ThomasDelteil commented on issue #11071: GPU memory usage for VGG16 prediction URL: https://github.com/apache/incubator-mxnet/issues/11071#issuecomment-402814070 @rachelmint do you have an update? @eric-haibin-lin I see much higher memory allocation than reported here using the VGG16 model. (Though the parameter file is indeed ~500MB) How do we explain this memory usage? Do we pre-allocate memory for feature maps? Using gluon I find that MXNet allocates 1.92GB in memory when loading the model. When running one image through the network, memory peak at 2.10GB and then go down to 2.02GB Using the Module API, it allocates 1.4GB ```python import mxnet as mx from mxnet import gluon # load model from model zoo net = gluon.model_zoo.vision.vgg16(pretrained=True, ctx=mx.gpu()) #1.9GB net(mx.nd.ones((1,3,224,224), mx.gpu())) #2.1GB # export the parameters net.hybridize() net(mx.nd.ones((1,3,224,224), mx.gpu())) net.export('vgg16') # Load in symbol sym, arg_params, aux_params = mx.model.load_checkpoint('vgg16', 0) mod = mx.mod.Module(symbol=sym, context=mx.gpu(0), label_names=None) mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))]) mod.set_params(arg_params, aux_params) # 1.4 GB ```
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