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Memory builds up when creating size-zero ndarray in a loop
----------Python Info----------
('Version :', '2.7.15')
('Compiler :', 'GCC 7.2.0')
('Build :', ('default', 'May 1 2018 23:32:55'))
('Arch :', ('64bit', ''))
------------Pip Info-----------
('Version :', '10.0.1')
('Directory :', '/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/pip')
----------MXNet Info-----------
('Version :', '1.4.0')
('Directory :', '/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet')
('Commit Hash :', 'a03d59ed867ba334d78d61246a1090cd1868f5da')
----------System Info----------
('Platform :', 'Linux-4.4.0-1075-aws-x86_64-with-debian-stretch-sid')
('system :', 'Linux')
('node :', 'ip-172-31-4-52')
('release :', '4.4.0-1075-aws')
('version :', '#85-Ubuntu SMP Thu Jan 17 17:15:12 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): 32
On-line CPU(s) list: 0-31
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
Stepping: 1
CPU MHz: 2699.984
CPU max MHz: 3000.0000
CPU min MHz: 1200.0000
BogoMIPS: 4600.09
Hypervisor vendor: Xen
Virtualization type: full
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 46080K
NUMA node0 CPU(s): 0-31
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 rep_good nopl xtopology nonstop_tsc aperfmperf pni pclmulqdq 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 bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0021 sec, LOAD: 0.6245 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0071 sec, LOAD: 0.3581 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0265 sec, LOAD: 0.0987 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0080 sec, LOAD: 0.0543 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1285 sec, LOAD: 0.1622 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.3537 sec, LOAD: 0.3427 sec.
Package used (Python/R/Scala/Julia): Python
If you run watch -n5 nvidia-smi, you may observe memory growth every by 2MB every few seconds.
import mxnet as mx
import time
count = 0
while True:
a = mx.nd.array([], ctx=mx.gpu(0))
a.asnumpy()
time.sleep(0.01)
count += 1
if count % 1000 == 0:
print(count)
(Paste the commands you ran that produced the error.)
Related to #13951
Hey, this is the MXNet Label Bot.
Thank you for submitting the issue! I will try and suggest some labels so that the appropriate MXNet community members can help resolve it.
Here are my recommended labels: Bug
@mxnet-label-bot update [Bug, NDArray, CUDA]
We still observe the same issue after changing context from mx.gpu(0) to mx.cpu(0).
@mxnet-label-bot update [Bug, NDArray]
@mxnet-label-bot add [Backend, Memory]
Nice catch !
@anirudh2290 Could you please reopen this? The original fix has been reverted due to test flakiness. I am working on alternative fix.