.. Licensed to the Apache Software Foundation (ASF) under one
   or more contributor license agreements.  See the NOTICE file
   distributed with this work for additional information
   regarding copyright ownership.  The ASF licenses this file
   to you under the Apache License, Version 2.0 (the
   "License"); you may not use this file except in compliance
   with the License.  You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing,
   software distributed under the License is distributed on an
   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
   KIND, either express or implied.  See the License for the
   specific language governing permissions and limitations
   under the License.

Python Tutorials
=====

Getting started
---------------

.. container:: cards

   .. card::
      :title: A 60-minute Gluon crash course
      :link: getting-started/crash-course/index.html

      A quick overview of the core concepts of MXNet using the Gluon API.

   .. card::
      :title: Moving from other frameworks
      :link: getting-started/to-mxnet/index.html

      Guides that ease your transition to MXNet from other framework.


Packages & Modules
------------------

.. container:: cards

   .. card::
      :title: Gluon
      :link: packages/gluon/index.html

      MXNet's imperative interface for Python. If you're new to MXNet, start here!

   .. card::
      :title: NP and NPX
      :link: packages/np/index.html

      This section contains the `mxnet.np` and `mxnet.npx` usage hints.

   .. card::
      :title: Autograd API
      :link: /api/python/docs/tutorials/packages/autograd/index.html

      How to use Automatic Differentiation with the Autograd API.



Performance
-----------
.. container:: cards

   .. card::
      :title: Improving Performance
      :link: performance/index.html

      How to get the best performance from MXNet.

   .. card::
      :title: Profiler
      :link: performance/backend/profiler.html

      How to profile MXNet models.

   .. card::
      :title: Compression: int8
      :link: performance/compression/int8.html

      How to use int8 in your model to boost training speed.

   .. card::
      :title: MKL-DNN
      :link: performance/backend/mkldnn/index.html

      How to get the most from your CPU by using Intel's MKL-DNN.

   .. card::
      :title: TVM
      :link: performance/backend/tvm.html

      How to use TVM to boost performance.


Deployment
----------
.. container:: cards

   .. card::
      :title: MXNet on EC2
      :link: deploy/run-on-aws/use_ec2.html

      How to deploy MXNet on an Amazon EC2 instance.

   .. card::
      :title: MXNet on SageMaker
      :link: deploy/run-on-aws/use_sagemaker.html

      How to run MXNet using Amazon SageMaker.

      ..
         PLACEHOLDER
         .. card::
            :title: Export
            :link: deploy/export/index.html

            How to export MXNet models.

         .. card::
            :title: C++
            :link: deploy/inference/cpp.html

            How to use MXNet models in a C++ environment.

         .. card::
            :title: Scala and Java
            :link: deploy/inference/scala.html

            How to use MXNet models in a Scala or Java environment.

         PLACEHOLDER
      ..


Customization
-------------
.. container:: cards

Coming Soon (CustomOps and Custom Operators)

Next steps
----------

- To learn more about using MXNet to implement various deep learning algorithms
  from scratch, we recommend the `Dive into Deep Learning
  <https://d2l.ai>`_ book.

- Check out the `API Reference docs <../api/index.html>`_.

.. raw:: html

   <style> h1 {display: none;} </style>
   <style>.localtoc { display: none; }</style>


.. toctree::
   :hidden:
   :maxdepth: 3

   getting-started/index
   packages/index
   performance/index
   deploy/index
   extend/index