[v1.x]Update git repo reference (apache#20510)

* update git repo url

* remove incubator

* fix typo

Co-authored-by: Wei Chu <weichu@amazon.com>

3rdparty/mshadow/README.md

@@ -50,5 +50,5 @@ Version
 
 Projects Using MShadow
 ----------------------
-* [MXNet: Efficient and Flexible Distributed Deep Learning Framework](https://github.com/dmlc/mxnet)
+* [MXNet: Efficient and Flexible Distributed Deep Learning Framework](https://github.com/apache/mxnet)
 * [CXXNet: A lightweight  C++ based deep learnig framework](https://github.com/dmlc/cxxnet)

NEWS.md

@@ -3572,7 +3572,7 @@ For more information and examples, see [full release notes](https://cwiki.apache
   - ImageRecordIter now stores data in pinned memory to improve GPU memcopy speed.
 ### Bugfixes
   - Cython interface is fixed. `make cython` and `python setup.py install --with-cython` should install the cython interface and reduce overhead in applications that use imperative/bucketing.
-  - Fixed various bugs in Faster-RCNN example: https://github.com/dmlc/mxnet/pull/6486
+  - Fixed various bugs in Faster-RCNN example: https://github.com/apache/mxnet/pull/6486
   - Fixed various bugs in SSD example.
   - Fixed `out` argument not working for `zeros`, `ones`, `full`, etc.
   - `expand_dims` now supports backward shape inference.
@@ -3648,9 +3648,9 @@ This is the last release before the NNVM refactor.
 - Support CuDNN v5 by @antinucleon
 - More applications
   - Speech recognition by @yzhang87
-  - [Neural art](https://github.com/dmlc/mxnet/tree/master/example/neural-style) by @antinucleon
-  - [Detection](https://github.com/dmlc/mxnet/tree/master/example/rcnn), RCNN bt @precedenceguo
-  - [Segmentation](https://github.com/dmlc/mxnet/tree/master/example/fcn-xs), FCN by @tornadomeet
+  - [Neural art](https://github.com/apache/mxnet/tree/v0.7.0/example/neural-style) by @antinucleon
+  - [Detection](https://github.com/apache/mxnet/tree/v0.7.0/example/rcnn), RCNN bt @precedenceguo
+  - [Segmentation](https://github.com/apache/mxnet/tree/v0.7.0/example/fcn-xs), FCN by @tornadomeet
   - [Face identification](https://github.com/tornadomeet/mxnet-face) by @tornadomeet
   - More on the example
 

R-package/R/zzz.R

@@ -63,7 +63,7 @@ NULL
   if (!interactive() || stats::runif(1) > 0.1) return()
 
   tips <- c(
-    "Need help? Feel free to open an issue on https://github.com/dmlc/mxnet/issues",
+    "Need help? Feel free to open an issue on https://github.com/apache/mxnet/issues",
     "For more documents, please visit https://mxnet.io",
     "Use suppressPackageStartupMessages() to eliminate package startup messages."
   )

R-package/vignettes/CallbackFunction.Rmd

@@ -75,7 +75,7 @@ head(logger$eval)
 ## How to write your own callback functions
 
 
-You can find the source code for two callback functions from [here](https://github.com/dmlc/mxnet/blob/master/R-package/R/callback.R) and they can be used as your template:
+You can find the source code for two callback functions from [here](https://github.com/apache/mxnet/blob/v1.x/R-package/R/callback.R) and they can be used as your template:
 
 Basically, all callback functions follow the structure below:
 

R-package/vignettes/CustomIterator.Rmd

@@ -19,7 +19,7 @@ You'll get two files, `mnist_train.csv` that contains 60.000 examples of hand wr
 
 ## Custom CSV Iterator
 
-Next we are going to create a custom CSV Iterator based on the [C++ CSVIterator class](https://github.com/dmlc/mxnet/blob/master/src/io/iter_csv.cc).
+Next we are going to create a custom CSV Iterator based on the [C++ CSVIterator class](https://github.com/apache/mxnet/blob/master/src/io/iter_csv.cc).
 
 For that we are going to use the R function `mx.io.CSVIter` as a base class. This class has as parameters `data.csv, data.shape, batch.size` and two main functions, `iter.next()` that calls the iterator in the next batch of data and `value()` that returns the train data and the label.
 

R-package/vignettes/mnistCompetition.Rmd

@@ -1,7 +1,7 @@
 # Handwritten Digits Classification Competition
 
 [MNIST](http://yann.lecun.com/exdb/mnist/) is a handwritten digits image data set created by Yann LeCun. Every digit is represented by a 28x28 image. It has become a standard data set to test classifiers on simple image input. Neural network is no doubt a strong model for image classification tasks. There's a [long-term hosted competition](https://www.kaggle.com/c/digit-recognizer) on Kaggle using this data set.
-We will present the basic usage of [mxnet](https://github.com/dmlc/mxnet/tree/master/R-package) to compete in this challenge.
+We will present the basic usage of [mxnet](https://github.com/apache/mxnet/tree/v1.x/R-package) to compete in this challenge.
 
 ## Data Loading
 

README.md

@@ -81,10 +81,10 @@ What's New
 * [0.12.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.12.0) - MXNet 0.12.0 Release.
 * [0.11.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.11.0) - MXNet 0.11.0 Release.
 * [Apache Incubator](http://incubator.apache.org/projects/mxnet.html) - We are now an Apache Incubator project.
-* [0.10.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.10.0) - MXNet 0.10.0 Release.
+* [0.10.0 Release](https://github.com/apache/mxnet/releases/tag/v0.10.0) - MXNet 0.10.0 Release.
 * [0.9.3 Release](./docs/architecture/release_note_0_9.md) - First 0.9 official release.
 * [0.9.1 Release (NNVM refactor)](./docs/architecture/release_note_0_9.md) - NNVM branch is merged into master now. An official release will be made soon.
-* [0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0)
+* [0.8.0 Release](https://github.com/apache/mxnet/releases/tag/v0.8.0)
 
 ### Ecosystem News
 

docker/Dockerfiles/Dockerfile.in.lib.cpu

@@ -24,6 +24,6 @@ FROM ubuntu:14.04
 COPY install/cpp.sh install/
 RUN install/cpp.sh
 
-RUN git clone --recursive https://github.com/dmlc/mxnet && cd mxnet && \
+RUN git clone --recursive https://github.com/apache/mxnet && cd mxnet && \
     make -j$(nproc) && \
     rm -r build

docker/Dockerfiles/Dockerfile.in.lib.gpu

@@ -25,5 +25,5 @@ COPY install/cpp.sh install/
 RUN install/cpp.sh
 
 ENV BUILD_OPTS "USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1"
-RUN git clone --recursive https://github.com/dmlc/mxnet && cd mxnet && \
+RUN git clone --recursive https://github.com/apache/mxnet && cd mxnet && \
     make -j$(nproc) $BUILD_OPTS

docs/static_site/src/pages/api/faq/caffe.md

@@ -32,7 +32,7 @@ Key topics covered include the following:
 ## Converting Caffe trained models to MXNet
 
 The converting tool is available at
-[tools/caffe_converter](https://github.com/dmlc/mxnet/tree/master/tools/caffe_converter). On
+[tools/caffe_converter](https://github.com/apache/mxnet/tree/v1.x/tools/caffe_converter). On
 the remaining of this section, we assume we are on the `tools/caffe_converter`
 directory.
 
@@ -205,4 +205,4 @@ train = mx.io.CaffeDataIter(
 ### Put it all together
 
 The complete example is available at
-[example/caffe](https://github.com/dmlc/mxnet/blob/master/example/caffe/)
+[example/caffe](https://github.com/apache/mxnet/blob/v1.x/example/caffe/)

docs/static_site/src/pages/api/faq/cloud.md

@@ -113,7 +113,7 @@ The following commands build _MXNet_ with CUDA/CUDNN, Amazon S3, and distributed
 training.
 
 ```bash
-git clone --recursive https://github.com/dmlc/mxnet
+git clone --recursive https://github.com/apache/mxnet
 cd mxnet; cp make/config.mk .
 echo "USE_CUDA=1" >>config.mk
 echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk
@@ -192,7 +192,7 @@ cat hosts | xargs -I{} ssh -o StrictHostKeyChecking=no {} 'uname -a; pgrep pytho
 ```
 
 ***Note:*** The preceding example is very simple to train and therefore isn't a good
-benchmark for distributed training. Consider using other [examples](https://github.com/dmlc/mxnet/tree/master/example/image-classification).
+benchmark for distributed training. Consider using other [examples](https://github.com/apache/mxnet/tree/v1.x/example/image-classification).
 
 ### More Options
 #### Use Multiple Data Shards

docs/static_site/src/pages/api/faq/multi_devices.md

@@ -71,7 +71,7 @@ import mxnet as mx
 module = mx.module.Module(context=[mx.gpu(0), mx.gpu(2)], ...)
 ```
 while if the program accepts a `--gpus` flag (as seen in
-[example/image-classification](https://github.com/dmlc/mxnet/tree/master/example/image-classification)),
+[example/image-classification](https://github.com/apache/mxnet/tree/v1.x/example/image-classification)),
 then we can try
 ```bash
 python train_mnist.py --gpus 0,2 ...
@@ -130,7 +130,7 @@ When using a large number of GPUs, e.g. >=4, we suggest using `device` for bette
 
 Launching a distributed job is a bit different from running on a single
 machine. MXNet provides
-[tools/launch.py](https://github.com/dmlc/mxnet/blob/master/tools/launch.py) to
+[tools/launch.py](https://github.com/apache/mxnet/blob/v1.x/tools/launch.py) to
 start a job by using `ssh`, `mpi`, `sge`, or `yarn`.
 
 An easy way to set up a cluster of EC2 instances for distributed deep learning
@@ -139,7 +139,7 @@ If you do not have a cluster, you can check the repository before you continue.
 
 Assume we are at the directory `mxnet/example/image-classification`
 and want to train LeNet to classify MNIST images, as demonstrated here:
-[train_mnist.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/train_mnist.py).
+[train_mnist.py](https://github.com/apache/mxnet/blob/v1.x/example/image-classification/train_mnist.py).
 
 On a single machine, we can run:
 

docs/static_site/src/pages/api/faq/new_op.md

@@ -144,12 +144,12 @@ To use the custom operator, create a mx.sym.Custom symbol with op_type as the re
 mlp = mx.symbol.Custom(data=fc3, name='softmax', op_type='softmax')
 ```
 
-Please see the full code for this example [here](https://github.com/dmlc/mxnet/blob/master/example/numpy-ops/custom_softmax.py).
+Please see the full code for this example [here](https://github.com/apache/mxnet/blob/v1.x/example/numpy-ops/custom_softmax.py).
 
 ## C++
 With MXNet v0.9 (the NNVM refactor) or later, creating new operators has become easier.
 Operators are now registered with NNVM.
-The following code is an example on how to register an operator (checkout [src/operator/tensor](https://github.com/dmlc/mxnet/tree/master/src/operator/tensor) for more examples):
+The following code is an example on how to register an operator (checkout [src/operator/tensor](https://github.com/apache/mxnet/tree/v1.x/src/operator/tensor) for more examples):
 
 ```c++
 NNVM_REGISTER_OP(abs)
@@ -189,7 +189,7 @@ In this section, we will go through the basic attributes MXNet expect for all op
 You can find the definition for them in the following two files:
 
 - [nnvm/op_attr_types.h](https://github.com/dmlc/nnvm/blob/master/include/nnvm/op_attr_types.h)
-- [mxnet/op_attr_types.h](https://github.com/dmlc/mxnet/blob/master/include/mxnet/op_attr_types.h)
+- [mxnet/op_attr_types.h](https://github.com/apache/mxnet/blob/v1.x/include/mxnet/op_attr_types.h)
 
 #### Descriptions (Optional)
 

docs/static_site/src/pages/api/faq/perf.md

@@ -66,7 +66,7 @@ So whether you specify `cpu(0)` or `cpu()`, _MXNet_ will use all CPU cores on th
 ### Scoring results
 The following table shows performance of MXNet-1.2.0.rc1,
 namely number of images that can be predicted per second.
-We used [example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
+We used [example/image-classification/benchmark_score.py](https://github.com/apache/mxnet/blob/v1.x/example/image-classification/benchmark_score.py)
 to measure the performance on different AWS EC2 machines.
 
 AWS EC2 C5.18xlarge:
@@ -150,7 +150,7 @@ and V100 (EC2 p3.2xlarge).
 ### Scoring results
 
 Based on
-[example/image-classification/benchmark_score.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark_score.py)
+[example/image-classification/benchmark_score.py](https://github.com/apache/mxnet/blob/v1.x/example/image-classification/benchmark_score.py)
 and  MXNet-1.2.0.rc1, with cuDNN 7.0.5
 
 - K80 (single GPU)
@@ -213,7 +213,7 @@ Below is the performance result on V100 using float 16.
 ### Training results
 
 Based on
-[example/image-classification/train_imagenet.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/train_imagenet.py)
+[example/image-classification/train_imagenet.py](https://github.com/apache/mxnet/blob/v1.x/example/image-classification/train_imagenet.py)
 and  MXNet-1.2.0.rc1, with CUDNN 7.0.5. The benchmark script is available at
 [here](https://github.com/mli/mxnet-benchmark/blob/master/run_vary_batch.sh),
 where the batch size for Alexnet is increased by 16x.
@@ -260,7 +260,7 @@ It's critical to use the proper type of `kvstore` to get the best performance.
 Refer to [multi_device.md](https://mxnet.io/api/faq/distributed_training.html) for more
 details.
 
-Besides, we can use [tools/bandwidth](https://github.com/dmlc/mxnet/tree/master/tools/bandwidth)
+Besides, we can use [tools/bandwidth](https://github.com/apache/mxnet/tree/v1.x/tools/bandwidth)
 to find the communication cost per batch.
 Ideally, the communication cost should be less than the time to compute a batch.
 To reduce the communication cost, we can consider:
@@ -293,7 +293,7 @@ by summarizing at the operator level, instead of a function, kernel, or instruct
 
 The profiler can be turned on with an [environment variable]({{'/api/faq/env_var#control-the-profiler' | relative_url}})
 for an entire program run, or programmatically for just part of a run. Note that by default the profiler hides the details of each individual operator, and you can reveal the details by setting environment variables `MXNET_EXEC_BULK_EXEC_INFERENCE`, `MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN` and `MXNET_EXEC_BULK_EXEC_TRAIN` to 0.
-See [example/profiler](https://github.com/dmlc/mxnet/tree/master/example/profiler)
+See [example/profiler](https://github.com/apache/mxnet/tree/v1.x/example/profiler)
 for complete examples of how to use the profiler in code, or [this tutorial](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/profiler.html) on how to profile MXNet performance.
 
 Briefly, the Python code looks like:

docs/static_site/src/pages/api/faq/recordio.md

@@ -34,8 +34,8 @@ RecordIO implements a file format for a sequence of records. We recommend storin
 
 We provide two tools for creating a RecordIO dataset.
 
-* [im2rec.cc](https://github.com/dmlc/mxnet/blob/master/tools/im2rec.cc) - implements the tool using the C++ API.
-* [im2rec.py](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py) - implements the tool using the Python API.
+* [im2rec.cc](https://github.com/apache/mxnet/blob/v1.x/tools/im2rec.cc) - implements the tool using the C++ API.
+* [im2rec.py](https://github.com/apache/mxnet/blob/v1.x/tools/im2rec.py) - implements the tool using the Python API.
 
 Both provide the same output: a RecordIO dataset.
 

docs/static_site/src/pages/api/faq/s3_integration.md

@@ -70,7 +70,7 @@ aws s3 sync ./training-data s3://bucket-name/training-data
 
 Once the data is in S3, it is very straightforward to use it from MXNet. Any data iterator that can read/write data from a local drive can also read/write data from S3.
 
-Let's modify an existing example code in MXNet repository to read data from S3 instead of local disk. [`mxnet/tests/python/train/test_conv.py`](https://github.com/dmlc/mxnet/blob/master/tests/python/train/test_conv.py) trains a convolutional network using MNIST data from local disk. We'll do the following change to read the data from S3 instead.
+Let's modify an existing example code in MXNet repository to read data from S3 instead of local disk. [`mxnet/tests/python/train/test_conv.py`](https://github.com/apache/mxnet/blob/v1.x/tests/python/train/test_conv.py) trains a convolutional network using MNIST data from local disk. We'll do the following change to read the data from S3 instead.
 
 ```
 ~/mxnet$ sed -i -- 's/data\//s3:\/\/bucket-name\/training-data\//g' ./tests/python/train/test_conv.py

docs/static_site/src/pages/api/faq/smart_device.md

@@ -39,7 +39,7 @@ All that's necessary to create the library is to compile that single file.
 This simplifies the problem of porting to various platforms.
 
 Thanks to [Jack Deng](https://github.com/jdeng),
-MXNet provides an [amalgamation](https://github.com/dmlc/mxnet/tree/master/amalgamation) script
+MXNet provides an [amalgamation](https://github.com/apache/mxnet/tree/v1.x/amalgamation) script
 that compiles all code needed for prediction based on trained DL models into a single `.cc` file,
 containing approximately 30K lines of code. This code only depends on the BLAS library.
 Moreover, we've also created an even more minimal version,
@@ -53,8 +53,8 @@ Porting to another language with a C foreign function interface requires little
 For examples, see the following examples on GitHub:
 
 - Go: [https://github.com/jdeng/gomxnet](https://github.com/jdeng/gomxnet)
-- Java: [https://github.com/dmlc/mxnet/tree/master/amalgamation/jni](https://github.com/dmlc/mxnet/tree/master/amalgamation/jni)
-- Python: [https://github.com/dmlc/mxnet/tree/master/amalgamation/python](https://github.com/dmlc/mxnet/tree/master/amalgamation/python)
+- Java: [https://github.com/apache/mxnet/tree/v1.x/amalgamation/jni](https://github.com/apache/mxnet/tree/v1.x/amalgamation/jni)
+- Python: [https://github.com/apache/mxnet/tree/v1.x/amalgamation/python](https://github.com/apache/mxnet/tree/v1.x/amalgamation/python)
 
 
 If you plan to amalgamate your system, there are a few guidelines you ought to observe when building the project:

docs/static_site/src/pages/api/faq/visualize_graph.md

@@ -84,5 +84,5 @@ You should see computation graph something like the following image:
 width=400/>
 
 # References
-* [Example MXNet Matrix Factorization](https://github.com/dmlc/mxnet/blob/master/example/recommenders/demo1-MF.ipynb)
+* [Example MXNet Matrix Factorization](https://github.com/apache/mxnet/blob/v1.x/example/recommenders/demo1-MF.ipynb)
 * [Visualizing CNN Architecture of MXNet Tutorials](http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/)

docs/static_site/src/pages/api/r/docs/tutorials/callback_function.md

@@ -175,7 +175,7 @@ You also can save the training and evaluation errors for later use by passing a
 How to Write Your Own Callback Functions
 ----------
 
-You can find the source code for the two callback functions on [GitHub](https://github.com/dmlc/mxnet/blob/master/R-package/R/callback.R) and use it as a template:
+You can find the source code for the two callback functions on [GitHub](https://github.com/apache/mxnet/blob/v1.x/R-package/R/callback.R) and use it as a template:
 
 Basically, all callback functions follow the following structure:
 

docs/static_site/src/pages/api/r/docs/tutorials/custom_iterator.md

@@ -43,7 +43,7 @@ You'll get two files, `mnist_train.csv` that contains 60.000 examples of hand wr
 
 Custom CSV Iterator
 ----------
-Next we are going to create a custom CSV Iterator based on the [C++ CSVIterator class](https://github.com/dmlc/mxnet/blob/master/src/io/iter_csv.cc).
+Next we are going to create a custom CSV Iterator based on the [C++ CSVIterator class](https://github.com/apache/mxnet/blob/v1.x/src/io/iter_csv.cc).
 
 For that we are going to use the R function `mx.io.CSVIter` as a base class. This class has as parameters `data.csv, data.shape, batch.size` and two main functions, `iter.next()` that calls the iterator in the next batch of data and `value()` that returns the train data and the label.
 

docs/static_site/src/pages/api/r/docs/tutorials/five_minutes_neural_network.md

@@ -116,7 +116,7 @@ To get an idea of what is happening, view the computation graph from R:
     graph.viz(model$symbol)
  ```
 
-[<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png">](https://github.com/dmlc/mxnet)
+[<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png">](https://github.com/apache/mxnet)
 
  ```r
     preds = predict(model, test.x)

docs/static_site/src/pages/api/r/docs/tutorials/mnist_competition.md

@@ -26,7 +26,7 @@ Handwritten Digits Classification Competition
 =============================================
 
 [MNIST](http://yann.lecun.com/exdb/mnist/) is a handwritten digits image data set created by Yann LeCun. Every digit is represented by a 28 x 28 pixel image. It's become a standard data set for testing classifiers on simple image input. A neural network is a strong model for image classification tasks. There's a [long-term hosted competition](https://www.kaggle.com/c/digit-recognizer) on Kaggle using this data set.
-This tutorial shows how to use [MXNet](https://github.com/dmlc/mxnet/tree/master/R-package) to compete in this challenge.
+This tutorial shows how to use [MXNet](https://github.com/apache/mxnet/tree/v1.x/R-package) to compete in this challenge.
 
 ## Loading the Data