cudaMalloc(and CPU memory allocation also) failed on asnumpy()


reproducible code

import mxnet as mx
from mxnet import nd, gluon, autograd, test_utils
from mxnet.gluon import nn
from mxnet.gluon.model_zoo import vision
import numpy as np

if __name__=='__main__':
    net =, prefix='model_')
    inputs = mx.sym.var('data')
    out = net(inputs)
    internals = out.get_internals()
    outputs = [internals['model_conv0_fwd_output'],
    upsample2x = mx.sym.UpSampling(outputs[1], #internals['model_stage1_activation2_output'],
    upsample4x = mx.sym.UpSampling(outputs[2], scale=4, sample_type='nearest')
    upsample8x = mx.sym.UpSampling(outputs[3], scale=8, sample_type='nearest')
    upsample16x = mx.sym.UpSampling(outputs[4], scale=16, sample_type='nearest')
    stacked = mx.sym.concat(upsample2x, upsample4x, upsample8x, upsample16x, dim=1)
    outputx = mx.sym.Convolution(stacked, kernel=(9,9), num_filter=64, pad=(4,4))
    outputx = mx.sym.Convolution(outputx, kernel=(5,5), num_filter=32, pad=(2,2))
    outputx = mx.sym.Convolution(outputx, kernel=(5,5), num_filter=1, pad=(2,2))

    net3 = gluon.SymbolBlock(outputs=outputx, inputs=inputs)

    # train
    lossf = gluon.loss.L2Loss()
    batch_sz = 2
    dataset =
    train_data_loader =, batch_sz, shuffle=True)
    trainer = gluon.Trainer(net3.collect_params(), 'sgd', {'learning_rate':0.1})

    cnt = 0
    for data, label in train_data_loader:
        data = data.as_in_context(mx.gpu(0))
        label = label.as_in_context(mx.gpu(0))
        with autograd.record():
            output = net3(data)
            loss = lossf(output, label)
        #loss = nd.log(loss).asscalar()


Traceback (most recent call last):
  File "", line 55, in <module>
  File "/lib/python3.7/site-packages/mxnet/ndarray/", line 1980, in asnumpy
  File “/lib/python3.7/site-packages/mxnet/", line 252, in check_call
    raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [09:05:43] src/storage/./pooled_storage_manager.h:143: cudaMalloc failed: out of memory

when I change all contexts to mx.cpu(), I can see below error(Failed to allocate CPU Memory). Almost same with the above case.

Traceback (most recent call last):
  File "", line 56, in <module>
  File "/lib/python3.7/site-packages/mxnet/ndarray/", line 189, in __repr__
    return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()),
  File "/lib/python3.7/site-packages/mxnet/ndarray/", line 1980, in asnumpy
  File "/lib/python3.7/site-packages/mxnet/", line 252, in check_call
    raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [10:53:55] src/storage/./cpu_device_storage.h:73: Failed to allocate CPU Memory

I think my batch size is small enough and GPU have much memory(32G)
if print(loss.asnumpy()) is removed, this code works well. But I cannot be sure this works really well because I can’t check the loss.
How can I see the loss? And what causes this error?


Due to asynchronous nature of the MXNet engine, you learn about exception happened on GPU only when you do a synchronous call - in your case it is asnumpy(). That’s why it may seems that it is asnumpy function that fails, where it is actually failing somewhere internally.

The problem is related to the amount of memory needed for storing activations While your batch size is indeed quite small, because you network is deep, creating each tensor of big dimensionality take a lot of memory. Take a look into the Output Shape column of the following table - you can get it if you call
print(mx.visualization.print_summary(net3(mx.sym.var('data')), shape={'data':(2,3,1024,1024)}))

I cannot paste the table here, because the length is too big for the post. But sizes are quite significant.

I can recommend 2 approaches:

  1. Reduce size from 1024x1024 to 512x512 - it will save a lot of memory
  2. Try to play with mixed precision training - it also should decrease amount of required memory:

For more information about how memory is utilized in DL I find this article quite an interesting read -


I’m very thankful to @Sergey
This problem is solved by reducing the input size to 512