Metadata-Version: 2.1
Name: fastsom
Version: 0.1.9
Summary: A PyTorch and Fastai based implementation of Self-Organizing Maps
Home-page: https://github.com/kireygroup/fastsom
Author: Riccardo Sayn
Author-email: riccardo.sayn@kireygroup.com
License: MIT
Download-URL: https://github.com/kireygroup/fastsom/archive/v0.1.9.tar.gz
Description: # Fastsom
        
        A PyTorch and Fastai based implementation of Self-Organizing Maps.
        
        You can find documentation and examples [here](https://kireygroup.github.io/fastsom/).
        
        ## Contents
        
        - [Fastsom](#fastsom)
          - [Contents](#contents)
          - [Getting started](#getting-started)
            - [Install as a dependency](#install-as-a-dependency)
          - [Docker boilerplate](#docker-boilerplate)
            - [Prerequisites](#prerequisites)
            - [Building the image](#building-the-image)
            - [Running the container](#running-the-container)
            - [Developing inside the container](#developing-inside-the-container)
          - [Documentation setup](#documentation-setup)
            - [Documenting the code](#documenting-the-code)
            - [Building the docs](#building-the-docs)
            - [Deploying the docs on GH Pages](#deploying-the-docs-on-gh-pages)
        
        ## Getting started
        
        ### Install as a dependency
        
        To install Fastsom, you can use `pip` to install the [PyPi package](https://pypi.org/project/fastsom/):
        
        ```bash
        pip install fastsom
        ```
        
        or you can install directly from Github:
        
        ```bash
        pip install git+ssh://github.com/kireygroup/fastsom
        # or
        pip install git+https://github.com/kireygroup/fastsom
        ```
        
        Alternatively, you can clone the repository and then install as follows:
        
        ```bash
        git clone git@github.com:kireygroup/fastsom
        cd fastsom
        python setup.py install
        ```
        
        ## Docker boilerplate
        
        This project was bootstrapped with the [cookiecutter-dl-docker](https://github.com/rsayn/cookiecutter-dl-docker) template.
        
        ### Prerequisites
        
        To run examples for this project you can either use Docker / Nvidia-Docker or recreate the environment on your local machine by using the provided `requirements.txt`.
        
        Steps for Docker are described below.
        
        ### Building the image
        
        An utility script can be found in `bin/build.sh`:
        
        ```bash
        ./bin/build.sh
        ```
        
        ### Running the container
        
        A run script is available:
        
        ```bash
        ./bin/run.sh
        ```
        
        This will mount the directories `/fastsom` and `/nbs` inside the container, allowing for code changes to be automatically replicated.
        
        Note: if you plan on using Nvidia-Docker, you should use one of the images available on the Nvidia Container Repository.
        
        The container will start a new Jupyter Notebook server on port 8888. Jupyter Lab is also available.
        
        Note that the `fastsom` folder will be mounted inside the container, so any change you make to the source files or notebooks will be replicated on both host and container.
        
        ### Developing inside the container
        
        With Visual Studio Code and PyCharm, it is possible to use the container Python interpreter for development.
        
        An SSH server has been configured inside the container to allow connection via PyCharm's remote interpreter feature.
        
        In Visual Studio Code, this can be done via the [Remote - Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers).
        
        ## Documentation setup
        
        Fastsom's documentation is built with [Sphinx](https://www.sphinx-doc.org/) and deployed to Gihtub Pages via the [`gh-pages` branch](https://github.com/kireygroup/fastsom/tree/gh-pages).
        
        ### Documenting the code
        
        We use a Numpy docstring notation (check out [this link](http://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html) for more information about the various docstring styles).
        
        ### Building the docs
        
        To generate the static HTML documentation, use the following:
        
        ```bash
        cd docs
        make docs
        ```
        
        ### Deploying the docs on GH Pages
        
        Docs are automatically built from the master branch and pushed to the `gh-pages` branch on each version tag.
        
Keywords: self-organizing-map,fastai,pytorch,python
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
