-one-hot ground truth (#11) Support non-one-hot ground truth * adds tests * update docstring * [MONAI] Keep track of best validation scores (#12) Keep track of best validation scores * model saving * adds window sampling * update readme * update docs * fixes flake8 error * update window sampling * fixes model name * fixes channel size issue * [MONAI] Update --pretrain, --lr (#13) + lr from 5e-4 to 1e-3 because we use mean for class channel instead of sum for class channel. + pretrain path is consistent with current model_name. * [MONAI] Pad image; elastic; best class model (#14) * [MONAI] Pad image; elastic; best class model + Pad image bigger than crop_size, avoid potential issues in RandCropByPosNegLabeld + Use Rand3DElasticd + Save best model for each class * Update train.py Co-authored-by: Wenqi Li <[email protected]> * flake8 fixes * removes -1 cropsize deform * testing commands * fixes unit tests * update spatial padding * [MONAI] Add full image deform augmentation (#15) + Add full image deform augmentation by Rand3DElasticd + Please use latest MONAI in #623 * Adding py.typed * updating setup.py to comply with black * update based on comments * excluding research from packaging * update tests * update setup.py Co-authored-by: Wentao Zhu <[email protected]> Co-authored-by: Neil Tenenholtz <[email protected]> Co-authored-by: Nic Ma <[email protected]>
Additional comment from #21
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