From ab2214beeb855c768f6b2deec5c144630f5f65e7 Mon Sep 17 00:00:00 2001 From: Aaron Markham Date: Wed, 25 Sep 2019 13:41:18 +0100 Subject: [PATCH] fix broken links (#16255) --- README.md | 27 +++++++++---------- .../_includes/get_started/get_started.html | 8 +++--- .../src/_includes/get_started/pip_snippet.md | 2 +- docs/static_site/src/pages/api/cpp/index.md | 4 +-- .../src/pages/get_started/index.html | 2 +- .../src/pages/get_started/windows_setup.md | 2 +- 6 files changed, 21 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index 04b7bcf9e7fa..8db55af4ff87 100644 --- a/README.md +++ b/README.md @@ -28,14 +28,14 @@ Apache MXNet (incubating) for Deep Learning ![banner](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/banner.png) Apache MXNet (incubating) is a deep learning framework designed for both *efficiency* and *flexibility*. -It allows you to ***mix*** [symbolic and imperative programming](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts) +It allows you to ***mix*** [symbolic and imperative programming](https://mxnet.incubator.apache.org/api/architecture/program_model) to ***maximize*** efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. MXNet is more than a deep learning project. It is a collection of -[blue prints and guidelines](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts) for building +[blue prints and guidelines](https://mxnet.incubator.apache.org/api/architecture/overview) for building deep learning systems, and interesting insights of DL systems for hackers. Ask Questions @@ -71,24 +71,21 @@ What's New * [MKLDNN for Faster CPU Performance](./docs/tutorials/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) -* [Embedding Torch layers and functions in MXNet](https://mxnet.incubator.apache.org/faq/torch.html) -* [MXNet.js: Javascript Package for Deep Learning in Browser (without server) -](https://github.com/dmlc/mxnet.js/) -* [Design Note: Design Efficient Deep Learning Data Loading Module](https://mxnet.incubator.apache.org/architecture/note_data_loading.html) -* [MXNet on Mobile Device](https://mxnet.incubator.apache.org/faq/smart_device.html) -* [Distributed Training](https://mxnet.incubator.apache.org/faq/multi_devices.html) -* [Guide to Creating New Operators (Layers)](https://mxnet.incubator.apache.org/faq/new_op.html) +* [MXNet.js: Javascript Package for Deep Learning in Browser (without server)](https://github.com/dmlc/mxnet.js/) +* [Guide to Creating New Operators (Layers)](https://mxnet.incubator.apache.org/api/faq/new_op) * [Go binding for inference](https://github.com/songtianyi/go-mxnet-predictor) * [Amalgamation and Go Binding for Predictors](https://github.com/jdeng/gomxnet/) - Outdated * [Large Scale Image Classification](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification) Contents -------- -* [Documentation](https://mxnet.incubator.apache.org/) and [Tutorials](https://mxnet.incubator.apache.org/tutorials/) -* [Design Notes](https://mxnet.incubator.apache.org/architecture/index.html) +* [Website](https://mxnet.incubator.apache.org) +* [Documentation](https://mxnet.incubator.apache.org/api) +* [Blog](https://mxnet.incubator.apache.org/blog) * [Code Examples](https://github.com/apache/incubator-mxnet/tree/master/example) -* [Installation](https://mxnet.incubator.apache.org/install/index.html) -* [Pretrained Models](http://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html) +* [Installation](https://mxnet.incubator.apache.org/get_started) +* [Features](https://mxnet.incubator.apache.org/features) +* [Ecosystem](https://mxnet.incubator.apache.org/ecosystem) Features -------- @@ -97,8 +94,8 @@ Features * Mix and match imperative and symbolic programming to maximize flexibility and efficiency * Lightweight, memory efficient and portable to smart devices * Scales up to multi GPUs and distributed setting with auto parallelism -* Support for [Python](https://github.com/apache/incubator-mxnet/tree/master/python), [Scala](https://github.com/apache/incubator-mxnet/tree/master/scala-package), [C++](https://github.com/apache/incubator-mxnet/tree/master/cpp-package), [Java](https://github.com/apache/incubator-mxnet/tree/master/scala-package), [Clojure](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package), [R](https://github.com/apache/incubator-mxnet/tree/master/R-package), [Go](https://github.com/jdeng/gomxnet/), [Javascript](https://github.com/dmlc/mxnet.js/), [Perl](https://github.com/apache/incubator-mxnet/tree/master/perl-package), [Matlab](https://github.com/apache/incubator-mxnet/tree/master/matlab), and [Julia](https://github.com/apache/incubator-mxnet/tree/master/julia) -* Cloud-friendly and directly compatible with S3, HDFS, and Azure +* Support for [Python](https://mxnet.incubator.apache.org/api/python), [Scala](https://mxnet.incubator.apache.org/api/scala), [C++](https://mxnet.incubator.apache.org/api/cpp), [Java](https://mxnet.incubator.apache.org/api/java), [Clojure](https://mxnet.incubator.apache.org/api/clojure), [R](https://mxnet.incubator.apache.org/api/r), [Go](https://github.com/jdeng/gomxnet/), [Javascript](https://github.com/dmlc/mxnet.js/), [Perl](https://mxnet.incubator.apache.org/api/perl), [Matlab](https://github.com/apache/incubator-mxnet/tree/master/matlab), and [Julia](https://mxnet.incubator.apache.org/api/julia) +* Cloud-friendly and directly compatible with AWS S3, AWS Deep Learning AMI, AWS SageMaker, HDFS, and Azure License ------- diff --git a/docs/static_site/src/_includes/get_started/get_started.html b/docs/static_site/src/_includes/get_started/get_started.html index 4999518431fd..614b4a409773 100644 --- a/docs/static_site/src/_includes/get_started/get_started.html +++ b/docs/static_site/src/_includes/get_started/get_started.html @@ -256,8 +256,8 @@

Installing MXNet


- For more installation options, refer to the Ubuntu installation guide and - CentOS installation guide. + For more installation options, refer to the Ubuntu installation guide and + CentOS installation guide. @@ -354,7 +354,7 @@

Installing MXNet


- For more installation options, refer to the MXNet macOS installation guide. + For more installation options, refer to the MXNet macOS installation guide. @@ -440,7 +440,7 @@

Installing MXNet

- For more installation options, refer to the MXNet Windows installation guide. + For more installation options, refer to the MXNet Windows installation guide. diff --git a/docs/static_site/src/_includes/get_started/pip_snippet.md b/docs/static_site/src/_includes/get_started/pip_snippet.md index e67c3331c91c..0ca854005ffd 100644 --- a/docs/static_site/src/_includes/get_started/pip_snippet.md +++ b/docs/static_site/src/_includes/get_started/pip_snippet.md @@ -1,6 +1,6 @@ MXNet offers MKL pip packages that will be much faster when running on Intel hardware. Check the chart below for other options, refer to PyPI for -other MXNet pip packages, or validate your MXNet installation. +other MXNet pip packages, or validate your MXNet installation.
), desired [BLAS libraries]() and optional [OpenCV, CUDA, and cuDNN]() for building MXNet from source. +3. Install the [prerequisites](), desired [BLAS libraries]() and optional [OpenCV, CUDA, and cuDNN]() for building MXNet from source. 4. There is a configuration file for make, [make/config.mk]() that contains all the compilation options. You can edit this file and set the appropriate options prior to running the **make** command. -5. Please refer to [platform specific build instructions]() and available [build configurations](https://mxnet.incubator.apache.org/install/build_from_source#build-configurations) for more details. +5. Please refer to [platform specific build instructions]() and available [build configurations](https://mxnet.incubator.apache.org/get_started/build_from_source#build-configurations) for more details. 5. For enabling the build of C++ Package, set the **USE\_CPP\_PACKAGE = 1** in [make/config.mk](). Optionally, the compilation flag can also be specified on **make** command line as follows. ``` make -j USE_CPP_PACKAGE=1 diff --git a/docs/static_site/src/pages/get_started/index.html b/docs/static_site/src/pages/get_started/index.html index 3e0e7601f387..e89b5e3b36e8 100644 --- a/docs/static_site/src/pages/get_started/index.html +++ b/docs/static_site/src/pages/get_started/index.html @@ -28,6 +28,6 @@

Download from source

-

The signed source code for Apache MXNet (incubating) is available for download here

+

The signed source code for Apache MXNet (incubating) is available for download here

diff --git a/docs/static_site/src/pages/get_started/windows_setup.md b/docs/static_site/src/pages/get_started/windows_setup.md index 9bf413826a17..06e8c6eff6e5 100644 --- a/docs/static_site/src/pages/get_started/windows_setup.md +++ b/docs/static_site/src/pages/get_started/windows_setup.md @@ -74,7 +74,7 @@ Install MXNet with CPU support with Python: pip install mxnet ``` -Now [validate your MXNet installation with Python](validate_mxnet). +Now [validate your MXNet installation with Python](get_started/validate_mxnet). ### Install with Intel CPUs