apache · ThomasDelteil · Sep 27, 2019 · Sep 26, 2019 · Sep 26, 2019 · Sep 26, 2019
@@ -14,7 +14,7 @@ Please feel free to remove inapplicable items for your PR.
 - For user-facing API changes, API doc string has been updated. 
 - For new C++ functions in header files, their functionalities and arguments are documented. 
 - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
-- Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
+- Check the API doc at https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
 - [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
 
 ### Changes ###

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
@@ -15,4 +15,4 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-File is moved to [docs/tutorials/mkldnn/MKLDNN_README.md](docs/tutorials/mkldnn/MKLDNN_README.md).
+File is moved to [docs/tutorials/mkldnn/MKLDNN_README.md](docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md).
diff --git a/NEWS.md b/NEWS.md
@@ -1213,7 +1213,7 @@ MKLDNN backend takes advantage of MXNet subgraph to implement the most of possib
 ##### Quantization
 Performance of reduced-precision (INT8) computation is also dramatically improved after the graph optimization feature is applied on CPU Platforms. Various models are supported and can benefit from reduced-precision computation, including symbolic models, Gluon models and even custom models. Users can run most of the pre-trained models with only a few lines of commands and a new quantization script imagenet_gen_qsym_mkldnn.py. The observed accuracy loss is less than 0.5% for popular CNN networks, like ResNet-50, Inception-BN, MobileNet, etc.
 
-Please find detailed information and performance/accuracy numbers here: [MKLDNN README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md), [quantization README](https://github.com/apache/incubator-mxnet/tree/master/example/quantization#1) and [design proposal](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN)
+Please find detailed information and performance/accuracy numbers here: [MKLDNN README](https://mxnet.incubator.apache.org/api/python/docs/tutorials/performance/backend/mkldnn/mkldnn_readme.html), [quantization README](https://github.com/apache/incubator-mxnet/tree/master/example/quantization#1) and [design proposal](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN)
 
 ### New Operators
 
@@ -1756,7 +1756,7 @@ For more information and examples, see [full release notes](https://cwiki.apache
 
 ### New Features - Clojure package (experimental)
 - MXNet now supports the Clojure programming language. The MXNet Clojure package brings flexible and efficient GPU computing and state-of-art deep learning to Clojure. It enables you to write seamless tensor/matrix computation with multiple GPUs in Clojure. It also lets you construct and customize the state-of-art deep learning models in Clojure, and apply them to tasks, such as image classification and data science challenges.([#11205](https://github.com/apache/incubator-mxnet/pull/11205))
-- Checkout examples and API documentation [here](http://mxnet.incubator.apache.org/api/clojure/index.html).
+- Checkout examples and API documentation [here](https://mxnet.incubator.apache.org/api/clojure/index.html).
 
 ### New Features - Synchronized Cross-GPU Batch Norm (experimental)
 - Gluon now supports Synchronized Batch Normalization (#11502).
@@ -1786,8 +1786,8 @@ For more information and examples, see [full release notes](https://cwiki.apache
 - Set environment variable `MXNET_KVSTORE_USETREE=1` to enable.
 
 ### New Features - Export MXNet models to ONNX format (experimental)
-- With this feature, now MXNet models can be exported to ONNX format([#11213](https://github.com/apache/incubator-mxnet/pull/11213)). Currently, MXNet supports ONNX v1.2.1. [API documentation](http://mxnet.incubator.apache.org/api/python/contrib/onnx.html).
-- Checkout this [tutorial](http://mxnet.incubator.apache.org/tutorials/onnx/export_mxnet_to_onnx.html) which shows how to use MXNet to ONNX exporter APIs. ONNX protobuf so that those models can be imported in other frameworks for inference.
+- With this feature, now MXNet models can be exported to ONNX format([#11213](https://github.com/apache/incubator-mxnet/pull/11213)). Currently, MXNet supports ONNX v1.2.1. [API documentation](https://mxnet.incubator.apache.org/api/python/contrib/onnx.html).
+- Checkout this [tutorial](https://mxnet.incubator.apache.org/tutorials/onnx/export_mxnet_to_onnx.html) which shows how to use MXNet to ONNX exporter APIs. ONNX protobuf so that those models can be imported in other frameworks for inference.
 
 ### New Features - TensorRT Runtime Integration (experimental)
 - [TensorRT](https://developer.nvidia.com/tensorrt) provides significant acceleration of model inference on NVIDIA GPUs compared to running the full graph in MxNet using unfused GPU operators. In addition to faster fp32 inference, TensorRT optimizes fp16 inference, and is capable of int8 inference (provided the quantization steps are performed). Besides increasing throughput, TensorRT significantly reduces inference latency, especially for small batches.

@@ -54,7 +54,7 @@ NULL
 
   tips <- c(
     "Need help? Feel free to open an issue on https://github.com/dmlc/mxnet/issues",
-    "For more documents, please visit http://mxnet.io",
+    "For more documents, please visit https://mxnet.io",
     "Use suppressPackageStartupMessages() to eliminate package startup messages."
   )
 

@@ -24,7 +24,7 @@ options(repos = cran)
 install.packages("mxnet")
 ```
 
-To use the GPU version or to use it on Linux, please follow [Installation Guide](http://mxnet.io/install/index.html)
+To use the GPU version or to use it on Linux, please follow [Installation Guide](https://mxnet.io/install/index.html)
 
 License
 -------

diff --git a/README.md b/README.md
@@ -68,7 +68,7 @@ What's New
 * [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0)
 * [Updated Image Classification with new Pre-trained Models](./example/image-classification)
 * [Notebooks How to Use MXNet](https://github.com/d2l-ai/d2l-en)
-* [MKLDNN for Faster CPU Performance](./docs/tutorials/mkldnn/MKLDNN_README.md)
+* [MKLDNN for Faster CPU Performance](docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md)
 * [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost](https://github.com/dmlc/mxnet-memonger)
 * [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial)
 * [MXNet.js: Javascript Package for Deep Learning in Browser (without server)](https://github.com/dmlc/mxnet.js/)

@@ -21,7 +21,7 @@
 # the whole docker cache for the image
 
 # Important Maintenance Instructions:
-#  Align changes with installation instructions in /docs/install/ubuntu_setup.md
+#  Align changes with installation instructions in /get_started/ubuntu_setup.md
 #  Align with R install script: /docs/install/install_mxnet_ubuntu_r.sh
 
 set -ex

@@ -29,4 +29,4 @@
 set -ex
 
 aws s3 sync --delete . s3://mxnet-ci-doc/$1/$2 \
-    && echo "Doc is hosted at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/$1/$2/index.html"
+    && echo "Doc is hosted at https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/$1/$2/index.html"
@@ -189,8 +189,8 @@ If you have previous builds and other unwanted files lying around in the working
 
 Detailed instructions for building MXNet core from source can be found [in the MXNet installation documentation](https://mxnet.incubator.apache.org/install/index.html). The relevant sections are:
 
-- For Ubuntu Linux: [CUDA Dependencies](https://mxnet.incubator.apache.org/install/ubuntu_setup.html#cuda-dependencies) and [Building MXNet from Source](https://mxnet.incubator.apache.org/install/ubuntu_setup.html#build-mxnet-from-source)
-- For Mac OSX: [Build the Shared Library](https://mxnet.incubator.apache.org/install/osx_setup.html#build-the-shared-library)
+- For Ubuntu Linux: [CUDA Dependencies](https://mxnet.incubator.apache.org/get_started/ubuntu_setup#cuda-dependencies) and [Building MXNet from Source](https://mxnet.incubator.apache.org/get_started/ubuntu_setup#build-mxnet-from-source)
+- For Mac OSX: [Build the Shared Library](https://mxnet.incubator.apache.org/get_started/osx_setup.html#build-the-shared-library)
 
 In particular, ignore all of the language-interface-specific sections.
 

@@ -17,8 +17,8 @@
 
 # Building and Updating MXNet Documentation
 
-The website is hosted at http://mxnet.incubator.apache.org/.
-http://mxnet.io redirects to this site and advised to use links with http://mxnet.incubator.apache.org/ instead of http://mxnet.io/.
+The website is hosted at https://mxnet.incubator.apache.org/.
+https://mxnet.io redirects to this site and advised to use links with https://mxnet.incubator.apache.org/ instead of https://mxnet.io/.
 
 ## Website & Documentation Contributions
 
@@ -36,7 +36,7 @@ If you plan to contribute changes to the documentation or website, please submit
 
 ## Python Docs
 
-MXNet's Python documentation is built with [Sphinx](http://www.sphinx-doc.org) and a variety of plugins including [pandoc](https://pandoc.org/), and [recommonmark](https://github.com/rtfd/recommonmark).
+MXNet's Python documentation is built with [Sphinx](https://www.sphinx-doc.org) and a variety of plugins including [pandoc](https://pandoc.org/), and [recommonmark](https://github.com/rtfd/recommonmark).
 
 More information on the dependencies can be found in the [CI folder's installation scripts](https://github.com/apache/incubator-mxnet/tree/master/ci/docker/install/ubuntu_docs.sh).
 

@@ -31,9 +31,9 @@
 # General information about the project.
 project = u'Apache MXNet'
 author = u'%s developers' % project
-copyright = u'2015-2018, %s' % author
+copyright = u'2015-2019, %s' % author
 github_doc_root = 'https://github.com/apache/incubator-mxnet/tree/master/docs/'
-doc_root = 'http://mxnet.io/'
+doc_root = 'https://mxnet.incubator.apache.org/'
 
 # add markdown parser
 source_parsers = {

@@ -26,9 +26,16 @@ but you also have the option to export most models to the ONNX format.
 
    .. card::
       :title: Export ONNX Models
+      :link: onnx.html
 
+      Export your MXNet model to the Open Neural Exchange Format
+
+   .. card::
+      :title: Save / Load Parameters
+      :link: ../../packages/gluon/blocks/save_load_params.html
+
+      Save and Load your model parameters with MXnet
 
-      Coming Soon!
 
    .. card::
       :title: Export with GluonCV
@@ -39,4 +46,8 @@ but you also have the option to export most models to the ONNX format.
 .. toctree::
    :hidden:
    :maxdepth: 1
+   :glob:
 
+   *
+   Export Gluon CV Models <https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html>
+   Save / Load Parameters <https://mxnet.incubator.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html>
@@ -0,0 +1,150 @@
+<!--- 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. -->
+
+# Exporting to ONNX format
+
+[Open Neural Network Exchange (ONNX)](https://github.com/onnx/onnx) provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
+
+In this tutorial, we will show how you can save MXNet models to the ONNX format.
+
+MXNet-ONNX operators coverage and features are updated regularly. Visit the [ONNX operator coverage](https://cwiki.apache.org/confluence/display/MXNET/ONNX+Operator+Coverage) page for the latest information.
+
+In this tutorial, we will learn how to use MXNet to ONNX exporter on pre-trained models.
+
+## Prerequisites
+
+To run the tutorial you will need to have installed the following python modules:
+- [MXNet >= 1.3.0](http://mxnet.incubator.apache.org/install/index.html)
+- [onnx]( https://github.com/onnx/onnx#installation) v1.2.1 (follow the install guide)
+
+*Note:* MXNet-ONNX importer and exporter follows version 7 of ONNX operator set which comes with ONNX v1.2.1.
+
+
+```python
+import mxnet as mx
+import numpy as np
+from mxnet.contrib import onnx as onnx_mxnet
+import logging
+logging.basicConfig(level=logging.INFO)
+```
+
+## Downloading a model from the MXNet model zoo
+
+We download the pre-trained ResNet-18 [ImageNet](http://www.image-net.org/) model from the [MXNet Model Zoo](http://data.mxnet.io/models/imagenet/).
+We will also download synset file to match labels.
+
+```python
+# Download pre-trained resnet model - json and params by running following code.
+path='http://data.mxnet.io/models/imagenet/'
+[mx.test_utils.download(path+'resnet/18-layers/resnet-18-0000.params'),
+ mx.test_utils.download(path+'resnet/18-layers/resnet-18-symbol.json'),
+ mx.test_utils.download(path+'synset.txt')]
+```
+
+Now, we have downloaded ResNet-18 symbol, params and synset file on the disk.
+
+## MXNet to ONNX exporter API
+
+Let us describe the MXNet's `export_model` API. 
+
+```python
+help(onnx_mxnet.export_model)
+```
+
+Output:
+
+```text
+Help on function export_model in module mxnet.contrib.onnx.mx2onnx.export_model:
+
+export_model(sym, params, input_shape, input_type=<type 'numpy.float32'>, onnx_file_path=u'model.onnx', verbose=False)
+    Exports the MXNet model file, passed as a parameter, into ONNX model.
+    Accepts both symbol,parameter objects as well as json and params filepaths as input.
+    Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/MXNet-ONNX+Integration
+
+    Parameters
+    ----------
+    sym : str or symbol object
+        Path to the json file or Symbol object
+    params : str or symbol object
+        Path to the params file or params dictionary. (Including both arg_params and aux_params)
+    input_shape : List of tuple
+        Input shape of the model e.g [(1,3,224,224)]
+    input_type : data type
+        Input data type e.g. np.float32
+    onnx_file_path : str
+        Path where to save the generated onnx file
+    verbose : Boolean
+        If true will print logs of the model conversion
+
+    Returns
+    -------
+    onnx_file_path : str
+        Onnx file path
+```
+
+`export_model` API can accept the MXNet model in one of the following two ways.
+
+1. MXNet sym, params objects:
+    * This is useful if we are training a model. At the end of training, we just need to invoke the `export_model` function and provide sym and params objects as inputs with other attributes to save the model in ONNX format.
+2. MXNet's exported json and params files:
+    * This is useful if we have pre-trained models and we want to convert them to ONNX format.
+
+Since we have downloaded pre-trained model files, we will use the `export_model` API by passing the path for symbol and params files.
+
+## How to use MXNet to ONNX exporter API
+
+We will use the downloaded pre-trained model files (sym, params) and define input variables.
+
+```python
+# Downloaded input symbol and params files
+sym = './resnet-18-symbol.json'
+params = './resnet-18-0000.params'
+
+# Standard Imagenet input - 3 channels, 224*224
+input_shape = (1,3,224,224)
+
+# Path of the output file
+onnx_file = './mxnet_exported_resnet50.onnx'
+```
+
+We have defined the input parameters required for the `export_model` API. Now, we are ready to covert the MXNet model into ONNX format.
+
+```python
+# Invoke export model API. It returns path of the converted onnx model
+converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32, onnx_file)
+```
+
+This API returns path of the converted model which you can later use to import the model into other frameworks.
+
+## Check validity of ONNX model
+
+Now we can check validity of the converted ONNX model by using ONNX checker tool. The tool will validate the model by checking if the content contains valid protobuf:
+
+```python
+from onnx import checker
+import onnx
+
+# Load onnx model
+model_proto = onnx.load_model(converted_model_path)
+
+# Check if converted ONNX protobuf is valid
+checker.check_graph(model_proto.graph)
+```
+
+If the converted protobuf format doesn't qualify to ONNX proto specifications, the checker will throw errors, but in this case it successfully passes. 
+
+This method confirms exported model protobuf is valid. Now, the model is ready to be imported in other frameworks for inference!
@@ -112,7 +112,7 @@ Security
 
 .. toctree::
    :hidden:
-   :maxdepth: 0
+   :maxdepth: 1
 
    export/index
    inference/index

@@ -33,25 +33,35 @@ The following tutorials will help you learn how to deploy MXNet models for infer
       :link: https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html
 
       How to use quantized GluonCV models for inference on Intel Xeon Processors to gain higher performance.
-   ..
-      PLACEHOLDER
 
-      .. card::
-         :title: Scala and Java
-         :link: scala.html
 
-         How to use MXNet models in a Scala or Java environment.
+The following tutorials will help you learn how to deploy MXNet models for inference applications.
+
+.. container:: cards
+
+   .. card::
+      :title: Scala and Java
+      :link: scala.html
+
+      How to use MXNet models in a Scala or Java environment.
+
+   .. card::
+      :title: C++
+      :link: cpp.html
+
+      How to use MXNet models in a C++ environment.
 
-      .. card::
-         :title: C++
-         :link: cpp.html
 
-         How to use MXNet models in a C++ environment.
-      PLACEHOLDER
-   ..
+   .. card::
+      :title: Raspberry Pi
+      :link: wine_detector.html
+
+      Example of running a wine detector on a raspberry pi.
+
 
 .. toctree::
    :hidden:
-   :maxdepth: 0
-
+   :maxdepth: 1
    :glob:
+
+   *