idealboy opened a new issue #13075: Performence with multi thead inference is slow URL: https://github.com/apache/incubator-mxnet/issues/13075 Note: Providing complete information in the most concise form is the best way to get help. This issue template serves as the checklist for essential information to most of the technical issues and bug reports. For non-technical issues and feature requests, feel free to present the information in what you believe is the best form. For Q & A and discussion, please start a discussion thread at https://discuss.mxnet.io ## Description (Brief description of the problem in no more than 2 sentences.) when I do inference with multi thread(each thread will create one predictor handle with the same libmxnet.so), I found it is very slow. I use MXPredReshape in some code to adapt for different input shape ## Environment info (Required) ``` What to do: 1. Download the diagnosis script from https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py 2. Run the script using `python diagnose.py` and paste its output here. ``` ----------Python Info---------- ('Version :', '2.7.5') ('Compiler :', 'GCC 4.8.2 20140120 (Red Hat 4.8.2-16)') ('Build :', ('default', 'Jun 17 2014 18:11:42')) ('Arch :', ('64bit', 'ELF')) ------------Pip Info----------- ('Version :', '9.0.1') ('Directory :', '/usr/lib/python2.7/site-packages/pip') ----------MXNet Info----------- No MXNet installed. ----------System Info---------- ('Platform :', 'Linux-4.1.5-1.el7.centos.x86_64-x86_64-with-centos-7.0.1406-Core') ('system :', 'Linux') ('node :', 'face00') ('release :', '4.1.5-1.el7.centos.x86_64') ('version :', '#1 SMP Tue Aug 11 13:53:50 EDT 2015') ----------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): 16 On-line CPU(s) list: 0-15 Thread(s) per core: 1 Core(s) per socket: 1 Socket(s): 16 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 22 Model name: Stepping: 3 CPU MHz: 2494.224 BogoMIPS: 4988.44 Hypervisor vendor: KVM Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 30720K NUMA node0 CPU(s): 0-15 Package used (Python/R/Scala/Julia): (I'm using Python) For Scala user, please provide: 1. Java version: (`java -version`) 2. Maven version: (`mvn -version`) 3. Scala runtime if applicable: (`scala -version`) For R user, please provide R `sessionInfo()`: ## Build info (Required if built from source) Compiler (gcc/clang/mingw/visual studio): gcc4.8.5 MXNet commit hash: (Paste the output of `git rev-parse HEAD` here.) 0a286a002c6f3c98843389fedfedf89d97324fda mxnet1.3.x Build config: (Paste the content of config.mk, or the build command.) 40 export CC = gcc 41 export CXX = g++ 42 export NVCC = nvcc 44 # whether compile with options for MXNet developer 45 DEV = 0 46 47 # whether compile with debug 48 DEBUG = 0 49 50 # whether to turn on segfault signal handler to log the stack trace 51 USE_SIGNAL_HANDLER = 52 53 # the additional link flags you want to add 54 ADD_LDFLAGS = -L/usr/local/lib 55 56 # the additional compile flags you want to add 57 ADD_CFLAGS = -I/usr/local/include 64 USE_CUDA = 0 65 66 # add the path to CUDA library to link and compile flag 67 # if you have already add them to environment variable, leave it as NONE 68 USE_CUDA_PATH = /usr/local/cuda-9.1 69 # USE_CUDA_PATH = /usr/local/cuda 70 71 # whether to enable CUDA runtime compilation 72 ENABLE_CUDA_RTC = 1 93 USE_OPENMP = 1 94 95 # whether use MKL-DNN library 96 USE_MKLDNN = 0 97 98 # whether use NNPACK library 99 USE_NNPACK = 0 100 101 # choose the version of blas you want to use 102 # can be: mkl, blas, atlas, openblas 103 # in default use atlas for linux while apple for osx 104 UNAME_S := $(shell uname -s) 105 ifeq ($(UNAME_S), Darwin) 106 USE_BLAS = apple 107 else 108 USE_BLAS = openblas 109 endif 110 111 # whether use lapack during compilation 112 # only effective when compiled with blas versions openblas/apple/atlas/mkl 113 USE_LAPACK = 0 114 115 # path to lapack library in case of a non-standard installation 116 USE_LAPACK_PATH = 117 118 # add path to intel library, you may need it for MKL, if you did not add the path 119 # to environment variable 120 USE_INTEL_PATH = NONE 121 122 # If use MKL only for BLAS, choose static link automatically to allow python wrapper 123 ifeq ($(USE_BLAS), mkl) 124 USE_STATIC_MKL = 1 125 else 126 USE_STATIC_MKL = NONE 157 USE_HDFS = 0 158 159 # path to libjvm.so. required if USE_HDFS=1 160 LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server 161 162 # whether or not allow to read and write AWS S3 directly. If yes, then 163 # libcurl4-openssl-dev is required, it can be installed on Ubuntu by 164 # sudo apt-get install -y libcurl4-openssl-dev 165 USE_S3 = 0 166 167 #---------------------------- 168 # performance settings 169 #---------------------------- 170 # Use operator tuning 171 USE_OPERATOR_TUNING = 1 172 173 # Use gperftools if found 174 USE_GPERFTOOLS = 0 175 176 # Use JEMalloc if found, and not using gperftools 177 USE_JEMALLOC = 1 178 179 #---------------------------- 180 # additional operators 181 #---------------------------- 182 183 # path to folders containing projects specific operators that you don't want to put in src/operators 184 EXTRA_OPERATORS = 185 186 #---------------------------- 187 # other features 188 #---------------------------- 189 190 # Create C++ interface package 191 USE_CPP_PACKAGE = 1 ## Error Message: (Paste the complete error message, including stack trace.) ## Minimum reproducible example (If you are using your own code, please provide a short script that reproduces the error. Otherwise, please provide link to the existing example.) ## Steps to reproduce (Paste the commands you ran that produced the error.) 1. 2. ## What have you tried to solve it? 1. 2.
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