docs/python_docs/python/tutorials/performance/index.rst

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Performance
===========
The following tutorials will help you learn how to tune MXNet or use tools that will improve training and inference performance.

Essential
---------

.. container:: cards

   .. card::
      :title: Improving Performance
      :link: /api/faq/perf

      How to get the best performance from MXNet.

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

      How to profile MXNet models.


Compression
-----------

.. container:: cards

   .. card::
      :title: Compression: float16
      :link: /api/faq/float16

      How to use float16 in your model to boost training speed.

   .. card::
      :title: Gradient Compression
      :link: /api/faq/gradient_compression

      How to use gradient compression to reduce communication bandwidth and increase speed.
   ..
      .. card::
         :title: Compression: int8
         :link: compression/int8.html

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


Accelerated Backend
-------------------

.. container:: cards

   .. card::
      :title: TensorRT
      :link: backend/tensorrt/index.html

      How to use NVIDIA's TensorRT to boost inference performance.

   ..
      TBD Content
      .. card::
         :title: oneDNN
         :link: backend/dnnl/dnnl_readme

         How to get the most from your CPU by using oneDNN.

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

         How to use TVM to boost performance.
   ..


Distributed Training
--------------------

.. container:: cards

   .. card::
      :title: Distributed Training Using the KVStore API
      :link: /api/faq/distributed_training.html

      How to use the KVStore API to use multiple GPUs when training a model.

   .. card::
      :title: Training with Multiple GPUs Using Model Parallelism
      :link: /api/faq/model_parallel_lstm.html

      An overview of using multiple GPUs when training an LSTM.

   .. card::
      :title: Distributed training in MXNet
      :link: /api/faq/distributed_training

      An overview of distributed training strategies.

   .. card::
      :title: MXNet with Horovod
      :link: https://github.com/apache/mxnet/tree/master/example/distributed_training-horovod

      A set of example scripts demonstrating MNIST and ImageNet training with Horovod as the distributed training backend.

.. toctree::
   :hidden:
   :maxdepth: 1

   compression/index
   backend/index