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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/01/15 13:37:04 UTC
[GitHub] kirk86 opened a new issue #13885: imagenet example failure to run
properly
kirk86 opened a new issue #13885: imagenet example failure to run properly
URL: https://github.com/apache/incubator-mxnet/issues/13885
## Description
I am following this example https://mxnet.incubator.apache.org/tutorials/vision/large_scale_classification.html to run imagenet training on multi-gpu single node, but it throws errors. The only step that I have omitted from the example is the optional one.
## Environment info (Required)
```
----------Python Info----------
Version : 3.6.8
Compiler : GCC 7.3.0
Build : ('default', 'Dec 30 2018 01:22:34')
Arch : ('64bit', '')
------------Pip Info-----------
Version : 18.1
Directory : /home/user/miniconda3/envs/mxnet/lib/python3.6/site-packages/pip
----------MXNet Info-----------
Version : 1.2.1
Directory : /home/user/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet
Hashtag not found. Not installed from pre-built package.
----------System Info----------
Platform : Linux-4.15.0-36-generic-x86_64-with-debian-buster-sid
system : Linux
node : theengine
release : 4.15.0-36-generic
version : #39-Ubuntu SMP Mon Sep 24 16:19:09 UTC 2018
----------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: 79
Model name: Intel(R) Xeon(R) CPU E5-2695 v4 @ 2.10GHz
Stepping: 1
CPU MHz: 1990.164
CPU max MHz: 3300.0000
CPU min MHz: 1200.0000
BogoMIPS: 4190.74
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 46080K
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 dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts flush_l1d
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0038 sec, LOAD: 0.7055 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0036 sec, LOAD: 0.5789 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0077 sec, LOAD: 0.6072 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0063 sec, LOAD: 0.9507 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0141 sec, LOAD: 0.5458 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0032 sec, LOAD: 0.0308 sec.
```
Package used (Python/R/Scala/Julia):
Python
## Build info (Required if built from source)
Installed through conda: mxnet-cu92
MXNet commit hash:
4fe5461eb98bdede589c511f486c1b934bfa6393
Build config:
```
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#-------------------------------------------------------------------------------
# Template configuration for compiling mxnet
#
# If you want to change the configuration, please use the following
# steps. Assume you are on the root directory of mxnet. First copy the this
# file so that any local changes will be ignored by git
#
# $ cp make/config.mk .
#
# Next modify the according entries, and then compile by
#
# $ make
#
# or build in parallel with 8 threads
#
# $ make -j8
#-------------------------------------------------------------------------------
#---------------------
# choice of compiler
#--------------------
ifndef CC
export CC = gcc
endif
ifndef CXX
export CXX = g++
endif
ifndef NVCC
export NVCC = nvcc
endif
# whether compile with options for MXNet developer
DEV = 0
# whether compile with debug
DEBUG = 0
# whether to turn on segfault signal handler to log the stack trace
USE_SIGNAL_HANDLER =
# the additional link flags you want to add
ADD_LDFLAGS =
# the additional compile flags you want to add
ADD_CFLAGS =
#---------------------------------------------
# matrix computation libraries for CPU/GPU
#---------------------------------------------
# whether use CUDA during compile
USE_CUDA = 0
# add the path to CUDA library to link and compile flag
# if you have already add them to environment variable, leave it as NONE
# USE_CUDA_PATH = /usr/local/cuda
USE_CUDA_PATH = NONE
# whether to enable CUDA runtime compilation
ENABLE_CUDA_RTC = 1
# whether use CuDNN R3 library
USE_CUDNN = 0
#whether to use NCCL library
USE_NCCL = 0
#add the path to NCCL library
USE_NCCL_PATH = NONE
# whether use opencv during compilation
# you can disable it, however, you will not able to use
# imbin iterator
USE_OPENCV = 1
#whether use libjpeg-turbo for image decode without OpenCV wrapper
USE_LIBJPEG_TURBO = 0
#add the path to libjpeg-turbo library
USE_LIBJPEG_TURBO_PATH = NONE
# use openmp for parallelization
USE_OPENMP = 1
# whether use MKL-DNN library: 0 = disabled, 1 = enabled
# if USE_MKLDNN is not defined, MKL-DNN will be enabled by default on x86 Linux.
# you can disable it explicity with USE_MKLDNN = 0
USE_MKLDNN =
# whether use NNPACK library
USE_NNPACK = 0
# choose the version of blas you want to use
# can be: mkl, blas, atlas, openblas
# in default use atlas for linux while apple for osx
UNAME_S := $(shell uname -s)
ifeq ($(UNAME_S), Darwin)
USE_BLAS = apple
else
USE_BLAS = atlas
endif
# whether use lapack during compilation
# only effective when compiled with blas versions openblas/apple/atlas/mkl
USE_LAPACK = 1
# path to lapack library in case of a non-standard installation
USE_LAPACK_PATH =
# add path to intel library, you may need it for MKL, if you did not add the path
# to environment variable
USE_INTEL_PATH = NONE
# If use MKL only for BLAS, choose static link automatically to allow python wrapper
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
else
USE_STATIC_MKL = NONE
endif
#----------------------------
# Settings for power and arm arch
#----------------------------
ARCH := $(shell uname -a)
ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
USE_SSE=0
USE_F16C=0
else
USE_SSE=1
endif
#----------------------------
# F16C instruction support for faster arithmetic of fp16 on CPU
#----------------------------
# For distributed training with fp16, this helps even if training on GPUs
# If left empty, checks CPU support and turns it on.
# For cross compilation, please check support for F16C on target device and turn off if necessary.
USE_F16C =
#----------------------------
# distributed computing
#----------------------------
# whether or not to enable multi-machine supporting
USE_DIST_KVSTORE = 0
# whether or not allow to read and write HDFS directly. If yes, then hadoop is
# required
USE_HDFS = 0
# path to libjvm.so. required if USE_HDFS=1
LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
# whether or not allow to read and write AWS S3 directly. If yes, then
# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
# sudo apt-get install -y libcurl4-openssl-dev
USE_S3 = 0
#----------------------------
# performance settings
#----------------------------
# Use operator tuning
USE_OPERATOR_TUNING = 1
# Use gperftools if found
USE_GPERFTOOLS = 1
# path to gperftools (tcmalloc) library in case of a non-standard installation
USE_GPERFTOOLS_PATH =
# Link gperftools statically
USE_GPERFTOOLS_STATIC =
# Use JEMalloc if found, and not using gperftools
USE_JEMALLOC = 1
# path to jemalloc library in case of a non-standard installation
USE_JEMALLOC_PATH =
# Link jemalloc statically
USE_JEMALLOC_STATIC =
#----------------------------
# additional operators
#----------------------------
# path to folders containing projects specific operators that you don't want to put in src/operators
EXTRA_OPERATORS =
#----------------------------
# other features
#----------------------------
# Create C++ interface package
USE_CPP_PACKAGE = 0
#----------------------------
# plugins
#----------------------------
# whether to use caffe integration. This requires installing caffe.
# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
# CAFFE_PATH = $(HOME)/caffe
# MXNET_PLUGINS += plugin/caffe/caffe.mk
# WARPCTC_PATH = $(HOME)/warp-ctc
# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
# whether to use sframe integration. This requires build sframe
# git@github.com:dato-code/SFrame.git
# SFRAME_PATH = $(HOME)/SFrame
# MXNET_PLUGINS += plugin/sframe/plugin.mk
```
## Error Message:
```
Traceback (most recent call last):
File "./incubator-mxnet/example/image-classification/train_imagenet.py", line 66, in <module>
fit.fit(args, sym, data.get_rec_iter)
File "/home/john/.kaggle/competitions/imagenet-object-localization-challenge/ILSVRC/Data/CLS-LOC/incubator-mxnet/example/image-classification/common/fit.py", line 180, in fit
(train, val) = data_loader(args, kv)
File "/home/john/.kaggle/competitions/imagenet-object-localization-challenge/ILSVRC/Data/CLS-LOC/incubator-mxnet/example/image-classification/common/data.py", line 184, in get_rec_iter
part_index = rank)
File "/home/john/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet/io.py", line 936, in creator
ctypes.byref(iter_handle)))
File "/home/john/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet/base.py", line 149, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [13:34:40] src/io/input_split_base.cc:24: Check failed: files_[i].size % align_bytes == 0 file do not align by 4 bytes
Stack trace returned 10 entries:
[bt] (0) /home/john/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5a) [0x7f2d9e96712a]
[bt] (1) /home/john/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28) [0x7f2d9e967ba8]
[bt] (2) /home/john/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet/libmxnet.so(dmlc::io::InputSplitBase::Init(dmlc::io::FileSystem*, char const*, unsigned long, bool)+0x416) [0x7f2da176c546]
[bt] (3) /home/john/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet/libmxnet.so(dmlc::InputSplit::Create(char const*, char const*, unsigned int, unsigned int, char const*, bool, int, unsigned long, bool)+0x429) [0x7f2da1733e39]
[bt] (4) /home/john/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet/libmxnet.so(mxnet::io::ImageRecordIOParser2<float>::Init(std::vector<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > > const&)+0xe4e) [0x7f2da10fe5de]
[bt] (5) /home/john/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet/libmxnet.so(mxnet::io::ImageRecordIter2<float>::Init(std::vector<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > > const&)+0x8a) [0x7f2da10ff17a]
[bt] (6) /home/john/miniconda3/envs/mxnet/lib/python3.6/site-packages/mxnet/libmxnet.so(MXDataIterCreateIter+0x3c1) [0x7f2da16be9a1]
[bt] (7) /home/john/miniconda3/envs/mxnet/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c) [0x7f2dba662ec0]
[bt] (8) /home/john/miniconda3/envs/mxnet/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call+0x22d) [0x7f2dba66287d]
[bt] (9) /home/john/miniconda3/envs/mxnet/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2ce) [0x7f2dbbbeaede]
```
## Minimum reproducible example
https://mxnet.incubator.apache.org/tutorials/vision/large_scale_classification.html
## Steps to reproduce
Followed steps in the link https://mxnet.incubator.apache.org/tutorials/vision/large_scale_classification.html
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