RoeePeleg commented on issue #16427: mxnet.recordio.pack example fails in
python3
URL:
https://github.com/apache/incubator-mxnet/issues/16427#issuecomment-544228137
> Use `s = b'...'` @RoeePeleg
> You can learn the difference of _string_ between python2 and python3
(encoding).
i
RoeePeleg edited a comment on issue #16427: mxnet.recordio.pack example fails
in python3
URL:
https://github.com/apache/incubator-mxnet/issues/16427#issuecomment-544228137
> Use `s = b'...'` @RoeePeleg
> You can learn the difference of _string_ between python2 and python3
(encoding).
kshitij12345 commented on issue #15909: [numpy] random.rand
URL: https://github.com/apache/incubator-mxnet/pull/15909#issuecomment-544229905
@reminisce @haojin2 @xidulu Sorry for the delayed action. Please review.
This is an a
kshitij12345 commented on issue #15331: [fix] missing input log higher order.
URL: https://github.com/apache/incubator-mxnet/pull/15331#issuecomment-544230413
@sxjscience @apeforest @larroy Gentle Ping.
This is an automated me
pedro-abundio-wang commented on issue #16527: ErrStr:no kernel image is
available for execution on the device
URL:
https://github.com/apache/incubator-mxnet/issues/16527#issuecomment-544230734
I am working on my laptop using GeForce GT 750M
TaoLv opened a new pull request #16555: Upgrade MKL-DNN dependency to v1.0
URL: https://github.com/apache/incubator-mxnet/pull/16555
## Description ##
This is an effort from the whole Intel MXNet team. Credits belong to
everyone in the team.
This PR upgrades the 3rdparty/mkldnn de
kshitij12345 commented on issue #13958: Windows builds running out of heap
space in CI
URL:
https://github.com/apache/incubator-mxnet/issues/13958#issuecomment-544235887
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fwindows-cpu/detail/PR-15909/6/
---
kshitij12345 edited a comment on issue #13958: Windows builds running out of
heap space in CI
URL:
https://github.com/apache/incubator-mxnet/issues/13958#issuecomment-544235887
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fwindows-cpu/detail/PR-15909/
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reminisce merged pull request #16554: [DO NOT SQUASH MERGE] Batch merge PRs
URL: https://github.com/apache/incubator-mxnet/pull/16554
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reminisce pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.
from 1648f4c [Numpy] SVD outputs tuple (#16530)
add 5accae0 numpy op doc: max, min, prod (#16506)
reminisce commented on issue #15909: [numpy] random.rand
URL: https://github.com/apache/incubator-mxnet/pull/15909#issuecomment-54427
@kshitij12345 Thanks for your contribution. It's merged through
https://github.com/apache/incubator-mxnet/pull/16554. I added missing
implementation in
gigasquid commented on issue #14415: [Test Failure] Clojure Integration
URL:
https://github.com/apache/incubator-mxnet/issues/14415#issuecomment-544275775
looking into it
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gigasquid commented on issue #14415: [Test Failure] Clojure Integration
URL:
https://github.com/apache/incubator-mxnet/issues/14415#issuecomment-544280438
The failing test is a bit too brittle - it is verifying the saved model of
the mnist. A new attribute has been added so the test is fai
haojin2 closed issue #14415: [Test Failure] Clojure Integration
URL: https://github.com/apache/incubator-mxnet/issues/14415
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haojin2 commented on issue #14415: [Test Failure] Clojure Integration
URL:
https://github.com/apache/incubator-mxnet/issues/14415#issuecomment-544285908
@gigasquid okay I see the cause. But no hurries on the fix for the test, I
think @reminisce has also made changes to make that test worki
haojin2 merged pull request #16537: Fix numpy bugs
URL: https://github.com/apache/incubator-mxnet/pull/16537
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haoj pushed a change to branch master
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from b949716 add interface for rand
add 217ae02 Fix numpy bugs (#16537)
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haojin2 commented on a change in pull request #16391: cuDNN non-persistant
bidirectional RNN dgrad sync fix
URL: https://github.com/apache/incubator-mxnet/pull/16391#discussion_r336800317
##
File path: src/operator/rnn-inl.h
##
@@ -1484,6 +1493,20 @@ class RNNOp {
}
haojin2 commented on a change in pull request #16391: cuDNN non-persistant
bidirectional RNN dgrad sync fix
URL: https://github.com/apache/incubator-mxnet/pull/16391#discussion_r336800317
##
File path: src/operator/rnn-inl.h
##
@@ -1484,6 +1493,20 @@ class RNNOp {
}
haojin2 commented on a change in pull request #16391: cuDNN non-persistant
bidirectional RNN dgrad sync fix
URL: https://github.com/apache/incubator-mxnet/pull/16391#discussion_r336800317
##
File path: src/operator/rnn-inl.h
##
@@ -1484,6 +1493,20 @@ class RNNOp {
}
KellenSunderland merged pull request #15399: Add unit tests for TensorRT
integration and fix some bugs
URL: https://github.com/apache/incubator-mxnet/pull/15399
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kellen pushed a change to branch master
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from 217ae02 Fix numpy bugs (#16537)
add 746cbc5 Add unit tests for TensorRT integration and fix some bugs
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kellen pushed a change to branch master
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from 217ae02 Fix numpy bugs (#16537)
add 746cbc5 Add unit tests for TensorRT integration and fix some bugs
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aaronmarkham pushed a commit to branch asf-site
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ZHAIXINGZHAIYUE commented on issue #16531: Just use fp16 is slower than
mixed_precision! Why?
URL:
https://github.com/apache/incubator-mxnet/issues/16531#issuecomment-544313684
@anirudh2290 I mean the train speed. mixed_precision: 1000 samples/s, just
single fp16: 900 samples/s
-
szha commented on issue #16531: Just use fp16 is slower than mixed_precision!
Why?
URL:
https://github.com/apache/incubator-mxnet/issues/16531#issuecomment-544314191
Hi @ZHAIXINGZHAIYUE, please pay attention to the request for your script as
it's impossible for others to help without know
JiangZhaoh opened a new pull request #16556: [numpy]op test in new pattern
URL: https://github.com/apache/incubator-mxnet/pull/16556
## Description ##
Add numpy test:
- stack
- var
- vdot
- vstack
- zeros_like
some test who use tvm can not pass CI because environment
szha commented on a change in pull request #16207: Bump numpy version >=1.17.0
URL: https://github.com/apache/incubator-mxnet/pull/16207#discussion_r336814314
##
File path: python/setup.py
##
@@ -30,7 +30,12 @@
else:
from setuptools import setup
from setuptools.e
roywei commented on a change in pull request #16532: fix dropout gpu seed
URL: https://github.com/apache/incubator-mxnet/pull/16532#discussion_r336821250
##
File path: src/operator/nn/dropout-inl.h
##
@@ -262,7 +262,7 @@ class DropoutOp {
Tensor(reinterpret_cast(wo
roywei commented on issue #16532: fix dropout gpu seed
URL: https://github.com/apache/incubator-mxnet/pull/16532#issuecomment-544327630
Still investigate why local unit test passed, but CI constantly failed,
seems seed is not fixed in CI.
On local GPU, running the following passed:
roywei commented on issue #16532: fix dropout gpu seed
URL: https://github.com/apache/incubator-mxnet/pull/16532#issuecomment-544327665
cc @eric-haibin-lin @sxjscience
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roywei edited a comment on issue #16532: fix dropout gpu seed
URL: https://github.com/apache/incubator-mxnet/pull/16532#issuecomment-544327630
@wkcn Thanks for the review!
Still investigate why local unit test passed, but CI constantly failed,
seems seed is not fixed in CI.
On loc
szha commented on a change in pull request #16532: fix dropout gpu seed
URL: https://github.com/apache/incubator-mxnet/pull/16532#discussion_r336825080
##
File path: src/operator/nn/dropout-inl.h
##
@@ -262,7 +262,7 @@ class DropoutOp {
Tensor(reinterpret_cast(work
knjwhn commented on issue #13991: Telling about libopenblas to mxnet config
files
URL:
https://github.com/apache/incubator-mxnet/issues/13991#issuecomment-54480
Hello,guys,I made some modification in openblas (add a new cblas_mygemm)and
I wanna to use it in mxnet , is there have a pla
knjwhn opened a new issue #16557: Where is the place that mxnet call cblas_gemm
if I use openblas?
URL: https://github.com/apache/incubator-mxnet/issues/16557
Hello,guys,I made some modification in openblas (add a new function
cblas_mygemm)and I wanna to use it in mxnet , is there have a p
szha commented on issue #16553: Fix for wrong reqs set after switching from
training to inference
URL: https://github.com/apache/incubator-mxnet/pull/16553#issuecomment-544334924
As follow-up:
1. ideally we should be able to test for this explicitly. given that we lack
the utility for s
ptrendx commented on issue #16553: Fix for wrong reqs set after switching from
training to inference
URL: https://github.com/apache/incubator-mxnet/pull/16553#issuecomment-544336113
For 1: this error was found when working on pointwise fusion, which uses
reqs to compile the right code. I d
szha commented on issue #16553: Fix for wrong reqs set after switching from
training to inference
URL: https://github.com/apache/incubator-mxnet/pull/16553#issuecomment-544339535
sounds good to both
This is an automated messa
szha commented on issue #16234: [MXNET-1426] Fix the wrong result of sum, mean,
argmin, argmax when inputs contain inf or nan
URL: https://github.com/apache/incubator-mxnet/pull/16234#issuecomment-544342329
cc @reminisce
Thi
szha commented on issue #16508: Distributed Training using MXNet with Horovod
URL:
https://github.com/apache/incubator-mxnet/issues/16508#issuecomment-544343011
@gentelyang while you're waiting for a response, I'd like to share a
full-fledged example in GluonNLP for BERT-pretraining with h
szha opened a new pull request #16558: [GITHUB] split issue templates
URL: https://github.com/apache/incubator-mxnet/pull/16558
## Description ##
This PR makes use of the multiple issue template feature on GitHub by
splitting the issue templates into bug report, feature request, and flak
kshitij12345 commented on issue #15909: [numpy] random.rand
URL: https://github.com/apache/incubator-mxnet/pull/15909#issuecomment-544349290
@reminisce Oh did not notice it. Thank You. Will close this one.
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kshitij12345 closed pull request #15909: [numpy] random.rand
URL: https://github.com/apache/incubator-mxnet/pull/15909
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reminisce opened a new issue #16559: Tracking mxnet.numpy operator issues for
1.6.0 release
URL: https://github.com/apache/incubator-mxnet/issues/16559
## Issues from running D2L
[numpy2](https://github.com/d2l-ai/d2l-en/tree/numpy2) branch
1. chapter_preliminaries/probability.md
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