You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository was archived by the owner on Nov 17, 2023. It is now read-only.
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.
120
+
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.
121
121
[Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is recommended.
122
122
123
123
**Visual Studio 2015**
@@ -202,7 +202,7 @@ conv_layer.initialize()
202
202
203
203
data = np.random.normal(size=shape)
204
204
o = conv_layer(data)
205
-
print(o)
205
+
o.wait_to_read()
206
206
```
207
207
208
208
More detailed debugging and profiling information can be logged by setting the environment variable 'DNNL_VERBOSE':
@@ -250,7 +250,7 @@ from mxnet import np
250
250
coeff = np.array([[7, 0], [5, 2]])
251
251
y = np.array([14, 18])
252
252
x = np.linalg.solve(coeff, y)
253
-
print(x)
253
+
x.wait_to_read()
254
254
```
255
255
256
256
You can get the verbose log output from mkl library by setting environment variable:
0 commit comments