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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/09/09 15:59:00 UTC
[GitHub] [incubator-mxnet] igolan opened a new issue #16130: Imperative
execution in MXNET with multiple GPUs does not run in parallel
igolan opened a new issue #16130: Imperative execution in MXNET with multiple GPUs does not run in parallel
URL: https://github.com/apache/incubator-mxnet/issues/16130
## Description
When running MXNET in imperative (not hybrid) mode using multiple GPUs, it seems like the GPUs do not run in parallel.
## Environment info (Required)
```
(mxnet_p36) ubuntu:~$ python diagnose.py
----------Python Info----------
Version : 3.6.5
Compiler : GCC 7.2.0
Build : ('default', 'Apr 29 2018 16:14:56')
Arch : ('64bit', '')
------------Pip Info-----------
Version : 10.0.1
Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/pip
----------MXNet Info-----------
Version : 1.4.1
Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet
Commit hash file "/home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/COMMIT_HASH" not found. Not installed from pre-built package or built from source.
Library : ['/home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/libmxnet.so']
Build features:
No runtime build feature info available
----------System Info----------
Platform : Linux-4.4.0-1092-aws-x86_64-with-debian-stretch-sid
system : Linux
node : ip-XXX-XX-XX-XXX
release : 4.4.0-1092-aws
version : #103-Ubuntu SMP Tue Aug 27 10:21:48 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): 64
On-line CPU(s) list: 0-63
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
Stepping: 1
CPU MHz: 2193.175
CPU max MHz: 3000.0000
CPU min MHz: 1200.0000
BogoMIPS: 4600.13
Hypervisor vendor: Xen
Virtualization type: full
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 46080K
NUMA node0 CPU(s): 0-15,32-47
NUMA node1 CPU(s): 16-31,48-63
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc aperfmperf pni pclmulqdq monitor est 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 bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt ida
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0020 sec, LOAD: 0.5027 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1331 sec, LOAD: 0.4725 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.2106 sec, LOAD: 0.5541 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0124 sec, LOAD: 0.2240 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0128 sec, LOAD: 0.2566 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0133 sec, LOAD: 0.0977 sec.
----------Environment----------
```
Package used: Python
## Build info (Required if built from source)
N/A
## Error Message:
N/A
## Minimum reproducible example
Running GluonCV modelzoo cifar_resnet110_v2 on CIFAR10:
[HYBRID profiler output](https://imgur.com/KKvQ29e)
[IMPERATIVE profiler output](https://imgur.com/B1Gahl7)
[IMPERATIVE profiler output zoomed](https://imgur.com/cf4fShO)
## Steps to reproduce
Reproduce using the train_cifar10.py script from [https://gluon-cv.mxnet.io/model_zoo/classification.html#cifar10](https://gluon-cv.mxnet.io/model_zoo/classification.html#cifar10) (download link [https://gluon-cv.mxnet.io/_downloads/54189a15ba652c5a2587928303cc2171/train_cifar10.py](https://gluon-cv.mxnet.io/_downloads/54189a15ba652c5a2587928303cc2171/train_cifar10.py) ).
and add MXNET's profiler to the forward pass.
Or use the train_cifar10.py script including profiler code that can be found in [https://gist.github.com/igolan/511b61d17da0694a817a1ac3f9bd8f95](https://gist.github.com/igolan/511b61d17da0694a817a1ac3f9bd8f95)
Run:
`python train_cifar10.py --num-epochs 200 --mode hybrid --num-gpus 4 -j 2 --batch-size 128 --wd 0.0001 --lr 0.1 --lr-decay 0.1 --lr-decay-epoch 100,150 --model cifar_resnet110_v2`
Vs.
`python train_cifar10.py --num-epochs 200 --mode imperative --num-gpus 4 -j 2 --batch-size 128 --wd 0.0001 --lr 0.1 --lr-decay 0.1 --lr-decay-epoch 100,150 --model cifar_resnet110_v2`
## What have you tried to solve it?
N/A
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