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Showing content from https://github.com/apache/mxnet/issues/14080 below:

Adam Optimizer Memory Leak in Scala · Issue #14080 · apache/mxnet · GitHub

Memory leak issue while using Adam optimizer with MXNet Scala Bindings. Running the code below will keep consuming more and more memory till you run out.

// Simple MLP network
def mlpNetwork(): Symbol = {
    val input = Symbol.Variable("data")
    val label = Symbol.Variable("label")
    val fc1 = Symbol.FullyConnected(name = "fc1")()(Map("data" -> input, "num_hidden" -> 128))
    val act1 = Symbol.Activation(name = "relu")()(Map("data" -> fc1, "act_type" -> "relu"))
    val fc2 = Symbol.FullyConnected(name = "fc2")()(Map("data" -> act1, "num_hidden" -> 1))
    val loss = Symbol.LinearRegressionOutput(name="loss")()(Map("data" -> fc2, "label" -> label))
    loss
  }

def getNDArrayIter: NDArrayIter = {
    val f = NDArray.zeros(100, 20, 20)
    val l = NDArray.zeros(100, 1)
    val data = Array(f)
    val labels = Array(l)
    val batchSize = 10
    val iter = new NDArrayIter(data, labels, batchSize)
    iter
  }

val net = mlpNetwork()
val iter = getNDArrayIter()
val optimizer = new Adam(0.001f, 0.9f, 0.999f, 1e-8f, 1 - 1e-8f, 0f, 10f, null);
val init = new Normal(0.01f);
val model = FeedForward.newBuilder(modelSpec)
                .setContext(Array(Context.gpu(0)))
                .setInitializer(init)
                .setNumEpoch(100000)
                .setOptimizer(optimizer)
                .setTrainData(iter)
                .setEvalData(iter)
                .build();

The issue is (I think) some temporary NDArrays are not getting disposed in Adam optimizer when using disposeDepsExcept.

The places exactly where the memory leak occurs is in 3 locations where the method disposeDepsExcept is used in Adam's update method.

Replace all the 3 lines that use disposeDepsExcept in update method of Adam.scala by explicitly disposing the temporary NDArrays that were created as shown below

Instead of the 3 following lines in Adam.scala

val meanT = (beta1t * mean + (1.0 - beta1t) * resdGrad)
      .disposeDepsExcept(mean, resdGrad)

val varianceT = (beta2 * variance + (1.0f - beta2) * resdGrad * resdGrad)
      .disposeDepsExcept(variance, resdGrad)

val step = (learningRate * meanT / (NDArray.sqrt(varianceT) + epsilon))
      .disposeDepsExcept(meanT, varianceT)

Replace it by:

val beta1Mean = beta1 * mean
    val beta1ResGrad = (1.0 - beta1t) * resdGrad
    val meanT = beta1Mean + beta1ResGrad
    // dipose temp NDArrays
    betaMean.dispose()
    betaResGrad.dispose()

val beta2Variance = beta2 * variance
    val beta2ResGrad = (1.0f - beta2) * resdGrad
    val beta2ResGradSquare = beta2ResGrad * resdGrad
    val varianceT = beta2Variance + beta2ResGradSquare
    // dipose temp NDArrays
    beta2Variance.dispose()
    beta2ResGrad.dispose()
    beta2ResGradSquare.dispose()


    val lrMeanT = learningRate * meanT
    val sqrtVar = NDArray.sqrt(varianceT)
    val sqrtVarPlusEpsilon = sqrtVar + epsilon
    val step = lrMeanT / sqrtVarPlusEpsilon
    // dipose temp NDArrays
    lrMeanT.dispose()
    sqrtVar.dispose()
    sqrtVarPlusEpsilon.dispose()

The above changes fixes things for now, but for some reason disposeDepsExcept is not doing its job in this case.

----------Python Info----------
Version      : 3.7.1
Compiler     : GCC 7.3.0
Build        : ('default', 'Dec 14 2018 19:28:38')
Arch         : ('64bit', '')
------------Pip Info-----------
Version      : 18.1
Directory    : /home/satya/anaconda3/lib/python3.7/site-packages/pip
----------MXNet Info-----------
Version      : 1.3.1
Directory    : /home/satya/Documents/workspace/mxnet_1.3.x/python/mxnet
Hashtag not found. Not installed from pre-built package.
----------System Info----------
Platform     : Linux-4.4.0-141-generic-x86_64-with-debian-stretch-sid
system       : Linux
node         : DS5
release      : 4.4.0-141-generic
version      : #167-Ubuntu SMP Wed Dec 5 10:40:15 UTC 2018
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0405 sec, LOAD: 0.6186 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1403 sec, LOAD: 0.4726 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.2418 sec, LOAD: 0.4049 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0445 sec, LOAD: 0.1894 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0779 sec, LOAD: 0.2447 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0409 sec, LOAD: 0.0746 sec.

Package used (Python/R/Scala/Julia): Scala

For Scala user, please provide:

1. Java version: 1.8.0_201
2. Maven version: 3.6.0
3. Scala runtime if applicable: 2.11.6

Compiler (gcc/clang/mingw/visual studio): gcc

MXNet commit hash: 96b4b6e

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