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From Chris Olivier <cjolivie...@gmail.com>
Subject Re: [VOTE] Release Apache MXNet (incubating) version 1.5.0.rc1
Date Sat, 29 Jun 2019 19:35:26 GMT
for batch norm, I mean. max*

On Sat, Jun 29, 2019 at 12:34 PM Chris Olivier <cjolivier01@gmail.com>
wrote:

> what’s with the mac memory usage being 2x in 1.4? As I am not sure where
> the number is coming from (if it’s my profiler code, I wouldn’t consider it
> terribly meaningful), but it is the same everywhere else, so it kind of
> sticks out.
>
> On Thu, Jun 27, 2019 at 3:36 PM sandeep krishnamurthy <
> sandeep.krishna98@gmail.com> wrote:
>
>> Hello Ciyong/Pedro,
>>
>> Ran operator benchmarks on 1.4.1 and 1.5.0.rc2. (Not complete, doesn’t
>> cover all MXNet operators, not presented in best possible way, still WIP)
>>
>> https://gist.github.com/sandeep-krishnamurthy/e0a2be893c8c4d484390c9c8813bdf50
>>
>> Following operators looks slower in 1.5 compared to 1.4.1:
>> - BatchNorm
>> - Pooling
>> - FullyConnected
>> - batch_dot
>> - Dot
>> - broadcast_mul
>> - log_softmax
>> and few other operators
>>
>> Also, several operators runs a lot faster on 1.5 compared to 1.4.1. For
>> example - Convolution, flatten, elementwise operators etc. So I see that
>> likely few operators have regressed noticeably, however, due to other
>> operator performance improvements, the end effect is not that significant
>> hiding a lot of regression. We need more detailed analysis per operator
>> performance. We will not be able to do this for current release, we should
>> have a more concrete way to determining such performance regression before
>> next release.
>>
>> Setup:
>> 1.5 => Build from source (head of 1.5.rc2 tag), built with MKLDNN
>> 1.4.1 => PyPi mxnet-mkl==1.4.1
>> Machine: C5.18X
>> No explicit environment variable were set
>> Operator benchmark code -
>> https://github.com/apache/incubator-mxnet/tree/master/benchmark/opperf
>>
>> Best,
>> Sandeep
>>
>>
>> On Thu, Jun 27, 2019 at 10:42 AM Pedro Larroy <
>> pedro.larroy.lists@gmail.com>
>> wrote:
>>
>> > I will try to run a few benchmarks in a bare metal instance tonight to
>> > remove virtualization variance for the measurements and provide some
>> > numbers.
>> >
>> > Please propose a set of models / examples that would be desirable to
>> > run before the release and provide a link to an easy to run script
>> > with instructions so we can validate the release better.
>> >
>> > Thank you.
>> >
>> > On Thu, Jun 27, 2019 at 10:01 AM Lai Wei <royweilai@gmail.com> wrote:
>> > >
>> > > Dear @dev,
>> > >
>> > > I m cancelling the vote for cached op fix:
>> > >
>> > > https://github.com/apache/incubator-mxnet/pull/15298
>> > >
>> > > As for the possible cpu training regression, it looks like not a
>> blocker
>> > > for now.
>> > >
>> > > I will start a new rc2 vote, please help to validate.
>> > >
>> > > Thanks!
>> > >
>> > >
>> > > On Thu, Jun 27, 2019 at 10:06 PM Chen, Ciyong <ciyong.chen@intel.com>
>> > wrote:
>> > >
>> > > > Hi Pedro,
>> > > >
>> > > > I was able to reproduced the similar result (v1.5 is ~%5.6 slower
>> than
>> > > > v1.4, I was using 18 cores for computing) with your script on
>> > C5.18xlarge.
>> > > > But need to bind the cores with below command when running the
>> script,
>> > > > (without setting the env variables, I got a close time (<1%) with
>> v1.5
>> > and
>> > > > v1.4)
>> > > >         export
>> KMP_AFFINITY=granularity=fine,noduplicates,compact,1,0
>> > > >         export OMP_NUM_THREADS=18
>> > > >
>> > > > Did you set any env variables during running?
>> > > >
>> > > > The performance result I got as below:
>> > > > 1) 1.4.1.rc0 (1a7199691f5cbc6012bb53eecbf884bed5ae6590)
>> > > > real    12m10.856s
>> > > > user    234m49.576s
>> > > > sys     4m38.044s
>> > > >
>> > > > 2) 1.5.0.rc1 (4d9667121ae6fb643f2a02ab15e25231ed756cde)
>> > > > real    12m52.140s
>> > > > user    246m30.740s
>> > > > sys     5m8.188s
>> > > >
>> > > > As I looked at the profiling data, most of the ops have same perf
>> > between
>> > > > v1.4 and v1.5. But some ops like " _backward_BatchNorm" and
>> "Pooling"
>> > is
>> > > > ~1.37x slower on v1.5 compared with v1.4.
>> > > > Will do further analysis on these ops.
>> > > >
>> > > > Here's the hardware/OS info from my side:
>> > > > ----------Python Info----------
>> > > > Version      : 3.6.8
>> > > > Compiler     : GCC 7.3.0
>> > > > Build        : ('default', 'Dec 30 2018 01:22:34')
>> > > > Arch         : ('64bit', '')
>> > > > ------------Pip Info-----------
>> > > > Version      : 19.0.3
>> > > > Directory    :
>> > > >
>> /home/ubuntu/anaconda3/envs/perf-mxnet/lib/python3.6/site-packages/pip
>> > > > ----------MXNet Info-----------
>> > > > Version      : 1.5.0
>> > > > Directory    : /home/ubuntu/ws/incubator-mxnet/python/mxnet
>> > > > Hashtag not found. Not installed from pre-built package.
>> > > > ----------System Info----------
>> > > > Platform     : Linux-4.4.0-1085-aws-x86_64-with-debian-stretch-sid
>> > > > system       : Linux
>> > > > node         : ip-172-31-32-129
>> > > > release      : 4.4.0-1085-aws
>> > > > version      : #96-Ubuntu SMP Tue Jun 11 09:08:32 UTC 2019
>> > > > ----------Hardware Info----------
>> > > > machine      : x86_64
>> > > > processor    : x86_64
>> > > > Architecture:          x86_64
>> > > > CPU op-mode(s):        32-bit, 64-bit
>> > > > Byte Order:            Little Endian
>> > > > CPU(s):                72
>> > > > On-line CPU(s) list:   0-71
>> > > > Thread(s) per core:    2
>> > > > Core(s) per socket:    18
>> > > > Socket(s):             2
>> > > > NUMA node(s):          2
>> > > > Vendor ID:             GenuineIntel
>> > > > CPU family:            6
>> > > > Model:                 85
>> > > > Model name:            Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz
>> > > > Stepping:              3
>> > > > CPU MHz:               3000.000
>> > > > BogoMIPS:              6000.00
>> > > > Hypervisor vendor:     KVM
>> > > > Virtualization type:   full
>> > > > L1d cache:             32K
>> > > > L1i cache:             32K
>> > > > L2 cache:              1024K
>> > > > L3 cache:              25344K
>> > > > NUMA node0 CPU(s):     0-17,36-53
>> > > > NUMA node1 CPU(s):     18-35,54-71
>> > > > Flags:                 fpu vme de pse tsc msr pae mce cx8 apic sep
>> mtrr
>> > > > pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx
>> > pdpe1gb
>> > > > rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology
>> nonstop_tsc
>> > > > aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid
>> > sse4_1
>> > > > sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c
>> rdrand
>> > > > hypervisor lahf_lm abm 3dnowprefetch invpcid_single kaiser fsgsbase
>> > > > tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f
>> rdseed
>> > adx
>> > > > smap clflushopt clwb avx512cd xsaveopt xsavec xgetbv1 ida arat pku
>> > > > ----------Network Test----------
>> > > >
>> > > >
>> > > > -Ciyong
>> > > >
>> > > >
>> > > > -----Original Message-----
>> > > > From: Zhao, Patric [mailto:patric.zhao@intel.com]
>> > > > Sent: Thursday, June 27, 2019 9:55 AM
>> > > > To: dev@mxnet.incubator.apache.org
>> > > > Cc: dev@mxnet.apache.org
>> > > > Subject: RE: [VOTE] Release Apache MXNet (incubating) version
>> 1.5.0.rc1
>> > > >
>> > > > Could we run more epochs to see the performance difference or
>> profiling
>> > > > the difference between good and bad run?
>> > > >
>> > > > > -----Original Message-----
>> > > > > From: Pedro Larroy [mailto:pedro.larroy.lists@gmail.com]
>> > > > > Sent: Thursday, June 27, 2019 9:35 AM
>> > > > > To: dev@mxnet.incubator.apache.org
>> > > > > Cc: dev@mxnet.apache.org
>> > > > > Subject: Re: [VOTE] Release Apache MXNet (incubating) version
>> > > > > 1.5.0.rc1
>> > > > >
>> > > > > I run again and the gap is again bigger, I guess we need to
>> average
>> > > > > out the times across several runs:
>> > > > >
>> > > > > piotr@ip-172-31-63-171:0:~/deeplearning-benchmark/dawnbench
>> > > > > (master)+$ time ~/mxnet_1.4/py3_venv/bin/python cifar10.py
>> --epochs 5
>> > > > > && time ~/mxnet_1.5/py3_venv/bin/python cifar10.py --epochs 5
>> > > > > [23:17:09] ../src/io/iter_image_recordio_2.cc:172:
>> > > > > ImageRecordIOParser2:
>> > > > > /home/piotr/deeplearning-benchmark/data/cifar/train.rec, use 4
>> > threads
>> > > > > for decoding..
>> > > > > [23:17:09] ../src/io/iter_image_recordio_2.cc:230: Load mean image
>> > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > [23:17:09] ../src/io/iter_image_recordio_2.cc:248: Load mean image
>> > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> completed
>> > > > > [23:17:09] ../src/io/iter_image_recordio_2.cc:172:
>> > > > > ImageRecordIOParser2:
>> > > > > /home/piotr/deeplearning-benchmark/data/cifar/test.rec, use 4
>> threads
>> > > > > for decoding..
>> > > > > [23:17:09] ../src/io/iter_image_recordio_2.cc:230: Load mean image
>> > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > [23:17:09] ../src/io/iter_image_recordio_2.cc:248: Load mean image
>> > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> completed
>> > > > > lr_schedule: {0: 0.05, 82: 0.005000000000000001, 123: 0.0005, 300:
>> > > > > 0.0001} Epoch 0, Changed learning rate to 0.05 [23:17:09]
>> > > > > ../src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate
>> > > > > 147456 bytes with malloc directly
>> > > > > [23:17:09] ../src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate
>> > > > > 589824 bytes with malloc directly
>> > > > > [23:17:09] ../src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate
>> > > > > 2359296 bytes with malloc directly
>> > > > > [23:17:09] ../src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate
>> > > > > 9437184 bytes with malloc directly
>> > > > > Epoch 0, Batch 199, Speed=384.149839
>> > > > > Epoch 0, Duration=140.919567
>> > > > > Epoch 0, Training accuracy=0.115169
>> > > > > Epoch 0, Validation accuracy=0.141317
>> > > > > Epoch 1, Batch 199, Speed=433.380512
>> > > > > Epoch 1, Duration=119.553233
>> > > > > Epoch 1, Training accuracy=0.170956
>> > > > > Epoch 1, Validation accuracy=0.216146
>> > > > > Epoch 2, Batch 199, Speed=434.864699
>> > > > > Epoch 2, Duration=123.278490
>> > > > > Epoch 2, Training accuracy=0.209455
>> > > > > Epoch 2, Validation accuracy=0.247296
>> > > > > Epoch 3, Batch 199, Speed=433.401854
>> > > > > Epoch 3, Duration=118.327797
>> > > > > Epoch 3, Training accuracy=0.248701
>> > > > > Epoch 3, Validation accuracy=0.302083
>> > > > > Epoch 4, Batch 199, Speed=419.713707
>> > > > > Epoch 4, Duration=126.468409
>> > > > > Epoch 4, Training accuracy=0.260949
>> > > > > Epoch 4, Validation accuracy=0.269030
>> > > > >
>> > > > > real    10m55.796s
>> > > > > user    399m33.567s
>> > > > > sys     13m55.904s
>> > > > > [23:28:04] ../src/io/iter_image_recordio_2.cc:172:
>> > > > > ImageRecordIOParser2:
>> > > > > /home/piotr/deeplearning-benchmark/data/cifar/train.rec, use 4
>> > threads
>> > > > > for decoding..
>> > > > > [23:28:04] ../src/io/iter_image_recordio_2.cc:230: Load mean image
>> > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > [23:28:04] ../src/io/iter_image_recordio_2.cc:248: Load mean image
>> > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> completed
>> > > > > [23:28:04] ../src/io/iter_image_recordio_2.cc:172:
>> > > > > ImageRecordIOParser2:
>> > > > > /home/piotr/deeplearning-benchmark/data/cifar/test.rec, use 4
>> threads
>> > > > > for decoding..
>> > > > > [23:28:04] ../src/io/iter_image_recordio_2.cc:230: Load mean image
>> > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > [23:28:04] ../src/io/iter_image_recordio_2.cc:248: Load mean image
>> > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> completed
>> > > > > lr_schedule: {0: 0.05, 82: 0.005000000000000001, 123: 0.0005, 300:
>> > > > > 0.0001} Epoch 0, Changed learning rate to 0.05 Epoch 0, Batch 199,
>> > > > > Speed=419.039188 Epoch 0, Duration=143.934903 Epoch 0, Training
>> > > > > accuracy=0.122542 Epoch 0, Validation accuracy=0.164359 Epoch 1,
>> > Batch
>> > > > > 199, Speed=445.257048 Epoch 1, Duration=135.248399 Epoch 1,
>> Training
>> > > > > accuracy=0.178828 Epoch 1, Validation accuracy=0.199419 Epoch 2,
>> > Batch
>> > > > > 199, Speed=447.115215 Epoch 2, Duration=132.003770 Epoch 2,
>> Training
>> > > > > accuracy=0.217808 Epoch 2, Validation accuracy=0.233073 Epoch 3,
>> > Batch
>> > > > > 199, Speed=441.079477 Epoch 3, Duration=126.543316 Epoch 3,
>> Training
>> > > > > accuracy=0.248102 Epoch 3, Validation accuracy=0.293870 Epoch 4,
>> > Batch
>> > > > > 199, Speed=449.329787 Epoch 4, Duration=138.398325 Epoch 4,
>> Training
>> > > > > accuracy=0.270021 Epoch 4, Validation accuracy=0.311498
>> > > > >
>> > > > > real    11m45.329s
>> > > > > user    426m13.908s
>> > > > > sys     16m45.093s
>> > > > >
>> > > > > On Wed, Jun 26, 2019 at 4:18 PM Pedro Larroy
>> > > > > <pedro.larroy.lists@gmail.com> wrote:
>> > > > > >
>> > > > > > The difference looks smaller now, more like your numbers. I
>> wonder
>> > > > > > if something happened during the previous benchmark like a
>> system
>> > > > > > update...
>> > > > > >
>> > > > > >
>> > > > > > piotr@ip-172-31-63-171:0:~/deeplearning-benchmark/dawnbench
>> > > > > (master)+$
>> > > > > > time ~/mxnet_1.4/py3_venv/bin/python cifar10.py --epochs 5 &&
>> time
>> > > > > > ~/mxnet_1.5/py3_venv/bin/python cifar10.py --epochs 5 [22:49:41]
>> > > > > > ../src/io/iter_image_recordio_2.cc:172:
>> > > > > > ImageRecordIOParser2:
>> > > > > > /home/piotr/deeplearning-benchmark/data/cifar/train.rec, use 4
>> > > > > > threads for decoding..
>> > > > > > [22:49:41] ../src/io/iter_image_recordio_2.cc:230: Load mean
>> image
>> > > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > > [22:49:41] ../src/io/iter_image_recordio_2.cc:248: Load mean
>> image
>> > > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > completed
>> > > > > > [22:49:41] ../src/io/iter_image_recordio_2.cc:172:
>> > > > > > ImageRecordIOParser2:
>> > > > > > /home/piotr/deeplearning-benchmark/data/cifar/test.rec, use 4
>> > > > > > threads for decoding..
>> > > > > > [22:49:41] ../src/io/iter_image_recordio_2.cc:230: Load mean
>> image
>> > > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > > [22:49:41] ../src/io/iter_image_recordio_2.cc:248: Load mean
>> image
>> > > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > completed
>> > > > > > lr_schedule: {0: 0.05, 82: 0.005000000000000001, 123: 0.0005,
>> 300:
>> > > > > > 0.0001} Epoch 0, Changed learning rate to 0.05 [22:49:42]
>> > > > > > ../src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate
>> > > > > > 147456 bytes with malloc directly
>> > > > > > [22:49:42] ../src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate
>> > > > > > 589824 bytes with malloc directly
>> > > > > > [22:49:42] ../src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate
>> > > > > > 2359296 bytes with malloc directly
>> > > > > > [22:49:42] ../src/operator/nn/mkldnn/mkldnn_base.cc:74: Allocate
>> > > > > > 9437184 bytes with malloc directly
>> > > > > > Epoch 0, Batch 199, Speed=426.182733 Epoch 0,
>> Duration=134.868458
>> > > > > > Epoch 0, Training accuracy=0.127238 Epoch 0, Validation
>> > > > > > accuracy=0.206388 Epoch 1, Batch 199, Speed=313.127156 Epoch 1,
>> > > > > > Duration=128.041775 Epoch 1, Training accuracy=0.182065 Epoch 1,
>> > > > > > Validation accuracy=0.202524 Epoch 2, Batch 199,
>> Speed=410.931187
>> > > > > > Epoch 2, Duration=124.920588 Epoch 2, Training accuracy=0.202584
>> > > > > > Epoch 2, Validation accuracy=0.245693 Epoch 3, Batch 199,
>> > > > > > Speed=419.119335 Epoch 3, Duration=120.948349 Epoch 3, Training
>> > > > > > accuracy=0.235854 Epoch 3, Validation accuracy=0.291066 Epoch 4,
>> > > > > > Batch 199, Speed=430.473733 Epoch 4, Duration=130.181724 Epoch
>> 4,
>> > > > > > Training accuracy=0.257773 Epoch 4, Validation accuracy=0.304988
>> > > > > >
>> > > > > > real    11m7.356s
>> > > > > > user    406m9.910s
>> > > > > > sys     14m18.349s
>> > > > > > [23:00:49] ../src/io/iter_image_recordio_2.cc:172:
>> > > > > > ImageRecordIOParser2:
>> > > > > > /home/piotr/deeplearning-benchmark/data/cifar/train.rec, use 4
>> > > > > > threads for decoding..
>> > > > > > [23:00:49] ../src/io/iter_image_recordio_2.cc:230: Load mean
>> image
>> > > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > > [23:00:49] ../src/io/iter_image_recordio_2.cc:248: Load mean
>> image
>> > > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > completed
>> > > > > > [23:00:49] ../src/io/iter_image_recordio_2.cc:172:
>> > > > > > ImageRecordIOParser2:
>> > > > > > /home/piotr/deeplearning-benchmark/data/cifar/test.rec, use 4
>> > > > > > threads for decoding..
>> > > > > > [23:00:49] ../src/io/iter_image_recordio_2.cc:230: Load mean
>> image
>> > > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > > [23:00:49] ../src/io/iter_image_recordio_2.cc:248: Load mean
>> image
>> > > > > > from /home/piotr/deeplearning-benchmark/data/cifar/mean.bin
>> > > > > completed
>> > > > > > lr_schedule: {0: 0.05, 82: 0.005000000000000001, 123: 0.0005,
>> 300:
>> > > > > > 0.0001} Epoch 0, Changed learning rate to 0.05 Epoch 0, Batch
>> 199,
>> > > > > > Speed=348.618154 Epoch 0, Duration=146.469352 Epoch 0, Training
>> > > > > > accuracy=0.124121 Epoch 0, Validation accuracy=0.167227 Epoch 1,
>> > > > > > Batch 199, Speed=452.790825 Epoch 1, Duration=130.199421 Epoch
>> 1,
>> > > > > > Training
>> > > > > > accuracy=0.183863 Epoch 1, Validation accuracy=0.237079 Epoch 2,
>> > > > > > Batch 199, Speed=451.406559 Epoch 2, Duration=126.320823 Epoch
>> 2,
>> > > > > > Training
>> > > > > > accuracy=0.214844 Epoch 2, Validation accuracy=0.244692 Epoch 3,
>> > > > > > Batch 199, Speed=403.161873 Epoch 3, Duration=125.331660 Epoch
>> 3,
>> > > > > > Training
>> > > > > > accuracy=0.243506 Epoch 3, Validation accuracy=0.301182 Epoch 4,
>> > > > > > Batch 199, Speed=450.826598 Epoch 4, Duration=126.426253 Epoch
>> 4,
>> > > > > > Training
>> > > > > > accuracy=0.266424 Epoch 4, Validation accuracy=0.311899
>> > > > > >
>> > > > > > real    11m21.930s
>> > > > > > user    415m3.855s
>> > > > > > sys     13m53.975s
>> > > > > >
>> > > > > > On Wed, Jun 26, 2019 at 3:50 PM Pedro Larroy
>> > > > > > <pedro.larroy.lists@gmail.com> wrote:
>> > > > > > >
>> > > > > > > Hi Ciyong, thanks for trying to reproduce:
>> > > > > > >
>> > > > > > > I used this one:
>> > > > > > > https://github.com/awslabs/deeplearning-
>> > > > > benchmark/blob/master/dawnbe
>> > > > > > > nch/cifar10.py
>> > > > > > >
>> > > > > > > Could you provide hardware and OS details?
>> > > > > > >
>> > > > > > > I will rerun and repost numbers in a few minutes.
>> > > > > > >
>> > > > > > > Pedro.
>> > > > > > >
>> > > > > > > On Wed, Jun 26, 2019 at 4:18 AM Chen, Ciyong
>> > > > > > > <ciyong.chen@intel.com>
>> > > > > wrote:
>> > > > > > > >
>> > > > > > > > Hi Pedro,
>> > > > > > > >
>> > > > > > > > I'm looking at this case, and using the script of
>> > > > > > > >
>> "incubator-mxnet/example/image-classification/train_cifar10.py"
>> > > > > > > > to get
>> > > > > the timing data, but seems there's not much difference between
>> mxnet
>> > > > > 1.4.1.rc0 and 1.5.0.rc1 on C5.18xlarge.
>> > > > > > > >
>> > > > > > > > Not sure if there's any difference in the python script, can
>> > you
>> > > > > > > > point me
>> > > > > the link to get your script (cifar10.py)?
>> > > > > > > > Or you can also have a try with MXNet's script
>> > > > > > > > (train_cifar10.py) and see
>> > > > > the performance.
>> > > > > > > >
>> > > > > > > > Here's the command I used to collect the time:
>> > > > > > > >         python train_cifar10.py --num-epoch=5
>> > > > > > > >
>> > > > > > > > 1) 1.5.0.rc1 (4d9667121ae6fb643f2a02ab15e25231ed756cde)
>> > > > > > > >         real    9m4.880s
>> > > > > > > >         user    333m13.340s
>> > > > > > > >         sys     14m36.100s
>> > > > > > > >
>> > > > > > > > 2) 1.4.1.rc0 (1a7199691f5cbc6012bb53eecbf884bed5ae6590)
>> > > > > > > >         real    9m2.155s
>> > > > > > > >         user    329m37.092s
>> > > > > > > >         sys     16m8.668s
>> > > > > > > >
>> > > > > > > > -Ciyong
>> > > > > > > >
>> > > > > > > >
>> > > > > > > > -----Original Message-----
>> > > > > > > > From: Pedro Larroy [mailto:pedro.larroy.lists@gmail.com]
>> > > > > > > > Sent: Wednesday, June 26, 2019 6:28 AM
>> > > > > > > > To: dev@mxnet.incubator.apache.org
>> > > > > > > > Cc: dev@mxnet.apache.org
>> > > > > > > > Subject: Re: [VOTE] Release Apache MXNet (incubating)
>> version
>> > > > > > > > 1.5.0.rc1
>> > > > > > > >
>> > > > > > > > Hi these were my build flags and system info:
>> > > > > > > >
>> > > > > > > >
>> > > > > > > > --- # CMake configuration
>> > > > > > > > USE_CUDA: "OFF" # Build with CUDA support
>> > > > > > > > USE_OLDCMAKECUDA: "OFF" # Build with old cmake cuda
>> > > > > > > > USE_NCCL: "OFF" # Use NVidia NCCL with CUDA
>> > > > > > > > USE_OPENCV: "ON" # Build with OpenCV support
>> > > > > > > > USE_OPENMP: "ON" # Build with Openmp support
>> > > > > > > > USE_CUDNN: "ON" # Build with cudnn support) # one could set
>> > > > > > > > CUDNN_ROOT for search path
>> > > > > > > > USE_SSE: "ON" # Build with x86 SSE instruction support IF
>> NOT
>> > > > > > > > ARM
>> > > > > > > > USE_F16C: "ON" # Build with x86 F16C instruction support) #
>> > > > > autodetects support if "ON"
>> > > > > > > > USE_LAPACK: "ON" # Build with lapack support
>> > > > > > > > USE_MKL_IF_AVAILABLE: "ON" # Use MKL if found
>> > > > > > > > USE_MKLML_MKL: "ON" # Use MKLDNN variant of MKL (if MKL
>> found)
>> > > > > > > > IF USE_MKL_IF_AVAILABLE AND (NOT APPLE)
>> > > > > > > > USE_MKLDNN: "ON" # Use MKLDNN variant of MKL (if MKL found)
>> IF
>> > > > > > > > USE_MKL_IF_AVAILABLE AND (NOT APPLE)
>> > > > > > > > USE_OPERATOR_TUNING: "ON" # Enable auto-tuning of operators
>> IF
>> > > > > NOT
>> > > > > > > > MSVC
>> > > > > > > > USE_GPERFTOOLS: "ON" # Build with GPerfTools support (if
>> found)
>> > > > > > > > USE_JEMALLOC: "ON" # Build with Jemalloc support
>> > > > > > > > USE_PROFILER: "ON" # Build with Profiler support
>> > > > > > > > USE_DIST_KVSTORE: "OFF" # Build with DIST_KVSTORE support
>> > > > > > > > USE_PLUGINS_WARPCTC: "OFF" # Use WARPCTC Plugins
>> > > > > > > > USE_PLUGIN_CAFFE: "OFF" # Use Caffe Plugin
>> > > > > > > > USE_CPP_PACKAGE: "OFF" # Build C++ Package
>> > > > > > > > USE_MXNET_LIB_NAMING: "ON" # Use MXNet library naming
>> > > > > conventions.
>> > > > > > > > USE_GPROF: "OFF" # Compile with gprof (profiling) flag
>> > > > > > > > USE_CXX14_IF_AVAILABLE: "OFF" # Build with C++14 if the
>> > compiler
>> > > > > > > > supports it
>> > > > > > > > USE_VTUNE: "OFF" # Enable use of Intel Amplifier XE
>> (VTune)) #
>> > > > > > > > one could set VTUNE_ROOT for search path
>> > > > > > > > ENABLE_CUDA_RTC: "ON" # Build with CUDA runtime compilation
>> > > > > > > > support
>> > > > > > > > BUILD_CPP_EXAMPLES: "ON" # Build cpp examples
>> > > > > > > > INSTALL_EXAMPLES: "OFF" # Install the example source files.
>> > > > > > > > USE_SIGNAL_HANDLER: "ON" # Print stack traces on segfaults.
>> > > > > > > > USE_TENSORRT: "OFF" # Enable infeference optimization with
>> > > > TensorRT.
>> > > > > > > > USE_ASAN: "OFF" # Enable Clang/GCC ASAN sanitizers.
>> > > > > > > > ENABLE_TESTCOVERAGE: "OFF" # Enable compilation with test
>> > > > > > > > coverage metric output
>> > > > > > > > CMAKE_BUILD_TYPE: "Release"
>> > > > > > > > CMAKE_CUDA_COMPILER_LAUNCHER: "ccache"
>> > > > > > > > CMAKE_C_COMPILER_LAUNCHER: "ccache"
>> > > > > > > > CMAKE_CXX_COMPILER_LAUNCHER: "ccache"
>> > > > > > > >
>> > > > > > > > commit 4d9667121ae6fb643f2a02ab15e25231ed756cde (HEAD, tag:
>> > > > > > > > 1.5.0.rc1,
>> > > > > > > > upstream/v1.5.x)
>> > > > > > > > commit 1a7199691f5cbc6012bb53eecbf884bed5ae6590 (HEAD, tag:
>> > > > > > > > 1.4.1.rc0,
>> > > > > > > > upstream/v1.4.x)
>> > > > > > > >
>> > > > > > > > curl http://169.254.169.254/latest/meta-data/instance-type
>> > > > > > > > c5d.18xlarge
>> > > > > > > >
>> > > > > > > >
>> > > > > > > > Version      : 3.6.7
>> > > > > > > > Compiler     : GCC 8.2.0
>> > > > > > > > Build        : ('default', 'Oct 22 2018 11:32:17')
>> > > > > > > > Arch         : ('64bit', 'ELF')
>> > > > > > > > ------------Pip Info-----------
>> > > > > > > > Version      : 19.1.1
>> > > > > > > > Directory    :
>> > /home/piotr/mxnet_1.5/py3_venv/lib/python3.6/site-
>> > > > > packages/pip
>> > > > > > > > ----------MXNet Info-----------
>> > > > > > > > Version      : 1.5.0
>> > > > > > > > Directory    : /home/piotr/mxnet_1.5/python/mxnet
>> > > > > > > > Hashtag not found. Not installed from pre-built package.
>> > > > > > > > ----------System Info----------
>> > > > > > > > Platform     :
>> > > > Linux-4.15.0-1035-aws-x86_64-with-Ubuntu-18.04-bionic
>> > > > > > > > system       : Linux
>> > > > > > > > node         : ip-172-31-63-171
>> > > > > > > > release      : 4.15.0-1035-aws
>> > > > > > > > version      : #37-Ubuntu SMP Mon Mar 18 16:15:14 UTC 2019
>> > > > > > > > ----------Hardware Info----------
>> > > > > > > > machine      : x86_64
>> > > > > > > > processor    : x86_64
>> > > > > > > > Architecture:        x86_64
>> > > > > > > > CPU op-mode(s):      32-bit, 64-bit
>> > > > > > > > Byte Order:          Little Endian
>> > > > > > > > CPU(s):              72
>> > > > > > > > On-line CPU(s) list: 0-71
>> > > > > > > > Thread(s) per core:  2
>> > > > > > > > Core(s) per socket:  18
>> > > > > > > > Socket(s):           2
>> > > > > > > > NUMA node(s):        2
>> > > > > > > > Vendor ID:           GenuineIntel
>> > > > > > > > CPU family:          6
>> > > > > > > > Model:               85
>> > > > > > > > Model name:          Intel(R) Xeon(R) Platinum 8124M CPU @
>> > 3.00GHz
>> > > > > > > > Stepping:            4
>> > > > > > > > CPU MHz:             1326.446
>> > > > > > > > BogoMIPS:            6000.00
>> > > > > > > > Hypervisor vendor:   KVM
>> > > > > > > > Virtualization type: full
>> > > > > > > > L1d cache:           32K
>> > > > > > > > L1i cache:           32K
>> > > > > > > > L2 cache:            1024K
>> > > > > > > > L3 cache:            25344K
>> > > > > > > > NUMA node0 CPU(s):   0-17,36-53
>> > > > > > > > NUMA node1 CPU(s):   18-35,54-71
>> > > > > > > > Flags:               fpu vme de pse tsc msr pae mce cx8 apic
>> > sep
>> > > > mtrr
>> > > > > > > > pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht
>> syscall
>> > > > > > > > nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl
>> > > > > > > > xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq monitor
>> > > > > > > > ssse3 fma cx16 pcid
>> > > > > > > > sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes
>> xsave
>> > > > > > > > avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch
>> > > > > > > > invpcid_single pti fsgsbase tsc_adjust bmi1 hle avx2 smep
>> bmi2
>> > > > > > > > erms invpcid rtm mpx avx512f avx512dq rdseed adx smap
>> > clflushopt
>> > > > > > > > clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1
>> xsaves
>> > > > > > > > ida arat pku ospke ----------Network Test----------
>> > > > > > > >
>> > > > > > > > ----------Python Info----------
>> > > > > > > > Version      : 3.6.7
>> > > > > > > > Compiler     : GCC 8.2.0
>> > > > > > > > Build        : ('default', 'Oct 22 2018 11:32:17')
>> > > > > > > > Arch         : ('64bit', 'ELF')
>> > > > > > > > ------------Pip Info-----------
>> > > > > > > > Version      : 19.1.1
>> > > > > > > > Directory    :
>> > /home/piotr/mxnet_1.4/py3_venv/lib/python3.6/site-
>> > > > > packages/pip
>> > > > > > > > ----------MXNet Info-----------
>> > > > > > > > Version      : 1.4.1
>> > > > > > > > Directory    : /home/piotr/mxnet_1.4/python/mxnet
>> > > > > > > > Hashtag not found. Not installed from pre-built package.
>> > > > > > > > ----------System Info----------
>> > > > > > > > Platform     :
>> > > > Linux-4.15.0-1035-aws-x86_64-with-Ubuntu-18.04-bionic
>> > > > > > > > system       : Linux
>> > > > > > > > node         : ip-172-31-63-171
>> > > > > > > > release      : 4.15.0-1035-aws
>> > > > > > > > version      : #37-Ubuntu SMP Mon Mar 18 16:15:14 UTC 2019
>> > > > > > > > ----------Hardware Info----------
>> > > > > > > > machine      : x86_64
>> > > > > > > > processor    : x86_64
>> > > > > > > > Architecture:        x86_64
>> > > > > > > > CPU op-mode(s):      32-bit, 64-bit
>> > > > > > > > Byte Order:          Little Endian
>> > > > > > > > CPU(s):              72
>> > > > > > > > On-line CPU(s) list: 0-71
>> > > > > > > > Thread(s) per core:  2
>> > > > > > > > Core(s) per socket:  18
>> > > > > > > > Socket(s):           2
>> > > > > > > > NUMA node(s):        2
>> > > > > > > > Vendor ID:           GenuineIntel
>> > > > > > > > CPU family:          6
>> > > > > > > > Model:               85
>> > > > > > > > Model name:          Intel(R) Xeon(R) Platinum 8124M CPU @
>> > 3.00GHz
>> > > > > > > > Stepping:            4
>> > > > > > > > CPU MHz:             1223.344
>> > > > > > > > BogoMIPS:            6000.00
>> > > > > > > > Hypervisor vendor:   KVM
>> > > > > > > > Virtualization type: full
>> > > > > > > > L1d cache:           32K
>> > > > > > > > L1i cache:           32K
>> > > > > > > > L2 cache:            1024K
>> > > > > > > > L3 cache:            25344K
>> > > > > > > > NUMA node0 CPU(s):   0-17,36-53
>> > > > > > > > NUMA node1 CPU(s):   18-35,54-71
>> > > > > > > > Flags:               fpu vme de pse tsc msr pae mce cx8 apic
>> > sep
>> > > > mtrr
>> > > > > > > > pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht
>> syscall
>> > > > > > > > nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl
>> > > > > > > > xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq monitor
>> > > > > > > > ssse3 fma cx16 pcid
>> > > > > > > > sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes
>> xsave
>> > > > > > > > avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch
>> > > > > > > > invpcid_single pti fsgsbase tsc_adjust bmi1 hle avx2 smep
>> bmi2
>> > > > > > > > erms invpcid rtm mpx avx512f avx512dq rdseed adx smap
>> > clflushopt
>> > > > > > > > clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1
>> xsaves
>> > > > > > > > ida arat pku ospke ----------Network Test----------
>> > > > > > > >
>> > > > > > > > On Tue, Jun 25, 2019 at 2:35 PM Pedro Larroy
>> > > > > <pedro.larroy.lists@gmail.com> wrote:
>> > > > > > > > >
>> > > > > > > > > I did a training of cifar10 in CPU and seems there's some
>> > > > > > > > > regressions in the range of 7% increase of training time
>> > against
>> > > > 1.4.1:
>> > > > > > > > >
>> > > > > > > > > (py3_venv)
>> > > > > > > > > piotr@ip-172-31-63-171
>> :0:~/deeplearning-benchmark/dawnbench
>> > > > > > > > > (master)+$ time python cifar10.py --epochs 5
>> > > > > > > > > real    11m30.388s
>> > > > > > > > > user    417m7.766s
>> > > > > > > > > sys     16m57.315s
>> > > > > > > > >
>> > > > > > > > > VS 1.4.1:
>> > > > > > > > > real    10m41.994s
>> > > > > > > > > user    392m40.646s
>> > > > > > > > > sys     12m30.601s
>> > > > > > > > >
>> > > > > > > > >
>> > > > > > > > > On Thu, Jun 20, 2019 at 10:15 PM Lai Wei <
>> > royweilai@gmail.com>
>> > > > > wrote:
>> > > > > > > > > >
>> > > > > > > > > > Hi Anirudh,
>> > > > > > > > > >
>> > > > > > > > > > Thanks for jumping into this quickly, I followed up on
>> the
>> > > > issue.
>> > > > > > > > > >
>> > > > > > > > > > I was meant for sockeye developer/maintainers to help
>> setup
>> > > > > > > > > > nightly tests and raise issues early.
>> > > > > > > > > >
>> > > > > > > > > > Thanks!
>> > > > > > > > > >
>> > > > > > > > > > On Fri, Jun 21, 2019 at 10:10 AM Haibin Lin
>> > > > > > > > > > <haibin.lin.aws@gmail.com>
>> > > > > > > > > > wrote:
>> > > > > > > > > >
>> > > > > > > > > > > In GluonNLP we are testing with MXNET nightly build
>> for
>> > > > > > > > > > > each PR, and we did find some MXNet related issue
>> caught
>> > by
>> > > > the CI.
>> > > > > > > > > > > I recommend other toolkits also add integration tests
>> > with
>> > > > > > > > > > > MXNet
>> > > > > nightly.
>> > > > > > > > > > > It helps identify issues early.
>> > > > > > > > > > >
>> > > > > > > > > > > Best,
>> > > > > > > > > > > Haibin
>> > > > > > > > > > >
>> > > > > > > > > > > On Thu, Jun 20, 2019 at 18:52 Zhao, Patric
>> > > > > > > > > > > <patric.zhao@intel.com>
>> > > > > wrote:
>> > > > > > > > > > >
>> > > > > > > > > > > > Thanks to raise the issue and we will take a look
>> ASAP.
>> > > > > > > > > > > >
>> > > > > > > > > > > > The downstream cases is not in the MXNet CI so it's
>> > hard
>> > > > > > > > > > > > to catch the potential bugs or performance
>> degradation
>> > > > > > > > > > > > for
>> > > > > MXNet developers.
>> > > > > > > > > > > >
>> > > > > > > > > > > > In the future, I suggest adding the major downstream
>> > > > > > > > > > > > test cases, like
>> > > > > > > > > > > from
>> > > > > > > > > > > > sockeye, GluonNLP, GLuonCV, DGL, Gluon-TS, into the
>> > > > > > > > > > > > nightly
>> > > > > test.
>> > > > > > > > > > > > If it's still too heavy,  maybe testing it weekly or
>> > > > > > > > > > > > monthly :)
>> > > > > > > > > > > >
>> > > > > > > > > > > > Thanks,
>> > > > > > > > > > > >
>> > > > > > > > > > > > --Patric
>> > > > > > > > > > > >
>> > > > > > > > > > > > > -----Original Message-----
>> > > > > > > > > > > > > From: Anirudh Subramanian
>> > > > > > > > > > > > > [mailto:anirudh2290@gmail.com]
>> > > > > > > > > > > > > Sent: Friday, June 21, 2019 9:31 AM
>> > > > > > > > > > > > > To: dev@mxnet.incubator.apache.org
>> > > > > > > > > > > > > Cc: dev@mxnet.apache.org
>> > > > > > > > > > > > > Subject: Re: [VOTE] Release Apache MXNet
>> (incubating)
>> > > > > > > > > > > > > version
>> > > > > > > > > > > > > 1.5.0.rc1
>> > > > > > > > > > > > >
>> > > > > > > > > > > > > Hi Lai,
>> > > > > > > > > > > > >
>> > > > > > > > > > > > > I have opened an issue:
>> > > > > > > > > > > > >
>> > https://github.com/apache/incubator-mxnet/issues/15297
>> > > > > > > > > > > > > I came to know about this issue only today and I
>> have
>> > > > > > > > > > > > > not been
>> > > > > > > > > > > monitoring
>> > > > > > > > > > > > > sockeye.
>> > > > > > > > > > > > > I jumped onto this issue to make sure it wasn't
>> > caused
>> > > > > > > > > > > > > by the dlpack
>> > > > > > > > > > > > changes.
>> > > > > > > > > > > > > Also, I don't  think sockeye CI checks against
>> > master,
>> > > > > > > > > > > > > it is using
>> > > > > > > > > > > 1.4.1.
>> > > > > > > > > > > > >
>> > > > > > > > > > > > > Anirudh
>> > > > > > > > > > > > >
>> > > > > > > > > > > > >
>> > > > > > > > > > > > > On Thu, Jun 20, 2019 at 6:17 PM Lai Wei
>> > > > > > > > > > > > > <royweilai@gmail.com>
>> > > > > wrote:
>> > > > > > > > > > > > >
>> > > > > > > > > > > > > > Hi,
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > > Could you share which test failed and what’s the
>> > > > > > > > > > > > > > crash? How to reproduce it?
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > > I was able to install sockeye and run all tests
>> > passed.
>> > > > > > > > > > > > > > Using python setup.py test
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > > I have tested both nightly pip package and
>> > 1.5.0.rc1
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > > It would be great to create an issue with
>> > > > > > > > > > > > > > reproducible steps and move the discussion
>> there.
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > > Also I see sockeye nightly build[1] has been
>> > failing
>> > > > > > > > > > > > > > for some time,
>> > > > > > > > > > > if
>> > > > > > > > > > > > > > it’s due to MXNet change, please raise this
>> early
>> > so
>> > > > > > > > > > > > > > we can track and solve it in time rather than
>> block
>> > > > > > > > > > > > > > the release
>> > > > > during vote time.
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > > [1] https://travis-ci.org/awslabs/sockeye
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > > On Fri, Jun 21, 2019 at 7:01 AM Anirudh
>> Subramanian
>> > > > > > > > > > > > > > <anirudh2290@gmail.com
>> > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > wrote:
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > I was able to reproduce a crash with the
>> commit
>> > > > > > > > > > > > > > > 09202f7f261954383aa387144524d38f83f18d06 but
>> not
>> > > > > > > > > > > > > > > with the commit
>> > > > > a862270beb2d796c1ba311183f7f4a766a18ad6c.
>> > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > Anirudh
>> > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > On Thu, Jun 20, 2019 at 3:53 PM Lai Wei
>> > > > > > > > > > > > > > > <royweilai@gmail.com>
>> > > > > > > > > > > wrote:
>> > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > Hi Przemyslaw,
>> > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > Is there an issue with more details to track
>> > the
>> > > > problem?
>> > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > On Fri, Jun 21, 2019 at 6:04 AM Przemysław
>> > > > > > > > > > > > > > > > Trędak <ptrendx@apache.org>
>> > > > > > > > > > > > > > > > wrote:
>> > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > -1
>> > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > There is a crash in sockeye unit test
>> (python
>> > > > > > > > > > > > > > > > > setup.py
>> > > > > > > > > > > > > > > > > test) observed starting with nightly 1.5
>> > build
>> > > > > > > > > > > > > > > > > from
>> > > > > > > > > > > > > > > > > 6/13 and still occuring in
>> > > > > > > > > > > > > > > 1.5rc1. I
>> > > > > > > > > > > > > > > > > don't yet have the exact commit that is
>> > > > > > > > > > > > > > > > > responsible for it, but it is either
>> > > > > > > > > > > > > > > > > a862270beb2d796c1ba311183f7f4a766a18ad6c
>> > > > > > > > > > > > > > > > > (dlpack
>> > > > > > > > > > > > > > > > > related) or
>> > > > > > > > > > > > > > > > > 09202f7f261954383aa387144524d38f83f18d06
>> > > > > > > > > > > > > > > > > (cached op
>> > > > > > > > > > > > > optimization).
>> > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > On 2019/06/20 06:36:22, Lai Wei
>> > > > > > > > > > > > > > > > > <royweilai@gmail.com>
>> > > > > wrote:
>> > > > > > > > > > > > > > > > > > Dear MXNet community,
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > > This is the 3-day vote to release Apache
>> > > > > > > > > > > > > > > > > > MXNet
>> > > > > > > > > > > > > > > > > > (incubating) version
>> > > > > > > > > > > > > > > > > 1.5.0.
>> > > > > > > > > > > > > > > > > > Voting on dev@ will start June 19,
>> > > > > > > > > > > > > > > > > > 23:59:59(PST) and close
>> > > > > > > > > > > on
>> > > > > > > > > > > > > > June
>> > > > > > > > > > > > > > > > 22,
>> > > > > > > > > > > > > > > > > > 23:59:59.
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > > 1) Link to release notes:
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > >
>> > > > > > > > > > >
>> > https://cwiki.apache.org/confluence/display/MXNET/1.5.0+Re
>> > > > > > > > > > > le
>> > > > > > > > > > > ase+No
>> > > > > > > > > > > te
>> > > > > > > > > > > > > > > s
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > > 2) Link to release candidate:
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > >
>> > https://github.com/apache/incubator-mxnet/releases/tag/1.5
>> > > > > > > > > > > .0
>> > > > > > > > > > > .r
>> > > > > > > > > > > > > > > > > > c1
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > > 3) Link to source and signatures on
>> apache
>> > > > dist server:
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > >
>> > https://dist.apache.org/repos/dist/dev/incubator/mxnet/1.5
>> > > > > > > > > > > .0
>> > > > > > > > > > > .r
>> > > > > > > > > > > > > > > > > > c1/
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > > Please remember to TEST first before
>> voting
>> > > > > accordingly:
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > > +1 = approve
>> > > > > > > > > > > > > > > > > > +0 = no opinion
>> > > > > > > > > > > > > > > > > > -1 = disapprove (provide reason)
>> > > > > > > > > > > > > > > > > > --
>> > > > > > > > > > > > > > > > > > Best Regards
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > > > Lai
>> > > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > --
>> > > > > > > > > > > > > > > > Best Regards
>> > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > > > Lai
>> > > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > >
>> > > > > > > > > > > > > > --
>> > > > > > > > > > > > > > Best Regards
>> > > > > > > > > > > > > >
>> > > > > > > > > > > > > > Lai
>> > > > > > > > > > > > > >
>> > > > > > > > > > > >
>> > > > > > > > > > >
>> > > > > > > > > > --
>> > > > > > > > > > Best Regards
>> > > > > > > > > >
>> > > > > > > > > > Lai
>> > > >
>> > > --
>> > > Best Regards
>> > >
>> > > Lai
>> >
>> >
>
>
>>
>> --
>> Sandeep Krishnamurthy
>>
>

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