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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/06/12 23:13:24 UTC
[GitHub] [incubator-mxnet] ThomasDelteil commented on a change in pull
request #15228: Cleaned up profiling tutorial
ThomasDelteil commented on a change in pull request #15228: Cleaned up profiling tutorial
URL: https://github.com/apache/incubator-mxnet/pull/15228#discussion_r293153561
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File path: docs/tutorials/python/profiler.md
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@@ -34,33 +34,18 @@ y = nd.dot(x, x)
print('Time for matrix multiplication: %f sec\n' % (time() - start))
start = time()
-print(y.asnumpy())
-print('Time for printing the output: %f sec' % (time() - start))
+y_np = y.asnumpy()
+print('Time for converting to numpy: %f sec' % (time() - start))
```
-
**Time for matrix multiplication: 0.005051 sec**<!--notebook-skip-line-->
-[[501.1584 508.29724 495.65237 ... 492.84705 492.69092 490.0481 ]<!--notebook-skip-line-->
-
- [508.81058 507.1822 495.1743 ... 503.10526 497.29315 493.67917]<!--notebook-skip-line-->
-
- [489.56598 499.47015 490.17722 ... 490.99945 488.05008 483.28836]<!--notebook-skip-line-->
-
- ...<!--notebook-skip-line-->
-
- [484.0019 495.7179 479.92142 ... 493.69952 478.89194 487.2074 ]<!--notebook-skip-line-->
-
- [499.64932 507.65094 497.5938 ... 493.0474 500.74512 495.82712]<!--notebook-skip-line-->
+**Time for converting to numpy: 0.167693 sec**<!--notebook-skip-line-->
- [516.0143 519.1715 506.354 ... 510.08878 496.35608 495.42523]]<!--notebook-skip-line-->
-**Time for printing the output: 0.167693 sec**<!--notebook-skip-line-->
+From the timings above, it seems as if converting to numpy takes lot more time than multiplying two large matrices. That doesn't feel right.
-
-From the output above, it seems as if printing the output takes lot more time that multiplying two large matrices. That doesn't feel right.
-
-This is because, in MXNet, all operations are executed asynchronously. So, when `nd.dot(x, x)` returns, the matrix multiplication is not complete, it has only been queued for execution. `asnumpy` in `print(y.asnumpy())` however, waits for the result to be computed and hence takes longer time.
+This is because, in MXNet, all operations are executed asynchronously. So, when `nd.dot(x, x)` returns, the matrix multiplication is not complete, it has only been queued for execution. `asnumpy` however, waits for the result to be computed and hence takes longer time. Other examples of 'blocking' operations include `asscalar` and `wait_to_read`.
Review comment:
`asnumpy` however, has to wait for the result to be actually computed in order to be able to convert it to a numpy array on CPU, hence taking a longer time.
or something like that. To express that it doesn't just wait for no reason.
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