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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/01/19 12:14:45 UTC
[GitHub] [incubator-mxnet] zhongyuchen opened a new issue #17381: High cpu
usage of gluon.data.Dataloader
zhongyuchen opened a new issue #17381: High cpu usage of gluon.data.Dataloader
URL: https://github.com/apache/incubator-mxnet/issues/17381
## Description
I found that if `num_workers=0`, there will be a high CPU usage (nearly ~50% of 48CPUs). This occurs whenever I enabling CUDA.
So when I have 4 threads run in parallel (e.g. data parallelism, each thread uses one GPU), **then there will be severe CPU resource preemption problem**. It will lead to extremely slow training speed. I have encountered this problem [here](https://github.com/bytedance/byteps/issues/194).
### Error Message
(Paste the complete error message. Please also include stack trace by setting environment variable `DMLC_LOG_STACK_TRACE_DEPTH=10` 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. `python example/gluon/mnist/mnist.py`
2. `htop` or `top`
2. see the high CPU usage
If you run `python example/gluon/mnist/mnist.py` for 4 times at one time, then you will observe all CPUs are running. And training process becomes extremely slow. **It is worth mentioning that in the figure below, `htop` reports blue CPU usage, which means low-priority threads are scheduled.**
This also occurs when enabling CUDA
## What have you tried to solve it?
1. setting `num_workers > 0` solves the problem temporarily. see the following figure where I set both training dataloader and val dataloader `num_workers=2`.
It also works when 4 threads run in parallel.
## Environment
g4.12xlarge
We recommend using our script for collecting the diagnositc information. Run the following command and paste the outputs below:
```
curl --retry 10 -s https://raw.githubusercontent.com/dmlc/gluon-nlp/master/tools/diagnose.py | python
# paste outputs here
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 48
On-line CPU(s) list: 0-47
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz
Stepping: 7
CPU MHz: 2500.000
BogoMIPS: 5000.00
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 32K
L1i cache: 32K
L2 cache: 1024K
L3 cache: 36608K
NUMA node0 CPU(s): 0-47
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 avx2 smep bmi2 erms invpcid mpx avx512f rdseed adx smap clflushopt clwb avx512cd xsaveopt xsavec xgetbv1 ida arat pku
----------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 : 1.6.0.rc0
Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet
Num GPUs : 4
Hashtag not found. Not installed from pre-built package.
----------System Info----------
Platform : Linux-4.4.0-1098-aws-x86_64-with-debian-stretch-sid
system : Linux
node : ip-172-31-57-215
release : 4.4.0-1098-aws
version : #109-Ubuntu SMP Fri Nov 8 09:30:18 UTC 2019
----------Hardware Info----------
machine : x86_64
processor : x86_64
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0008 sec, LOAD: 0.3803 sec.
Timing for GluonNLP GitHub: https://github.com/dmlc/gluon-nlp, DNS: 0.0004 sec, LOAD: 0.3689 sec.
Timing for GluonNLP: http://gluon-nlp.mxnet.io, DNS: 0.0003 sec, LOAD: 0.0175 sec.
Timing for D2L: http://d2l.ai, DNS: 0.0004 sec, LOAD: 0.0027 sec.
Timing for D2L (zh-cn): http://zh.d2l.ai, DNS: 0.0002 sec, LOAD: 0.0026 sec.
Timing for FashionMNIST: https://repo.mxnet.io/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0002 sec, LOAD: 0.0152 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0030 sec, LOAD: 0.0457 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0002 sec, LOAD: 0.0286 sec.
```
___
This is a trap more than a bug, but I feel it is necessary to tell all about this.
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