Reverting namespace change since this needs a major version change an…

…d breaks backward compatibility.

Revert "Refactor scala code/tests/examples/docs to use org.apache.mxnet (apache#9324)"

This reverts commit deb25bc.

docs/api/scala/index.md

@@ -1,6 +1,6 @@
 # MXNet - Scala API
 
-See the [MXNet Scala API Documentation](http://mxnet.incubator.apache.org/api/scala/docs/index.html).
+See the [MXNet Scala API Documentation](http://mxnet.io/api/scala/docs/index.html).
 
 MXNet supports the Scala programming language. The MXNet Scala package brings flexible and efficient GPU
 computing and state-of-art deep learning to Scala. It enables you to write seamless tensor/matrix computation with multiple GPUs in Scala. It also lets you construct and customize the state-of-art deep learning models in Scala,
@@ -9,20 +9,20 @@ computing and state-of-art deep learning to Scala. It enables you to write seaml
 You can perform tensor or matrix computation in pure Scala:
 
 ```scala
-   scala> import org.apache.mxnet._
-   import org.apache.mxnet._
+   scala> import ml.dmlc.mxnet._
+   import ml.dmlc.mxnet._
 
    scala> val arr = NDArray.ones(2, 3)
-   arr: org.apache.mxnet.NDArray = org.apache.mxnet.NDArray@f5e74790
+   arr: ml.dmlc.mxnet.NDArray = ml.dmlc.mxnet.NDArray@f5e74790
 
    scala> arr.shape
-   res0: org.apache.mxnet.Shape = (2,3)
+   res0: ml.dmlc.mxnet.Shape = (2,3)
 
    scala> (arr * 2).toArray
    res2: Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
 
    scala> (arr * 2).shape
-   res3: org.apache.mxnet.Shape = (2,3)
+   res3: ml.dmlc.mxnet.Shape = (2,3)
 ```
 
  ## Scala API Reference
@@ -36,7 +36,7 @@ You can perform tensor or matrix computation in pure Scala:
 
 ## Resources
 
-* [MXNet Scala API Documentation](http://mxnet.incubator.apache.org/api/scala/docs/index.html)
-* [Handwritten Digit Classification in Scala](http://mxnet.incubator.apache.org/tutorials/scala/mnist.html)
-* [Neural Style in Scala on MXNet](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/NeuralStyle.scala)
-* [More Scala Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples)
+* [MXNet Scala API Documentation](http://mxnet.io/api/scala/docs/index.html)
+* [Handwritten Digit Classification in Scala](http://mxnet.io/tutorials/scala/mnist.html)
+* [Neural Style in Scala on MXNet](https://github.com/dmlc/mxnet/blob/master/scala-package/examples/src/main/scala/ml/dmlc/mxnetexamples/neuralstyle/NeuralStyle.scala)
+* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/src/main/scala/ml/dmlc/mxnetexamples)

docs/api/scala/io.md

@@ -8,7 +8,7 @@ Topics:
 * [Data Iterator Parameters](#parameters-for-data-iterator) clarifies the different usages for dataiter parameters.
 * [Create a Data Iterator](#create-a-data-iterator) introduces how to create a data iterator in MXNet for Scala.
 * [How to Get Data](#how-to-get-data) introduces the data resource and data preparation tools.
-* [IO API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.IO$) explains the IO API.
+* [IO API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.IO$) explains the IO API.
 
 
 ## Data Iterator Parameters
@@ -83,7 +83,7 @@ First, explicitly specify the kind of data (MNIST, ImageRecord, etc.) to fetch.
 ## How to Get Data
 
 
-We provide [scripts](https://github.com/apache/incubator-mxnet/tree/master/scala-package/core/scripts) to download MNIST data and CIFAR10 ImageRecord data. If you want to create your own dataset, we recommend using the Image RecordIO data format.
+We provide [scripts](https://github.com/dmlc/mxnet/tree/master/scala-package/core/scripts) to download MNIST data and CIFAR10 ImageRecord data. If you want to create your own dataset, we recommend using the Image RecordIO data format.
 
 ## Create a Dataset Using RecordIO
 
@@ -93,7 +93,7 @@ RecordIO implements a file format for a sequence of records. We recommend storin
 * Packing data together allows continuous reading on the disk.
 * RecordIO has a simple way to partition, simplifying distributed setting. We provide an example later.
 
-We provide the [im2rec tool](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.cc) so you can create an Image RecordIO dataset by yourself. The following walkthrough shows you how.
+We provide the [im2rec tool](https://github.com/dmlc/mxnet/blob/master/tools/im2rec.cc) so you can create an Image RecordIO dataset by yourself. The following walkthrough shows you how.
 
 ### Prerequisites
 Download the data. You don't need to resize the images manually. You can use `im2rec` to resize them automatically. For details, see "Extension: Using Multiple Labels for a Single Image," later in this topic.

docs/api/scala/kvstore.md

@@ -3,7 +3,7 @@
 Topics:
 * [Basic Push and Pull](#basic-push-and-pull)
 * [List Key-Value Pairs](#list-key-value-pairs)
-* [API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.KVStore)
+* [API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.KVStore)
 
 
 ## Basic Push and Pull
@@ -103,3 +103,6 @@ the interface for generating a list of key-value pairs. For a single device, use
     scala> b(1).toArray
     Array[Float] = Array(3.0, 3.0, 3.0, 3.0, 3.0, 3.0)
 ```
+
+## Next Steps
+* [Scala Tutorials](http://mxnet.io/tutorials/index.html#Python-Tutorials)

docs/api/scala/model.md

@@ -10,7 +10,7 @@ Topics:
 * [Save the Model](#save-the-model)
 * [Periodic Checkpoint](#periodic-checkpointing)
 * [Multiple Devices](#use-multiple-devices)
-* [Model API Reference](#http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Model)
+* [Model API Reference](#http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.Model)
 
 ## Train the Model
 
@@ -48,7 +48,7 @@ You can also use the `scikit-learn-style` construct and `fit` function to create
 
   model.fit(trainData = train)
 ```
-For more information, see [API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html).
+For more information, see [API Reference](http://mxnet.io/api/scala/docs/index.html).
 
 ## Save the Model
 

docs/api/scala/module.md

@@ -7,8 +7,8 @@ A subclass of modules might have extra interface functions. This topic provides
 To construct a module, refer to the constructors for the module class. For example, the `Module` class accepts a `Symbol` as input:
 
 ```scala
-    import org.apache.mxnet._
-    import org.apache.mxnet.module.{FitParams, Module}
+    import ml.dmlc.mxnet._
+    import ml.dmlc.mxnet.module.{FitParams, Module}
 
     // construct a simple MLP
     val data = Symbol.Variable("data")
@@ -40,21 +40,21 @@ Now you can compute with the module using functions like `forward()`, `backward(
 Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the `fit()` function with some `DataIter`s:
 
 ```scala
-    import org.apache.mxnet.optimizer.SGD
+    import ml.dmlc.mxnet.optimizer.SGD
     val mod = new Module(softmax)
 
     mod.fit(train_dataiter, evalData = scala.Option(eval_dataiter), \
     numEpoch = n_epoch, fitParams = new FitParams()\
     .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f)))
 ```
 
-The interface is very similar to the old `FeedForward` class. You can pass in batch-end callbacks using `setBatchEndCallback` and epoch-end callbacks using `setEpochEndCallback`. You can also set parameters using methods like `setOptimizer` and `setEvalMetric`. To learn more about the `FitParams()`, see the [API page](http://mxnet.io/api/scala/docs/index.html#org.apache.mxnet.module.FitParams). To predict with a module, call `predict()` with a `DataIter`:
+The interface is very similar to the old `FeedForward` class. You can pass in batch-end callbacks using `setBatchEndCallback` and epoch-end callbacks using `setEpochEndCallback`. You can also set parameters using methods like `setOptimizer` and `setEvalMetric`. To learn more about the `FitParams()`, see the [API page](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.module.FitParams). To predict with a module, call `predict()` with a `DataIter`:
 
 ```scala
     mod.predict(val_dataiter)
 ```
 
-The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict()` function](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.module.BaseModule).
+The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the [`predict()` function](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.module.BaseModule).
 
 When prediction results might be too large to fit in memory, use the `predictEveryBatch` API:
 

docs/api/scala/ndarray.md

@@ -7,14 +7,14 @@ Topics:
 
 * [Create NDArray](#create-ndarray)
 * [NDArray Operations](#ndarray-operations)
-* [NDArray API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.NDArray)
+* [NDArray API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.NDArray)
 
 ## Create NDArray
 
 Create `mxnet.ndarray` as follows:
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> // all-zero array of dimension 100x50
     scala> val a = NDArray.zeros(100, 50)
     scala> // all-one array of dimension 256x32x128x1
@@ -30,10 +30,10 @@ We provide some basic ndarray operations, like arithmetic and slice operations.
 ### Arithmetic Operations
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val a = NDArray.zeros(100, 50)
     scala> a.shape
-    org.apache.mxnet.Shape = (100,50)
+    ml.dmlc.mxnet.Shape = (100,50)
     scala> val b = NDArray.ones(100, 50)
     scala> // c and d will be calculated in parallel here!
     scala> val c = a + b
@@ -45,7 +45,7 @@ We provide some basic ndarray operations, like arithmetic and slice operations.
 ### Multiplication/Division Operations
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     //Multiplication
     scala> val ndones = NDArray.ones(2, 1)
     scala> val ndtwos = ndones * 2
@@ -79,7 +79,7 @@ We provide some basic ndarray operations, like arithmetic and slice operations.
 ### Slice Operations
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val a = NDArray.array(Array(1f, 2f, 3f, 4f, 5f, 6f), shape = Shape(3, 2))
     scala> val a1 = a.slice(1)   
     scala> assert(a1.shape === Shape(1, 2))
@@ -93,12 +93,12 @@ We provide some basic ndarray operations, like arithmetic and slice operations.
 ### Dot Product
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val arr1 = NDArray.array(Array(1f, 2f), shape = Shape(1, 2))
     scala> val arr2 = NDArray.array(Array(3f, 4f), shape = Shape(2, 1))   
     scala> val res = NDArray.dot(arr1, arr2)
     scala> res.shape
-    org.apache.mxnet.Shape = (1,1)
+    ml.dmlc.mxnet.Shape = (1,1)
     scala> res.toArray
     Array[Float] = Array(11.0)
 ```
@@ -108,7 +108,7 @@ We provide some basic ndarray operations, like arithmetic and slice operations.
 You can use MXNet functions to save and load a list or dictionary of NDArrays from file systems, as follows:
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val a = NDArray.zeros(100, 200)
     scala> val b = NDArray.zeros(100, 200)
     scala> // save list of NDArrays
@@ -128,20 +128,20 @@ The good thing about using the `save` and `load` interface is that you can use t
 Device information is stored in the `mxnet.Context` structure. When creating NDArray in MXNet, you can use the context argument (the default is the CPU context) to create arrays on specific devices as follows:
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val cpu_a = NDArray.zeros(100, 200)
     scala> cpu_a.context
-    org.apache.mxnet.Context = cpu(0)
+    ml.dmlc.mxnet.Context = cpu(0)
     scala> val ctx = Context.gpu(0)
     scala> val gpu_b = NDArray.zeros(Shape(100, 200), ctx)
     scala> gpu_b.context
-    org.apache.mxnet.Context = gpu(0)
+    ml.dmlc.mxnet.Context = gpu(0)
 ```
 
 Currently, we *do not* allow operations among arrays from different contexts. To manually enable this, use the `copyto` member function to copy the content to different devices, and continue computation:
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val x = NDArray.zeros(100, 200)
     scala> val ctx = Context.gpu(0)
     scala> val y = NDArray.zeros(Shape(100, 200), ctx)

docs/api/scala/symbol.md

@@ -6,9 +6,9 @@ Topics:
 * [Symbol Attributes](#symbol-attributes) describes how to attach attributes to symbols.
 * [Serialization](#serialization) explains how to save and load symbols.
 * [Executing Symbols](#executing-symbols) explains how to evaluate the symbols with data.
-* [Execution API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Executor) documents the execution APIs.
+* [Execution API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.Executor) documents the execution APIs.
 * [Multiple Outputs](#multiple-outputs) explains how to configure multiple outputs.
-* [Symbol Creation API Reference](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Symbol) documents functions.
+* [Symbol Creation API Reference](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.Symbol) documents functions.
 
 We also highly encourage you to read [Symbolic Configuration and Execution in Pictures](symbol_in_pictures.md).
 
@@ -20,14 +20,14 @@ You can configure the graphs either at the level of neural network layer operati
 The following example configures a two-layer neural network.
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val data = Symbol.Variable("data")
     scala> val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
     scala> val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
     scala> val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
     scala> val net = Symbol.SoftmaxOutput(name = "out")()(Map("data" -> fc2))
     scala> :type net
-    org.apache.mxnet.Symbol
+    ml.dmlc.mxnet.Symbol
 ```
 
 The basic arithmetic operators (plus, minus, div, multiplication) are overloaded for
@@ -36,7 +36,7 @@ The basic arithmetic operators (plus, minus, div, multiplication) are overloaded
 The following example creates a computation graph that adds two inputs together.
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val a = Symbol.Variable("a")
     scala> val b = Symbol.Variable("b")
     scala> val c = a + b
@@ -79,12 +79,12 @@ To attach attributes, you can use ```AttrScope```. ```AttrScope``` automatically
 There are two ways to save and load the symbols. You can use the `mxnet.Symbol.save` and `mxnet.Symbol.load` functions to serialize the ```Symbol``` objects.
 The advantage of using `save` and `load` functions is that it is language agnostic and cloud friendly.
 The symbol is saved in JSON format. You can also get a JSON string directly using `mxnet.Symbol.toJson`.
-Refer to [API documentation](http://mxnet.incubator.apache.org/api/scala/docs/index.html#org.apache.mxnet.Symbol) for more details.
+Refer to [API documentation](http://mxnet.io/api/scala/docs/index.html#ml.dmlc.mxnet.Symbol) for more details.
 
 The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string.
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val a = Symbol.Variable("a")
     scala> val b = Symbol.Variable("b")
     scala> val c = a + b
@@ -112,7 +112,7 @@ which is typically constructed by calling the [`simpleBind(<parameters>)`] metho
 To group the symbols together, use the [mxnet.symbol.Group](#mxnet.symbol.Group) function.
 
 ```scala
-    scala> import org.apache.mxnet._
+    scala> import ml.dmlc.mxnet._
     scala> val data = Symbol.Variable("data")
     scala> val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
     scala> val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))

docs/mxdoc.py

@@ -80,13 +80,13 @@ def build_r_docs(app):
 
 def build_scala_docs(app):
     """build scala doc and then move the outdir"""
-    scala_path = app.builder.srcdir + '/../scala-package/core/src/main/scala/org/apache/mxnet'
+    scala_path = app.builder.srcdir + '/../scala-package/core/src/main/scala/ml/dmlc/mxnet'
     # scaldoc fails on some apis, so exit 0 to pass the check
     _run_cmd('cd ' + scala_path + '; scaladoc `find . | grep .*scala`; exit 0')
     dest_path = app.builder.outdir + '/api/scala/docs'
     _run_cmd('rm -rf ' + dest_path)
     _run_cmd('mkdir -p ' + dest_path)
-    scaladocs = ['index', 'index.html', 'org', 'lib', 'index.js', 'package.html']
+    scaladocs = ['index', 'index.html', 'ml', 'lib', 'index.js', 'package.html']
     for doc_file in scaladocs:
         _run_cmd('cd ' + scala_path + ' && mv -f ' + doc_file + ' ' + dest_path)