From 6a4671b864ced129444542fbc36c7bb4ac7abc4f Mon Sep 17 00:00:00 2001 From: Aaron Markham Date: Fri, 26 Jan 2018 21:27:33 -0800 Subject: [PATCH] Tutorials index page update (#9535) * changed url references from dmlc to apache/incubator-mxnet * added selector to tutorials page; added fixed tutorials; fixed links * fixed a link * changed gluon intro * updated release version to 1.0.1 on the mainpage and re-arranged news tiles * removed 3 tutorials from gluon category; already in symbol * bigger button; changed category to module * refactored the selector, removed categories, removed 13 tutorials --- docs/_static/js/sidebar.js | 18 +- docs/_static/mxnet-theme/index.html | 23 ++- docs/_static/mxnet-theme/layout.html | 2 +- docs/_static/mxnet-theme/navbar.html | 8 +- docs/_static/mxnet.css | 2 +- docs/faq/index.md | 2 + docs/tutorials/index.md | 260 ++++++++++++++++++++++----- example/README.md | 4 +- 8 files changed, 245 insertions(+), 74 deletions(-) diff --git a/docs/_static/js/sidebar.js b/docs/_static/js/sidebar.js index 31e1450154af..1c27b7e0007b 100644 --- a/docs/_static/js/sidebar.js +++ b/docs/_static/js/sidebar.js @@ -17,7 +17,7 @@ function render_left_helper(toc) { $('.leftsidebar > .sphinxsidebarwrapper').append(lefttoc); addToggle('.leftsidebar'); - + $('.leftsidebar li a').click(function () { $('.leftsidebar li a').css('color', '#337ab7'); $(this).css('color', 'black'); @@ -77,18 +77,18 @@ function render_lefttoc() { /*Render contents inside page*/ function render_righttoc() { var url = window.location.href, indexTrailing = 'index.html'; - + var rightTocTitle = "Page Contents"; $("div.rightsidebar > div.sphinxsidebarwrapper > h3").children().remove(); $("div.rightsidebar > div.sphinxsidebarwrapper > h3").html(rightTocTitle); - + addToggle('.rightsidebar'); - + $('.rightsidebar li a').click(function () { $('.rightsidebar li a').css('color', '#337ab7'); $(this).css('color', 'black'); }); - + if (url.indexOf(indexTrailing) != -1 || isAPI) { $('.rightsidebar').hide(); } @@ -175,7 +175,7 @@ function keepExpand() { break; } } - + //Merge right toc into left toc for API pages since they are quite long if (isAPI) { var rootEntry = currentEntry; @@ -234,6 +234,10 @@ $(document).ready(function () { $('div.sphinxsidebar').hide(); $('.content').css('width', '100%'); } + if (url.indexOf('/tutorials/index.html') != -1) { + $('div.sphinxsidebar').hide(); + $('.content').css('width', '100%'); + } if (showRightToc) { $(window).scroll(function () { scroll_righttoc(); @@ -251,4 +255,4 @@ $(document).ready(function () { if ($('div.sphinxsidebar').css('visibility') == 'hidden') $('.content').css('width', '100%'); return; } -}); \ No newline at end of file +}); diff --git a/docs/_static/mxnet-theme/index.html b/docs/_static/mxnet-theme/index.html index f3f98f7bdce4..9dfb7d6268fa 100644 --- a/docs/_static/mxnet-theme/index.html +++ b/docs/_static/mxnet-theme/index.html @@ -17,27 +17,26 @@ -
-

Apache MXNet 1.0 Released

-

We're excited to announce the release of MXNet 1.0! Check out the release notes for latest updates.

- Learn More +

Apache MXNet 1.0.1 Released

+

We're excited to announce the release of MXNet 1.0.1! Check out the release notes for latest updates.

+ Learn More
-

Introducing - Gluon

-

We’re happy to introduce a new elegant, easy to use, imperative interface for MXNet. +

MXNet Model Server

+

Apache MXNet Model Server (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX).

- Learn More + Learn More
-

MXNet Joining Apache

-

We’re excited to announce that MXNet has been accepted to the Apache Incubator. -

- Learn More -
+

Introducing - Gluon

+

We’re happy to introduce a new elegant, easy to use, imperative interface for MXNet. +

+ Learn More +
diff --git a/docs/_static/mxnet-theme/layout.html b/docs/_static/mxnet-theme/layout.html index 08e305c02f6b..653f5d791611 100644 --- a/docs/_static/mxnet-theme/layout.html +++ b/docs/_static/mxnet-theme/layout.html @@ -148,7 +148,7 @@ - +
{%- include "navbar.html" %} diff --git a/docs/_static/mxnet-theme/navbar.html b/docs/_static/mxnet-theme/navbar.html index e5619f17fcaa..bcf87f1ca7f4 100644 --- a/docs/_static/mxnet-theme/navbar.html +++ b/docs/_static/mxnet-theme/navbar.html @@ -6,7 +6,6 @@

diff --git a/docs/_static/mxnet.css b/docs/_static/mxnet.css index 20db74bbd80f..6e116dbb745b 100644 --- a/docs/_static/mxnet.css +++ b/docs/_static/mxnet.css @@ -654,7 +654,7 @@ li.dropdown-submenu ul.dropdown-menu a { } .section-tout .container { - height: 200px; + height: 220px; } .section-tout .row { diff --git a/docs/faq/index.md b/docs/faq/index.md index 60b75ec8b05c..099cd509b14a 100644 --- a/docs/faq/index.md +++ b/docs/faq/index.md @@ -56,6 +56,8 @@ and full working examples, visit the [tutorials section](../tutorials/index.md). * [How do I create new operators in MXNet?](http://mxnet.io/faq/new_op.html) +* [How do I contribute an example or tutorial?](https://github.com/apache/incubator-mxnet/tree/master/example#contributing) + * [How do I set MXNet's environmental variables?](http://mxnet.io/faq/env_var.html) ## Questions about Using MXNet diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index d691ecc427f8..aca091c41c3f 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -1,84 +1,250 @@ # Tutorials -## Gluon +MXNet has two primary high-level interfaces for its deep learning engine: the Gluon API and the Module API. Tutorials for each are provided below. -Gluon is the high-level interface for MXNet. It is more intuitive and easier to use than the lower level interface. -Gluon supports dynamic (define-by-run) graphs with JIT-compilation to achieve both flexibility and efficiency. +The difference between the two is an imperative versus symbolic programming style. Gluon makes it easy to prototype, build, and train deep learning models without sacrificing training speed by enabling both (1) intuitive imperative Python code development and (2) faster execution by automatically generating a symbolic execution graph using the hybridization feature. -This is a selected subset of Gluon tutorials that explain basic usage of Gluon and fundamental concepts in deep learning. For a comprehensive tutorial on Gluon that covers topics from basic statistics and probability theory to reinforcement learning and recommender systems, please see [gluon.mxnet.io](http://gluon.mxnet.io). +`TL;DR:` If you are new to deep learning or MXNet, you should start with the Gluon tutorials. -### Basics +The Gluon and Module tutorials are in Python, but you can also find a variety of other MXNet tutorials, such as R, Scala, and C++ in the [Other Languages API Tutorials](#other-mxnet-api-tutorials) section below. + +[Example scripts and applications](#example-scripts-and-applications) as well as [contribution](#contributing-tutorials) info is below. + + + + +## Python API Tutorials + + +
+ + +
+ + + +
+
+ + +
+
+ + + +
+
+ + + +
+
+ + + + + + +
+ +
+
+ + +
- [Manipulate data the MXNet way with ndarray](http://gluon.mxnet.io/chapter01_crashcourse/ndarray.html) + - [Automatic differentiation with autograd](http://gluon.mxnet.io/chapter01_crashcourse/autograd.html) + - [Linear regression with gluon](http://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html) + - [Serialization - saving, loading and checkpointing](http://gluon.mxnet.io/chapter03_deep-neural-networks/serialization.html) -### Neural Networks +
+ + +
- [Multilayer perceptrons in gluon](http://gluon.mxnet.io/chapter03_deep-neural-networks/mlp-gluon.html) -- [Convolutional Neural Networks in gluon](http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html) -- [Recurrent Neural Networks with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html) -### Advanced +- [Multi-class object detection using CNNs in gluon](http://gluon.mxnet.io/chapter04_convolutional-neural-networks/cnn-gluon.html) + +- [Advanced RNNs with gluon](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/rnns-gluon.html) + +
+ + +
- [Plumbing: A look under the hood of gluon](http://gluon.mxnet.io/chapter03_deep-neural-networks/plumbing.html) + - [Designing a custom layer with gluon](http://gluon.mxnet.io/chapter03_deep-neural-networks/custom-layer.html) + - [Fast, portable neural networks with Gluon HybridBlocks](http://gluon.mxnet.io/chapter07_distributed-learning/hybridize.html) + - [Training on multiple GPUs with gluon](http://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html) -## MXNet +
+ +
+ + +
+ +- [Creating custom operators with numpy](http://mxnet.incubator.apache.org/tutorials/gluon/customop.html) + +- [Handwritten digit recognition (MNIST)](http://mxnet.incubator.apache.org/tutorials/gluon/mnist.html) + +- [Hybrid network example](http://mxnet.incubator.apache.org/tutorials/gluon/hybrid.html) + +- [Neural network building blocks with gluon](http://mxnet.incubator.apache.org/tutorials/gluon/gluon.html) + +- [Simple autograd example](http://mxnet.incubator.apache.org/tutorials/gluon/autograd.html) + +
+ +
+ + +
+ + +
+ + +
+ +- [Imperative tensor operations on CPU/GPU](http://mxnet.incubator.apache.org/tutorials/basic/ndarray.html) + +- [NDArray Indexing](http://mxnet.incubator.apache.org/tutorials/basic/ndarray_indexing.html) + +- [Symbol API](http://mxnet.incubator.apache.org/tutorials/basic/symbol.html) + +- [Module API](http://mxnet.incubator.apache.org/tutorials/basic/module.html) + +- [Iterators - Loading data](http://mxnet.incubator.apache.org/tutorials/basic/data.html) + +
+ + +
+ +- [Linear regression](http://mxnet.incubator.apache.org/tutorials/python/linear-regression.html) + +- [MNIST - handwriting recognition](http://mxnet.incubator.apache.org/tutorials/python/mnist.html) + +- [Large scale image classification](http://mxnet.incubator.apache.org/tutorials/vision/large_scale_classification.html) + + +
+ + +
+ +- [NDArray in Compressed Sparse Row Storage Format](http://mxnet.incubator.apache.org/tutorials/sparse/csr.html) + +- [Sparse Gradient Updates](http://mxnet.incubator.apache.org/tutorials/sparse/row_sparse.html) + +- [Train a Linear Regression Model with Sparse Symbols](http://mxnet.incubator.apache.org/tutorials/sparse/train.html) + +
+ +
+ + +
+ +- [Connectionist Temporal Classification](http://mxnet.incubator.apache.org/tutorials/speech_recognition/ctc.html) + +- [Distributed key-value store](http://mxnet.incubator.apache.org/tutorials/python/kvstore.html) + +- [Fine-tuning a pre-trained ImageNet model with a new dataset](http://mxnet.incubator.apache.org/faq/finetune.html) + +- [Generative Adversarial Networks](http://mxnet.incubator.apache.org/tutorials/unsupervised_learning/gan.html) + +- [Matrix factorization in recommender systems](http://mxnet.incubator.apache.org/tutorials/python/matrix_factorization.html) + +- [Text classification (NLP) on Movie Reviews](http://mxnet.incubator.apache.org/tutorials/nlp/cnn.html) + +
+ +
+ + +
+ +## Other Languages API Tutorials + + +
+ + + +
+
+ +
+ +- [MNIST with the MXNet C++ API](http://mxnet.incubator.apache.org/tutorials/c%2B%2B/basics.html) +
+ + +
-These tutorials introduce a few fundamental concepts in deep learning and how to implement them in _MXNet_. The _Basics_ section contains tutorials on manipulating arrays, building networks, loading/preprocessing data, etc. The _Training and Inference_ section talks about implementing Linear Regression, training a Handwritten digit classifier using MLP and CNN, running inferences using a pre-trained model, and lastly, efficiently training a large scale image classifier. +- [NDArray: Vectorized Tensor Computations on CPUs and GPUs with R](http://mxnet.incubator.apache.org/tutorials/r/ndarray.html) +- [Symbol API with R](http://mxnet.incubator.apache.org/tutorials/r/symbol.html) +- [Custom Iterator](http://mxnet.incubator.apache.org/tutorials/r/CustomIterator.html) +- [Callback Function](http://mxnet.incubator.apache.org/tutorials/r/CallbackFunction.html) +- [Five minute neural network](http://mxnet.incubator.apache.org/tutorials/r/fiveMinutesNeuralNetwork.html) +- [MNIST with R](http://mxnet.incubator.apache.org/tutorials/r/mnistCompetition.html) +- [Classify images via R with a pre-trained model](http://mxnet.incubator.apache.org/tutorials/r/classifyRealImageWithPretrainedModel.html) +- [Char RNN Example with R](http://mxnet.incubator.apache.org/tutorials/r/charRnnModel.html) +- [Custom loss functions in R](http://mxnet.incubator.apache.org/tutorials/r/CustomLossFunction.html) -### Basics -```eval_rst -.. toctree:: - :maxdepth: 1 +
- basic/ndarray - basic/ndarray_indexing - basic/symbol - basic/module - basic/data -``` -### Training and Inference +
-```eval_rst -.. toctree:: - :maxdepth: 1 +- [Setup your MXNet with Scala on InelliJ](http://mxnet.incubator.apache.org/tutorials/scala/mxnet_scala_on_intellij.html) +- [MNIST with the Scala API](http://mxnet.incubator.apache.org/tutorials/scala/mnist.html) +- [Use Scala to build a Long Short-Term Memory network that generates Barack Obama's speech patterns](http://mxnet.incubator.apache.org/tutorials/scala/char_lstm.html) +
- python/linear-regression - python/mnist - python/predict_image - vision/large_scale_classification -``` +
-### Sparse NDArray -```eval_rst -.. toctree:: - :maxdepth: 1 +## Example Scripts and Applications - sparse/csr - sparse/row_sparse - sparse/train -``` +More tutorials and examples are available in the [GitHub repository](https://github.com/apache/incubator-mxnet/tree/master/example). -### Advanced Neural networks -```eval_rst -.. toctree:: - :maxdepth: 1 +## Learn More About Gluon! - unsupervised_learning/gan -``` +Most of the Gluon tutorials are hosted on [gluon.mxnet.io](http://gluon.mxnet.io), and you may want to follow the chapters on directly the Gluon site. -
-More tutorials and examples are available in the GitHub [repository](https://github.com/dmlc/mxnet/tree/master/example). ## Contributing Tutorials -Want to contribute an MXNet tutorial? To get started, download the [tutorial template](https://github.com/dmlc/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb). +Want to contribute an MXNet tutorial? To get started, [review these details](https://github.com/apache/incubator-mxnet/tree/master/example#contributing) on example and tutorial writing. diff --git a/example/README.md b/example/README.md index d6bb464183e1..49484a09e97e 100644 --- a/example/README.md +++ b/example/README.md @@ -65,7 +65,7 @@ The site expects the format to be markdown, so export your notebook as a .md via * [CTC with MXNet](ctc) - a modification of warpctc * [Deep Embedded Clustering](deep-embedded-clustering) - unsupervised deep embedding for clustering analysis * [Dense-Sparse-Dense Training](dsd) - Dense-Sparse-Dense Training for deep neural networks -* [Fully Convolutional Networks](fcn) - fully convolutional networks for semantic segmentation +* [Fully Convolutional Networks](fcn-xs) - fully convolutional networks for semantic segmentation * [Generative Adversarial Networks with R](gan/CGAN_mnist_R) - GAN examples in R * [Gluon Examples](gluon) - several examples using the Gluon API * [Style Transfer](gluon/style_transfer) - a style transfer example using gluon @@ -78,7 +78,7 @@ The site expects the format to be markdown, so export your notebook as a .md via * [Model Parallelism with LSTM](model-parallel/lstm) - an example showing how to do model parallelism with a LSTM * [Model Parallelism with Matrix Factorization](model-parallel/lstm) - a matrix factorization algorithm for recommendations * [Module API](module) - examples with the Python Module API -* [Multi-task Learning] - how to use MXNet for multi-task learning +* [Multi-task Learning](multi-task) - how to use MXNet for multi-task learning * [MXNet Adversarial Variational Autoencoder](mxnet_adversarial_vae) - combines a variational autoencoder with a generative adversarial network * [Noise-contrastive estimation loss](nce-loss) - used to speedup multi-class classification * [Neural Style](neural-style) - use deep learning for style transfer in images