[v2.0][LICENSE] Port #20496

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
 

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/cloud.md

@@ -112,7 +112,7 @@ 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/master/example/image-classification).
 
 ### More Options
 #### Use Multiple Data Shards

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/master/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/master/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/master/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/master/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/master/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/master/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 [Distributed Training](https://mxnet.apache.org/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/master/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/master/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

@@ -34,7 +34,7 @@ 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.cc](https://github.com/apache/incubator-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.
 
 Both provide the same output: a RecordIO dataset.

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

@@ -67,7 +67,7 @@ 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/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.
 
 ```
 ~/mxnet$ sed -i -- 's/data\//s3:\/\/bucket-name\/training-data\//g' ./tests/python/train/test_conv.py

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/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.
 

example/README.md

@@ -48,7 +48,7 @@ Example applications or scripts should be submitted in this `example` folder.
 
 ### Tutorials
 
-If you have a tutorial idea for the website, download the [Jupyter notebook tutorial template](https://github.com/dmlc/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb).
+If you have a tutorial idea for the website, download the [Jupyter notebook tutorial template](https://github.com/apache/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb).
 
 #### Tutorial location
 
@@ -122,7 +122,7 @@ If your tutorial depends on specific packages, simply add them to this provision
 * "Learn to sort by LSTM" by [xlvector](https://github.com/xlvector) [github link](https://github.com/xlvector/learning-dl/tree/master/mxnet/lstm_sort) [Blog in Chinese](http://blog.xlvector.net/2016-05/mxnet-lstm-example/)
 * [Neural Art using extremely lightweight (<500K) neural network](https://github.com/pavelgonchar/neural-art-mini) Lightweight version of mxnet neural art implementation by [Pavel Gonchar](https://github.com/pavelgonchar)
 * [Neural Art with generative networks](https://github.com/zhaw/neural_style) by [zhaw](https://github.com/zhaw)
-* [Faster R-CNN in MXNet with distributed implementation and data parallelization](https://github.com/dmlc/mxnet/tree/master/example/rcnn)
+* [Faster R-CNN in MXNet with distributed implementation and data parallelization](https://github.com/apache/mxnet/tree/master/example/rcnn)
 * [Asynchronous Methods for Deep Reinforcement Learning in MXNet](https://github.com/zmonoid/Asyn-RL-MXNet/blob/master/mx_asyn.py) by [zmonoid](https://github.com/zmonoid)
 * [Deep Q-learning in MXNet](https://github.com/zmonoid/DQN-MXNet) by [zmonoid](https://github.com/zmonoid)
 * [Face Detection with End-to-End Integration of a ConvNet and a 3D Model (ECCV16)](https://github.com/tfwu/FaceDetection-ConvNet-3D) by [tfwu](https://github.com/tfwu), source code for paper Yunzhu Li, Benyuan Sun, Tianfu Wu and Yizhou Wang, "Face Detection with End-to-End Integration of a ConvNet and a 3D Model", ECCV 2016 <https://arxiv.org/abs/1606.00850>

src/engine/naive_engine.cc

@@ -250,7 +250,7 @@ class NaiveEngine final : public Engine {
 #endif
   /*!
    * \brief Holding a shared_ptr to the object pool to prevent it from being destructed too early
-   * See also #309 (https://github.com/dmlc/mxnet/issues/309) and similar fix in threaded_engine.h.
+   * See also #309 (https://github.com/apache/mxnet/issues/309) and similar fix in threaded_engine.h.
    * Without this, segfaults seen on CentOS7 in
    * test_operator_gpu.py:test_convolution_multiple_streams
    */

src/engine/threaded_engine.h

@@ -586,7 +586,7 @@ class ThreadedEngine : public Engine {
 
   /*!
    * \brief Holding a shared_ptr to the object pool to prevent it from being destructed too early
-   * See also #309 (https://github.com/dmlc/mxnet/issues/309)
+   * See also #309 (https://github.com/apache/mxnet/issues/309)
    */
   std::shared_ptr<common::ObjectPool<ThreadedOpr>> objpool_opr_ref_;
   std::shared_ptr<common::ObjectPool<OprBlock>> objpool_blk_ref_;

src/operator/svm_output.cc

@@ -88,7 +88,7 @@ MXNET_REGISTER_OP_PROPERTY(SVMOutput, SVMOutputProp)
     .describe(R"code(Computes support vector machine based transformation of the input.
 
 This tutorial demonstrates using SVM as output layer for classification instead of softmax:
-https://github.com/dmlc/mxnet/tree/v1.x/example/svm_mnist.
+https://github.com/apache/mxnet/tree/v1.x/example/svm_mnist.
 
 )code")
     .add_argument("data", "NDArray-or-Symbol", "Input data for SVM transformation.")