Skip to main content

Descriptive deep learning

Project 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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kur-0.5.0.tar.gz (1.1 MB view details)

Uploaded Source

Built Distribution

kur-0.5.0-py3-none-any.whl (216.6 kB view details)

Uploaded Python 3

File details

Details for the file kur-0.5.0.tar.gz.

File metadata

  • Download URL: kur-0.5.0.tar.gz
  • Upload date:
  • Size: 1.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for kur-0.5.0.tar.gz
Algorithm Hash digest
SHA256 6231335f882a4b494325fa13dd48e0159ae0f2eddb2a684c25557f7dce6b3e9e
MD5 83a08d23561c9df7769260a903e2138b
BLAKE2b-256 1be4df566e17e43c195f960842c21949275c77ec3dd5d09ade17f252c4c13131

See more details on using hashes here.

File details

Details for the file kur-0.5.0-py3-none-any.whl.

File metadata

  • Download URL: kur-0.5.0-py3-none-any.whl
  • Upload date:
  • Size: 216.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for kur-0.5.0-py3-none-any.whl
Algorithm Hash digest
SHA256 c39e2aa607cd3a1aa32dfc9e50f58acf19fe3af13a70a952281a95cba8144af4
MD5 e8ce96e68600068c15ab6e66d5b763f1
BLAKE2b-256 43ee6da2182c3e49b89a80093fe0814d153125380772d09f1726bebea93d93ce

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page