Docs & website sphinx errors squished 🌦

docs/api/scala/index.md

@@ -32,20 +32,19 @@ The Infer API can be used for single and batch image classification. More inform
 You can perform tensor or matrix computation in pure Scala:
 
 ```scala
-   scala> import org.apache.mxnet._
    import org.apache.mxnet._
 
-   scala> val arr = NDArray.ones(2, 3)
-   arr: org.apache.mxnet.NDArray = org.apache.mxnet.NDArray@f5e74790
+   val arr = NDArray.ones(2, 3)
+   // arr: org.apache.mxnet.NDArray = org.apache.mxnet.NDArray@f5e74790
 
-   scala> arr.shape
-   res0: org.apache.mxnet.Shape = (2,3)
+   arr.shape
+   // org.apache.mxnet.Shape = (2,3)
 
-   scala> (arr * 2).toArray
-   res2: Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
+   (arr * 2).toArray
+   // 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)
+   (arr * 2).shape
+   // org.apache.mxnet.Shape = (2,3)
 ```
 
 

docs/api/scala/kvstore.md

@@ -16,61 +16,61 @@ Let's consider a simple example. It initializes
 a (`int`, `NDArray`) pair into the store, and then pulls the value out.
 
 ```scala
-    scala> val kv = KVStore.create("local") // create a local kv store.
-    scala> val shape = Shape(2,3)
-    scala> kv.init(3, NDArray.ones(shape)*2)
-    scala> val a = NDArray.zeros(shape)
-    scala> kv.pull(3, out = a)
-    scala> a.toArray
-    Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
+val kv = KVStore.create("local") // create a local kv store.
+val shape = Shape(2,3)
+kv.init(3, NDArray.ones(shape)*2)
+val a = NDArray.zeros(shape)
+kv.pull(3, out = a)
+a.toArray
+// Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
 ```
 
 ### Push, Aggregation, and Updater
 
 For any key that's been initialized, you can push a new value with the same shape to the key, as follows:
 
 ```scala
-    scala> kv.push(3, NDArray.ones(shape)*8)
-    scala> kv.pull(3, out = a) // pull out the value
-    scala> a.toArray
-    Array[Float] = Array(8.0, 8.0, 8.0, 8.0, 8.0, 8.0)
+kv.push(3, NDArray.ones(shape)*8)
+kv.pull(3, out = a) // pull out the value
+a.toArray
+// Array[Float] = Array(8.0, 8.0, 8.0, 8.0, 8.0, 8.0)
 ```
 
 The data that you want to push can be stored on any device. Furthermore, you can push multiple
 values into the same key, where KVStore first sums all of these
 values, and then pushes the aggregated value, as follows:
 
 ```scala
-    scala> val gpus = Array(Context.gpu(0), Context.gpu(1), Context.gpu(2), Context.gpu(3))
-    scala> val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)), \
-    scala> NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
-    scala> kv.push(3, b)
-    scala> kv.pull(3, out = a)
-    scala> a.toArray
-    Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
+val gpus = Array(Context.gpu(0), Context.gpu(1), Context.gpu(2), Context.gpu(3))
+val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)), \
+NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
+kv.push(3, b)
+kv.pull(3, out = a)
+a.toArray
+// Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
 ```
 
 For each push command, KVStore applies the pushed value to the value stored by an
 `updater`. The default updater is `ASSIGN`. You can replace the default to
 control how data is merged.
 
 ```scala
-    scala> val updater = new MXKVStoreUpdater {
-              override def update(key: Int, input: NDArray, stored: NDArray): Unit = {
-                println(s"update on key $key")
-                stored += input * 2
-              }
-              override def dispose(): Unit = {}
-           }
-    scala> kv.setUpdater(updater)
-    scala> kv.pull(3, a)
-    scala> a.toArray
-    Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
-    scala> kv.push(3, NDArray.ones(shape))
-    update on key 3
-    scala> kv.pull(3, a)
-    scala> a.toArray
-    Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
+val updater = new MXKVStoreUpdater {
+          override def update(key: Int, input: NDArray, stored: NDArray): Unit = {
+            println(s"update on key $key")
+            stored += input * 2
+          }
+          override def dispose(): Unit = {}
+       }
+kv.setUpdater(updater)
+kv.pull(3, a)
+a.toArray
+// Array[Float] = Array(4.0, 4.0, 4.0, 4.0, 4.0, 4.0)
+kv.push(3, NDArray.ones(shape))
+// update on key 3
+kv.pull(3, a)
+a.toArray
+// Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
 ```
 
 ### Pull
@@ -79,11 +79,11 @@ You've already seen how to pull a single key-value pair. Similar to the way that
 pull the value into several devices with a single call.
 
 ```scala
-    scala> val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)),\
-    scala> NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
-    scala> kv.pull(3, outs = b)
-    scala> b(1).toArray
-    Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
+val b = Array(NDArray.ones(shape, gpus(0)), NDArray.ones(shape, gpus(1)),\
+NDArray.ones(shape, gpus(2)), NDArray.ones(shape, gpus(3)))
+kv.pull(3, outs = b)
+b(1).toArray
+// Array[Float] = Array(6.0, 6.0, 6.0, 6.0, 6.0, 6.0)
 ```
 
 ## List Key-Value Pairs
@@ -92,14 +92,14 @@ All of the operations that we've discussed so far are performed on a single key.
 the interface for generating a list of key-value pairs. For a single device, use the following:
 
 ```scala
-    scala> val keys = Array(5, 7, 9)
-    scala> kv.init(keys, Array.fill(keys.length)(NDArray.ones(shape)))
-    scala> kv.push(keys, Array.fill(keys.length)(NDArray.ones(shape)))
-    update on key: 5
-    update on key: 7
-    update on key: 9
-    scala> val b = Array.fill(keys.length)(NDArray.zeros(shape))
-    scala> kv.pull(keys, outs = b)
-    scala> b(1).toArray
-    Array[Float] = Array(3.0, 3.0, 3.0, 3.0, 3.0, 3.0)
+val keys = Array(5, 7, 9)
+kv.init(keys, Array.fill(keys.length)(NDArray.ones(shape)))
+kv.push(keys, Array.fill(keys.length)(NDArray.ones(shape)))
+// update on key: 5
+// update on key: 7
+// update on key: 9
+val b = Array.fill(keys.length)(NDArray.zeros(shape))
+kv.pull(keys, outs = b)
+b(1).toArray
+// Array[Float] = Array(3.0, 3.0, 3.0, 3.0, 3.0, 3.0)
 ```