[jira] [Created] (MXNET-362) ensure same mkldnn engine is used for consistency

["Ashok Emani (JIRA)" <ji...@apache.org>]

Ashok Emani created MXNET-362:
---------------------------------

             Summary: ensure same mkldnn engine is used for consistency
                 Key: MXNET-362
                 URL: https://issues.apache.org/jira/browse/MXNET-362
             Project: Apache MXNet
          Issue Type: Bug
            Reporter: Ashok Emani


Gluon data iterators may trigger different thread for execution context, this causes mkl-dnn engine to be inconsistent. Following snippet reproduces this issue.

 
{code:java}
// import numpy as np
import mxnet as mx
from mxnet import gluon, nd
 
net = gluon.nn.HybridSequential()
with net.name_scope():
    net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None))
net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=mx.cpu())
 
val_data = gluon.data.DataLoader(
    gluon.data.vision.CIFAR10(train=False),
    batch_size=32, shuffle=False,num_workers=1)
 
# output should be 0.57521844
X = (32,3,32,32)
y = net(nd.array(np.ones(X))).asnumpy()
print(y[0][0][0][0])
 
# below line works!
# for _ in range(1):
# below line causes bug
for _ in val_data:
    y = net(nd.array(np.ones(X))).asnumpy()
    print(y[0][0][0][0])
    break
{code}



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