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https://github.com/giotto-ai/giotto-deep

Deep learning made topological.
https://github.com/giotto-ai/giotto-deep

computational-geometry deep-learning image-processing nlp pytorch tda

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Deep learning made topological.

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# 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](http://localhost:6006/) 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](

    https://giotto-ai.github.io/gtda-docs/latest/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:[email protected]/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.