Metadata-Version: 2.1
Name: dtoolai
Version: 0.2.0
Summary: Reproducible Deep Learning tools and examples
Home-page: http://github.com/JIC-CSB/dtoolai
Author: Matthew Hartley
Author-email: Matthew.Hartley@jic.ac.uk
License: MIT
Description: dtoolAI - reproducible deep learning
        ====================================
        
        .. image:: https://badge.fury.io/py/dtoolai.svg
           :target: https://badge.fury.io/py/dtoolai
           :alt: PyPi package
        
        dtoolAI is a library for supporting reproducible deep learning.
        
        
        Quick start
        -----------
        
        If you'd like to see what dtoolAI can do without installing anything, two of the
        Jupyter notebooks in this repository highlighting dtoolAI functions can be run
        through Google Colab without any local software installation:
        
        * `Training a character recognition network <https://colab.research.google.com/drive/1vqKmQFK2utX8Bn0LQ_6lx_xB56r3nnUA?usp=sharing>`_
        * `Retraining a network on new data <https://colab.research.google.com/drive/1vYS90QH7pW-9PLGXD9CKNXtqiTT6o3O1?usp=sharing>`_
        
        You'll need a Google account to run these, and when you load the notebooks,
        click "Open in playground" to be able to execute code.
        
        Installation
        ------------
        
        Dependencies
        ~~~~~~~~~~~~
        
        dtoolAI is dependent on the following Python packages:
        
        * pytorch
        * torchvision
        * dtoolcore
        * dtool-http
        * click
        * pillow
        
        If you install dtoolAI with ``pip`` or ``conda`` as described below, these
        dependencies will be installed automatically. If you wish to install manually,
        you'll need to install these before installing dtoolAI.
        
        For Windows users, we recommend installing pytorch and torchvision through
        anaconda/conda. See the section below for details.
        
        Through ``pip``
        ~~~~~~~~~~~~~~~
        
        dtoolAI requires Python version 3 and Pytorch.
        
        .. warning:: Install Pytorch before installing dtoolAI. For information on how to install Pytorch this see the
                     `Pytorch getting started guide <https://pytorch.org/get-started/locally/>`_
                     for details.
        
        Once Pytorch has been installed dtoolAI can be installed through pip:
        
        .. code-block:: bash
        
            pip install dtoolai
        
        Through ``conda``
        ~~~~~~~~~~~~~~~~~
        
        You can also install dtoolAI through conda. To optionally create a conda environment in which to install
        dtoolAI:
        
        .. code-block:: bash
        
            conda create -n dtoolai
            conda activate dtoolai
        
        Then you can install with:
        
        .. code-block:: bash
        
            conda install pytorch==1.4.0 torchvision==0.5.0 -c pytorch
            conda install dtoolcore dtool-http dtoolai -c dtool
        
        To install the dtool command line utilities, you'll need to use pip:
        
        .. code-block:: bash
            
            pip install dtool
            
        With ``setup.py``
        ~~~~~~~~~~~~~~~~~
        
        You can also download this repository and install through:
        
        .. code-block:: bash
        
            python setup.py install
        
        Documentation
        -------------
        
        Primary documentation: https://dtoolai.readthedocs.io/en/latest/
        
        Detailed examples of API use are provided in the notebooks/ directory in this
        repository.
        
        Tests
        -----
        
        Running the tests requires pytest.
        
        To run the faster tests in the test suite, use:
        
        .. code-block:: bash
        
            pytest tests/ -m "not slow"
        
        The test suite also includes full end-to-end tests that create datasets, train
        models and evaluate them on those datasets. These are much slower, to run them
        use:
        
        .. code-block:: bash
        
            pytest tests/
        
        
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