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Fix dnnl_readme.md
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docs/python_docs/python/tutorials/performance/backend/dnnl/dnnl_readme.md

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@@ -117,7 +117,7 @@ LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu) CC=$(brew -
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<h2 id="3">Windows</h2>
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On Windows, you can use [Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) and [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) to compile MXNet with Intel oneDNN.
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On Windows, you can use [Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) and [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) to compile MXNet with oneDNN.
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[Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is recommended.
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**Visual Studio 2015**
@@ -202,7 +202,7 @@ conv_layer.initialize()
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data = np.random.normal(size=shape)
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o = conv_layer(data)
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print(o)
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o.wait_to_read()
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```
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More detailed debugging and profiling information can be logged by setting the environment variable 'DNNL_VERBOSE':
@@ -250,7 +250,7 @@ from mxnet import np
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coeff = np.array([[7, 0], [5, 2]])
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y = np.array([14, 18])
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x = np.linalg.solve(coeff, y)
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print(x)
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x.wait_to_read()
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```
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You can get the verbose log output from mkl library by setting environment variable:
@@ -287,16 +287,16 @@ net.add(nn.Activation('relu'))
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net.initialize()
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print("=" * 5, " Not optimized ", "=" * 5)
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o = net(data)
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print(o)
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o.wait_to_read()
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net.optimize_for(data, backend='ONEDNN')
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print("=" * 5, " Optimized ", "=" * 5)
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o = net(data)
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print(o)
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o.wait_to_read()
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```
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Above code snippet should produce similar output to the following one (printed tensors are omitted) :
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Above code snippet should produce following output:
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```
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===== Not optimized =====
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[15:05:43] ../src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU

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