[GitHub] [incubator-mxnet] anko-intel commented on a change in pull request #20670: [master] Bring dnnl_readme.md on master up-to-date

[GitBox <gi...@apache.org>]

anko-intel commented on a change in pull request #20670:
URL: https://github.com/apache/incubator-mxnet/pull/20670#discussion_r731722277



##########
File path: docs/python_docs/python/tutorials/performance/backend/dnnl/dnnl_readme.md
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@@ -183,29 +185,24 @@ Expected Output:
 [[ 2.  2.  2.]
  [ 2.  2.  2.]]
 ```
-### Verify whether ONEDNN works
+### Verify whether oneDNN works
 
-After MXNet is installed, you can verify if ONEDNN backend works well with a single Convolution layer.
+After MXNet is installed, you can verify if oneDNN backend works well with a single Convolution layer.
 ```
-import mxnet as mx
-import numpy as np
+from mxnet import np
+from mxnet.gluon import nn
 
 num_filter = 32
 kernel = (3, 3)
 pad = (1, 1)
 shape = (32, 32, 256, 256)
 
-x = mx.sym.Variable('x')
-w = mx.sym.Variable('w')
-y = mx.sym.Convolution(data=x, weight=w, num_filter=num_filter, kernel=kernel, no_bias=True, pad=pad)
-exe = y.simple_bind(mx.cpu(), x=shape)
+conv_layer = nn.Conv2D(channels=num_filter, kernel_size=kernel, padding=pad)
+conv_layer.initialize()
 
-exe.arg_arrays[0][:] = np.random.normal(size=exe.arg_arrays[0].shape)
-exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape)
-
-exe.forward(is_train=False)
-o = exe.outputs[0]
-t = o.asnumpy()
+data = np.random.normal(size=shape)
+o = conv_layer(data)
+o.wait_to_read()

Review comment:
       it could suggest that wait_to_read is required in normal usage, but as far as I understand is not.




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