.. 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.

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