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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/21 23:32:42 UTC
[GitHub] kalpitdixit opened a new issue #9171: MXNet: Using FusedRNNCell with its "bidirectional" flag turned True, can lead to hanging of training run.
kalpitdixit opened a new issue #9171: MXNet: Using FusedRNNCell with its "bidirectional" flag turned True, can lead to hanging of training run.
URL: https://github.com/apache/incubator-mxnet/issues/9171
## Description
MXNet
Using FusedRNNCell with its "bidirectional" flag turned True, can lead to hanging (i.e. infinite pause without progress/error/crash) of training run.
## Details
I am running a single training run of a Sequence-to-Sequence model using the BucketingModule. Iam using an Encoder-Decoder network. I am using a FusedRNNCell with its "bidirectional" flag turned on for the Encoder and an unfused RNNCell for the Decoder.
GPU utilization is 15000MB / 16000MB. CPU utilization is 95%.
For each batch during training, I do a forward() pass and a backward() pass. After a 5-15 epochs, the training run gets stuck in the forward() pass of one of the mini-batches. The forward pass does not complete. No errors are thrown nor does anything crash. GPU/CPU utilization remains identically the same.
I have tried an ablation of many-many things in my training run (architecture, data, code etc). The conclusion is that specifically using the FusedRNNCell with the "bidirectional" flag turned True causes this problem.
## Package used
Python
## Environment info
----------Python Info----------
Version : 3.5.2
Compiler : GCC 5.4.0 20160609
Build : ('default', 'Nov 23 2017 16:37:01')
Arch : ('64bit', 'ELF')
------------Pip Info-----------
Version : 9.0.1
Directory : /usr/local/lib/python3.5/dist-packages/pip
----------MXNet Info-----------
Version : 1.0.0
Directory : /usr/local/lib/python3.5/dist-packages/mxnet
Commit Hash : 25720d0e3c29232a37e2650f3ba3a2454f9367bb
----------System Info----------
Platform : Linux-4.4.0-1039-aws-x86_64-with-Ubuntu-16.04-xenial
system : Linux
node : ip-172-31-85-194
release : 4.4.0-1039-aws
version : #48-Ubuntu SMP Wed Oct 11 15:15:01 UTC 2017
----------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: 1200.582
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): 0-15,32-47
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 eagerfpu 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 fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt ida
----------Network Test----------
Setting timeout: 10
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0300 sec, LOAD: 0.0514 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1141 sec, LOAD: 0.1956 sec.
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0016 sec, LOAD: 0.4062 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.1799 sec, LOAD: 0.3847 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0046 sec, LOAD: 0.0126 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0154 sec, LOAD: 0.1567 sec.
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