[GitHub] [mxnet] github-actions[bot] commented on issue #21171: adding model evaluation plots to improve the examples

[GitBox]

github-actions[bot] commented on issue #21171:
URL: https://github.com/apache/mxnet/issues/21171#issuecomment-1397460307

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] edublancas opened a new issue, #21171: adding model evaluation plots to improve the examples

[GitBox]

edublancas opened a new issue, #21171:
URL: https://github.com/apache/mxnet/issues/21171

   ## Description
   
   Hi mxnet team!
   
   I noticed the 
[examples](https://mxnet.apache.org/versions/1.9.1/api/python/docs/tutorials/getting-started/crash-course/5-predict.html)
 do not contain model evaluation plots; adding them would be great so beginners 
can visualize their model's performance. I'm the maintainer of a [package for 
ML model 
visualization](https://sklearn-evaluation.ploomber.io/en/latest/api/plot.html), 
I'm happy to open a PR editing some of the examples! Let me know what you think!
   
   ## References
   
   - [Github repo](https://github.com/ploomber/sklearn-evaluation)
   


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[GitHub] [mxnet] DSDanielPark commented on issue #20118: Fail to install MXNet with cu110

[GitBox]

DSDanielPark commented on issue #20118:
URL: https://github.com/apache/mxnet/issues/20118#issuecomment-1386867669

   This issue is still unsolved.


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[GitHub] [mxnet] cristobal140 commented on issue #11277: No matching distribution found for mxnet when install mxnet everywhere

[GitBox]

cristobal140 commented on issue #11277:
URL: https://github.com/apache/mxnet/issues/11277#issuecomment-1384368793

   Im in python 3.7 but still the same error: 
   (python3.7) PS C:\Users\Pc> pip install --force-reinstall mxnet==1.8.0
   ERROR: Could not find a version that satisfies the requirement mxnet==1.8.0 
(from versions: 0.11.0, 0.12.0, 0.12.1, 1.0.0, 1.0.0.post1, 1.0.0.post3, 
1.0.0.post4, 1.1.0.post0, 1.2.0, 1.2.1, 1.2.1.post1, 1.3.0, 1.3.1, 1.4.0, 
1.4.0.post0, 1.4.1, 1.5.0, 1.6.0, 1.7.0.post1, 1.7.0.post2)
   ERROR: No matching distribution found for mxnet==1.8.0


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[GitHub] [mxnet] Yimin-Yi commented on issue #20028: Mac-ARM build failure

[GitBox]

Yimin-Yi commented on issue #20028:
URL: https://github.com/apache/mxnet/issues/20028#issuecomment-138324

   Can I install this precompiled build into a conda environment? @ankane 


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[GitHub] [mxnet] marcontk opened a new issue, #21170: Installing MXnet 1.9.1 from source in macOS M1 Ventura

[GitBox]

marcontk opened a new issue, #21170:
URL: https://github.com/apache/mxnet/issues/21170

   ## Description
   I downloaded version 1.9.1 for MacOS and the compilation is not progressing. 
I followed the general instructions inside darwin.cmake :
   
   mkdir build; cd build
   cmake ..
   cmake --build .
   
   Obs: previous version 1.7 if it is compilable on MacOS M1 Ventura 13.1  but 
not 1.8 and later. 
   
   marcont@MacBookAir build % cmake -DUSE_F16C=OFF ..
   -- The C compiler identification is AppleClang 14.0.0.1429
   -- The CXX compiler identification is AppleClang 14.0.0.1429
   -- Detecting C compiler ABI info
   -- Detecting C compiler ABI info - done
   -- Check for working C compiler: 
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/cc
 - skipped
   -- Detecting C compile features
   -- Detecting C compile features - done
   -- Detecting CXX compiler ABI info
   -- Detecting CXX compiler ABI info - done
   -- Check for working CXX compiler: 
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/c++
 - skipped
   -- Detecting CXX compile features
   -- Detecting CXX compile features - done
   -- CMAKE_CROSSCOMPILING FALSE
   -- CMAKE_HOST_SYSTEM_PROCESSOR arm64
   -- CMAKE_SYSTEM_PROCESSOR arm64
   -- CMAKE_SYSTEM_NAME Darwin
   fatal: not a git repository (or any of the parent directories): .git
   fatal: not a git repository (or any of the parent directories): .git
   -- CMake version '3.24.3' using generator 'Unix Makefiles'
   -- Found Accelerate: 
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX13.1.sdk/System/Library/Frameworks/vecLib.framework/Headers
  
   -- Found Accelerate (include: 
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX13.1.sdk/System/Library/Frameworks/vecLib.framework/Headers,
 library: )
   After choosing blas, linking to -framework Accelerate
   -- Found OpenCV: / (found version "3.4.16") found components: core highgui 
imgproc imgcodecs 
   -- OpenCV 3.4.16 found (/opt/local/libexec/opencv3/cmake)
   --  OpenCV_LIBS=opencv_core;opencv_highgui;opencv_imgproc;opencv_imgcodecs
   -- Performing Test CMAKE_HAVE_LIBC_PTHREAD
   -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
   -- Found Threads: TRUE  
   USE_LAPACK is ON
   CMake Error at CMakeLists.txt:520 (add_subdirectory):
 add_subdirectory given source
 "/Users/marcont/mxnet-1.9.1/3rdparty/googletest/googletest" which is not an
 existing directory.
   
   
   -- Found GTest: gtest  
   -- Determining F16C support
   -- Performing Test COMPILER_SUPPORT_MF16C
   -- Performing Test COMPILER_SUPPORT_MF16C - Success
   CPU does not support F16C instructions
   -- Found Python3: /opt/local/bin/python3.11 (found version "3.11.1") found 
components: Interpreter 
   -- Configuring incomplete, errors occurred!
   See also "/Users/marcont/mxnet-1.9.1/build/CMakeFiles/CMakeOutput.log".
   See also "/Users/marcont/mxnet-1.9.1/build/CMakeFiles/CMakeError.log".
   
   
   ## Environment
   ***We recommend using our script for collecting the diagnostic information 
with the following command***
   
   `curl --retry 10 -s 
https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
 | python3`
   
   --Python Info--
   Version  : 3.9.6
   Compiler : Clang 14.0.0 (clang-1400.0.29.202)
   Build: ('default', 'Oct 18 2022 12:41:40')
   Arch : ('64bit', '')
   Pip Info---
   Version  : 21.2.4
   Directory: 
/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages/pip
   --MXNet Info---
   No MXNet installed.
   --System Info--
   Platform : macOS-13.1-arm64-arm-64bit
   system   : Darwin
   node : MacBookAir.local
   release  : 22.2.0
   version  : Darwin Kernel Version 22.2.0: Fri Nov 11 02:04:44 PST 2022; 
root:xnu-8792.61.2~4/RELEASE_ARM64_T8103
   --Hardware Info--
   machine  : arm64
   processor: arm
   b'hw.features.allows_security_research: 0'
   b'machdep.cpu.brand_string: Apple M1'
   --Network Test--
   Setting timeout: 10
   Timing for MXNet: https://github.com/apache/mxnet, DNS: 0.0115 sec, LOAD: 
0.8860 sec.
   Error open Gluon Tutorial(en): http://gluon.mxnet.io, HTTP Error 404: Not 
Found, DNS finished in 0.07685995101928711 sec.
   Error open Gluon Tutorial(cn): https://zh.gluon.ai, , DNS finished in 0.0953969955444336 sec.
   Timing for FashionMNIST: 
https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz,
 DNS: 0.0425 sec, LOAD: 0.3538 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0180 sec, LOAD: 
0.6607 sec.
   Error open Conda: https://repo.continuum.io/pkgs/free/, HTTP Error 403: 
Forbidden, DNS finished in 0.0006

[GitHub] [mxnet] github-actions[bot] commented on issue #21169: Installing MXNet Perl Bindings - mxnet_wrap.cxx 'c_api.h' file not found

[GitBox]

github-actions[bot] commented on issue #21169:
URL: https://github.com/apache/mxnet/issues/21169#issuecomment-1381147414

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] marcontk opened a new issue, #21169: Installing MXNet Perl Bindings - mxnet_wrap.cxx 'c_api.h' file not found

[GitBox]

marcontk opened a new issue, #21169:
URL: https://github.com/apache/mxnet/issues/21169

   After Installing MXNet from source on OS X (Mac), I trying to resolve MxNet 
perl bindings dependency from source
   
   marcont@MacBookAir AI-MXNet-1.5 % perl Makefile.PL
   Warning: prerequisite AI::MXNetCAPI 1.5 not found.
   Warning: prerequisite AI::NNVMCAPI 1.3 not found.
   Warning: prerequisite GraphViz 2.14 not found.
   Generating a Unix-style Makefile
   Writing Makefile for AI::MXNet
   Writing MYMETA.yml and MYMETA.json
   
   I think the error may be related to what is mentioned in the following 
feedback
   
https://discuss.mxnet.apache.org/t/c-api-header-files-are-not-generated-to-one-directory/453
   
   
   ### Error Message
   1) Resolver MXNetCAPI :
   sudo perl -MCPAN -e -shell
   password:***
   
   cpan[1]> install AI::MXNetCAPI   

   Reading '/Users/marcont/.cpan/Metadata'
 Database was generated on Thu, 12 Jan 2023 19:17:01 GMT
   Running install for module 'AI::MXNetCAPI'
   Checksum for 
/Users/marcont/.cpan/sources/authors/id/S/SK/SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz 
ok
   Scanning cache /Users/marcont/.cpan/build for sizes
   
DONE
   'YAML' not installed, will not store persistent state
   Configuring S/SK/SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz with Makefile.PL
   Checking if your kit is complete...
   Looks good
   Warning (mostly harmless): No library found for -lmxnet
   Generating a Unix-style Makefile
   Writing Makefile for AI::MXNetCAPI
   Writing MYMETA.yml and MYMETA.json
 SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz
 /opt/local/bin/perl5.34 Makefile.PL -- OK
   Running make for S/SK/SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz
   cp lib/AI/MXNetCAPI.pm blib/lib/AI/MXNetCAPI.pm
   Running Mkbootstrap for MXNetCAPI ()
   chmod 644 "MXNetCAPI.bs"
   "/opt/local/bin/perl5.34" -MExtUtils::Command::MM -e 'cp_nonempty' -- 
MXNetCAPI.bs blib/arch/auto/AI/MXNetCAPI/MXNetCAPI.bs 644
   /usr/bin/cc -c  -I../../include/mxnet -fno-common -DPERL_DARWIN -pipe -Os 
-fno-strict-aliasing -fstack-protector-strong -I/opt/local/include 
-DPERL_USE_SAFE_PUTENV -O3   -DVERSION=\"1.5\" -DXS_VERSION=\"1.5\"  
"-I/opt/local/lib/perl5/5.34/darwin-thread-multi-2level/CORE"   mxnet_wrap.cxx
   **mxnet_wrap.cxx:1626:10: fatal error: 'c_api.h' file not found**
   #include 
^
   1 error generated.
   make: *** [mxnet_wrap.o] Error 1
 SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz
 /usr/bin/make -- NOT OK
   Failed during this command:
SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz: make NO
   
   
   ### Steps to reproduce
   
   1. sudo perl -MCPAN -e -shell
   2. install AI::MXNetCAPI  
   
   ## What have you tried to solve it?
   1. prerequisites for AI-MXNet
   
   


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[GitHub] [mxnet] marcontk commented on issue #7078: cannot build MXNet Package for Perl

[GitBox]

marcontk commented on issue #7078:
URL: https://github.com/apache/mxnet/issues/7078#issuecomment-1380990730

   I have the same problem on MacOS M1 Ventura 13.1. after installing 
swing-perl with macports I get the following:
   
   
   cpan[1]> install AI::MXNetCAPI   

   Reading '/Users/marcont/.cpan/Metadata'
 Database was generated on Thu, 12 Jan 2023 19:17:01 GMT
   Running install for module 'AI::MXNetCAPI'
   Checksum for 
/Users/marcont/.cpan/sources/authors/id/S/SK/SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz 
ok
   Scanning cache /Users/marcont/.cpan/build for sizes
   
DONE
   'YAML' not installed, will not store persistent state
   Configuring S/SK/SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz with Makefile.PL
   Checking if your kit is complete...
   Looks good
   Warning (mostly harmless): No library found for -lmxnet
   Generating a Unix-style Makefile
   Writing Makefile for AI::MXNetCAPI
   Writing MYMETA.yml and MYMETA.json
 SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz
 /opt/local/bin/perl5.34 Makefile.PL -- OK
   Running make for S/SK/SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz
   cp lib/AI/MXNetCAPI.pm blib/lib/AI/MXNetCAPI.pm
   Running Mkbootstrap for MXNetCAPI ()
   chmod 644 "MXNetCAPI.bs"
   "/opt/local/bin/perl5.34" -MExtUtils::Command::MM -e 'cp_nonempty' -- 
MXNetCAPI.bs blib/arch/auto/AI/MXNetCAPI/MXNetCAPI.bs 644
   /usr/bin/cc -c  -I../../include/mxnet -fno-common -DPERL_DARWIN -pipe -Os 
-fno-strict-aliasing -fstack-protector-strong -I/opt/local/include 
-DPERL_USE_SAFE_PUTENV -O3   -DVERSION=\"1.5\" -DXS_VERSION=\"1.5\"  
"-I/opt/local/lib/perl5/5.34/darwin-thread-multi-2level/CORE"   mxnet_wrap.cxx
   mxnet_wrap.cxx:1626:10: fatal error: 'c_api.h' file not found
   #include 
^
   1 error generated.
   make: *** [mxnet_wrap.o] Error 1
 SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz
 /usr/bin/make -- NOT OK
   Failed during this command:
SKOLYCHEV/AI-MXNetCAPI-1.5.tar.gz: make NO
   


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[GitHub] [mxnet] ddelange commented on issue #20251: [RFC] MXNet AArch64 wheels

[GitBox]

ddelange commented on issue #20251:
URL: https://github.com/apache/mxnet/issues/20251#issuecomment-1378460668

   first hickup ^ 
[`libquadmath0`](https://packages.ubuntu.com/search?keywords=libquadmath0&searchon=names)
 is missing arm64


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[GitHub] [mxnet] matteosal commented on issue #21143: Gradient calculation for recurrent operators is wrong

[GitBox]

matteosal commented on issue #21143:
URL: https://github.com/apache/mxnet/issues/21143#issuecomment-1377561019

   Updating this to report that it behaves correctly with oneDNN


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[GitHub] [mxnet] anilrgukt commented on issue #21166: mxnet 1.9.1 cuda support for Jetson AGX Orin (Jetpack 5.0 with cuda 11.4)

[GitBox]

anilrgukt commented on issue #21166:
URL: https://github.com/apache/mxnet/issues/21166#issuecomment-1376556547

   It works now, I had to modify the arch from 72 to 87 for Orin modules 
`-gencode arch=compute_72,code=sm_72 for Xavier NX and AGX Xavier


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[GitHub] [mxnet] anilrgukt commented on issue #20774: install mxnet with cuda11.4

[GitBox]

anilrgukt commented on issue #20774:
URL: https://github.com/apache/mxnet/issues/20774#issuecomment-1376479101

   Did it work after building from source with cuda 11.4?


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[GitHub] [mxnet] github-actions[bot] commented on issue #21166: mxnet 1.9.1 cuda error with cuda 11.4 on Jetson AGX Orin (Jetpack 5.0)

[GitBox]

github-actions[bot] commented on issue #21166:
URL: https://github.com/apache/mxnet/issues/21166#issuecomment-1376375294

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] anilrgukt opened a new issue, #21166: mxnet 1.9.1 cuda error with cuda 11.4 on Jetson AGX Orin (Jetpack 5.0)

[GitBox]

anilrgukt opened a new issue, #21166:
URL: https://github.com/apache/mxnet/issues/21166

   ## Description
   mxnet (1.9.1) is raising a cuda error with cuda 11.4 on Jetson AGX Orin 
series with Jetpack 5.0 
   
   ### Error Message
   Check failed: err == cudaSuccess (209 vs. 0) : mxnet_generic_kernel 
ErrStr:no kernel image is available for execution on the device
   
   ## To Reproduce
   I ran the installation steps recommended for the jetson and built things 
manually from source with cuda path and other variables. 
   
   Everything went fine with the make and importing mxnet and version check. 
When I run the gpu check the error pops up. 
   
   ### Steps to reproduce
   (Paste the commands you ran that produced the error.)
   
   1.
   2.
   
   ## What have you tried to solve it?
   
   1.
   2.
   
   ## Environment
   Ubuntu 20.04, Nvidia jetson AGX Orin developer kit 
   
   Environment Information
   
   ```
   --Python Info--
   Version  : 3.8.10
   Compiler : GCC 9.4.0
   Build: ('default', 'Nov 14 2022 12:59:47')
   Arch : ('64bit', 'ELF')
   Pip Info---
   Version  : 22.3.1
   Directory: /home/flashsys002/py38/lib/python3.8/site-packages/pip
   --MXNet Info---
   Version  : 1.9.1
   Directory: /home/flashsys002/mxnet/python/mxnet
   Commit hash file "/home/flashsys002/mxnet/python/mxnet/COMMIT_HASH" not 
found. Not installed from pre-built package or built from source.
   Library  : ['/home/flashsys002/mxnet/lib/libmxnet.so', 
'/home/flashsys002/mxnet/python/mxnet/../../lib/libmxnet.so']
   Build features:
   ✔ CUDA
   ✔ CUDNN
   ✖ NCCL
   ✖ CUDA_RTC
   ✖ TENSORRT
   ✖ CPU_SSE
   ✖ CPU_SSE2
   ✖ CPU_SSE3
   ✖ CPU_SSE4_1
   ✖ CPU_SSE4_2
   ✖ CPU_SSE4A
   ✖ CPU_AVX
   ✖ CPU_AVX2
   ✔ OPENMP
   ✖ SSE
   ✖ F16C
   ✖ JEMALLOC
   ✔ BLAS_OPEN
   ✖ BLAS_ATLAS
   ✖ BLAS_MKL
   ✖ BLAS_APPLE
   ✖ LAPACK
   ✖ MKLDNN
   ✖ OPENCV
   ✖ CAFFE
   ✖ PROFILER
   ✖ DIST_KVSTORE
   ✖ CXX14
   ✖ INT64_TENSOR_SIZE
   ✔ SIGNAL_HANDLER
   ✖ DEBUG
   ✖ TVM_OP
   --System Info--
   Platform : Linux-5.10.65-tegra-aarch64-with-glibc2.29
   system   : Linux
   node : flashsys002-desktop
   release  : 5.10.65-tegra
   version  : #1 SMP PREEMPT Mon May 16 20:58:07 PDT 2022
   --Hardware Info--
   machine  : aarch64
   processor: aarch64
   Architecture:aarch64  
   CPU op-mode(s):  32-bit, 64-bit
   Byte Order:  Little Endian
   CPU(s):  12
   On-line CPU(s) list: 0-11
   Thread(s) per core:  1
   Core(s) per socket:  4
   Socket(s):   3
   Vendor ID:   ARM
   Model:   1
   Model name:  ARMv8 Processor rev 1 (v8l)
   Stepping:r0p1
   CPU max MHz: 2201.6001
   CPU min MHz: 115.2000 
   BogoMIPS:62.50
   L1d cache:   768 KiB  
   L1i cache:   768 KiB  
   L2 cache:3 MiB
   L3 cache:6 MiB
   Vulnerability Itlb multihit: Not affected
   Vulnerability L1tf:  Not affected
   Vulnerability Mds:   Not affected
   Vulnerability Meltdown:  Not affected
   Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass 
disabled via prctl
   Vulnerability Spectre v1:Mitigation; __user pointer sanitization
   Vulnerability Spectre v2:Not affected
   Vulnerability Srbds: Not affected
   Vulnerability Tsx async abort:   Not affected
   Flags:   fp asimd evtstrm aes pmull sha1 sha2 crc32 
atomics fphp asimdhp cpuid asimdrdm lrcpc dcpop asimddp uscat ilrcpc flagm
   --Network Test--
   Setting timeout: 10
   Timing for MXNet: https://github.com/apache/mxnet, DNS: 0.0029 sec, LOAD: 
0.4289 sec.
   Error open Gluon Tutorial(en): http://gluon.mxnet.io, HTTP Error 404: Not 
Found, DNS finished in 0.07585453987121582 sec.
   Error open Gluon Tutorial(cn): https://zh.gluon.ai, , DNS finished in 0.035755157470703125 sec.
   Timing for FashionMNIST: 
https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz,
 DNS: 0.0161 sec, LOAD: 0.4418 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0113 sec, LOAD: 
0.2129 sec.
   Error open Conda: https://repo.continuum.io/pkgs/free/, HTTP Error 403: 
Forbidden, DNS finished in 0.021700382232666016 sec.
   --Environment--
   MXNET_HOME="/home/flashsys002/mxnet/"
   KMP_DUPLICATE_LIB_OK="True"
   KMP_INIT_AT_FORK="FALSE"
   ```
   
   
   


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[GitHub] [mxnet] ddelange commented on issue #20251: [RFC] MXNet AArch64 wheels

[GitBox]

ddelange commented on issue #20251:
URL: https://github.com/apache/mxnet/issues/20251#issuecomment-1374954163

   hi @mseth10,
   
   fkrst of all, thanks a lot for the above PR. could you give an indication of 
complexity to cherry-pick these aarch64 builds onto the [cuda 
wheels](https://pypi.org/project/mxnet-cu116/1.9.1/#files)?
   
   would be cool to run GPU inference on 
[g5.xlarge](https://aws.amazon.com/ec2/instance-types/g5/)


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[GitHub] [mxnet] github-actions[bot] commented on issue #21165: MXNet does not support NumPy 1.24

[GitBox]

github-actions[bot] commented on issue #21165:
URL: https://github.com/apache/mxnet/issues/21165#issuecomment-1373454399

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] rkazants opened a new issue, #21165: MXNet does not support NumPy 1.24

[GitBox]

rkazants opened a new issue, #21165:
URL: https://github.com/apache/mxnet/issues/21165

   ## Description
   MXNet does not support NumPy 1.24
   
   ### Error Message
   Conversion of MXNet models by using OpenVINO Model Optimizer will raise this 
issue
   
   python3.8/site-packages/mxnet/numpy/utils.py:37: FutureWarning: In the 
future `np.bool` will be defined as the corresponding NumPy scalar.  (This may 
have returned Python scalars in past versions.
 bool = onp.bool
   [ ERROR ]  -
   [ ERROR ]  - INTERNAL ERROR 
   [ ERROR ]  Unexpected exception happened.
   [ ERROR ]  Please contact Model Optimizer developers and forward the 
following information:
   [ ERROR ]  module 'numpy' has no attribute 'bool'
   [ ERROR ]  Traceback (most recent call last):
 File "openvino/tools/mo/openvino/tools/mo/main.py", line 50, in main
   ngraph_function = convert_model(**argv)
 File "openvino/tools/mo/openvino/tools/mo/convert.py", line 47, in 
convert_model
   return _convert(**args)
 File "openvino/tools/mo/openvino/tools/mo/convert_impl.py", line 932, in 
_convert
   raise e.with_traceback(None)
   AttributeError: module 'numpy' has no attribute 'bool'
   [ ERROR ]   END OF BUG REPORT --
   [ ERROR ]  -
   
   (Paste the complete error message. Please also include stack trace by 
setting environment variable `DMLC_LOG_STACK_TRACE_DEPTH=100` before running 
your script.)
   
   ## To Reproduce
   install numpy 1.24
   install mxnet 1.9.1
   
   ### Steps to reproduce
   
   ## What have you tried to solve it?
   installation of numpy 1.23 should solve this problem
   
   ## Environment
   install numpy 1.24
   install mxnet 1.9.1
   


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[GitHub] [mxnet] AmitPress closed issue #21161: mx.context.num_gpus() not working as expected

[GitBox]

AmitPress closed issue #21161: mx.context.num_gpus() not working as expected
URL: https://github.com/apache/mxnet/issues/21161


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[GitHub] [mxnet] AmitPress commented on issue #21161: mx.context.num_gpus() not working as expected

[GitBox]

AmitPress commented on issue #21161:
URL: https://github.com/apache/mxnet/issues/21161#issuecomment-1368351898

   Solved: It was a cumbersome process but find out they release specific gpu 
packages such as mxnet-cu117 etc for gpu support. Still the process is very 
hazy as you will still need mxnet package and the versions need to be same.


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[GitHub] [mxnet] Lehdari commented on issue #18221: C++, custom loss and network output?

[GitBox]

Lehdari commented on issue #18221:
URL: https://github.com/apache/mxnet/issues/18221#issuecomment-1367314783

   I would also like to find an answer to this question. It seems that there's 
no C++ example for using loss other than `SoftmaxOutput` anywhere on the 
internet.


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[GitHub] [mxnet] github-actions[bot] commented on issue #21161: mx.context.num_gpus() not working as expected

[GitBox]

github-actions[bot] commented on issue #21161:
URL: https://github.com/apache/mxnet/issues/21161#issuecomment-1367260470

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] AmitPress opened a new issue, #21161: mx.context.num_gpus() not working as expected

[GitBox]

AmitPress opened a new issue, #21161:
URL: https://github.com/apache/mxnet/issues/21161

   ## Description
   In kaggle, I turned on the T4 x2, but the function can't detect the gpu.
   
   ```python
   # setting up context
   # if mx.context.num_gpus()>0:
   # ctx = mx.gpu()
   # else:
   # ctx = mx.cpu()
   # print(ctx)
   mx.context.num_gpus()
   ```
   gives "0" as result
   


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[GitHub] [mxnet] fhieber closed issue #20636: MXNet 2.x significantly slower than 1.x in Sockeye

[GitBox]

fhieber closed issue #20636: MXNet 2.x significantly slower than 1.x in Sockeye
URL: https://github.com/apache/mxnet/issues/20636


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[GitHub] [mxnet] pribadihcr commented on issue #13774: KeyError: 'identity1'

[GitBox]

pribadihcr commented on issue #13774:
URL: https://github.com/apache/mxnet/issues/13774#issuecomment-1365622145

   +1


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[GitHub] [mxnet] szha commented on issue #19521: [RFC] Integration with AndroidNN

[GitBox]

szha commented on issue #19521:
URL: https://github.com/apache/mxnet/issues/19521#issuecomment-1364663824



   Bonjour,

   Je vous contacte car j’avais noté un échange avec vous courant décembre 2022
   afin de faire le point sur vos besoins informatiques (prestation et
   recrutement) pour les mois à venir.

   Avez-vous un moment à m’accorder à ce sujet ?
   
   Je vous remercie,
   Cordialement, 
   Caroline Revin 
   IT Resourcer 
   Prestation et Recrutement
   24 rue Poncelet
   75017 Paris

***@***.***>
   ***@***.***

   


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[GitHub] [mxnet] dai-ichiro commented on issue #21160: mxnet support windows10 or windows 11 on RTX30 serial

[GitBox]

dai-ichiro commented on issue #21160:
URL: https://github.com/apache/mxnet/issues/21160#issuecomment-1364602028

   This might help you.
   https://github.com/apache/mxnet/discussions/21131


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[GitHub] [mxnet] github-actions[bot] commented on issue #21160: mxnet support windows10 or windows 11 on RTX30 serial

[GitBox]

github-actions[bot] commented on issue #21160:
URL: https://github.com/apache/mxnet/issues/21160#issuecomment-1364511651

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] futureflsl opened a new issue, #21160: mxnet support windows10 or windows 11 on RTX30 serial

[GitBox]

futureflsl opened a new issue, #21160:
URL: https://github.com/apache/mxnet/issues/21160

   windows 10 RTX3090
   I want to install mxnet,but I do not find mxnet version to match with 
RTX3090.so whl file can supply or how can compile it with windows 10 or win11.
   


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[GitHub] [mxnet] pribadihcr commented on issue #18970: Unsuported operator when importing an ONNX model

[GitBox]

pribadihcr commented on issue #18970:
URL: https://github.com/apache/mxnet/issues/18970#issuecomment-1357375749

   any solution?


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[GitHub] [mxnet] github-actions[bot] commented on issue #21156: [bug] training end report recordio.py super error

[GitBox]

github-actions[bot] commented on issue #21156:
URL: https://github.com/apache/mxnet/issues/21156#issuecomment-1352986394

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] wangjiangben-hw opened a new issue, #21156: [bug] training end report recordio.py super error

[GitBox]

wangjiangben-hw opened a new issue, #21156:
URL: https://github.com/apache/mxnet/issues/21156

   ## Description
   (A clear and concise description of what the bug is.)
   
   ### Error Message
   (Paste the complete error message. Please also include stack trace by 
setting environment variable `DMLC_LOG_STACK_TRACE_DEPTH=100` before running 
your script.)
   
   ## To Reproduce
   (If you developed your own code, please provide a short script that 
reproduces the error. For existing examples, please provide link.)
   
   ### Steps to reproduce
   (Paste the commands you ran that produced the error.)
   
   1.
   2.
   
   ## What have you tried to solve it?
   
   1.
   2.
   
   ## Environment
   
   ***We recommend using our script for collecting the diagnostic information 
with the following command***
   `curl --retry 10 -s 
https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
 | python3`
   
   
   Environment Information
   
   ```
   # Paste the diagnose.py command output here
   ```
   
   
   


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[GitHub] [mxnet] github-actions[bot] commented on issue #21154: Installing MXNet Perl Bindings - undefined symbol: MXListAllOpNames

[GitBox]

github-actions[bot] commented on issue #21154:
URL: https://github.com/apache/mxnet/issues/21154#issuecomment-1343728626

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] ajaff opened a new issue, #21154: Installing MXNet Perl Bindings - undefined symbol: MXListAllOpNames

[GitBox]

ajaff opened a new issue, #21154:
URL: https://github.com/apache/mxnet/issues/21154

   I have followed the installation guide for installing MXNet as well as the 
perl bindings however I am running into the below error. 
   I was able to verify my MXNet installation by installing and using the 
python bindings so I'm fairly confident it is not an issue with the base MXNet 
library.
   
   **Guide:**
   
https://mxnet.apache.org/versions/1.5.0/install/ubuntu_setup.html#install-the-mxnet-package-for-perl
   
   **Error:**
   ` perl: symbol lookup error: 
/root/perl5/perlbrew/perls/perl-5.22.0/lib/site_perl/5.22.0/x86_64-linux/auto/AI/MXNetCAPI/MXNetCAPI.so:
 undefined symbol: MXListAllOpNames
   `
   I have tried installing the binding with Perl 5.30, 5.22, and 5.18 now but 
am seeing this error always.
   
   Has anyone else run into this, or have any hints on how to debug? 


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[GitHub] [mxnet] melanopsis commented on issue #20897: Loosen Graphviz version requirements

[GitBox]

melanopsis commented on issue #20897:
URL: https://github.com/apache/mxnet/issues/20897#issuecomment-1341498296

   Currently, graphviz is at v0.20.1. The version that mxnet brings (v0.8.4) 
was released on Jul 1, 2018. 


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[GitHub] [mxnet] Kaiser-Yang commented on issue #21153: mxnet 1.2.1 random_generator.h:154:5: error: there are no arguments to 'CUDA_CALL' that depend on a template parameter, so a declaration of 'CUD

[GitBox]

Kaiser-Yang commented on issue #21153:
URL: https://github.com/apache/mxnet/issues/21153#issuecomment-1336343458

   after adding this code, the problem is solved. So I wonder why this problem 
occurs even when including cuda_utils.h;
   CUDA_CALL is defined in cuda_utils.h as below:
   
![image](https://user-images.githubusercontent.com/58209855/205480180-542eee43-a5f8-4a80-b92d-35de1cf36523.png)
   


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[GitHub] [mxnet] github-actions[bot] commented on issue #21153: mxnet 1.2.1 random_generator.h:154:5: error: there are no arguments to 'CUDA_CALL' that depend on a template parameter, so a declaration

[GitBox]

github-actions[bot] commented on issue #21153:
URL: https://github.com/apache/mxnet/issues/21153#issuecomment-1336343207

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] Kaiser-Yang opened a new issue, #21153: mxnet 1.2.1 random_generator.h:154:5: error: there are no arguments to 'CUDA_CALL' that depend on a template parameter, so a declaration of 'CU

[GitBox]

Kaiser-Yang opened a new issue, #21153:
URL: https://github.com/apache/mxnet/issues/21153

   ## Description
   when compiling mxnet 1.2.1, I found that there are some compilation errors.
   I think these ERRORs are weird. It is described below.
   ### Error Message
   /src/common/random_generator.h:154:5: error: there are no arguments to 
'CUDA_CALL' that depend on a template parameter, so a declaration of 
'CUDA_CALL' must be available [-fpermissive]
CUDA_CALL(cudaMalloc(&inst->states_,
   
   ## To Reproduce
   clone mxnet-1.2.1 from github and set root/make/config.mk USE_CUDA CUDA_PATH 
USE_DIST_KVSTORE and enter root to start with command make
   
   ## What have you tried to solve it?
   I add the below code into random_generator.h
   
![image](https://user-images.githubusercontent.com/58209855/205480062-7e81c3d4-bd4b-4d83-b159-68e77f622e4a.png)
   
   
   ## Environment
   cuda-11.2
   ubuntu 16.04
   
   What perplexes me is the CUDA_CALL is define in ../common/cuda_utils.h, and 
included by random_generator.h but getting a compilation error
   


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[GitHub] [mxnet] Kaiser-Yang commented on issue #11339: compiling error when USE_CUDA=1 and USE_CUDNN=0 with make command

[GitBox]

Kaiser-Yang commented on issue #11339:
URL: https://github.com/apache/mxnet/issues/11339#issuecomment-1336125183

   when I downloaded source from https://mxnet.apache.org/get_started/download 
(version 1.2.1) I got the same question; I recommend you to update codes on 
this page.


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[GitHub] [mxnet] nimashiri commented on issue #21150: Can't get mxnet function decorators

[GitBox]

nimashiri commented on issue #21150:
URL: https://github.com/apache/mxnet/issues/21150#issuecomment-1328576045

   
   
   Just found the answer.
   
   The problem is that I should do the above stuff inside ```__init__.py``` 
located in the root folder of mxnet library (installed via pip).
   


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[GitHub] [mxnet] github-actions[bot] commented on issue #21150: Can't get mxnet function decorators

[GitBox]

github-actions[bot] commented on issue #21150:
URL: https://github.com/apache/mxnet/issues/21150#issuecomment-1328524081

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [mxnet] nimashiri opened a new issue, #21150: Can't get mxnet function decorators

[GitBox]

nimashiri opened a new issue, #21150:
URL: https://github.com/apache/mxnet/issues/21150

   ## Description
   
   I want to get mxnet function decorators in python. I can get the decorators 
for Tensorflow as follows:
   
   Given that we have the following tensorflow API:
   
   ```
   tf.math.floor(2.5)
   ```
   
   When I run the code, the function arguments are set inside tensorflow object.
   
   ```
   APIname = "tf.math.floor"
   apit_split = APIname.split('.')
   func_name = apit_split[-1]
   
   module_obj = tf
   if len(func_name_list) > 1:
  for module_name in apit_split[:-1]:
module_obj = getattr(module_obj, module_name)
   myfunction = getattr(module_obj, func_name)
   ```
   
   And the output is:
   
   
![deco](https://user-images.githubusercontent.com/17060581/204193845-3f94fcc7-6c60-4e9d-9a58-a4699cfb1ae1.png)
   
   As you can see, I have the decorators for the function. Now for mxnet, I 
have the following code snippet:
   
   Given that we have the following mxnet API:
   
   ```
   from mxnet import ndarray 
   x = ndarray.ones((2,3))
   ```
   
   When I run the code, the function arguments are set inside ndarray object.
   
   ```
   APIname = "ndarray.ones"
   apit_split = APIname.split('.')
   func_name = apit_split[-1]
   
   module_obj = ndarray
   if len(func_name_list) > 1:
  for module_name in apit_split[:-1]:
module_obj = getattr(module_obj, module_name)
   myfunction = getattr(module_obj, func_name)
   ```
   The output is:
   
   
![decomx](https://user-images.githubusercontent.com/17060581/204193935-f995bce3-838d-43c8-bd80-f696389c2a60.png)
   
   As you can see, there is no decorator for the function. Any idea? thanks.
   
   ## References
   https://fossies.org/linux/tensorflow/tensorflow/python/util/tf_decorator.py
   


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[GitHub] [mxnet] djuarezr closed issue #15610: Elasticnet Regression slower than pure Numpy

[GitBox]

djuarezr closed issue #15610: Elasticnet Regression slower than pure Numpy
URL: https://github.com/apache/mxnet/issues/15610


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[GitHub] [mxnet] DNXie closed issue #18988: Documentation Bug: Format issue in mxnet.util.use_np document

[GitBox]

DNXie closed issue #18988: Documentation Bug: Format issue in mxnet.util.use_np 
document
URL: https://github.com/apache/mxnet/issues/18988


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[GitHub] [mxnet] DNXie commented on issue #18988: Documentation Bug: Format issue in mxnet.util.use_np document

[GitBox]

DNXie commented on issue #18988:
URL: https://github.com/apache/mxnet/issues/18988#issuecomment-1305782504

   @vijaya-lakshmi-venkatraman  It looks good now. I will close the issue. 


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[GitHub] [mxnet] szha commented on issue #21147: Graduation Tasks

[GitBox]

szha commented on issue #21147:
URL: https://github.com/apache/mxnet/issues/21147#issuecomment-1304884479

   We probably don't need the -events or -test repos. Also, Infra asked us to 
change the website publishing and reduce the website size, or otherwise the 
existing workflow tends to time out.


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[GitHub] [mxnet] matteosal commented on issue #21143: Gradient calculation for recurrent operators is wrong

[GitBox]

matteosal commented on issue #21143:
URL: https://github.com/apache/mxnet/issues/21143#issuecomment-1302488256

   @bgawrych @barry-jin @szha I think this deserves attention


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[GitHub] [incubator-mxnet] josephevans commented on issue #21147: Graduation Tasks

[GitBox]

josephevans commented on issue #21147:
URL: 
https://github.com/apache/incubator-mxnet/issues/21147#issuecomment-1300998852

   I am collecting what we need to ask Apache Infra to update now that we have 
graduated. These are the git repos for MXNet under Apache. Some of these are 
stale and have not been updated recently. Do we still need all of these repos?
   
   The following repos should be renamed to remove the "incubator-" prefix. 
   
   https://github.com/apache/incubator-mxnet-site
   https://github.com/apache/incubator-mxnet-ci
   https://github.com/apache/incubator-mxnet-events
   https://github.com/apache/incubator-mxnet-examples
   https://github.com/apache/incubator-mxnet-test
   


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[GitHub] [incubator-mxnet] bkmgit commented on issue #19583: Incorrect images in documentation

[GitBox]

bkmgit commented on issue #19583:
URL: 
https://github.com/apache/incubator-mxnet/issues/19583#issuecomment-1296339102

   The images are correct in the version available at 
https://github.com/apache/incubator-mxnet/blob/v1.7.x/docs/python_docs/python/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.md
 but not on the deployed site 
https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html
 maybe the site needs to be re-generated?


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[GitHub] [incubator-mxnet] bkmgit commented on issue #17895: update CentOS installation docs

[GitBox]

bkmgit commented on issue #17895:
URL: 
https://github.com/apache/incubator-mxnet/issues/17895#issuecomment-1296277164

   Updated link to [CI 
script](https://github.com/apache/incubator-mxnet/blob/master/ci/docker/Dockerfile.build.centos7)


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[GitHub] [incubator-mxnet] Rijulsin commented on issue #21074: Build for Cuda 11.3

[GitBox]

Rijulsin commented on issue #21074:
URL: 
https://github.com/apache/incubator-mxnet/issues/21074#issuecomment-1293300252

   Hi @josephevans , I'm unable to import mxnet after using "pip install 
mxnet-cu113==1.9.1" for installation. The error : 
   
   OSError: libnccl.so.2: cannot open shared object file: No such file or 
directory
   


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[GitHub] [incubator-mxnet] AlexanderSerov closed issue #19521: [RFC] Integration with AndroidNN

[GitBox]

AlexanderSerov closed issue #19521: [RFC] Integration with AndroidNN
URL: https://github.com/apache/incubator-mxnet/issues/19521


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[GitHub] [incubator-mxnet] josephevans opened a new issue, #21147: Graduation Tasks

[GitBox]

josephevans opened a new issue, #21147:
URL: https://github.com/apache/incubator-mxnet/issues/21147

   The ASF board has approved a resolution to graduate MXNet into a full top 
level project. Thanks to everyone for your help to get to this point. 
   
   To transition from the Apache Incubator to a new TLP, there's a few action 
items we need to do to complete the transition. These are identified by 
https://incubator.apache.org/guides/transferring.html#life_after_graduation.
   
   If you can help with these tasks, please respond in this issue.
   
   - Update Incubator status records
 - [ ] Update the incubator status file (in svn) - @josephevans
 - [ ] Update podling status page - @josephevans
   - Source repo changes
 - [ ] Create ASF Infra TLP request
 - [ ] Git repos will be renamed to drop the "incubator-" prefix, notify 
developers to change their remotes
 - [ ] Post announcement to dev list telling everyone the repo is about to 
be moved
 - [ ] Post an announcement containing instructions for developers 
describing how to change their git remotes.
 - [ ] Update website, Jenkins, wikis, pom.xml, and other resources to 
point to the new repository locationi
   - Websites
 - [ ] If you have any fully qualified links to your 
podling.incubator.apache.org on your website change them
   - Mailing Lists
 - [ ] If you use podling.incubator.apache.org format email addresses, 
please start using podling.apache.org
 - [ ] Check project-private mailing list membership. Mentors should be 
allowed to remain if they wish to do so. The subscriber list should otherwise 
match that on the resolution. See 
[this](http://www.apache.org/dev/committers.html#mail-moderate) and the 
[EZMLM](http://www.ezmlm.org/) "Moderator’s and Administrator’s Manual".
 - [ ] Update mail addresses including: issue tracking messages (see 
administration documentation)
 - [ ] Double-check that all of your lists have sufficient active 
[moderators](http://www.apache.org/dev/committers.html#mailing-list-moderators).
   - Issue Tracking
 - [ ] Ask infra to move the podling to its own top level category in JIRA, 
if using JIRA (I think we previously used JIRA)
   - Distribution mirrors
 - [ ] Create new distribution area (dist.apache.org) release/${project} 
and dev/${project} folders can be created by PMC members. - new releases will 
go here. Need to decide on what to do with previous releases under incubator 
(see section under distribution mirrors 
[here](https://incubator.apache.org/guides/transferring.html#life_after_graduation).



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[GitHub] [incubator-mxnet] fazaza commented on issue #20992: Trying to install MxNet and GluonCV on Mac M1

[GitBox]

fazaza commented on issue #20992:
URL: 
https://github.com/apache/incubator-mxnet/issues/20992#issuecomment-1287120731

   any news please ?


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[GitHub] [incubator-mxnet] MichaelCurrie commented on issue #11277: No matching distribution found for mxnet when install mxnet everywhere

[GitBox]

MichaelCurrie commented on issue #11277:
URL: 
https://github.com/apache/incubator-mxnet/issues/11277#issuecomment-1286741947

   It's because mxnet hasn't made a wheel for Windows since 2019.  Very 
confusing.  So you need to downgrade Python to 3.7.


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[GitHub] [incubator-mxnet] KristofVandewynckel commented on issue #11277: No matching distribution found for mxnet when install mxnet everywhere

[GitBox]

KristofVandewynckel commented on issue #11277:
URL: 
https://github.com/apache/incubator-mxnet/issues/11277#issuecomment-1286730045

   ERROR: Could not find a version that satisfies the requirement mxnet==1.9.* 
(from versions: 0.11.0, 0.12.0, 0.12.1, 1.0.0, 1.0.0.post1, 1.0.0.post3, 
1.0.0.post4, 1.1.0.post0, 1.2.0, 1.2.1, 1.2.1.post1, 1.3.0, 1.3.1, 1.4.0, 
1.4.0.post0, 1.4.1, 1.5.0, 1.6.0, 1.7.0.post1, 1.7.0.post2)
   ERROR: No matching distribution found for mxnet==1.9.*
   
   
   Anyone has any ideas? I've been trying to solve/understand for the past days 
now but can't get it to work.


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[GitHub] [incubator-mxnet] github-actions[bot] commented on issue #21146: Gluon RNN cannot perform deferred initialization when specifying sequence_length parameter in the forward process

[GitBox]

github-actions[bot] commented on issue #21146:
URL: 
https://github.com/apache/incubator-mxnet/issues/21146#issuecomment-1286529479

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [incubator-mxnet] Davidham3 opened a new issue, #21146: Gluon RNN cannot perform deferred initialization when specifying sequence_length parameter in the forward process

[GitBox]

Davidham3 opened a new issue, #21146:
URL: https://github.com/apache/incubator-mxnet/issues/21146

   ## Description
   ```python
   from mxnet import nd
   from mxnet.gluon import rnn
   
   # create a simple GRU network
   net = rnn.GRU(hidden_size=1, num_layers=2, bidirectional=True, layout="NTC", 
use_sequence_length=True)
   net.initialize()
   
   x = nd.random.uniform(shape=(2, 3, 2))
   valid_length = nd.array([1, 2])
   
   # pass the sequence_length parameter
   print(net(x, sequence_length=nd.array([1, 2])))
   ```
   
   ### Error Message
   ```
   ---
   DeferredInitializationError   Traceback (most recent call last)
   File 
~/miniconda3/envs/autogluon/lib/python3.8/site-packages/mxnet/gluon/block.py:1495,
 in HybridBlock.forward(self, x, *args)
  1494 try:
   -> 1495 params = {k: v.data(ctx) for k, v in self._reg_params.items()}
  1496 except DeferredInitializationError:
   
   File 
~/miniconda3/envs/autogluon/lib/python3.8/site-packages/mxnet/gluon/block.py:1495,
 in (.0)
  1494 try:
   -> 1495 params = {k: v.data(ctx) for k, v in self._reg_params.items()}
  1496 except DeferredInitializationError:
   
   File 
~/miniconda3/envs/autogluon/lib/python3.8/site-packages/mxnet/gluon/parameter.py:574,
 in Parameter.data(self, ctx)
   571 raise RuntimeError("Cannot return a copy of Parameter '%s' on 
ctx %s via data() " \
   572"because its storage type is %s. Please use 
row_sparse_data() " \
   573"instead." % (self.name, str(ctx), 
self._stype))
   --> 574 return self._check_and_get(self._data, ctx)
   
   File 
~/miniconda3/envs/autogluon/lib/python3.8/site-packages/mxnet/gluon/parameter.py:230,
 in Parameter._check_and_get(self, arr_list, ctx)
   229 if self._deferred_init:
   --> 230 raise DeferredInitializationError(
   231 "Parameter '%s' has not been initialized yet because 
initialization was " \
   232 "deferred. Actual initialization happens during the first 
forward pass. " \
   233 "Please pass one batch of data through the network before 
accessing Parameters. " \
   234 "You can also avoid deferred initialization by specifying 
in_units, " \
   235 "num_features, etc., for network layers."%(self.name))
   236 raise RuntimeError(
   237 "Parameter '%s' has not been initialized. Note that " \
   238 "you should initialize parameters and create Trainer " \
   239 "with Block.collect_params() instead of Block.params " \
   240 "because the later does not include Parameters of " \
   241 "nested child Blocks"%(self.name))
   
   DeferredInitializationError: Parameter 'gru0_l0_i2h_weight' has not been 
initialized yet because initialization was deferred. Actual initialization 
happens during the first forward pass. Please pass one batch of data through 
the network before accessing Parameters. You can also avoid deferred 
initialization by specifying in_units, num_features, etc., for network layers.
   
   During handling of the above exception, another exception occurred:
   
   MXNetErrorTraceback (most recent call last)
   File 
~/miniconda3/envs/autogluon/lib/python3.8/site-packages/mxnet/gluon/block.py:1192,
 in HybridBlock._deferred_infer_shape(self, *args)
  1191 try:
   -> 1192 self.infer_shape(*args)
  1193 except Exception as e:
   
   File 
~/miniconda3/envs/autogluon/lib/python3.8/site-packages/mxnet/gluon/block.py:1410,
 in HybridBlock.infer_shape(self, *args)
  1409 """Infers shape of Parameters from inputs."""
   -> 1410 self._infer_attrs('infer_shape', 'shape', *args)
   
   File 
~/miniconda3/envs/autogluon/lib/python3.8/site-packages/mxnet/gluon/block.py:1394,
 in HybridBlock._infer_attrs(self, infer_fn, attr, *args)
  1393 """Generic infer attributes."""
   -> 1394 inputs, out = self._get_graph(*args)
  1395 args, _ = _flatten(args, "input")
   
   File 
~/miniconda3/envs/autogluon/lib/python3.8/site-packages/mxnet/gluon/block.py:1060,
 in HybridBlock._get_graph(self, *args)
  1059 with self.name_scope():
   -> 1060 out = self.hybrid_forward(symbol, *grouped_inputs, **params)  # 
pylint: disable=no-value-for-parameter
  1061 out, self._out_format = _flatten(out, "output")
   
   File 
~/miniconda3/envs/autogluon/lib/python3.8/site-packages/mxnet/gluon/rnn/rnn_layer.py:254,
 in _RNNLayer.hybrid_forward(self, F, inputs, states, sequence_length, **kwargs)
   251 raise ValueError(
   252 "Invalid recurrent state shape. Expecting %s, got 
%s."%(
   253 str(info['shape']), str(state.shape)))
   --> 254 out = self._forward_kernel(F, inputs, states, sequence_length, 
**kwargs)
   256 # out is (output, state)
   
   File 
~/miniconda3/envs/autogluon/lib/python3.8/sit

[GitHub] [incubator-mxnet] yaosting commented on issue #20992: Trying to install MxNet and GluonCV on Mac M1

[GitBox]

yaosting commented on issue #20992:
URL: 
https://github.com/apache/incubator-mxnet/issues/20992#issuecomment-1284292403

   > 
   Did you successfully install by adjusting these two files
I installed according  
https://mxnet.apache.org/versions/1.9.1/get_started/osx_setup.html#install-mxnet-for-python,
  and replaced two files after the build, but the following error occurred
   https://user-images.githubusercontent.com/39480659/196751920-92c270d4-5575-4be2-a409-96e0629e8fab.png";>
   
   What should I do？
   


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[GitHub] [incubator-mxnet] leezu closed issue #20028: Mac-ARM build failure

[GitBox]

leezu closed issue #20028: Mac-ARM build failure
URL: https://github.com/apache/incubator-mxnet/issues/20028


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[GitHub] [incubator-mxnet] Yimin-Yi commented on issue #20992: Trying to install MxNet and GluonCV on Mac M1

[GitBox]

Yimin-Yi commented on issue #20992:
URL: 
https://github.com/apache/incubator-mxnet/issues/20992#issuecomment-1279626649

   > I was able to get mxnet version 1.6 compiling for m1 macs by basically 
copying some of their stuff from the 2.0 code: top of cmakelists script that 
loads the config.cmake file, moved the darwin config.cmake to root, and 
replaced the scripts the blas and accelerate framework package finders. I could 
fork it if it's wanted. I had to disable USE_SSE for it to function 
(config.cmake) and I don't have enough time to research what was wrong there.
   > 
   > Remove .txt extension to this config.cmake.txt here, place it in the root, 
and then replace the chooseblas and findaccerlerate with the ones from the 2.0 
branch.
   > 
   > 
[CMakeLists.txt](https://github.com/apache/incubator-mxnet/files/8902060/CMakeLists.txt)
 
[config.cmake.txt](https://github.com/apache/incubator-mxnet/files/8902071/config.cmake.txt)
 .
   
   Can you please fork it? I also want to install mxnet 1.6. Thanks


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[GitHub] [incubator-mxnet] szha commented on issue #21000: [RFC] v1.9.1 release

[GitBox]

szha commented on issue #21000:
URL: 
https://github.com/apache/incubator-mxnet/issues/21000#issuecomment-1279285326

   Hi there,
   I am a security researcher and I have found a DMARC vulnerability which I
   reported from this attack. An attacker can spam your users by sending them
   email using your website's official email address, kindly forward this to
   the development team.
   hoping for a bounty for my ethical disclosure of this vulnerability to you.
   Waiting for your response.
   Always Best Regards
   Arslan
   
   On Thu, Oct 13, 2022 at 9:51 PM Joe Evans ***@***.***>
   wrote:
   
   > Closed #21000 as completed.
   >
   > --
   > Reply to this email directly or view it on GitHub:
   > https://github.com/apache/incubator-mxnet/issues/21000#event-7583309255
   > You are receiving this because you are subscribed to this thread.
   >
   > Message ID:  ***@***.***>
   


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[GitHub] [incubator-mxnet] jens-maus commented on issue #21144: undefined reference to `void mxnet::op::ElemwiseBinaryOp::DnsCsrDnsOp` for GPU

[GitBox]

jens-maus commented on issue #21144:
URL: 
https://github.com/apache/incubator-mxnet/issues/21144#issuecomment-1278097508

   After some further investigation I actually found the config option that 
seems to trigger the mentioned `undefined reference` linker error. It is the 
`USE_DIST_KVSTORE` config option that - if enabled - seem to trigger these 
error messages within the environment. Thus, setting it to `OFF`...
   
   ```cmake
   set(USE_DIST_KVSTORE OFF CACHE BOOL "Build with DIST_KVSTORE support")
   ```
   
   seem to help and the linker error goes away. However, it is still unclear 
why this config option seem to trigger the mentioned linker error.
   


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[GitHub] [incubator-mxnet] josephevans closed issue #21000: [RFC] v1.9.1 release

[GitBox]

josephevans closed issue #21000: [RFC] v1.9.1 release
URL: https://github.com/apache/incubator-mxnet/issues/21000


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[GitHub] [incubator-mxnet] josephevans commented on issue #21138: It looks like some files are missing from the source code in the mxnet version 0.9.5

[GitBox]

josephevans commented on issue #21138:
URL: 
https://github.com/apache/incubator-mxnet/issues/21138#issuecomment-1277890439

   Hello, MXNet 0.9.5 is a very old version; the latest release is 1.9.1. 
   
   The files missing appear to be Intel MKL header files. Do you have MKL 
installed properly?


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[GitHub] [incubator-mxnet] josephevans commented on issue #20984: [RFC] Graduate MXNet from Apache Incubator

[GitBox]

josephevans commented on issue #20984:
URL: 
https://github.com/apache/incubator-mxnet/issues/20984#issuecomment-1277725664

   Now that the vote has passed, I will create another issue for 
post-graduation activities to track.


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[GitHub] [incubator-mxnet] josephevans closed issue #20984: [RFC] Graduate MXNet from Apache Incubator

[GitBox]

josephevans closed issue #20984: [RFC] Graduate MXNet from Apache Incubator
URL: https://github.com/apache/incubator-mxnet/issues/20984


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[GitHub] [incubator-mxnet] josephevans closed issue #21074: Build for Cuda 11.3

[GitBox]

josephevans closed issue #21074: Build for Cuda 11.3
URL: https://github.com/apache/incubator-mxnet/issues/21074


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[GitHub] [incubator-mxnet] josephevans commented on issue #21074: Build for Cuda 11.3

[GitBox]

josephevans commented on issue #21074:
URL: 
https://github.com/apache/incubator-mxnet/issues/21074#issuecomment-1277723530

   Hi David, we've recently published MXNet 1.9.1 packages for Cuda 11.0 
through 11.7. You can get the 11.3 wheel using the command `pip install 
mxnet-cu113==1.9.1`.
   
   https://pypi.org/project/mxnet-cu113/


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[GitHub] [incubator-mxnet] jens-maus commented on issue #21144: undefined reference to `void mxnet::op::ElemwiseBinaryOp::DnsCsrDnsOp` for GPU

[GitBox]

jens-maus commented on issue #21144:
URL: 
https://github.com/apache/incubator-mxnet/issues/21144#issuecomment-1277329189

   Please note, that I am seeing the same/similar issue when trying to compile 
the older mxnet 1.9.0 in the same environment:
   
   ```
   $ cmake --build . --parallel 1
   Consolidate compiler generated dependencies of target objects
   [  9%] Built target objects
   [  9%] Built target libzmq-static
   Consolidate compiler generated dependencies of target dnnl_cpu_x64
   [ 25%] Built target dnnl_cpu_x64
   Consolidate compiler generated dependencies of target dnnl_common
   [ 28%] Built target dnnl_common
   Consolidate compiler generated dependencies of target dnnl_cpu
   [ 35%] Built target dnnl_cpu
   [ 36%] Built target dnnl
   Consolidate compiler generated dependencies of target intgemm
   [ 36%] Built target intgemm
   [ 37%] Built target libomp-needed-headers
   Consolidate compiler generated dependencies of target omp
   [ 40%] Built target omp
   [ 40%] Built target proto_python
   Consolidate compiler generated dependencies of target pslite
   [ 40%] Built target pslite
   Consolidate compiler generated dependencies of target mxnet_static
   [ 91%] Built target mxnet_static
   Consolidate compiler generated dependencies of target dmlc
   [ 92%] Built target dmlc
   Consolidate compiler generated dependencies of target mxnet
   [ 92%] Built target mxnet
   Consolidate compiler generated dependencies of target customop_lib
   [ 92%] Built target customop_lib
   Consolidate compiler generated dependencies of target transposecsr_lib
   [ 92%] Built target transposecsr_lib
   Consolidate compiler generated dependencies of target transposerowsp_lib
   [ 93%] Built target transposerowsp_lib
   Consolidate compiler generated dependencies of target subgraph_lib
   [ 93%] Built target subgraph_lib
   Consolidate compiler generated dependencies of target pass_lib
   [ 94%] Built target pass_lib
   Consolidate compiler generated dependencies of target customop_gpu_lib
   [ 94%] Built target customop_gpu_lib
   Consolidate compiler generated dependencies of target im2rec
   [ 94%] Linking CXX executable im2rec
   /usr/bin/ld: libmxnet.a(elemwise_binary_broadcast_op_basic.cu.o): in 
function `void mxnet::op::BinaryBroadcastComputeDenseEx(nnvm::NodeAttrs const&, mxnet::OpContext const&, 
std::vector > const&, 
std::vector > const&, 
std::vector > const&)':
   
tmpxft_001b66c6_-6_elemwise_binary_broadcast_op_basic.compute_80.cudafe1.cpp:(.text._ZN5mxnet2op29BinaryBroadcastComputeDenseExIN7mshadow3gpuENS0_10mshadow_op5minusEEEvRKN4nnvm9NodeAttrsERKNS_9OpContextERKSt6vectorINS_7NDArrayESaISE_EERKSD_INS_9OpReqTypeESaISJ_EESI_[_ZN5mxnet2op29BinaryBroadcastComputeDenseExIN7mshadow3gpuENS0_10mshadow_op5minusEEEvRKN4nnvm9NodeAttrsERKNS_9OpContextERKSt6vectorINS_7NDArrayESaISE_EERKSD_INS_9OpReqTypeESaISJ_EESI_]+0x34c):
 undefined reference to `void 
mxnet::op::ElemwiseBinaryOp::DnsCsrDnsOp(mshadow::Stream*,
 nnvm::NodeAttrs const&, mxnet::OpContext const&, mxnet::NDArray const&, 
mxnet::NDArray const&, mxnet::OpReqType, mxnet::NDArray const&, bool)'
   /usr/bin/ld: libmxnet.a(elemwise_binary_broadcast_op_basic.cu.o): in 
function `void mxnet::op::BinaryBroadcastComputeDenseEx(nnvm::NodeAttrs const&, mxnet::OpContext const&, 
std::vector > const&, 
std::vector > const&, 
std::vector > const&)':
   
tmpxft_001b66c6_-6_elemwise_binary_broadcast_op_basic.compute_80.cudafe1.cpp:(.text._ZN5mxnet2op29BinaryBroadcastComputeDenseExIN7mshadow3gpuENS0_10mshadow_op4plusEEEvRKN4nnvm9NodeAttrsERKNS_9OpContextERKSt6vectorINS_7NDArrayESaISE_EERKSD_INS_9OpReqTypeESaISJ_EESI_[_ZN5mxnet2op29BinaryBroadcastComputeDenseExIN7mshadow3gpuENS0_10mshadow_op4plusEEEvRKN4nnvm9NodeAttrsERKNS_9OpContextERKSt6vectorINS_7NDArrayESaISE_EERKSD_INS_9OpReqTypeESaISJ_EESI_]+0x14de):
 undefined reference to `void 
mxnet::op::ElemwiseBinaryOp::DnsCsrDnsOp(mshadow::Stream*,
 nnvm::NodeAttrs const&, mxnet::OpContext const&, mxnet::NDArray const&, 
mxnet::NDArray const&, mxnet::OpReqType, mxnet::NDArray const&, bool)'
   collect2: error: ld returned 1 exit status
   gmake[2]: *** [CMakeFiles/im2rec.dir/build.make:125: im2rec] Error 1
   gmake[1]: *** [CMakeFiles/Makefile2:783: CMakeFiles/im2rec.dir/all] Error 2
   gmake: *** [Makefile:146: all] Error 2
   ```
   
   Perhaps anyone has a clue where this might originate from!?!?


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[GitHub] [incubator-mxnet] github-actions[bot] commented on issue #21144: undefined reference to `void mxnet::op::ElemwiseBinaryOp::DnsCsrDnsOp` for GPU

[GitBox]

github-actions[bot] commented on issue #21144:
URL: 
https://github.com/apache/incubator-mxnet/issues/21144#issuecomment-1276679963

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [incubator-mxnet] jens-maus opened a new issue, #21144: undefined reference to `void mxnet::op::ElemwiseBinaryOp::DnsCsrDnsOp` for GPU

[GitBox]

jens-maus opened a new issue, #21144:
URL: https://github.com/apache/incubator-mxnet/issues/21144

   ## Description
   After switching to Ubuntu 22.04 with latest gcc/g++ v11 and CUDA 11.7 with 
NVIDIA driver 515.65.01 for Tesla V100S GPU cards I tried to compile mxnet 
1.9.1 for our new environment because we need to get the R-package 
installed/updated as well. However, while most of the mxnet build seem to 
suceed fine, the build unfortunately stops right at trying to link `img2rec` 
with an error message mentioned in the next section. Trying to skip the 
`img2rec` build ends up in similar linker errors for other tools for which I 
could not find any solution. Also looking at similar issue tickets like 
https://github.com/apache/incubator-mxnet/pull/18761 and 
https://github.com/apache/incubator-mxnet/pull/18357 did not end up in a fix we 
could apply for the issue.
   
   Any help in trying to solve this issue would be highly appreciated.
   
   ### Error Message
   ```
   $ cmake --build . --parallel 1
   Consolidate compiler generated dependencies of target objects
   [  8%] Built target objects
   [  8%] Built target libzmq-static
   Consolidate compiler generated dependencies of target dnnl_cpu_x64
   [ 27%] Built target dnnl_cpu_x64
   Consolidate compiler generated dependencies of target dnnl_common
   [ 32%] Built target dnnl_common
   Consolidate compiler generated dependencies of target dnnl_cpu
   [ 41%] Built target dnnl_cpu
   [ 41%] Built target dnnl
   Consolidate compiler generated dependencies of target intgemm
   [ 41%] Built target intgemm
   [ 42%] Built target libomp-needed-headers
   Consolidate compiler generated dependencies of target omp
   [ 45%] Built target omp
   Consolidate compiler generated dependencies of target dmlc
   [ 46%] Built target dmlc
   [ 46%] Built target proto_python
   Consolidate compiler generated dependencies of target pslite
   [ 47%] Built target pslite
   Consolidate compiler generated dependencies of target mxnet
   [ 94%] Built target mxnet
   Consolidate compiler generated dependencies of target customop_lib
   [ 94%] Built target customop_lib
   Consolidate compiler generated dependencies of target transposecsr_lib
   [ 94%] Built target transposecsr_lib
   Consolidate compiler generated dependencies of target transposerowsp_lib
   [ 95%] Built target transposerowsp_lib
   Consolidate compiler generated dependencies of target subgraph_lib
   [ 95%] Built target subgraph_lib
   Consolidate compiler generated dependencies of target pass_lib
   [ 95%] Built target pass_lib
   Consolidate compiler generated dependencies of target customop_gpu_lib
   [ 95%] Built target customop_gpu_lib
   Consolidate compiler generated dependencies of target im2rec
   [ 95%] Linking CXX executable im2rec
   /usr/bin/ld: libmxnet.so: undefined reference to `void 
mxnet::op::ElemwiseBinaryOp::DnsCsrDnsOp(mshadow::Stream*,
 nnvm::NodeAttrs const&, mxnet::OpContext const&, mxnet::NDArray const&, 
mxnet::NDArray const&, mxnet::OpReqType, mxnet::NDArray const&, bool)'
   /usr/bin/ld: libmxnet.so: undefined reference to `void 
mxnet::op::ElemwiseBinaryOp::DnsCsrDnsOp(mshadow::Stream*,
 nnvm::NodeAttrs const&, mxnet::OpContext const&, mxnet::NDArray const&, 
mxnet::NDArray const&, mxnet::OpReqType, mxnet::NDArray const&, bool)'
   collect2: error: ld returned 1 exit status
   gmake[2]: *** [CMakeFiles/im2rec.dir/build.make:130: im2rec] Error 1
   gmake[1]: *** [CMakeFiles/Makefile2:749: CMakeFiles/im2rec.dir/all] Error 2
   gmake: *** [Makefile:146: all] Error 2
   ```
   
   ## To Reproduce
   This is the `config.cmake` file we are using to build mxnet 1.9.1 in our 
ubuntu 22.04 environment:
   
   ```cmake
   set(CMAKE_BUILD_TYPE "Distribution" CACHE STRING "Build type")
   set(CFLAGS "-mno-avx" CACHE STRING "CFLAGS")
   set(CXXFLAGS "-mno-avx" CACHE STRING "CXXFLAGS")
   set(USE_CUDA ON CACHE BOOL "Build with CUDA support")
   set(USE_CUDNN ON CACHE BOOL "Build with CUDA support")
   set(USE_NCCL ON CACHE BOOL "Build with NCCL support")
   set(USE_OPENCV ON CACHE BOOL "Build with OpenCV support")
   set(USE_OPENMP ON CACHE BOOL "Build with Openmp support")
   set(USE_MKL_IF_AVAILABLE OFF CACHE BOOL "Use Intel MKL if found")
   set(USE_MKLDNN ON CACHE BOOL "Build with MKL-DNN support")
   set(USE_LAPACK ON CACHE BOOL "Build with lapack support")
   set(USE_TVM_OP OFF CACHE BOOL "Enable use of TVM operator build system.")
   set(USE_SSE ON CACHE BOOL "Build with x86 SSE instruction support")
   set(USE_F16C OFF CACHE BOOL "Build with x86 F16C instruction support")
   set(USE_LIBJPEG_TURBO ON CACHE BOOL "Build with libjpeg-turbo")
   set(USE_DIST_KVSTORE ON CACHE BOOL "Build with DIST_KVSTORE support")
   set(MXNET_CUDA_ARCH "5.0;6.0;7.0;8.0;8.6" CACHE STRING "Cuda architectures")
   set(CMAKE_CUDA_COMPILER "/usr/local/cuda-11.7/bin/nvcc" CACHE STRING "Cuda 
compiler")
   set(OPENMP_FILECHECK_EXECUTABLE "/usr/lib/llvm-14/bin/FileCheck")
   set(OPENMP_LLVM_LIT_EXECUTABLE "/

[GitHub] [incubator-mxnet] matteosal commented on issue #21070: Repeated evaluations of a large convolution allocate more and more memory

[GitBox]

matteosal commented on issue #21070:
URL: 
https://github.com/apache/incubator-mxnet/issues/21070#issuecomment-1276197539

   Behaviour is way better when oneDNN is enabled:
   ```
   Initial memory: 201.998336
   215.732224
   215.732224
   215.732224
   215.732224
   215.732224
   215.732224
   215.732224
   215.732224
   215.732224
   215.732224
   ```
   Closing this issue.


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[GitHub] [incubator-mxnet] matteosal closed issue #21070: Repeated evaluations of a large convolution allocate more and more memory

[GitBox]

matteosal closed issue #21070: Repeated evaluations of a large convolution 
allocate more and more memory
URL: https://github.com/apache/incubator-mxnet/issues/21070


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[GitHub] [incubator-mxnet] vijaya-lakshmi-venkatraman commented on issue #18988: Documentation Bug: Format issue in mxnet.util.use_np document

[GitBox]

vijaya-lakshmi-venkatraman commented on issue #18988:
URL: 
https://github.com/apache/incubator-mxnet/issues/18988#issuecomment-1276005851

   @DNXie Isn't master the current live version? Do you prefer to have it fixed 
in 1.6 branch as well?


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[GitHub] [incubator-mxnet] matteosal opened a new issue, #21143: Gradient calculation for recurrent operators is wrong

[GitBox]

matteosal opened a new issue, #21143:
URL: https://github.com/apache/incubator-mxnet/issues/21143

   This script creates an `RNN` operator and computes its input gradient 5 
times for sequence lengths = 1, 2, 3, 4, 5. Then it shows each gradient element 
at a fixed sequence position for all the computed sequence lengths:
   ```
   import mxnet as mx
   from mxnet import autograd
   import numpy as np
   
   batch_size = 1
   data_len = 5
   input_size = 2
   output_size = 3
   
   param_shapes = {
'wx': [output_size, input_size], 
'ws': [output_size, output_size], 
'bx': [output_size],
'bs': [output_size]
   }
   fused_param_len = np.sum(
[np.prod(v) for v in param_shapes.values()]
   )
   
   shapes = {
'data': [data_len, batch_size, input_size], 
'par': [fused_param_len], 
's0': [1, batch_size, output_size]
   }
   
   sym = mx.symbol.RNN(
*[mx.symbol.Variable(name) for name in shapes.keys()],
state_size=output_size,
num_layers=1,
mode='rnn_tanh'
   )
   op = mx.ndarray.CachedOp(sym)
   
   args = [mx.np.random.uniform(size=shape, ctx=mx.cpu()) for shape in 
shapes.values()]
   
   def get_grad(seq_len):
input_data = args[0][:seq_len]
with autograd.record(train_mode=True):
input_data.attach_grad()
output = op(input_data, args[1], args[2], default_ctx=mx.cpu())
autograd.backward(output, head_grads=mx.np.ones([data_len, batch_size, 
output_size], ctx=mx.cpu()))
return input_data.grad
   
   results = []
   for i in range(1, 6):
print('**')
print('Input gradient for sequence length = ' + str(i) + '\n')
results.append(get_grad(i))
print(results[-1])
print('\n')
   
   for i in range(4):
print('++')
print('Element #' + str(i) + ' of all input gradients')
for j in range(i, 5):
print('sequence length: ' + str(j+1) + ': ' + 
str(results[j][i]))
# [print('sequence length: ' + str(i+1) + ': ' + str(grad[i])) for grad 
in results[i:]]
print('\n')
   ```
   
   The output is:
   ```
   **
   Input gradient for sequence length = 1
   
   [[[0.14385478 0.05408207]]]
   
   
   **
   Input gradient for sequence length = 2
   
   [[[0.14385478 0.05408207]]
[[0.01706791 0.00660894]]]
   
   
   **
   Input gradient for sequence length = 3
   
   [[[0.14385478 0.05408207]]
[[0.01706791 0.00660894]]
[[0.0178871  0.00672178]]]
   
   
   **
   Input gradient for sequence length = 4
   
   [[[0.14385478 0.05408207]]
[[0.01706791 0.00660894]]
[[0.0178871  0.00672178]]
[[0.01958952 0.00729937]]]
   
   
   **
   Input gradient for sequence length = 5
   
   [[[0.14385478 0.05408207]]
[[0.01706791 0.00660894]]
[[0.0178871  0.00672178]]
[[0.01958952 0.00729937]]
[[0.02612576 0.00999804]]]
   
   
   ++
   Element #0 of all input gradients
   sequence length: 1: [[0.14385478 0.05408207]]
   sequence length: 2: [[0.14385478 0.05408207]]
   sequence length: 3: [[0.14385478 0.05408207]]
   sequence length: 4: [[0.14385478 0.05408207]]
   sequence length: 5: [[0.14385478 0.05408207]]
   
   
   ++
   Element #1 of all input gradients
   sequence length: 2: [[0.01706791 0.00660894]]
   sequence length: 3: [[0.01706791 0.00660894]]
   sequence length: 4: [[0.01706791 0.00660894]]
   sequence length: 5: [[0.01706791 0.00660894]]
   
   
   ++
   Element #2 of all input gradients
   sequence length: 3: [[0.0178871  0.00672178]]
   sequence length: 4: [[0.0178871  0.00672178]]
   sequence length: 5: [[0.0178871  0.00672178]]
   
   
   ++
   Element #3 of all input gradients
   sequence length: 4: [[0.01958952 0.00729937]]
   sequence length: 5: [[0.01958952 0.00729937]]
   
   ```
   In the last 4 sections starting with `++`, it can be seen that 
gradient elements at the same sequence position are equal across all the 5 
gradient computations with sequence length 1, 2, 3, 4, 5 (if they are long 
enough to have that element, e.g. gradient with sequence length 2 cannot have 
element 3 obviously). This means that `RNN` behaves as if the presence of later 
elements in the sequence does not affect the gradient for earlier elements. 
   But this is clearly wrong, because by the nature of recurrent computations 
earlier elements in the sequence DO affect later ones, hence gradient elements 
at the same sequence position should change if the sequence length is 
different. With a longer input sequence having an additional element, the 
gradient of all earlier elements should get an additional contribution from the 
new element, changing their value.
   
   This is not a direct comparison with a manual computation of the gradient, 
but pointing out this behavior is enough to conclude that the gradients 
computed 

[GitHub] [incubator-mxnet] ankane commented on issue #20028: Mac-ARM build failure

[GitBox]

ankane commented on issue #20028:
URL: 
https://github.com/apache/incubator-mxnet/issues/20028#issuecomment-1272381981

   fwiw, was able to get the latest version (1.9.1) built with Homebrew with 
`-DUSE_SSE=OFF`. Mac ARM users should now be able to install a precompiled 
build with:
   
   ```sh
   brew install mxnet
   ```
   
   https://github.com/Homebrew/homebrew-core/pull/111872


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[GitHub] [incubator-mxnet] ashsyal commented on issue #19234: import mxnet in arm64 error

[GitBox]

ashsyal commented on issue #19234:
URL: 
https://github.com/apache/incubator-mxnet/issues/19234#issuecomment-1264536223

   Here are my steps for reference:
   1. wget 
https://developer.arm.com/-/media/Files/downloads/hpc/arm-performance-libraries/22-0-2/Ubuntu20.04/arm-performance-libraries_22.0.2_Ubuntu-20.04_gcc-11.2.tar?rev=577d3dbcff7847b9af57399b2978f9a6&revision=577d3dbc-ff78-47b9-af57-399b2978f9a6
   2. tar -xvf 
arm-performance-libraries_22.0.2_Ubuntu-20.04_gcc-11.2.tar\?rev\=577d3dbcff7847b9af57399b2978f9a6
   3. sudo 
arm-performance-libraries_22.0.2_Ubuntu-20.04/arm-performance-libraries_22.0.2_Ubuntu-20.04.sh
 -a
   4. export 
LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/opt/arm/armpl-22.0.2-gcc-11.2/lib
   5.  wget 
https://repo.mxnet.io/dist/python/cpu/mxnet-1.9.0-py3-none-manylinux2014_aarch64.whl
   6. pip3 install mxnet-1.9.0-py3-none-manylinux2014_aarch64.whl
   
   I am using a Qualcomm QRB5165 aarch64 chipset


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[GitHub] [incubator-mxnet] ashsyal commented on issue #19234: import mxnet in arm64 error

[GitBox]

ashsyal commented on issue #19234:
URL: 
https://github.com/apache/incubator-mxnet/issues/19234#issuecomment-1264535754

   > wget 
https://repo.mxnet.io/dist/python/cpu/mxnet-1.9.0-py3-none-manylinux2014_aarch64.whl
   > python3 -c 'import mxnet'
   
   I ran through all these steps on aarch64 using ubuntu 20.04 but didnt seem 
to work.
   
   root@qrb5165-rb5:~/mxnet# python3.6
   Python 3.6.9 (default, Jun 29 2022, 11:45:57)
   [GCC 8.4.0] on linux
   Type "help", "copyright", "credits" or "license" for more information.
   >>> import mxnet
   Traceback (most recent call last):
 File "", line 1, in 
 File "/usr/local/lib/python3.6/dist-packages/mxnet/__init__.py", line 23, 
in 
   from .context import Context, current_context, cpu, gpu, cpu_pinned
 File "/usr/local/lib/python3.6/dist-packages/mxnet/context.py", line 23, 
in 
   from .base import classproperty, with_metaclass, 
_MXClassPropertyMetaClass
 File "/usr/local/lib/python3.6/dist-packages/mxnet/base.py", line 356, in 

   _LIB = _load_lib()
 File "/usr/local/lib/python3.6/dist-packages/mxnet/base.py", line 347, in 
_load_lib
   lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL)
 File "/usr/lib/python3.6/ctypes/__init__.py", line 348, in __init__
   self._handle = _dlopen(self._name, mode)
   OSError: libarmpl_lp64_mp.so: cannot open shared object file: No such file 
or directory
   
   
   Any way to resolve this?


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[GitHub] [incubator-mxnet] aW3ikun commented on issue #21081: win10+vs2019 link failed: error LNK2019: __int64 __cdecl mxnet::op::linalg_gesdd_workspace_query

[GitBox]

aW3ikun commented on issue #21081:
URL: 
https://github.com/apache/incubator-mxnet/issues/21081#issuecomment-1259472485

   Hi. Maybe u try using `python ./ci/build_windows.py -f WIN_GPU` to compile 
mxnet(v1.9.x) with VS2019.


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[GitHub] [incubator-mxnet] aW3ikun commented on issue #20954: Windows CPP_PACKAGE+GPU Build Fails inside python at opwrapper stage under certain conditions (using python version >= 3.8 or having certa

[GitBox]

aW3ikun commented on issue #20954:
URL: 
https://github.com/apache/incubator-mxnet/issues/20954#issuecomment-1259280200

   Hi, guys. I have compiled and installed successfully mxnet(v1.9.x) with 
python3.8 and cuda11.3(RTX 3060) on Windows11 in these days. Performance in a 
model of D2L is not bad. Actually, I also had the same problems. So, I leave my 
steps as a reference.
   1. Following the official website and installing all dependencies. 
https://mxnet.apache.org/get_started/build_from_source
   2. Compiling the mxnet with `mxnet/ci/build_windows.py`. Before the start, I 
modify the parameter [this 
line](https://github.com/apache/incubator-mxnet/blob/878c3c9e5d172755e73ba346fffac56719ab0331/ci/build_windows.py#L130)
 to "Auto", and I modify all folder addresses and add `CUDNN_ROOT` path to 
adapt the CMake configuration below [this line of 
code](https://github.com/apache/incubator-mxnet/blob/878c3c9e5d172755e73ba346fffac56719ab0331/ci/build_windows.py#L297).
   ```
   if 'CUTENSOR_ROOT' not in os.environ:
   os.environ["CUTENSOR_ROOT"] = "D:\\libcutensor"
   ```
   4. `python /ci/build_windows.py`, waiting to complete.
   5. The following is important. No Running python setup.py install in 
windows_package, since it occurs same problems. So, i copy compiled binaries 
like mxnet_xx.dll and libmxnet.dll to`mxnet\python\mxnet`. Then Coping all 
dependencies to the same folder. For many .dll names, please refer to the 
release in my 
[repository](https://github.com/aW3ikun/My_mxnet/releases/tag/mxnet) in which u 
could find all DLL files with  similar names.
   6. Using python -m pip install -e /mxnet/python/ to install the python 
module of mxnet. And u may test it with some code.


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[GitHub] [incubator-mxnet] zhcn381 commented on issue #20028: Mac-ARM build failure

[GitBox]

zhcn381 commented on issue #20028:
URL: 
https://github.com/apache/incubator-mxnet/issues/20028#issuecomment-1255733462

   Check your config.cmake file, make sure the USE_SSE/USE_F16C configuration 
is OFF.


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[GitHub] [incubator-mxnet] cyrusbehr closed issue #15261: C++ Can't load model with Symbol::Load when using static library

[GitBox]

cyrusbehr closed issue #15261: C++ Can't load model with Symbol::Load when 
using static library
URL: https://github.com/apache/incubator-mxnet/issues/15261


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[GitHub] [incubator-mxnet] cyrusbehr closed issue #20600: How to efficiently run inference with variable batch sizes with mxnet using C++ API?

[GitBox]

cyrusbehr closed issue #20600: How to efficiently run inference with variable 
batch sizes with mxnet using C++ API?
URL: https://github.com/apache/incubator-mxnet/issues/20600


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[GitHub] [incubator-mxnet] DNXie commented on issue #18988: Documentation Bug: Format issue in mxnet.util.use_np document

[GitBox]

DNXie commented on issue #18988:
URL: 
https://github.com/apache/incubator-mxnet/issues/18988#issuecomment-1248472610

   @vijaya-lakshmi-venkatraman 
   Hi, it is fixed in the the master version. But in the [1.6]( 
https://mxnet.apache.org/versions/1.6/api/python/docs/api/mxnet/util/index.html#mxnet.util.use_np)
 document, it still exists. Should we consider this as fixed?


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[GitHub] [incubator-mxnet] github-actions[bot] commented on issue #21138: It looks like some files are missing from the source code in the mxnet version 0.9.5

[GitBox]

github-actions[bot] commented on issue #21138:
URL: 
https://github.com/apache/incubator-mxnet/issues/21138#issuecomment-1246196600

   Welcome to Apache MXNet (incubating)! We are on a mission to democratize AI, 
and we are glad that you are contributing to it by opening this issue.
   Please make sure to include all the relevant context, and one of the 
@apache/mxnet-committers will be here shortly.
   If you are interested in contributing to our project, let us know! Also, be 
sure to check out our guide on [contributing to 
MXNet](https://mxnet.apache.org/community/contribute) and our [development 
guides wiki](https://cwiki.apache.org/confluence/display/MXNET/Developments).


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[GitHub] [incubator-mxnet] zhyaoch opened a new issue, #21138: It looks like some files are missing from the source code in the mxnet version 0.9.5

[GitBox]

zhyaoch opened a new issue, #21138:
URL: https://github.com/apache/incubator-mxnet/issues/21138

   ## Description
   When I was compiling the source code of Mxnet v0.9.5,there meet some Error 
Messages that said can't open the file of “mkl_dnn_types.h” and “mkl_dnn.h”,and 
it seems like these file isn't in the source code of Mxnet，so where should I 
find these files.
   
   ### Error Message
   fatal error: mkl_dnn.h: No such file or directory
   fatal error: mkl_dnn_types.h: No such file or directory
   
   


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[GitHub] [incubator-mxnet] MichaelCurrie commented on issue #15281: Gluon Inference failed

[GitBox]

MichaelCurrie commented on issue #15281:
URL: 
https://github.com/apache/incubator-mxnet/issues/15281#issuecomment-1242690246

   I suppressed the warning with:
   
   ```
   with warnings.catch_warnings():
   warnings.simplefilter("ignore")
   self.resnet = gcv.model_zoo.get_model(...)
   ```


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[GitHub] [incubator-mxnet] MichaelCurrie commented on issue #11277: No matching distribution found for mxnet when install mxnet everywhere

[GitBox]

MichaelCurrie commented on issue #11277:
URL: 
https://github.com/apache/incubator-mxnet/issues/11277#issuecomment-1240564917

   `ERROR: No matching distribution found for mxnet==1.9.1`


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[GitHub] [incubator-mxnet] matteosal opened a new issue, #21135: Hangs during operator tuning & how to disable it

[GitBox]

matteosal opened a new issue, #21135:
URL: https://github.com/apache/incubator-mxnet/issues/21135

   We have experienced problems with the mxnet library hanging at loading time 
on some Linux machines, specifically during the operator tuning phase 
(`mxnet::op::OperatorTune`). I don't have precise information about where the 
hang happened exactly and the machine details, but on a CentOS system we could 
avoid the hang by setting the environment variable 
`MXNET_USE_OPERATOR_TUNING=0` before loading, while on an ARM machine we also 
had to rebuild the library with the cmake option `USE_OPERATOR_TUNING=OFF`.
   
   So what's the relationship between the env variable and the build option?


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[GitHub] [incubator-mxnet] vijaya-lakshmi-venkatraman commented on issue #18988: Documentation Bug: Format issue in mxnet.util.use_np document

[GitBox]

vijaya-lakshmi-venkatraman commented on issue #18988:
URL: 
https://github.com/apache/incubator-mxnet/issues/18988#issuecomment-1237793235

   @DNXie I am looking at the [master 
](https://mxnet.apache.org/versions/master/api/python/docs/api/util/index.html#mxnet.util.use_np
 )version and dont see the second bullet point anymore however the description 
seems to be incomplete
   
   Am I looking at the right version & the right place?
   
   
   


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[GitHub] [incubator-mxnet] matteosal commented on issue #21111: cuDNN batchnorm behaviour is not consistent and it can output nan

[GitBox]

matteosal commented on issue #2:
URL: 
https://github.com/apache/incubator-mxnet/issues/2#issuecomment-1235713824

   So, to summarize the issues I found with the cuDNN implementation:
   
   1) Moving arrays are normally updated only if a BACKWARD pass in training 
mode is performed, but on GPU + cudnn they are changed by the FORWARD
   2) In training mode, all implementations compute the **biased** data 
variance during the forward but the cuDNN implementation uses the **unbiased** 
data variance to update the moving variance
   
   So the cuDNN implementation updates the moving variance using a different 
value (the unbiased one) and also at a different time (during the forward)


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[GitHub] [incubator-mxnet] matteosal commented on issue #21111: cuDNN batchnorm behaviour is not consistent and it can output nan

[GitBox]

matteosal commented on issue #2:
URL: 
https://github.com/apache/incubator-mxnet/issues/2#issuecomment-1235710738

   @DickJC123 you were in fact right about biased vs unbiased variance 
computation. This script tests such claim by letting a non-cudnn batchnorm and 
a cudnn-batchnorm update their moving variance, and checking that they are 
updated differently and that they respectively correspond to the biased 
(non-cudnn) and the unbiased (cudnn) computations:
   ```
   import mxnet as mx
   import numpy as np
   from mxnet import autograd
   
   print(" cudnn batchnorm variance")
   
   shapes = {'input': [1, 6, 5], 'gamma': [6], 'beta': [6], 'mean': [6], 'var': 
[6]}
   
   # Define batchnorms with identical specs except cudnn_off
   # Note that momentum is 0, so moving arrays are replaced everytime with the 
latest one
   sym1 = mx.symbol.BatchNorm(
*[mx.symbol.Variable(name) for name in shapes.keys()],
eps=0.001,
momentum=0,
fix_gamma=False,
use_global_stats=False,
axis=1,
cudnn_off=True
   )
   sym2 = mx.symbol.BatchNorm(
*[mx.symbol.Variable(name) for name in shapes.keys()],
eps=0.001,
momentum=0,
fix_gamma=False,
use_global_stats=False,
axis=1,
cudnn_off=False
   )
   op1 = mx.ndarray.CachedOp(sym1)
   op2 = mx.ndarray.CachedOp(sym2)
   
   # Define arrays for op1 and 
   # They are identical now, but they will be changed differently by the ops
   args1 = [mx.np.random.uniform(size=shape, ctx=mx.gpu()) for shape in 
shapes.values()]
   args2 = [mx.np.array(array, ctx=mx.gpu()) for array in args1]
   
   data, gamma, beta, mean, var = args1
   
   # Evaluation in training mode with backward that rewrites moving mean and var
   with autograd.record(train_mode=True):
[arg.attach_grad() for arg in args1]
[arg.attach_grad() for arg in args2]
dummy1 = op1(*args1, default_ctx=mx.gpu())
dummy2 = op2(*args2, default_ctx=mx.gpu())
   autograd.backward(dummy1, head_grads=mx.np.ones(shapes['input'], 
ctx=mx.gpu()))
   autograd.backward(dummy2, head_grads=mx.np.ones(shapes['input'], 
ctx=mx.gpu()))
   
   # Check that outputs are the same
   print()
   print("difference between training mode outputs")
   print(mx.np.max(mx.np.abs(dummy1 - dummy2))) 
   
   # Check updated moving vars and observe they are different
   print()
   print("variance updated by the non-cudnn batchnorm")
   print(args1[-1])
   print("variance updated by the cudnn batchnorm")
   print(args2[-1])
   
   # Manually compute biased and unbiased variance
   data_mean = mx.np.mean(data, axis=(-1))
   data_zeromean = data - data_mean[:, :, np.newaxis]
   var1 = mx.np.mean((data_zeromean * data_zeromean), axis=(-1))
   var2 = var1 * shapes['input'][-1] / (shapes['input'][-1] - 1)
   
   print()
   print("manual biased variance")
   print(var1)
   print("manual unbiased variance")
   print(var2)
   ```
   output is:
   ```
    cudnn batchnorm variance
   
   difference between training mode outputs
   2.3841858e-07
   
   variance updated by the non-cudnn batchnorm
   [0.12171984 0.03338415 0.03920404 0.04988261 0.02153183 0.02420242] @gpu(0)
   variance updated by the cudnn batchnorm
   [0.15214981 0.04173018 0.04900505 0.06235326 0.02691478 0.03025302] @gpu(0)
   
   manual biased variance
   [[0.12171984 0.03338414 0.03920404 0.04988261 0.02153182 0.02420242]] @gpu(0)
   manual unbiased variance
   [[0.1521498  0.04173018 0.04900505 0.06235326 0.02691478 0.03025302]] @gpu(0)
   ```
   
   So this shows that:
   1) The training mode output is the same between non-cudnn and cudnn 
implementations ("difference between training mode outputs"), so they are 
computing the data variance in the same way at this step.It can be checked 
manually that their result corresponds to using the biased variance
   2) However the way the end up changing their moving variance is different. 
In particular, the non-cudnn case uses the biased variance as before but the 
cudnn case uses the non-biased variance this time. Note that the momentum is 
set to 0 for both ops, which means that moving arrays are replaced with the 
latest ones, that makes it easy to check the results
   3) This explains the numerical error found in my original report. For a 
spatial size of 1, the unbiased variance gets multiplied by a factor **1 / (1 - 
1) = nan** which would make a subsequent evaluation fail for the cudnn case


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[GitHub] [incubator-mxnet] relaxli00 commented on issue #21125: Can't install mxnet cuda on Windows Server 2016!

[GitBox]

relaxli00 commented on issue #21125:
URL: 
https://github.com/apache/incubator-mxnet/issues/21125#issuecomment-1230492882

   As far as I know, there is no binary wheel for windows released after 1.7.0. 
You'll have to build from source. I tried to build 1.9.1 for Windows 10 with 
MKL BLAS. Not sure if it's helpful, my steps can be found 
[here](https://medium.com/@relaxli/build-mxnet-1-9-1-with-cuda-11-7-and-mkl-blas-using-visual-studio-2019-on-windows-10-a9ebecb1941b)
 
https://medium.com/@relaxli/build-mxnet-1-9-1-with-cuda-11-7-and-mkl-blas-using-visual-studio-2019-on-windows-10-a9ebecb1941b
 .


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[GitHub] [incubator-mxnet] TristonC commented on issue #20984: [RFC] Graduate MXNet from Apache Incubator

[GitBox]

TristonC commented on issue #20984:
URL: 
https://github.com/apache/incubator-mxnet/issues/20984#issuecomment-1228785958

   Thank you, @josephevans for leading the effort of the graduation!


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[GitHub] [incubator-mxnet] josephevans opened a new issue, #20984: [RFC] Graduate MXNet from Apache Incubator

[GitBox]

josephevans opened a new issue, #20984:
URL: https://github.com/apache/incubator-mxnet/issues/20984

   Hello MXNet Community,
   
   I'd like to start the discussion of graduating MXNet from an Apache 
Incubating project to a top-level ASF project. 
   
   MXNet has been an incubating project since January 23rd, 2017 [1]. Since 
then, the project has had 24 releases under the ASF Incubator (0.11.0, 0.12.0, 
0.12.1, 1.0.0, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.3.1, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 
1.6.0, 1.7.0, 1.8.0, 1.9.0, and 7 alpha/beta releases of 2.0.) There is a 
thriving community of developers and users from all over the world that 
regularly contribute to the project.
   
   I’m asking the community and mentors for feedback or suggestions on how to 
move this forward. Based on the graduation guidelines [2], the next step for 
graduation is having a community vote. Should we have a community (dev@) vote 
in a few weeks?
   
   One thing I also noticed is the project status page [1] may be a little 
out-of-date (the committers and incubation status reports are not up-to-date), 
would one of our PPMCs like to take on updating this?
   
   References:
   
   [1] https://incubator.apache.org/projects/mxnet.html
   [2] https://incubator.apache.org/guides/graduation.html
   


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[GitHub] [incubator-mxnet] josephevans closed issue #20984: [RFC] Graduate MXNet from Apache Incubator

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josephevans closed issue #20984: [RFC] Graduate MXNet from Apache Incubator
URL: https://github.com/apache/incubator-mxnet/issues/20984


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[GitHub] [incubator-mxnet] josephevans commented on issue #20984: [RFC] Graduate MXNet from Apache Incubator

[GitBox]

josephevans commented on issue #20984:
URL: 
https://github.com/apache/incubator-mxnet/issues/20984#issuecomment-1228613246

   The final IPMC vote has passed. Thanks to the everyone in the Apache MXNet 
community for all the work to get to this point. We will now submit the 
resolution to the Apache Board for consideration in the September board meeting.
   
   Voting thread: 
https://lists.apache.org/thread/hmd5gyq3xhqn48yqszwjcqx0q5wl7qv6


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[GitHub] [incubator-mxnet] bgawrych closed issue #21097: Wrong output from operator _npi_repeats?

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bgawrych closed issue #21097: Wrong output from operator _npi_repeats?
URL: https://github.com/apache/incubator-mxnet/issues/21097


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[GitHub] [incubator-mxnet] preethamam commented on issue #12982: Multiple GPUs crashes entire linux system

[GitBox]

preethamam commented on issue #12982:
URL: 
https://github.com/apache/incubator-mxnet/issues/12982#issuecomment-1227472233

   > [This issue in PyTorch](https://github.com/pytorch/pytorch/issues/1637) 
may be relevant. Specifically, [this 
comment](https://github.com/pytorch/pytorch/issues/1637#issuecomment-338268158) 
about either disabling IOMMU or changing to software IOMMU. I've had issues 
with the threadripper requiring `iommu=soft`; I'd recommend giving that a shot.
   
   I do have the same issue. I have 4 Nvidia RTX 2080Ti GPUs and whenever I use 
`nn.DataParallel` the Linux and Windows 11 systems crash after a few seconds 
when the data is pushed to the GPUs. In addition, I tried `NCCL_P2P_DISABLE=1` 
but none of the solutions helped me. Including the `iommu=soft` or `iommu=off'. 
I went through the comments and their suggestions none worked for me.
   
   I have AMD Threadripper 2950X
   RAM: 128 GB
   4 Nvidia RTX 2080Ti GPUs
   
   Any help is much appreciated!


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