Metadata-Version: 1.0
Name: kur
Version: 0.4.0
Summary: Descriptive deep learning
Home-page: https://github.com/deepgram/kur
Author: Adam Sypniewski
Author-email: adam@deepgram.com
License: Apache Software License (http://www.apache.org/licenses/LICENSE-2.0)
Description: .. _Tutorial: https://kur.deepgram.com/tutorial.html
        
        ******************************
        Kur: Descriptive Deep Learning
        ******************************
        
        Introduction
        ============
        
        Welcome to Kur! You've found the future of deep learning!
        
        - Install Kur easily with ``pip install kur``.
        - Design, train, and evaluate models *without ever needing to code*.
        - Describe your model with easily understandable concepts.
        - Quickly explore better versions of your model with the power of the `Jinja2
          <http://jinja.pocoo.org>`_ templating engine.
        - Supports Theano, TensorFlow, and PyTorch, and supports **multi-GPU**
          out-of-the-box.
        - **COMING SOON**: Share your models with the community, making it incredibly
          easy to collaborate on sophisticated models.
        
        Go ahead and give it a whirl: `Get the Code`_ and then jump into
        the `Examples`_! Then build your own model in our Tutorial_. Remember to check
        out our `homepage <https://kur.deepgram.com>`_ for complete documentation and
        the newest news.
        
        What is Kur?
        ------------
        
        Kur is a system for quickly building and applying state-of-the-art deep
        learning models to new and exciting problems. Kur was designed to appeal to the
        entire machine learning community, from novices to veterans. It uses
        specification files that are simple to read and author, meaning that you can
        get started building sophisticated models *without ever needing to code*. Even
        so, Kur exposes a friendly and extensible API to support advanced deep learning
        architectures or workflows. Excited? Jump straight into the `Examples`_.
        
        .. _get_the_code:
        
        Get the Code
        ============
        
        Kur is really easy to install! You can pick either one of these two options for
        installing Kur.
        
        **NOTE**: Kur requires **Python 3.4** or greater. Take a look at our
        `installation guide <https://kur.deepgram.com/installing.html>`_ for
        step-by-step instructions for installing Kur and setting up a `virtual
        environment <https://virtualenv.pypa.io/>`_.
        
        Latest Pip Release
        ------------------
        
        If you know what you are doing, then this is easy:
        
        .. code-block:: bash
        
        	pip install kur
        
        Latest Development Release
        --------------------------
        
        Just check it out and run the setup script:
        
        .. code-block:: bash
        
        	git clone https://github.com/deepgram/kur
        	cd kur
        	pip install .
        
        **Quick Start**: Or, if you already have `Python 3 installed
        <https://kur.deepgram.com/installing.html>`_, then here's a few quick-start
        lines to get you training your first model:
        
        **Quick Start For Using pip:**
        
        .. code-block:: bash
        
        	pip install virtualenv                      # Make sure virtualenv is present
        	virtualenv -p $(which python3) ~/kur-env    # Create a Python 3 environment for Kur
        	. ~/kur-env/bin/activate                    # Activate the Kur environment
        	pip install kur                             # Install Kur
        	kur --version                               # Check that everything works
        	git clone https://github.com/deepgram/kur   # Get the examples
        	cd kur/examples                             # Change directories
        	kur train mnist.yml                         # Start training!
        
        **Quick Start For Using git:**
        
        .. code-block:: bash
        
        	pip install virtualenv                      # Make sure virtualenv is present
        	virtualenv -p $(which python3) ~/kur-env    # Create a Python 3 environment for Kur
        	. ~/kur-env/bin/activate                    # Activate the Kur environment
        	git clone https://github.com/deepgram/kur   # Check out the latest code
        	cd kur                                      # Change directories
        	pip install .                               # Install Kur
        	kur --version                               # Check that everything works
        	cd examples                                 # Change directories
        	kur train mnist.yml                         # Start training!
        
        Usage
        -----
        
        If everything has gone well, you shoud be able to use Kur:
        
        .. code-block:: bash
        
        	kur --version
        
        You'll typically be using Kur in commands like ``kur train model.yml`` or ``kur
        test model.yml``. You'll see these in the `Examples`_, which is
        where you should head to next!
        
        Troubleshooting
        ---------------
        
        If you run into any problems installing or using Kur, please check out our
        `troubleshooting <https://kur.deepgram.com/troubleshooting.html>`_ page for
        lots of useful help. And if you want more detailed installation instructions,
        with help on setting up your environment, before sure to see our `installation
        <https://kur.deepgram.com/installing.html>`_ page.
Keywords: deep learning
Platform: UNKNOWN
