giotto-deep
giotto-deep: A Python Package for Topological Deep Learning - Published in JOSS (2022)
Science Score: 95.0%
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Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
Deep learning made topological.
Basic Info
Statistics
- Stars: 84
- Watchers: 4
- Forks: 11
- Open Issues: 6
- Releases: 4
Topics
Metadata Files
README.md
giotto-deep
The first library to bring seamless integration between topological data analysis and deep learning on top of PyTorch. The code for Persformer will be released open-source soon together with Giotto-deep. It aims to make the day-to-day of researchers easy, allowing them to focus on inventing new models and layers rather than dealing with the more standard deep-learning code. It comes with optimized implementations for multi-GPU/TPU computations and the ability to run benchmarks and hyperparameter optimization in a few lines of code.
Documentation
You can find the documentation of this repository here: https://giotto-ai.github.io/giotto-deep/
Run tensorboard for visualization
In order to analyse the results of your models, you need to start tensorboard. On the terminal, move inside the /examples folder. There, run the following command:
tensorboard --logdir=runs
Afterwards go here and, after running the notebooks of interest, you will see all the visualization results that you stored in the writer = SummaryWriter().
Install user version
The simplest way to install giotto-deep is using pip:
python -m pip install -U giotto-deep
If necessary, this command will also automatically install all the library dependencies.
Note: we recommend upgrading pip to a recent version as the above may fail on very old versions.
Install dev version
The first step to install the developer version of the package is to git clone this repository:
git clone https://github.com/giotto-ai/giotto-deep.git
The change the current working directory to the Repository root folder, e.g. cd giotto-deep.
It is best practice to create a virtual environment for the project, e.g. using virtualenv:
virtualenv -p python3.9 venv
Activate the virtual environment (e.g. source venv/bin/activate on Linux or venv\Scripts\activate on Windows).
First make sure you have upgraded to the last version of pip with
python -m pip install --upgrade pip
Make sure you have the latest version of pytorch installed.
You can do this by running the following command (if you have a GPU):
pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
Once you are in the root folder, install the package dynamically with:
pip install -e .
Contributing
The giotto-deep project welcomes contributions of all kinds. Please see our contributing guidelines for more information.
We are using pre-commit hooks to ensure that the code is formatted correctly. To install the pre-commit hooks, run the following command from the root folder:
pre-commit install
The pre-commit hooks will run automatically before each commit. If you want to run the pre-commit hooks manually, run the following command from the root folder:
pre-commit run --all-files
To run both unit and integration tests on macOS or Linux, simply run the following command from the root folder:
bash local_test.bh
TPU support in Google Colab
I order to run your analysis on TPU cores, you ca use the following lines:
!git clone https://username:token@github.com/giotto-ai/giotto-deep
!ls
!pip uninstall -y tensorflow
!pip install -e giotto-deep/
!pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl
Once you have run the lines above, please make sure to restart the runtime.
The code will automatically detect the TPU core and use it as default to run the experiments. GPUs are also automatically supported.
Owner
- Name: giotto.ai
- Login: giotto-ai
- Kind: organization
- Email: maintainers@giotto.ai
- Website: https://giotto.ai
- Repositories: 11
- Profile: https://github.com/giotto-ai
Adding a third dimension to AI
JOSS Publication
giotto-deep: A Python Package for Topological Deep Learning
Authors
L2F SA, Rue du Centre 9, Saint-Sulpice, 1025, CH
Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratory for topology and neuroscience, Lausanne, 1015, CH
Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratory for topology and neuroscience, Lausanne, 1015, CH
Editor
Øystein Sørensen
Tags
Python topological data analysis deep learning persformerGitHub Events
Total
- Watch event: 6
Last Year
- Watch event: 6
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| matteocao | m****s@g****i | 395 |
| raphaelreinauer | r****r@g****m | 256 |
| matteocao | 3****o | 61 |
| Abdul Jabbar | a****r@n****i | 22 |
| Raphael Reinauer | r****r@e****h | 21 |
| nberkouk | b****s@m****l | 18 |
| hkirvesl | h****i@e****h | 16 |
| Anthony I. Jaccard | a****d@h****h | 5 |
| nberkouk | 4****k | 4 |
| Matthias Kemper | m****8@i****e | 2 |
| act-reds | a****s@h****h | 1 |
| Sydney Hauke | s****e@h****h | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 53
- Total pull requests: 56
- Average time to close issues: 3 months
- Average time to close pull requests: 30 days
- Total issue authors: 10
- Total pull request authors: 9
- Average comments per issue: 1.43
- Average comments per pull request: 1.86
- Merged pull requests: 37
- Bot issues: 0
- Bot pull requests: 3
Past Year
- Issues: 1
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 1
- Average comments per issue: 1.0
- Average comments per pull request: 1.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- raphaelreinauer (32)
- matteocao (12)
- AnthoJack (2)
- fbarfi (1)
- SauravMaheshkar (1)
- hkirvesl (1)
- WurmD (1)
- shamsvahid2 (1)
- nberkouk (1)
- sehunfromdaegu (1)
Pull Request Authors
- raphaelreinauer (31)
- matteocao (7)
- dependabot[bot] (5)
- nberkouk (4)
- AnthoJack (4)
- hkirvesl (3)
- giotto-learn (3)
- yorickbrunet (1)
- sydneyhauke (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 38 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 4
- Total maintainers: 1
pypi.org: giotto-deep
Toolbox for Deep Learning and Topological Data Analysis.
- Homepage: https://github.com/giotto-ai/giotto-deep
- Documentation: https://giotto-deep.readthedocs.io/
- License: GNU AGPLv3
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Latest release: 0.0.4
published about 2 years ago
Rankings
Maintainers (1)
Dependencies
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- matteocao/giotto-deep gdeep-lab build
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