docs/api/scala/index.md

# MXNet - Scala API

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,
  and apply them to tasks, such as image classification and data science challenges.

You can perform tensor or matrix computation in pure Scala:

```scala
   scala> import ml.dmlc.mxnet._
   import ml.dmlc.mxnet._

   scala> val arr = NDArray.ones(2, 3)
   arr: ml.dmlc.mxnet.NDArray = ml.dmlc.mxnet.NDArray@f5e74790

   scala> arr.shape
   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: ml.dmlc.mxnet.Shape = (2,3)
```

 ## Scala API Reference
 * [Module API](module.md) is a flexible high-level interface for training neural networks.
 * [Model API](model.md) is an alternate simple high-level interface for training neural networks.
 * [Symbolic API](symbol.md) performs operations on NDArrays to assemble neural networks from layers.
 * [IO Data Loading API](io.md) performs parsing and data loading.
 * [NDArray API](ndarray.md) performs vector/matrix/tensor operations.
 * [KVStore API](kvstore.md) performs multi-GPU and multi-host distributed training.


## Resources

* [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/mxnet/examples/neuralstyle/NeuralStyle.scala)
* [More Scala Examples](https://github.com/dmlc/mxnet/tree/master/scala-package/examples/src/main/scala/ml/dmlc/mxnet/examples)