Deep Learning for Scala/Java

Here you find the MXNet Scala Package! It brings flexible and efficient GPU/CPU computing and state-of-art deep learning to JVM.

It enables you to write seamless tensor/matrix computation with multiple GPUs in Scala, Java and other languages built on JVM.
It also enables you to construct and customize the state-of-art deep learning models in JVM languages, and apply them to tasks such as image classification and data science challenges.

Install

Technically, all you need is the mxnet-full_2.10-{arch}-{xpu}-0.1.1.jar in your classpath. It will automatically extract the native library to a tempfile and load it.

Currently we provide linux-x86_64-gpu, linux-x86_64-cpu and osx-x86_64-cpu. Support for Windows will come soon. Use the following dependency in maven, change the artifactId according to your own architecture, e.g., mxnet-full_2.10-osx-x86_64-cpu for OSX (and cpu-only).

<dependency>
  <groupId>ml.dmlc.mxnet</groupId>
  <artifactId>mxnet-full_2.10-linux-x86_64-gpu</artifactId>
  <version>0.1.1</version>
</dependency>

You can also use mxnet-core_2.10-0.1.1.jar and put the compiled native library somewhere in your load path.

<dependency>
  <groupId>ml.dmlc.mxnet</groupId>
  <artifactId>mxnet-core_2.10</artifactId>
  <version>0.1.1</version>
</dependency>

If you have some native libraries conflict with the ones in the provided ‘full’ jar (e.g., you use openblas instead of atlas), this is a recommended way. Refer to the next section for how to build it from the very source.

Build

Checkout the Installation Guide contains instructions to install mxnet. Then you can compile the Scala Package by

make scalapkg

(Optional) run unit/integration tests by

make scalatest

Or run a subset of unit tests by, e.g.,

make SCALA_TEST_ARGS=-Dsuites=ml.dmlc.mxnet.NDArraySuite scalatest

If everything goes well, you will find jars for assembly, core and example modules. Also it produces the native library in native/{your-architecture}/target, which you can use to cooperate with the core module.

Once you've downloaded and unpacked MNIST dataset to ./data/, run the training example by

java -Xmx4G -cp \
  scala-package/assembly/{your-architecture}/target/*:scala-package/examples/target/*:scala-package/examples/target/classes/lib/* \
  ml.dmlc.mxnet.examples.imclassification.TrainMnist \
  --data-dir=./data/ \
  --num-epochs=10 \
  --network=mlp \
  --cpus=0,1,2,3

If you've compiled with USE_DIST_KVSTORE enabled, the python tools in mxnet/tracker can be used to launch distributed training. The following command runs the above example using 2 worker nodes (and 2 server nodes) in local. Refer to Distributed Training for more details.

tracker/dmlc_local.py -n 2 -s 2 \
  java -Xmx4G -cp \
  scala-package/assembly/{your-architecture}/target/*:scala-package/examples/target/*:scala-package/examples/target/classes/lib/* \
  ml.dmlc.mxnet.examples.imclassification.TrainMnist \
  --data-dir=./data/ \
  --num-epochs=10 \
  --network=mlp \
  --cpus=0 \
  --kv-store=dist_sync

Change the arguments and have fun!

Usage

Here is a Scala example of what training a simple 3-layer multilayer perceptron on MNIST looks like. You can download the MNIST dataset using get_mnist_data script.

import ml.dmlc.mxnet._
import ml.dmlc.mxnet.optimizer.SGD

// model definition
val data = Symbol.Variable("data")
val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> data, "num_hidden" -> 128))
val act1 = Symbol.Activation(name = "relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 64))
val act2 = Symbol.Activation(name = "relu2")()(Map("data" -> fc2, "act_type" -> "relu"))
val fc3 = Symbol.FullyConnected(name = "fc3")()(Map("data" -> act2, "num_hidden" -> 10))
val mlp = Symbol.SoftmaxOutput(name = "sm")()(Map("data" -> fc3))

// load MNIST dataset
val trainDataIter = IO.MNISTIter(Map(
  "image" -> "data/train-images-idx3-ubyte",
  "label" -> "data/train-labels-idx1-ubyte",
  "data_shape" -> "(1, 28, 28)",
  "label_name" -> "sm_label",
  "batch_size" -> "50",
  "shuffle" -> "1",
  "flat" -> "0",
  "silent" -> "0",
  "seed" -> "10"))

val valDataIter = IO.MNISTIter(Map(
  "image" -> "data/t10k-images-idx3-ubyte",
  "label" -> "data/t10k-labels-idx1-ubyte",
  "data_shape" -> "(1, 28, 28)",
  "label_name" -> "sm_label",
  "batch_size" -> "50",
  "shuffle" -> "1",
  "flat" -> "0", "silent" -> "0"))

// setup model and fit the training data
val model = FeedForward.newBuilder(mlp)
      .setContext(Context.cpu())
      .setNumEpoch(10)
      .setOptimizer(new SGD(learningRate = 0.1f, momentum = 0.9f, wd = 0.0001f))
      .setTrainData(trainDataIter)
      .setEvalData(valDataIter)
      .build()

Predict using the model in the following way:

val probArrays = model.predict(valDataIter)
// in this case, we do not have multiple outputs
require(probArrays.length == 1)
val prob = probArrays(0)

// get real labels
import scala.collection.mutable.ListBuffer
valDataIter.reset()
val labels = ListBuffer.empty[NDArray]
while (valDataIter.hasNext) {
  val evalData = valDataIter.next()
  labels += evalData.label(0).copy()
}
val y = NDArray.concatenate(labels)

// get predicted labels
val py = NDArray.argmax_channel(prob)
require(y.shape == py.shape)

// calculate accuracy
var numCorrect = 0
var numInst = 0
for ((labelElem, predElem) <- y.toArray zip py.toArray) {
  if (labelElem == predElem) {
    numCorrect += 1
  }
  numInst += 1
}
val acc = numCorrect.toFloat / numInst
println(s"Final accuracy = $acc")

Release

Version 0.1.1, March 24, 2016.
- Bug fix for MAE & MSE metrics.
Version 0.1.0, March 22, 2016.

License

MXNet Scala Package is licensed under Apache-2 license.