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

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

     [ https://issues.apache.org/jira/browse/MXNET-362?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ashok Emani reassigned MXNET-362:
---------------------------------

    Assignee: Ashok Emani

> 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
>            Assignee: Ashok Emani
>            Priority: Major
>          Time Spent: 10m
>  Remaining Estimate: 0h
>
> 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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