[GitHub] [incubator-mxnet] TaoLv commented on pull request #18827: Fix gelu to use erf based algorithm

[GitBox]

TaoLv commented on pull request #18827:
URL: https://github.com/apache/incubator-mxnet/pull/18827#issuecomment-667154138


   > is erf based implementation slower?
   
   They should be similar.



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[GitHub] [incubator-mxnet] TaoLv edited a comment on pull request #18827: Fix gelu to use erf based algorithm

[GitBox]

TaoLv edited a comment on pull request #18827:
URL: https://github.com/apache/incubator-mxnet/pull/18827#issuecomment-667154138


   > is erf based implementation slower?
   
   They should be similar in the library.



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[GitHub] [incubator-mxnet] saudet commented on issue #17783: [RFC] MXNet 2.0 JVM Language development

[GitBox]

saudet commented on issue #17783:
URL: 
https://github.com/apache/incubator-mxnet/issues/17783#issuecomment-667041968


   > We are looking for a robust solution for MXNet Java developers to use 
especially owned and maintained by the Apache MXNet's community. I will be more 
than happy to see if you would like to contribute the source code that generate 
MXNet JavaCpp package to this repo. So we can own the maintainance and 
responsible for the end users that the package is reliable.
   > 
   > At the beginning, we were discussing several ways that we can try to 
preserve a low level Java API for MXNet that anyone who use Java can start 
with. Most of the problems were lying under the ownership and maintainance 
part. I have placed JavaCpp option to option 5 so we can see which one works 
the best in the end.
   
   Sounds good, thanks! If you have any specific concerns about the above, 
please let me know. JNA seems to be maintained by a single person with 
apparently no connections to the AI industry 
(https://dzone.com/articles/scratch-netbeans-itch-matthias) whereas I have to 
maintain anyway as part of my work APIs mainly for OpenCV, FFmpeg, ONNX 
Runtime, and TensorFlow at the moment, but others as well and it tends to vary 
with time, MXNet could become part of those eventually, and I have users paying 
for commercial support of proprietary libraries too, so I think JavaCPP is the 
better option here, but I'm obviously biased. :)



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[GitHub] [incubator-mxnet] szha edited a comment on issue #18824: [RFC] Double dependency for ONNX

[GitBox]

szha edited a comment on issue #18824:
URL: 
https://github.com/apache/incubator-mxnet/issues/18824#issuecomment-666989953


   Can we drop onnx-tensorrt in favor of native tensorrt integration? It seems 
perfectly ok to run that outside of MXNet as it's more of an ONNX feature. It 
would be great to have some insights on what impact this simplification may have



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[GitHub] [incubator-mxnet] szha commented on issue #18824: [RFC] Double dependency for ONNX

[GitBox]

szha commented on issue #18824:
URL: 
https://github.com/apache/incubator-mxnet/issues/18824#issuecomment-666989953


   Can we drop onnx-tensorrt in favor of native tensorrt integration? It seems 
perfectly ok to run that outside of MXNet as it's more of an ONNX feature.



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[GitHub] [incubator-mxnet] bgawrych commented on pull request #18708: [v1.x Backport] Fix softmax, logsoftmax failed on empty ndarray (#18602)

[GitBox]

bgawrych commented on pull request #18708:
URL: https://github.com/apache/incubator-mxnet/pull/18708#issuecomment-666974960


   @mxnet-bot run ci [unix-gpu]



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18708: [v1.x Backport] Fix softmax, logsoftmax failed on empty ndarray (#18602)

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mxnet-bot commented on pull request #18708:
URL: https://github.com/apache/incubator-mxnet/pull/18708#issuecomment-666975045


   Jenkins CI successfully triggered : [unix-gpu]



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[GitHub] [incubator-mxnet] bgawrych commented on pull request #18777: [v1.7.x] ElementWiseSum fix for oneDNN

[GitBox]

bgawrych commented on pull request #18777:
URL: https://github.com/apache/incubator-mxnet/pull/18777#issuecomment-666974179


   > LGTM
   > please add a test case too
   
   @pengzhao-intel Done :)
   
   



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[GitHub] [incubator-mxnet] sandeep-krishnamurthy merged pull request #18816: Add Large Dim Checks for linalg Operators

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sandeep-krishnamurthy merged pull request #18816:
URL: https://github.com/apache/incubator-mxnet/pull/18816


   



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[GitHub] [incubator-mxnet] szha merged pull request #18748: Fixup move gluon.metric api docs

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szha merged pull request #18748:
URL: https://github.com/apache/incubator-mxnet/pull/18748


   



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[GitHub] [incubator-mxnet] jinboci removed a comment on pull request #18818: Fixing tvmgpu issue & not restoring tvmop checks

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jinboci removed a comment on pull request #18818:
URL: https://github.com/apache/incubator-mxnet/pull/18818#issuecomment-666905291


   @mxnet-bot run ci [all]
   



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[GitHub] [incubator-mxnet] jinboci commented on pull request #18818: Fixing tvmgpu issue & not restoring tvmop checks

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jinboci commented on pull request #18818:
URL: https://github.com/apache/incubator-mxnet/pull/18818#issuecomment-666906994


   @mxnet-bot run ci [all]



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[GitHub] [incubator-mxnet] jinboci edited a comment on pull request #18818: Fixing tvmgpu issue & not restoring tvmop checks

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jinboci edited a comment on pull request #18818:
URL: https://github.com/apache/incubator-mxnet/pull/18818#issuecomment-666905291


   @mxnet-bot run ci [all]
   



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[GitHub] [incubator-mxnet] jinboci commented on pull request #18818: Fixing tvmgpu issue & not restoring tvmop checks

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jinboci commented on pull request #18818:
URL: https://github.com/apache/incubator-mxnet/pull/18818#issuecomment-666905291


   run ci [all]
   



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[GitHub] [incubator-mxnet] bartekkuncer commented on pull request #18822: [v1.x][submodule] Upgrade to oneDNN v1.6

[GitBox]

bartekkuncer commented on pull request #18822:
URL: https://github.com/apache/incubator-mxnet/pull/18822#issuecomment-666893964


   @ChaiBapchya @pengzhao-intel all tests are passing now.



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18827: Fix gelu to use erf based algorithm

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mxnet-bot commented on pull request #18827:
URL: https://github.com/apache/incubator-mxnet/pull/18827#issuecomment-666886412


   Jenkins CI successfully triggered : [unix-cpu]



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[GitHub] [incubator-mxnet] szha commented on pull request #18827: Fix gelu to use erf based algorithm

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szha commented on pull request #18827:
URL: https://github.com/apache/incubator-mxnet/pull/18827#issuecomment-666886394


   @mxnet-bot run ci [unix-cpu]



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[GitHub] [incubator-mxnet] szha merged pull request #18796: Enable DIST_KVSTORE by default in staticbuild

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szha merged pull request #18796:
URL: https://github.com/apache/incubator-mxnet/pull/18796


   



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[GitHub] [incubator-mxnet] szha merged pull request #18828: add adaptive left margin for python site document body

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szha merged pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828


   



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[GitHub] [incubator-mxnet] szha merged pull request #18656: [NumPy] DLPack refactor and npx.from_numpy

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szha merged pull request #18656:
URL: https://github.com/apache/incubator-mxnet/pull/18656


   



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[GitHub] [incubator-mxnet] atulk79 commented on issue #14468: TypeError: 'float' object is not subscriptable`

[GitBox]

atulk79 commented on issue #14468:
URL: 
https://github.com/apache/incubator-mxnet/issues/14468#issuecomment-666862171


   As Wuxun Zhang  commented, define a function reset_local() in the class 
MultiBoxMetric to override the new method



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18828: add adaptive left margin for python site document body

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mxnet-bot commented on pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828#issuecomment-666850906


   Jenkins CI successfully triggered : [unix-cpu]



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[GitHub] [incubator-mxnet] ys2843 edited a comment on pull request #18828: add adaptive left margin for python site document body

[GitBox]

ys2843 edited a comment on pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828#issuecomment-666721621


   @mxnet-bot run ci [ unix-cpu]



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[GitHub] [incubator-mxnet] szha commented on pull request #18830: Fix nn.dense doc

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szha commented on pull request #18830:
URL: https://github.com/apache/incubator-mxnet/pull/18830#issuecomment-666821159


   thanks for the fix @eric-haibin-lin. and nice job spotting it, @jmracek!



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18830: Fix nn.dense doc

[GitBox]

mxnet-bot commented on pull request #18830:
URL: https://github.com/apache/incubator-mxnet/pull/18830#issuecomment-666818833


   Hey @eric-haibin-lin , Thanks for submitting the PR 
   All tests are already queued to run once. If tests fail, you can trigger one 
or more tests again with the following commands: 
   - To trigger all jobs: @mxnet-bot run ci [all] 
   - To trigger specific jobs: @mxnet-bot run ci [job1, job2] 
   *** 
   **CI supported jobs**: [centos-cpu, unix-gpu, unix-cpu, sanity, windows-cpu, 
clang, centos-gpu, windows-gpu, website, edge, miscellaneous]
   *** 
   _Note_: 
Only following 3 categories can trigger CI :PR Author, MXNet Committer, 
Jenkins Admin. 
   All CI tests must pass before the PR can be merged. 
   



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[GitHub] [incubator-mxnet] eric-haibin-lin opened a new pull request #18830: Fix nn.dense doc

[GitBox]

eric-haibin-lin opened a new pull request #18830:
URL: https://github.com/apache/incubator-mxnet/pull/18830


   ## Description ##
   Found by @jmracek 
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
   - [ ] To the best of my knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [ ] Feature1, tests, (and when applicable, API doc)
   - [ ] Feature2, tests, (and when applicable, API doc)
   
   ## Comments ##
   - If this change is a backward incompatible change, why must this change be 
made.
   - Interesting edge cases to note here
   



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[GitHub] [incubator-mxnet-ci] szha commented on pull request #27: Serverless based Lambda function for Labelling Successful PRs with pr-awaiting-review

[GitBox]

szha commented on pull request #27:
URL: https://github.com/apache/incubator-mxnet-ci/pull/27#issuecomment-666814614


   maybe we could automate the PR labeling as the following pseudo code 
describes:
   ```
   if WIP in title or PR is draft or CI failed:
   pr-work-in-progress
   elif CI has not started yet or CI is in progress:
   pr-awaiting-testing
   else: # CI passed checks
   if pr has at least one approval and no request changes:
   pr-awaiting-merge
   elif pr has no review or all reviews have been dismissed/re-requested:
   pr-awaiting-review
   else: # pr has a review that hasn't been dismissed yet no approval
   pr-awaiting-response
   ```
   



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18803: Add unit tests for potri and potrf backward and check output shape.

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mxnet-bot commented on pull request #18803:
URL: https://github.com/apache/incubator-mxnet/pull/18803#issuecomment-666810421


   Jenkins CI successfully triggered : [unix-gpu]



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[GitHub] [incubator-mxnet] josephevans commented on pull request #18803: Add unit tests for potri and potrf backward and check output shape.

[GitBox]

josephevans commented on pull request #18803:
URL: https://github.com/apache/incubator-mxnet/pull/18803#issuecomment-666810367


   @mxnet-bot run ci [unix-gpu]



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[GitHub] [incubator-mxnet] szha opened a new issue #18829: test_sparse_mathematical_core segmentation fault

[GitBox]

szha opened a new issue #18829:
URL: https://github.com/apache/incubator-mxnet/issues/18829


   ## Description
   unittest/test_sparse_operator.py::test_sparse_mathematical_core segmentation 
fault
   
   ## Occurrences
   
   
https://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fcentos-cpu/detail/PR-18656/18/pipeline/#step-259-log-1771
   



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18796: Enable DIST_KVSTORE by default in staticbuild

[GitBox]

mxnet-bot commented on pull request #18796:
URL: https://github.com/apache/incubator-mxnet/pull/18796#issuecomment-666762787


   Jenkins CI successfully triggered : [windows-gpu]



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[GitHub] [incubator-mxnet] leezu commented on pull request #18796: Enable DIST_KVSTORE by default in staticbuild

[GitBox]

leezu commented on pull request #18796:
URL: https://github.com/apache/incubator-mxnet/pull/18796#issuecomment-666762751


   @mxnet-bot run ci [windows-gpu]



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[GitHub] [incubator-mxnet] szha commented on a change in pull request #18656: [NumPy] DLPack refactor and npx.from_numpy

[GitBox]

szha commented on a change in pull request #18656:
URL: https://github.com/apache/incubator-mxnet/pull/18656#discussion_r463281160



##
File path: tests/python/unittest/test_numpy_ndarray.py
##
@@ -1369,3 +1369,31 @@ def test_dlpack(dtype, size):
 same(a_np+1, b)
 same(a_np+2, c)
 same(a_np+2, a_copy)
+
+@use_np
+@pytest.mark.parametrize('np_array', [
+# ordinary numpy array
+_np.array([[1, 2], [3, 4], [5, 6]], dtype="float32"),
+# 0-dim
+_np.array((1, )).reshape(()),
+# 0-size
+_np.array(()).reshape((1, 0, 2)),
+])
+@pytest.mark.parametrize('zero_copy', [False, True])
+def test_from_numpy(np_array, zero_copy):
+# Test zero_copy
+mx_array = mx.npx.from_numpy(np_array, zero_copy=zero_copy)
+mx.test_utils.assert_almost_equal(np_array, mx_array.asnumpy())
+
+def test_from_numpy_exception():
+np_array = _np.array([[1, 2], [3, 4], [5, 6]], dtype="float32")
+mx_array = mx.npx.from_numpy(np_array)
+with pytest.raises(ValueError):
+np_array[2, 1] = 0

Review comment:
   already covered in another test a few lines up.





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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18828: add adaptive left margin for python site document body

[GitBox]

mxnet-bot commented on pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828#issuecomment-666721655


   Jenkins CI successfully triggered : [unix-cpu]



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[GitHub] [incubator-mxnet] ys2843 commented on pull request #18828: add adaptive left margin for python site document body

[GitBox]

ys2843 commented on pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828#issuecomment-666721621


   @mxnet-bot run ci [unix-cpu]



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[GitHub] [incubator-mxnet] szha commented on a change in pull request #18656: [NumPy] DLPack refactor and npx.from_numpy

[GitBox]

szha commented on a change in pull request #18656:
URL: https://github.com/apache/incubator-mxnet/pull/18656#discussion_r463281160



##
File path: tests/python/unittest/test_numpy_ndarray.py
##
@@ -1369,3 +1369,31 @@ def test_dlpack(dtype, size):
 same(a_np+1, b)
 same(a_np+2, c)
 same(a_np+2, a_copy)
+
+@use_np
+@pytest.mark.parametrize('np_array', [
+# ordinary numpy array
+_np.array([[1, 2], [3, 4], [5, 6]], dtype="float32"),
+# 0-dim
+_np.array((1, )).reshape(()),
+# 0-size
+_np.array(()).reshape((1, 0, 2)),
+])
+@pytest.mark.parametrize('zero_copy', [False, True])
+def test_from_numpy(np_array, zero_copy):
+# Test zero_copy
+mx_array = mx.npx.from_numpy(np_array, zero_copy=zero_copy)
+mx.test_utils.assert_almost_equal(np_array, mx_array.asnumpy())
+
+def test_from_numpy_exception():
+np_array = _np.array([[1, 2], [3, 4], [5, 6]], dtype="float32")
+mx_array = mx.npx.from_numpy(np_array)
+with pytest.raises(ValueError):
+np_array[2, 1] = 0

Review comment:
   already covered in another test a few lines up.





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[GitHub] [incubator-mxnet] leezu commented on a change in pull request #18656: [NumPy] DLPack refactor and npx.from_numpy

[GitBox]

leezu commented on a change in pull request #18656:
URL: https://github.com/apache/incubator-mxnet/pull/18656#discussion_r463265822



##
File path: tests/python/unittest/test_numpy_ndarray.py
##
@@ -1369,3 +1369,31 @@ def test_dlpack(dtype, size):
 same(a_np+1, b)
 same(a_np+2, c)
 same(a_np+2, a_copy)
+
+@use_np
+@pytest.mark.parametrize('np_array', [
+# ordinary numpy array
+_np.array([[1, 2], [3, 4], [5, 6]], dtype="float32"),
+# 0-dim
+_np.array((1, )).reshape(()),
+# 0-size
+_np.array(()).reshape((1, 0, 2)),
+])
+@pytest.mark.parametrize('zero_copy', [False, True])
+def test_from_numpy(np_array, zero_copy):
+# Test zero_copy
+mx_array = mx.npx.from_numpy(np_array, zero_copy=zero_copy)
+mx.test_utils.assert_almost_equal(np_array, mx_array.asnumpy())
+
+def test_from_numpy_exception():
+np_array = _np.array([[1, 2], [3, 4], [5, 6]], dtype="float32")
+mx_array = mx.npx.from_numpy(np_array)
+with pytest.raises(ValueError):
+np_array[2, 1] = 0

Review comment:
   Test that no exception is thrown if `zero_copy=False`?





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[GitHub] [incubator-mxnet-ci] szha commented on a change in pull request #27: Serverless based Lambda function for Labelling Successful PRs with pr-awaiting-review

[GitBox]

szha commented on a change in pull request #27:
URL: https://github.com/apache/incubator-mxnet-ci/pull/27#discussion_r463262723



##
File path: services/lambda-label-successful-prs/CIBot.py
##
@@ -0,0 +1,199 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import json
+import os
+import logging
+import secret_manager
+
+from jenkinsapi.jenkins import Jenkins
+from github import Github
+
+
+class CIBot:
+def __init__(self,
+ repo=os.environ.get("repo"),
+ github_personal_access_token=None,
+ jenkins_url=os.environ.get("jenkins_url"),
+ jenkins_username=None,
+ jenkins_password=None,
+ apply_secret=True):
+"""
+Initializes the CI Bot
+:param repo: GitHub repository that is being referenced
+:param github_personal_access_token: GitHub authentication token 
(Personal access token)
+:param jenkins_url: Jenkins URL
+:param jenkins_username: Jenkins Username for JenkinsAPI
+:param jenkins_password: Jenkins Password for JenkinsAPI
+:param apply_secret: GitHub secret credential (Secret credential that 
is unique to a GitHub developer)
+"""
+self.repo = repo
+self.github_personal_access_token = github_personal_access_token
+self.jenkins_username = jenkins_username
+self.jenkins_password = jenkins_password
+if apply_secret:
+self._get_secret()
+self.all_jobs = None
+self.jenkins_url = jenkins_url
+self.prs_labeled = 0
+self.prs_already_labeled = 0
+self.prs_status_fail = 0
+self.prs_unknown_job = 0
+self.prs_no_build = 0
+
+def _get_secret(self):
+"""
+This method is to get secret value from Secrets Manager
+"""
+secret = json.loads(secret_manager.get_secret())
+self.github_personal_access_token = 
secret["github_personal_access_token"]
+self.jenkins_username = secret["jenkins_username"]
+self.jenkins_password = secret["jenkins_password"]
+
+def _get_github_object(self):
+"""
+This method returns github object initialized with Github personal 
access token
+"""
+github_obj = Github(self.github_personal_access_token)
+return github_obj
+
+def _find_all_open_prs(self, github_obj):
+"""
+This method identifies all the open PRs in the given repository
+It returns a Paginated list of Pull Request objects sorted in
+descending order of created date i.e. latest PR first
+:param github_obj
+"""
+repo = github_obj.get_repo(self.repo)
+pulls = repo.get_pulls(state='open', sort='created', direction='desc')
+return pulls
+
+def _get_jenkins_obj(self):
+"""
+This method returns an object of Jenkins instantiated using username, 
password
+"""
+return Jenkins(self.jenkins_url, username=self.jenkins_username, 
password=self.jenkins_password)
+
+def _get_checks(self):
+"""
+This method finds out all the checks that are currently supported as 
part of CI
+"""
+# for now hardcoding list of checks
+# ideally use Jenkins API to query list of checks and parse it (bit 
complicated)
+all_checks = ['clang', 'edge', 'centos-cpu', 'centos-gpu', 
'windows-cpu', 'windows-gpu', 'miscellaneous', 'unix-cpu', 'unix-gpu', 
'website', 'sanity']
+return all_checks
+
+def _is_successful(self, pr):
+"""
+This method checks if all the pipelines corresponding to a latest 
build of the PR are successful
+:param pr PullRequest object
+"""
+# get jenkins object
+jenkins_obj = self._get_jenkins_obj()
+
+# get PR number
+pr_number = pr.number
+
+# get a list of checks
+# Github says "status checks"
+# Jenkins refers to it as a job name
+checks = self._get_checks()
+
+# iterate over the checks
+for check in checks:
+# Jenkins Job name
+name = "mxnet-validation/" + check + "/PR-" + str(pr_number)
+
+try:
+   

[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18803: Add unit tests for potri and potrf backward and check output shape.

[GitBox]

mxnet-bot commented on pull request #18803:
URL: https://github.com/apache/incubator-mxnet/pull/18803#issuecomment-79145


   Jenkins CI successfully triggered : [unix-gpu]



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[GitHub] [incubator-mxnet] josephevans commented on pull request #18803: Add unit tests for potri and potrf backward and check output shape.

[GitBox]

josephevans commented on pull request #18803:
URL: https://github.com/apache/incubator-mxnet/pull/18803#issuecomment-79106


   @mxnet-bot run ci [unix-gpu]



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[GitHub] [incubator-mxnet] ChaiBapchya commented on pull request #18797: [v1.x] Mkldnn header fix v1x for nightly binaries

[GitBox]

ChaiBapchya commented on pull request #18797:
URL: https://github.com/apache/incubator-mxnet/pull/18797#issuecomment-47689


   Sure. Thanks Sheng for jumping in. I'll wait until the branch protection is 
fixed.



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[GitHub] [incubator-mxnet-ci] marcoabreu commented on a change in pull request #27: Serverless based Lambda function for Labelling Successful PRs with pr-awaiting-review

[GitBox]

marcoabreu commented on a change in pull request #27:
URL: https://github.com/apache/incubator-mxnet-ci/pull/27#discussion_r463235721



##
File path: services/lambda-label-successful-prs/CIBot.py
##
@@ -0,0 +1,199 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import json
+import os
+import logging
+import secret_manager
+
+from jenkinsapi.jenkins import Jenkins
+from github import Github
+
+
+class CIBot:
+def __init__(self,
+ repo=os.environ.get("repo"),
+ github_personal_access_token=None,
+ jenkins_url=os.environ.get("jenkins_url"),
+ jenkins_username=None,
+ jenkins_password=None,
+ apply_secret=True):
+"""
+Initializes the CI Bot
+:param repo: GitHub repository that is being referenced
+:param github_personal_access_token: GitHub authentication token 
(Personal access token)
+:param jenkins_url: Jenkins URL
+:param jenkins_username: Jenkins Username for JenkinsAPI
+:param jenkins_password: Jenkins Password for JenkinsAPI
+:param apply_secret: GitHub secret credential (Secret credential that 
is unique to a GitHub developer)
+"""
+self.repo = repo
+self.github_personal_access_token = github_personal_access_token
+self.jenkins_username = jenkins_username
+self.jenkins_password = jenkins_password
+if apply_secret:
+self._get_secret()
+self.all_jobs = None
+self.jenkins_url = jenkins_url
+self.prs_labeled = 0
+self.prs_already_labeled = 0
+self.prs_status_fail = 0
+self.prs_unknown_job = 0
+self.prs_no_build = 0
+
+def _get_secret(self):
+"""
+This method is to get secret value from Secrets Manager
+"""
+secret = json.loads(secret_manager.get_secret())
+self.github_personal_access_token = 
secret["github_personal_access_token"]
+self.jenkins_username = secret["jenkins_username"]
+self.jenkins_password = secret["jenkins_password"]
+
+def _get_github_object(self):
+"""
+This method returns github object initialized with Github personal 
access token
+"""
+github_obj = Github(self.github_personal_access_token)
+return github_obj
+
+def _find_all_open_prs(self, github_obj):
+"""
+This method identifies all the open PRs in the given repository
+It returns a Paginated list of Pull Request objects sorted in
+descending order of created date i.e. latest PR first
+:param github_obj
+"""
+repo = github_obj.get_repo(self.repo)
+pulls = repo.get_pulls(state='open', sort='created', direction='desc')
+return pulls
+
+def _get_jenkins_obj(self):
+"""
+This method returns an object of Jenkins instantiated using username, 
password
+"""
+return Jenkins(self.jenkins_url, username=self.jenkins_username, 
password=self.jenkins_password)
+
+def _get_checks(self):
+"""
+This method finds out all the checks that are currently supported as 
part of CI
+"""
+# for now hardcoding list of checks
+# ideally use Jenkins API to query list of checks and parse it (bit 
complicated)
+all_checks = ['clang', 'edge', 'centos-cpu', 'centos-gpu', 
'windows-cpu', 'windows-gpu', 'miscellaneous', 'unix-cpu', 'unix-gpu', 
'website', 'sanity']
+return all_checks
+
+def _is_successful(self, pr):
+"""
+This method checks if all the pipelines corresponding to a latest 
build of the PR are successful
+:param pr PullRequest object
+"""
+# get jenkins object
+jenkins_obj = self._get_jenkins_obj()
+
+# get PR number
+pr_number = pr.number
+
+# get a list of checks
+# Github says "status checks"
+# Jenkins refers to it as a job name
+checks = self._get_checks()
+
+# iterate over the checks
+for check in checks:
+# Jenkins Job name
+name = "mxnet-validation/" + check + "/PR-" + str(pr_number)
+
+

[GitHub] [incubator-mxnet] ys2843 edited a comment on pull request #18828: add adaptive left margin for python site document body

[GitBox]

ys2843 edited a comment on pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828#issuecomment-05001


   @mxnet-label-bot  add [Website]



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18828: add adaptive left margin for python site document body

[GitBox]

mxnet-bot commented on pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828#issuecomment-05014


   Undefined action detected. 
   Permissible actions are : run ci [all], run ci [job1, job2] 
   Example : @mxnet-bot run ci [all] 
   Example : @mxnet-bot run ci [centos-cpu, clang]



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[GitHub] [incubator-mxnet] ys2843 opened a new pull request #18828: add adaptive left margin for python site document body

[GitBox]

ys2843 opened a new pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828


   ## Description ##
#18798 refactored python site document body layout, it missed a left margin 
for small screen. This PR added the missing css.
   
   ### Changes ###
   - [ ] add adaptive left margin for python site document body
   
   ## Comments ##
   - Preview: pending
   
   



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[GitHub] [incubator-mxnet] ys2843 commented on pull request #18828: add adaptive left margin for python site document body

[GitBox]

ys2843 commented on pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828#issuecomment-05001


   @mxnet-bot add [Website]



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18828: add adaptive left margin for python site document body

[GitBox]

mxnet-bot commented on pull request #18828:
URL: https://github.com/apache/incubator-mxnet/pull/18828#issuecomment-04817


   Hey @ys2843 , Thanks for submitting the PR 
   All tests are already queued to run once. If tests fail, you can trigger one 
or more tests again with the following commands: 
   - To trigger all jobs: @mxnet-bot run ci [all] 
   - To trigger specific jobs: @mxnet-bot run ci [job1, job2] 
   *** 
   **CI supported jobs**: [windows-gpu, sanity, windows-cpu, centos-cpu, clang, 
unix-cpu, centos-gpu, unix-gpu, website, miscellaneous, edge]
   *** 
   _Note_: 
Only following 3 categories can trigger CI :PR Author, MXNet Committer, 
Jenkins Admin. 
   All CI tests must pass before the PR can be merged. 
   



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[GitHub] [incubator-mxnet] szha closed issue #18809: test_gluon_dirichlet intermittently fails on CPU

[GitBox]

szha closed issue #18809:
URL: https://github.com/apache/incubator-mxnet/issues/18809


   



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[GitHub] [incubator-mxnet] szha edited a comment on pull request #18817: Fix dirichlet flaky tests

[GitBox]

szha edited a comment on pull request #18817:
URL: https://github.com/apache/incubator-mxnet/pull/18817#issuecomment-666585695


   thanks for the fix. looks like we still have other stability issues with 
unix-cpu tests but they were not related to the dirichlet tests anymore.



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[GitHub] [incubator-mxnet] szha merged pull request #18817: Fix dirichlet flaky tests

[GitBox]

szha merged pull request #18817:
URL: https://github.com/apache/incubator-mxnet/pull/18817


   



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[GitHub] [incubator-mxnet] szha commented on pull request #18817: Fix dirichlet flaky tests

[GitBox]

szha commented on pull request #18817:
URL: https://github.com/apache/incubator-mxnet/pull/18817#issuecomment-666585695


   thanks. looks like we still have other stability issues with unix-cpu tests 
but they were not related to the dirichlet tests anymore.



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[GitHub] [incubator-mxnet] szha commented on pull request #18797: [v1.x] Mkldnn header fix v1x for nightly binaries

[GitBox]

szha commented on pull request #18797:
URL: https://github.com/apache/incubator-mxnet/pull/18797#issuecomment-666574831


   I think when enabling the branch protection, we accidentally turned on 
"Require branches to be up to date before merging". I'm requesting to disable 
it in https://issues.apache.org/jira/browse/INFRA-20616. Don't worry about 
updating the branch in this PR for now.



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[GitHub] [incubator-mxnet] szha commented on pull request #18706: [1.x] Revert Fix memory leaks in Gluon in 1.x branch

[GitBox]

szha commented on pull request #18706:
URL: https://github.com/apache/incubator-mxnet/pull/18706#issuecomment-666574683


   I think when enabling the branch protection, we accidentally turned on 
"Require branches to be up to date before merging". I'm requesting to disable 
it in https://issues.apache.org/jira/browse/INFRA-20616. Don't worry about 
updating the branch in this PR for now.



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[GitHub] [incubator-mxnet] szha commented on pull request #18785: [CI][1.x] Cherrypick: Upgrade unix gpu toolchain (#18186)

[GitBox]

szha commented on pull request #18785:
URL: https://github.com/apache/incubator-mxnet/pull/18785#issuecomment-666574414


   I think when enabling the branch protection, we accidentally turned on 
"Require branches to be up to date before merging". I'm requesting to disable 
it in https://issues.apache.org/jira/browse/INFRA-20616. Don't worry about 
updating the branch in this PR for now.



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[GitHub] [incubator-mxnet] Zha0q1 commented on a change in pull request #18816: Add Large Dim Checks for linalg Operators

[GitBox]

Zha0q1 commented on a change in pull request #18816:
URL: https://github.com/apache/incubator-mxnet/pull/18816#discussion_r463180088



##
File path: tests/nightly/test_large_array.py
##
@@ -1350,6 +1351,50 @@ def run_trsm(inp):
 check_batch_trsm()
 
 
+def test_linalg_large_dim():
+def check_gemm():
+A = nd.ones(shape=(1, INT32_MAX + 1, 1))

Review comment:
   Yeah that would make sense. I have moved the tests to 
test_large_vector.py, which I kept the original name to avoid naming 
discrepancy with master. Also comments were added to the tests





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[GitHub] [incubator-mxnet-ci] szha commented on a change in pull request #27: Serverless based Lambda function for Labelling Successful PRs with pr-awaiting-review

[GitBox]

szha commented on a change in pull request #27:
URL: https://github.com/apache/incubator-mxnet-ci/pull/27#discussion_r463176409



##
File path: services/lambda-label-successful-prs/CIBot.py
##
@@ -0,0 +1,199 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import json
+import os
+import logging
+import secret_manager
+
+from jenkinsapi.jenkins import Jenkins
+from github import Github
+
+
+class CIBot:
+def __init__(self,
+ repo=os.environ.get("repo"),
+ github_personal_access_token=None,
+ jenkins_url=os.environ.get("jenkins_url"),
+ jenkins_username=None,
+ jenkins_password=None,
+ apply_secret=True):
+"""
+Initializes the CI Bot
+:param repo: GitHub repository that is being referenced
+:param github_personal_access_token: GitHub authentication token 
(Personal access token)
+:param jenkins_url: Jenkins URL
+:param jenkins_username: Jenkins Username for JenkinsAPI
+:param jenkins_password: Jenkins Password for JenkinsAPI
+:param apply_secret: GitHub secret credential (Secret credential that 
is unique to a GitHub developer)
+"""
+self.repo = repo
+self.github_personal_access_token = github_personal_access_token
+self.jenkins_username = jenkins_username
+self.jenkins_password = jenkins_password
+if apply_secret:
+self._get_secret()
+self.all_jobs = None
+self.jenkins_url = jenkins_url
+self.prs_labeled = 0
+self.prs_already_labeled = 0
+self.prs_status_fail = 0
+self.prs_unknown_job = 0
+self.prs_no_build = 0
+
+def _get_secret(self):
+"""
+This method is to get secret value from Secrets Manager
+"""
+secret = json.loads(secret_manager.get_secret())
+self.github_personal_access_token = 
secret["github_personal_access_token"]
+self.jenkins_username = secret["jenkins_username"]
+self.jenkins_password = secret["jenkins_password"]
+
+def _get_github_object(self):
+"""
+This method returns github object initialized with Github personal 
access token
+"""
+github_obj = Github(self.github_personal_access_token)
+return github_obj
+
+def _find_all_open_prs(self, github_obj):
+"""
+This method identifies all the open PRs in the given repository
+It returns a Paginated list of Pull Request objects sorted in
+descending order of created date i.e. latest PR first
+:param github_obj
+"""
+repo = github_obj.get_repo(self.repo)
+pulls = repo.get_pulls(state='open', sort='created', direction='desc')
+return pulls
+
+def _get_jenkins_obj(self):
+"""
+This method returns an object of Jenkins instantiated using username, 
password
+"""
+return Jenkins(self.jenkins_url, username=self.jenkins_username, 
password=self.jenkins_password)
+
+def _get_checks(self):
+"""
+This method finds out all the checks that are currently supported as 
part of CI
+"""
+# for now hardcoding list of checks
+# ideally use Jenkins API to query list of checks and parse it (bit 
complicated)
+all_checks = ['clang', 'edge', 'centos-cpu', 'centos-gpu', 
'windows-cpu', 'windows-gpu', 'miscellaneous', 'unix-cpu', 'unix-gpu', 
'website', 'sanity']
+return all_checks
+
+def _is_successful(self, pr):
+"""
+This method checks if all the pipelines corresponding to a latest 
build of the PR are successful
+:param pr PullRequest object
+"""
+# get jenkins object
+jenkins_obj = self._get_jenkins_obj()
+
+# get PR number
+pr_number = pr.number
+
+# get a list of checks
+# Github says "status checks"
+# Jenkins refers to it as a job name
+checks = self._get_checks()
+
+# iterate over the checks
+for check in checks:
+# Jenkins Job name
+name = "mxnet-validation/" + check + "/PR-" + str(pr_number)
+
+try:
+   

[GitHub] [incubator-mxnet] szha commented on a change in pull request #18797: [v1.x] Mkldnn header fix v1x for nightly binaries

[GitBox]

szha commented on a change in pull request #18797:
URL: https://github.com/apache/incubator-mxnet/pull/18797#discussion_r463170714



##
File path: tools/pip/setup.py
##
@@ -156,10 +156,9 @@ def skip_markdown_comments(md):
 
 package_data = {'mxnet': [os.path.join('mxnet', 
os.path.basename(LIB_PATH[0]))],
 'dmlc_tracker': []}
-if variant.endswith('MKL'):
-if platform.system() == 'Darwin':
-shutil.copytree(os.path.join(CURRENT_DIR, 
'mxnet-build/3rdparty/mkldnn/build/install/include'),
-os.path.join(CURRENT_DIR, 'mxnet/include/mkldnn'))
+if variant != 'NATIVE':
+shutil.copytree(os.path.join(CURRENT_DIR, 
'mxnet-build/3rdparty/mkldnn/include'),
+os.path.join(CURRENT_DIR, 'mxnet/include/mkldnn'))

Review comment:
   there's a flag in mxnet runtime library on whether mkldnn is enabled. 
instead of relying on variant it will be more robust to switch to that as 
condition instead.





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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18574: Update the onnx-tensorrt submodule

[GitBox]

mxnet-bot commented on pull request #18574:
URL: https://github.com/apache/incubator-mxnet/pull/18574#issuecomment-666560198


   Jenkins CI successfully triggered : [centos-cpu]



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[GitHub] [incubator-mxnet] Kh4L commented on pull request #18574: Update the onnx-tensorrt submodule

[GitBox]

Kh4L commented on pull request #18574:
URL: https://github.com/apache/incubator-mxnet/pull/18574#issuecomment-666560169


   @mxnet-bot run ci [centos-cpu]



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[GitHub] [incubator-mxnet] sandeep-krishnamurthy merged pull request #18825: [v1.x][LT] Add forward & backward linalg.gemm test for large size

[GitBox]

sandeep-krishnamurthy merged pull request #18825:
URL: https://github.com/apache/incubator-mxnet/pull/18825


   



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[GitHub] [incubator-mxnet] mseth10 commented on pull request #18779: [WIP] Support extra inputs for subgraph ops

[GitBox]

mseth10 commented on pull request #18779:
URL: https://github.com/apache/incubator-mxnet/pull/18779#issuecomment-666549341


   > @mseth10 @rondogency should we change `supportedOps` to use the `Graph` 
class too instead of passing the JSON string like:
   > 
   > ```
   > MXReturnValue mySupportedOps(const Graph& g,
   >  std::vector* ids,
   >  const std::unordered_map& options)
   > ```
   
   Would we need to provide users with a Graph parser then, just like we 
provide a JsonParser currently?



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[GitHub] [incubator-mxnet] josephevans commented on pull request #18803: Add unit tests for potri and potrf backward and check output shape.

[GitBox]

josephevans commented on pull request #18803:
URL: https://github.com/apache/incubator-mxnet/pull/18803#issuecomment-666526667


   @mxnet-bot run ci [unix-gpu]



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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #18816: Add Large Dim Checks for linalg Operators

[GitBox]

access2rohit commented on a change in pull request #18816:
URL: https://github.com/apache/incubator-mxnet/pull/18816#discussion_r462769587



##
File path: tests/nightly/test_large_array.py
##
@@ -1350,6 +1351,50 @@ def run_trsm(inp):
 check_batch_trsm()
 
 
+def test_linalg_large_dim():
+def check_gemm():
+A = nd.ones(shape=(1, INT32_MAX + 1, 1))

Review comment:
   @Zha0q1 the vector file generally houses tests for operators with a 
single dimension that exceeds 2^32 range. Please address what is suggested by 
@ChaiBapchya and move it to vector tests. Feel free to rename the file to 
`test_large_dimensions.py`





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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #18816: Add Large Dim Checks for linalg Operators

[GitBox]

access2rohit commented on a change in pull request #18816:
URL: https://github.com/apache/incubator-mxnet/pull/18816#discussion_r462769587



##
File path: tests/nightly/test_large_array.py
##
@@ -1350,6 +1351,50 @@ def run_trsm(inp):
 check_batch_trsm()
 
 
+def test_linalg_large_dim():
+def check_gemm():
+A = nd.ones(shape=(1, INT32_MAX + 1, 1))

Review comment:
   @Zha0q1 the vector file generally houses tests for operators with a 
single dimension that exceeds 2^32 range. Please address what is suggested by 
@ChaiBapchya and move it to vector tests. Feel free to rename it to 
`test_large_dimensions.py`





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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #18816: Add Large Dim Checks for linalg Operators

[GitBox]

access2rohit commented on a change in pull request #18816:
URL: https://github.com/apache/incubator-mxnet/pull/18816#discussion_r462769587



##
File path: tests/nightly/test_large_array.py
##
@@ -1350,6 +1351,50 @@ def run_trsm(inp):
 check_batch_trsm()
 
 
+def test_linalg_large_dim():
+def check_gemm():
+A = nd.ones(shape=(1, INT32_MAX + 1, 1))

Review comment:
   @Zha0q1 the vector file generally houses tests for operators with a 
single dimension that exceeds 2^32 range. Please address what is suggested by 
@ChaiBapchya and move it to vector tests. 





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[GitHub] [incubator-mxnet] leezu commented on issue #18667: MXNet serialization format depends on endianness

[GitBox]

leezu commented on issue #18667:
URL: 
https://github.com/apache/incubator-mxnet/issues/18667#issuecomment-666464456


   Are you sure the build succeed? Above logs are only related to the build 
configuration and not the actual build



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[GitHub] [incubator-mxnet] Masquerade0097 commented on issue #18667: MXNet serialization format depends on endianness

[GitBox]

Masquerade0097 commented on issue #18667:
URL: 
https://github.com/apache/incubator-mxnet/issues/18667#issuecomment-666403574


   @leezu I tried to build the cpp package using cmake for your PR #18749 on 
s390x. Although the build succeeds, I'm unable to find the build files. 
`incubator-mxnet/build/lib`, `incubator-mxnet/build/bin` are empty. Can you 
please let me know if this is an issue or am I missing something?
   
   Here are the logs -
   
   ```
   root@e0e720e9a63c:~/mxnet-latest/incubator-mxnet/build# cmake .. 
-DUSE_MKLDNN=0 -DUSE_MKL_IF_AVAILABLE=0
   -- The C compiler identification is GNU 7.5.0
   -- The CXX compiler identification is GNU 7.5.0
   -- Check for working C compiler: /usr/bin/cc
   -- Check for working C compiler: /usr/bin/cc -- works
   -- Detecting C compiler ABI info
   -- Detecting C compiler ABI info - done
   -- Detecting C compile features
   -- Detecting C compile features - done
   -- Check for working CXX compiler: /usr/bin/c++
   -- Check for working CXX compiler: /usr/bin/c++ -- works
   -- Detecting CXX compiler ABI info
   -- Detecting CXX compiler ABI info - done
   -- Detecting CXX compile features
   -- Detecting CXX compile features - done
   -- CMAKE_CROSSCOMPILING FALSE
   -- CMAKE_HOST_SYSTEM_PROCESSOR s390x
   -- CMAKE_SYSTEM_PROCESSOR s390x
   -- CMAKE_SYSTEM_NAME Linux
   -- CMake version '3.13.4' using generator 'Unix Makefiles'
   -- Could not find CCache. Consider installing CCache to speed up compilation.
   -- Found OpenCV: /usr (found version "3.2.0") found components:  core 
highgui imgproc imgcodecs 
   -- OpenCV 3.2.0 found (/usr/share/OpenCV)
   --  OpenCV_LIBS=opencv_core;opencv_highgui;opencv_imgproc;opencv_imgcodecs
   -- Looking for pthread.h
   -- Looking for pthread.h - found
   -- Looking for pthread_create
   -- Looking for pthread_create - not found
   -- Check if compiler accepts -pthread
   -- Check if compiler accepts -pthread - yes
   -- Found Threads: TRUE  
   USE_LAPACK is ON
   CMake Warning at 3rdparty/googletest/googletest/CMakeLists.txt:47 (project):
 VERSION keyword not followed by a value or was followed by a value that
 expanded to nothing.
   
   
   -- Found PythonInterp: /usr/bin/python (found version "2.7.17") 
   -- Found GTest: gtest  
   -- Looking for clock_gettime in rt
   -- Looking for clock_gettime in rt - found
   -- Looking for fopen64
   -- Looking for fopen64 - not found
   -- Looking for C++ include cxxabi.h
   -- Looking for C++ include cxxabi.h - found
   -- Looking for nanosleep
   -- Looking for nanosleep - found
   -- Looking for backtrace
   -- Looking for backtrace - found
   -- backtrace facility detected in default set of libraries
   -- Found Backtrace: /usr/include  
   -- Check if the system is big endian
   -- Searching 16 bit integer
   -- Looking for sys/types.h
   -- Looking for sys/types.h - found
   -- Looking for stdint.h
   -- Looking for stdint.h - found
   -- Looking for stddef.h
   -- Looking for stddef.h - found
   -- Check size of unsigned short
   -- Check size of unsigned short - done
   -- Using unsigned short
   -- Check if the system is big endian - big endian
   -- 
/root/mxnet-latest/incubator-mxnet/3rdparty/dmlc-core/cmake/build_config.h.in 
-> include/dmlc/build_config.h
   -- Performing Test SUPPORT_MSSE2
   -- Performing Test SUPPORT_MSSE2 - Failed
   -- Found Python3: /usr/bin/python3.6 (found version "3.6.9") found 
components:  Interpreter 
   -- Configuring done
   -- Generating done
   -- Build files have been written to: /root/mxnet-latest/incubator-mxnet/build
   
   ```
   
   ```
   root@e0e720e9a63c:~/mxnet-latest/incubator-mxnet/build# cmake -DUSE_MKLDNN=0 
-DUSE_MKL_IF_AVAILABLE=0 --build .
   
   -- CMAKE_CROSSCOMPILING FALSE
   -- CMAKE_HOST_SYSTEM_PROCESSOR s390x
   -- CMAKE_SYSTEM_PROCESSOR s390x
   -- CMAKE_SYSTEM_NAME Linux
   -- CMake version '3.13.4' using generator 'Unix Makefiles'
   -- Could not find CCache. Consider installing CCache to speed up compilation.
   -- OpenCV 3.2.0 found (/usr/share/OpenCV)
   --  OpenCV_LIBS=opencv_core;opencv_highgui;opencv_imgproc;opencv_imgcodecs
   USE_LAPACK is ON
   CMake Warning at 3rdparty/googletest/googletest/CMakeLists.txt:47 (project):
 VERSION keyword not followed by a value or was followed by a value that
 expanded to nothing.
   
   -- 
/root/mxnet-latest/incubator-mxnet/3rdparty/dmlc-core/cmake/build_config.h.in 
-> include/dmlc/build_config.h
   -- Configuring done
   -- Generating done
   -- Build files have been written to: /root/mxnet-latest/incubator-mxnet/build
   
   ```



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[GitHub] [incubator-mxnet] bgawrych commented on pull request #18708: [v1.x Backport] Fix softmax, logsoftmax failed on empty ndarray (#18602)

[GitBox]

bgawrych commented on pull request #18708:
URL: https://github.com/apache/incubator-mxnet/pull/18708#issuecomment-666241371


   @mxnet-bot run ci [unix-gpu]



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18708: [v1.x Backport] Fix softmax, logsoftmax failed on empty ndarray (#18602)

[GitBox]

mxnet-bot commented on pull request #18708:
URL: https://github.com/apache/incubator-mxnet/pull/18708#issuecomment-666241463


   Jenkins CI successfully triggered : [unix-gpu]



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[GitHub] [incubator-mxnet] ChaiBapchya commented on a change in pull request #18825: [v1.x][LT] Add forward & backward linalg.gemm test for large size

[GitBox]

ChaiBapchya commented on a change in pull request #18825:
URL: https://github.com/apache/incubator-mxnet/pull/18825#discussion_r462822933



##
File path: tests/nightly/test_large_array.py
##
@@ -1211,23 +1211,49 @@ def check_syrk_batch():
 assert_almost_equal(A.grad[1,0,0], nd.array([0.4]), rtol=1e-3, 
atol=1e-5)
 
 def check_gemm2():
-def run_gemm2(inp1,inp2):
+def run_gemm2(inp1, inp2):
 inp1.attach_grad()
 inp2.attach_grad()
 with mx.autograd.record():
-out = mx.nd.linalg.gemm2(inp1,inp2)
+out = mx.nd.linalg.gemm2(inp1, inp2)
 return inp1.grad, inp2.grad, out
 
-inp1=mx.nd.ones(shape=(SMALL_Y, LARGE_X))
-inp1[0][0]=0.1
-inp2=mx.nd.ones(shape=(LARGE_X, SMALL_Y))
-inp1_grad, inp2_grad, out= run_gemm2(inp1,inp2)
+inp1 = mx.nd.ones(shape=(SMALL_Y, LARGE_X))
+perturbation = 0.2
+inp1[0][0] = perturbation
+inp2 = mx.nd.ones(shape=(LARGE_X, SMALL_Y))
+inp1_grad, inp2_grad, out = run_gemm2(inp1, inp2)
 assert out.asnumpy()[0][0] == LARGE_X
 assert out.shape == (SMALL_Y, SMALL_Y)
 out.backward()
 assert inp1_grad.shape == (SMALL_Y, LARGE_X)
 assert inp2_grad.shape == (LARGE_X, SMALL_Y)
-assert_almost_equal(inp2_grad.asnumpy()[0][0],49.1)
+assert_almost_equal(inp1_grad.asnumpy()[0][0], SMALL_Y)
+assert_almost_equal(inp2_grad.asnumpy()[0][0], SMALL_Y - (1 - 
perturbation))
+
+def check_gemm():
+def run_gemm(inp1,inp2, inp3):
+inp1.attach_grad()
+inp2.attach_grad()
+inp3.attach_grad()
+with mx.autograd.record():
+out = mx.nd.linalg.gemm(inp1, inp2, inp3, transpose_b=True)
+return inp1.grad, inp2.grad, inp3.grad, out
+
+inp1 = mx.nd.ones(shape=(MEDIUM_X, SMALL_Y, MEDIUM_X))
+perturbation = 0.2
+inp1[0][0][0] = perturbation
+inp2 = mx.nd.ones(shape=(MEDIUM_X, SMALL_Y, MEDIUM_X))

Review comment:
   It's incorrect according to PEP8 style guide Python
   https://www.python.org/dev/peps/pep-0008/
   https://user-images.githubusercontent.com/10992635/88897924-e7dbdc00-d200-11ea-987c-e36c6ac58fde.png;>
   

##
File path: tests/nightly/test_large_array.py
##
@@ -1211,23 +1211,49 @@ def check_syrk_batch():
 assert_almost_equal(A.grad[1,0,0], nd.array([0.4]), rtol=1e-3, 
atol=1e-5)
 
 def check_gemm2():
-def run_gemm2(inp1,inp2):
+def run_gemm2(inp1, inp2):
 inp1.attach_grad()
 inp2.attach_grad()
 with mx.autograd.record():
-out = mx.nd.linalg.gemm2(inp1,inp2)
+out = mx.nd.linalg.gemm2(inp1, inp2)
 return inp1.grad, inp2.grad, out
 
-inp1=mx.nd.ones(shape=(SMALL_Y, LARGE_X))
-inp1[0][0]=0.1
-inp2=mx.nd.ones(shape=(LARGE_X, SMALL_Y))
-inp1_grad, inp2_grad, out= run_gemm2(inp1,inp2)
+inp1 = mx.nd.ones(shape=(SMALL_Y, LARGE_X))
+perturbation = 0.2
+inp1[0][0] = perturbation
+inp2 = mx.nd.ones(shape=(LARGE_X, SMALL_Y))
+inp1_grad, inp2_grad, out = run_gemm2(inp1, inp2)
 assert out.asnumpy()[0][0] == LARGE_X
 assert out.shape == (SMALL_Y, SMALL_Y)
 out.backward()
 assert inp1_grad.shape == (SMALL_Y, LARGE_X)
 assert inp2_grad.shape == (LARGE_X, SMALL_Y)
-assert_almost_equal(inp2_grad.asnumpy()[0][0],49.1)
+assert_almost_equal(inp1_grad.asnumpy()[0][0], SMALL_Y)
+assert_almost_equal(inp2_grad.asnumpy()[0][0], SMALL_Y - (1 - 
perturbation))
+
+def check_gemm():
+def run_gemm(inp1,inp2, inp3):
+inp1.attach_grad()
+inp2.attach_grad()
+inp3.attach_grad()
+with mx.autograd.record():
+out = mx.nd.linalg.gemm(inp1, inp2, inp3, transpose_b=True)
+return inp1.grad, inp2.grad, inp3.grad, out
+
+inp1 = mx.nd.ones(shape=(MEDIUM_X, SMALL_Y, MEDIUM_X))
+perturbation = 0.2
+inp1[0][0][0] = perturbation
+inp2 = mx.nd.ones(shape=(MEDIUM_X, SMALL_Y, MEDIUM_X))
+inp3 = mx.nd.ones(shape=(MEDIUM_X, SMALL_Y, SMALL_Y))

Review comment:
   It's incorrect according to PEP8 style guide Python
   https://www.python.org/dev/peps/pep-0008/
   https://user-images.githubusercontent.com/10992635/88897924-e7dbdc00-d200-11ea-987c-e36c6ac58fde.png;>
   

##
File path: tests/nightly/test_large_array.py
##
@@ -1211,23 +1211,49 @@ def check_syrk_batch():
 assert_almost_equal(A.grad[1,0,0], nd.array([0.4]), rtol=1e-3, 
atol=1e-5)
 
 def check_gemm2():
-def run_gemm2(inp1,inp2):
+def run_gemm2(inp1, inp2):
 inp1.attach_grad()
 inp2.attach_grad()
 with mx.autograd.record():
-out = mx.nd.linalg.gemm2(inp1,inp2)
+ 

[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18825: [v1.x][LT] Add forward & backward linalg.gemm test for large size

[GitBox]

mxnet-bot commented on pull request #18825:
URL: https://github.com/apache/incubator-mxnet/pull/18825#issuecomment-666212089


   Jenkins CI successfully triggered : [centos-cpu]



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[GitHub] [incubator-mxnet] ChaiBapchya commented on issue #17369: flaky test: test_gluon_data_vision.test_random_transforms

[GitBox]

ChaiBapchya commented on issue #17369:
URL: 
https://github.com/apache/incubator-mxnet/issues/17369#issuecomment-666211883


   
https://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fcentos-cpu/detail/PR-18825/2/pipeline
   #18825 



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[GitHub] [incubator-mxnet] ChaiBapchya commented on pull request #18825: [v1.x][LT] Add forward & backward linalg.gemm test for large size

[GitBox]

ChaiBapchya commented on pull request #18825:
URL: https://github.com/apache/incubator-mxnet/pull/18825#issuecomment-666212039


   @mxnet-bot run ci [centos-cpu]



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[GitHub] [incubator-mxnet] waytrue17 edited a comment on pull request #18690: [WIP] optimize graph in presence of dynamic shape ops

[GitBox]

waytrue17 edited a comment on pull request #18690:
URL: https://github.com/apache/incubator-mxnet/pull/18690#issuecomment-665993052


   > Thanks @mseth10 for investigating the root cause. Could you add a new 
tests that contains the `reshape` after dynamic shape?
   
   Test case added. Thanks for your suggestions and help on this issue
   



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[GitHub] [incubator-mxnet] szha commented on issue #12432: Installation guide does not provide instructions how to build a specific release

[GitBox]

szha commented on issue #12432:
URL: 
https://github.com/apache/incubator-mxnet/issues/12432#issuecomment-665961812


   this definitely isn't a good first issue without pointers. there are 
separate issues that track the improvement for source build with version 
selector.



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[GitHub] [incubator-mxnet] pengzhao-intel removed a comment on pull request #18822: [v1.x][submodule] Upgrade to oneDNN v1.6

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pengzhao-intel removed a comment on pull request #18822:
URL: https://github.com/apache/incubator-mxnet/pull/18822#issuecomment-666010847







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[GitHub] [incubator-mxnet] szha closed issue #15935: unused -mf16c compiler flag during android build

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szha closed issue #15935:
URL: https://github.com/apache/incubator-mxnet/issues/15935


   



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[GitHub] [incubator-mxnet] samskalicky commented on pull request #18779: [WIP] Support extra inputs for subgraph ops

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samskalicky commented on pull request #18779:
URL: https://github.com/apache/incubator-mxnet/pull/18779#issuecomment-665911763


   Todo: 
https://github.com/apache/incubator-mxnet/pull/18815/files#diff-3ac18ad0381b998b34f8d31f6a566dc8



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[GitHub] [incubator-mxnet] szha closed issue #12048: reshape_like does not support different input types

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szha closed issue #12048:
URL: https://github.com/apache/incubator-mxnet/issues/12048


   



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[GitHub] [incubator-mxnet] TaoLv commented on issue #18826: [Activation] GELU precision mismatch between MXNet and PyTorch in the CPU version

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TaoLv commented on issue #18826:
URL: 
https://github.com/apache/incubator-mxnet/issues/18826#issuecomment-666098440







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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18807: adding error message when attempting to use Large tensor with linalg_syevd

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mxnet-bot commented on pull request #18807:
URL: https://github.com/apache/incubator-mxnet/pull/18807#issuecomment-665938808


   Jenkins CI successfully triggered : [clang]



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[GitHub] [incubator-mxnet] szha closed pull request #17492: Fix flaky test_gluon_data_vision.test_random_transforms

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szha closed pull request #17492:
URL: https://github.com/apache/incubator-mxnet/pull/17492


   



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[GitHub] [incubator-mxnet] szha commented on issue #12565: Improve error messages on shape inference (Operator)

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szha commented on issue #12565:
URL: 
https://github.com/apache/incubator-mxnet/issues/12565#issuecomment-665961123


   The improvement would need to happen per operator. Let's create issues for 
individual operators when we discover more of these clear messages.



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[GitHub] [incubator-mxnet] ChaiBapchya opened a new pull request #18825: [v1.x][LT] Add forward & backward linalg.gemm test for large size

[GitBox]

ChaiBapchya opened a new pull request #18825:
URL: https://github.com/apache/incubator-mxnet/pull/18825


   ## Description ##
   Tests mx.nd.linalg.gemm operator for large size tensor [not large dimension]
   
   ## Results ##
   ```
   python3 -m pytest -s --exitfirst --verbose 
tests/nightly/test_large_array.py::test_linalg
   
=
 test session starts 
==
   platform linux -- Python 3.7.7, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 -- 
/home/ubuntu/anaconda3/bin/python3
   cachedir: .pytest_cache
   hypothesis profile 'default' -> 
database=DirectoryBasedExampleDatabase('/home/ubuntu/chai-mxnet/.hypothesis/examples')
   rootdir: /home/ubuntu/chai-mxnet
   plugins: remotedata-0.3.2, openfiles-0.4.0, astropy-header-0.1.2, 
hypothesis-5.8.3, arraydiff-0.3, doctestplus-0.5.0
   collected 1 item
   
   tests/nightly/test_large_array.py::test_linalg
   ```
   
   ## Comments
   - FIxes indent in linalg.gemm2 test
   - Removes hardcode & replaces with "perturbation" for gradient checks



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[GitHub] [incubator-mxnet] szha commented on pull request #17492: Fix flaky test_gluon_data_vision.test_random_transforms

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szha commented on pull request #17492:
URL: https://github.com/apache/incubator-mxnet/pull/17492#issuecomment-665963786


   this test doesn't seem flaky anymore.



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[GitHub] [incubator-mxnet] aaronmarkham merged pull request #18811: website clipcoard patch - use regex that is supported by all browsers

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aaronmarkham merged pull request #18811:
URL: https://github.com/apache/incubator-mxnet/pull/18811


   



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[GitHub] [incubator-mxnet] sxjscience commented on issue #18826: [Activation] GELU precision mismatch between MXNet and PyTorch in the CPU version

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sxjscience commented on issue #18826:
URL: 
https://github.com/apache/incubator-mxnet/issues/18826#issuecomment-666100930







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[GitHub] [incubator-mxnet] szha closed issue #12565: Improve error messages on shape inference (Operator)

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szha closed issue #12565:
URL: https://github.com/apache/incubator-mxnet/issues/12565


   



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[GitHub] [incubator-mxnet] access2rohit commented on pull request #18825: [v1.x][LT] Add forward & backward linalg.gemm test for large size

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access2rohit commented on pull request #18825:
URL: https://github.com/apache/incubator-mxnet/pull/18825#issuecomment-666119663


   left nitpicky comments. Rest LGTM !



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[GitHub] [incubator-mxnet] aaronmarkham merged pull request #18783: remove other language bindings section from website api page

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aaronmarkham merged pull request #18783:
URL: https://github.com/apache/incubator-mxnet/pull/18783


   



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[GitHub] [incubator-mxnet] szha closed issue #14229: About the weighted softmax, the forward result is too small

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szha closed issue #14229:
URL: https://github.com/apache/incubator-mxnet/issues/14229


   



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[GitHub] [incubator-mxnet] szha commented on issue #13953: Perhaps a bug in mx.symbol.UpSampling

[GitBox]

szha commented on issue #13953:
URL: 
https://github.com/apache/incubator-mxnet/issues/13953#issuecomment-665842755


   @zhreshold could you take a look and see:
   - how we should deal with this in 2.x
   - whether we can/should patch it in 1.x



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[GitHub] [incubator-mxnet] szha closed issue #14064: Inconsistent behavior in split() when num_outputs=1

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szha closed issue #14064:
URL: https://github.com/apache/incubator-mxnet/issues/14064


   



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[GitHub] [incubator-mxnet] szha commented on issue #14064: Inconsistent behavior in split() when num_outputs=1

[GitBox]

szha commented on issue #14064:
URL: 
https://github.com/apache/incubator-mxnet/issues/14064#issuecomment-665840709


   in 2.x we will focus on numpy array instead of ndarray. the np.split is now 
behaving consistently.
   ```
   In [15]: mx.np.split(a, 2)
   Out[15]: [array([[1., 1., 1.]]), array([[1., 1., 1.]])]
   
   In [16]: mx.np.split(a, 1)
   Out[16]:
   [array([[1., 1., 1.],
   [1., 1., 1.]])]
   ```



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[GitHub] [incubator-mxnet] szha commented on issue #11557: Logging in Mxnet, with different level (verbose, warning, error)

[GitBox]

szha commented on issue #11557:
URL: 
https://github.com/apache/incubator-mxnet/issues/11557#issuecomment-665962947


   requires https://github.com/dmlc/dmlc-core/issues/623



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18803: Add unit tests for potri and potrf backward and check output shape.

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mxnet-bot commented on pull request #18803:
URL: https://github.com/apache/incubator-mxnet/pull/18803#issuecomment-665943826







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[GitHub] [incubator-mxnet] szha edited a comment on issue #12565: Improve error messages on shape inference (Operator)

[GitBox]

szha edited a comment on issue #12565:
URL: 
https://github.com/apache/incubator-mxnet/issues/12565#issuecomment-665961123


   The improvement would need to happen per operator. Let's create issues for 
individual operators when we discover more of these unclear messages.



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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #18817: Fix dirichlet flaky tests

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mxnet-bot commented on pull request #18817:
URL: https://github.com/apache/incubator-mxnet/pull/18817#issuecomment-666076585







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[GitHub] [incubator-mxnet] leezu commented on a change in pull request #18807: adding error message when attempting to use Large tensor with linalg_syevd

[GitBox]

leezu commented on a change in pull request #18807:
URL: https://github.com/apache/incubator-mxnet/pull/18807#discussion_r462554483



##
File path: src/operator/tensor/la_op.h
##
@@ -470,11 +470,14 @@ inline bool DetType(const nnvm::NodeAttrs& attrs,
 inline bool LaEigFactShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector* in_attrs,
mxnet::ShapeVector* out_attrs) {
+  using namespace mshadow;
   CHECK_EQ(in_attrs->size(), 1);
   CHECK_EQ(out_attrs->size(), 2);
   const mxnet::TShape& in_a = (*in_attrs)[0];
   const mxnet::TShape& out_u = (*out_attrs)[0];
   const mxnet::TShape& out_l = (*out_attrs)[1];
+  CHECK_LE(in_a.Size(), INT_MAX)

Review comment:
   It's better to be explicit about your dependencies.
   
   "Avoid surprises. Avoid having to change #includes if an #included header 
changes. Avoid accidentally becoming dependent on implementation details and 
logically separate entities included in a header."
   
   
https://github.com/isocpp/CppCoreGuidelines/blob/master/CppCoreGuidelines.md#Rs-implicit





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[GitHub] [incubator-mxnet] gilbertfrancois commented on issue #18751: gluon.nn.BatchNorm seems to swap updated values of moving_mean and moving_var on GPU.

[GitBox]

gilbertfrancois commented on issue #18751:
URL: 
https://github.com/apache/incubator-mxnet/issues/18751#issuecomment-665976180


   Apologies for the delay in answering. I was offline for a few days.
   
   @TristonC 
https://github.com/apache/incubator-mxnet/issues/18751#issuecomment-665436383 
Yes, that is correct. It is part of research I'm doing, where BatchNorm layers 
are placed in the tail between Dense layers to equalise the "importance" or 
"amplitude" of features. I noticed that when comparing feature vectors by 
computing the distance of similar objects with e.g. cosine distance or 
euclidian distance, the is_similar vs is_not_similar accuracy is slightly 
higher when the features are balanced with (N, C) BatchNorm layers, especially 
when objects are partially occluded.
   
   Whether or not this will lead to _generic_ higher accuracy, is still under 
research. It is too early to say if it will be useful in the end. What I 
noticed as an issue during experimenting and using Gluon, is that I see 
different results on CPU and GPU and NaN on GPU when doing a validation step 
with batch size = 1, which is in fact an inference step, not training. On CPU 
however, the inference or validation step goes well.
   
   As you mentioned in 
https://github.com/apache/incubator-mxnet/issues/18751#issuecomment-663168367 I 
believe that NaN is a valid result when training a network with 1D BatchNorm 
with a batch size of 1, but I don't think it should occur on inference, where 
moving mean/var are fixed, which is similar to the outcome of pyTorch, shown in 
https://github.com/apache/incubator-mxnet/issues/18751#issuecomment-662750082. 
   
   Please let me know if there is anything I can do to help.



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