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.li...@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.li...@gmail.com> wrote:
> >
> > Hi Ciyong, thanks for trying to reproduce:
> >
> > I used this one:
> > https://github.com/awslabs/deeplearning-benchmark/blob/master/dawnbench/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.c...@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.li...@gmail.com]
> > > Sent: Wednesday, June 26, 2019 6:28 AM
> > > To: dev@mxnet.incubator.apache.org
> > > Cc: d...@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.li...@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 <roywei...@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....@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.z...@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:anirudh2...@gmail.com]
> > > > > > > > Sent: Friday, June 21, 2019 9:31 AM
> > > > > > > > To: dev@mxnet.incubator.apache.org
> > > > > > > > Cc: d...@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 <roywei...@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
> > > > > > > > > <anirudh2...@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
> > > > > > > > > > <roywei...@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
> > > > > > > > > > > <ptre...@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 <roywei...@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+Release+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
