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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/15 12:23:48 UTC
[GitHub] elabeca opened a new issue #9087: MXNET 1.0.0 - marginal performance improvement Titan V (Volta) with half precision cuda 9.0 + cudnn 7.0.5
elabeca opened a new issue #9087: MXNET 1.0.0 - marginal performance improvement Titan V (Volta) with half precision cuda 9.0 + cudnn 7.0.5
URL: https://github.com/apache/incubator-mxnet/issues/9087
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
Running the following script: example/image-classification/train_cifar10.py on MXNET v1.0.0, seems to have marginal performance increase on Titan V (Volta) cards with half precision set and CUDA 9.0 / CUDNN 7.0.5
## Environment info (Required)
----------Python Info----------
('Version :', '2.7.12')
('Compiler :', 'GCC 5.4.0 20160609')
('Build :', ('default', 'Nov 20 2017 18:23:56'))
('Arch :', ('64bit', 'ELF'))
------------Pip Info-----------
('Version :', '9.0.1')
('Directory :', '/home/elie/.local/lib/python2.7/site-packages/pip')
----------MXNet Info-----------
('Version :', '1.0.0')
('Directory :', '/home/elie/mxnet/python/mxnet')
Hashtag not found. Not installed from pre-built package.
----------System Info----------
('Platform :', 'Linux-4.10.0-42-generic-x86_64-with-Ubuntu-16.04-xenial')
('system :', 'Linux')
('node :', 'zeus')
('release :', '4.10.0-42-generic')
('version :', '#46~16.04.1-Ubuntu SMP Mon Dec 4 15:57:59 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): 12
On-line CPU(s) list: 0-11
Thread(s) per core: 2
Core(s) per socket: 6
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 63
Model name: Intel(R) Core(TM) i7-5930K CPU @ 3.50GHz
Stepping: 2
CPU MHz: 1397.308
CPU max MHz: 4100.0000
CPU min MHz: 1200.0000
BogoMIPS: 6999.98
Virtualisation: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 15360K
NUMA node0 CPU(s): 0-11
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 aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx 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 epb intel_ppin tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm xsaveopt cqm_llc cqm_occup_llc dtherm ida arat pln pts
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0608 sec, LOAD: 0.9193 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0531 sec, LOAD: 0.3470 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0671 sec, LOAD: 0.4028 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0546 sec, LOAD: 0.2149 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.3649 sec, LOAD: 0.3953 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.3955 sec, LOAD: 0.8753 sec.
Package used (Python/R/Scala/Julia):
Python 2.7
## Build info (Required if built from source)
Compiler (gcc/clang/mingw/visual studio):
gcc
MXNet commit hash:
25720d0e3c29232a37e2650f3ba3a2454f9367bb
Build config:
#-------------------------------------------------------------------------------
# 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
#--------------------
export CC = gcc
export CXX = g++
export NVCC = nvcc
# whether compile with options for MXNet developer
DEV = 0
# whether compile with debug
DEBUG = 0
# whether compile with profiler
USE_PROFILER =
# whether to turn on signal handler (e.g. segfault logger)
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 = 1
# 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 = /usr/local/cuda
# whether use CuDNN R3 library
USE_CUDNN = 1
#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
# MKL ML Library for Intel CPU/Xeon Phi
# Please refer to MKL_README.md for details
# MKL ML Library folder, need to be root for /usr/local
# Change to User Home directory for standard user
# For USE_BLAS!=mkl only
MKLML_ROOT=/usr/local
# whether use MKL2017 library
USE_MKL2017 = 0
# whether use MKL2017 experimental feature for high performance
# Prerequisite USE_MKL2017=1
USE_MKL2017_EXPERIMENTAL = 0
# 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 =
# by default, disable lapack when using MKL
# switch on when there is a full installation of MKL available (not just MKL2017/MKL_ML)
ifeq ($(USE_BLAS), mkl)
USE_LAPACK = 0
endif
# add path to intel library, you may need it for MKL, if you did not add the path
# to environment variable
USE_INTEL_PATH = NONE
# If use MKL only for BLAS, choose static link automatically to allow python wrapper
ifeq ($(USE_MKL2017), 0)
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
endif
else
USE_STATIC_MKL = NONE
endif
#----------------------------
# Settings for power and arm arch
#----------------------------
ARCH := $(shell uname -a)
ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
USE_SSE=0
else
USE_SSE=1
endif
#----------------------------
# distributed computing
#----------------------------
# whether or not to enable multi-machine supporting
USE_DIST_KVSTORE = 0
# whether or not allow to read and write HDFS directly. If yes, then hadoop is
# required
USE_HDFS = 0
# path to libjvm.so. required if USE_HDFS=1
LIBJVM=$(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
# Use JEMalloc if found, and not using gperftools
USE_JEMALLOC = 1
#----------------------------
# 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
# whether to use torch integration. This requires installing torch.
# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
# TORCH_PATH = $(HOME)/torch
# MXNET_PLUGINS += plugin/torch/torch.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:
None
## Minimum reproducible example
time python2 train_cifar10.py --dtype float16 --network resnet --num-epochs 1 --num-layers 110 --batch-size 512 --gpus 0
vs
time python2 train_cifar10.py --network resnet --num-epochs 1 --num-layers 110 --batch-size 512 --gpus 0
## Steps to reproduce
time python2 train_cifar10.py --dtype float16 --network resnet --num-epochs 1 --num-layers 110 --batch-size 512 --gpus 0
vs
time python2 train_cifar10.py --network resnet --num-epochs 1 --num-layers 110 --batch-size 512 --gpus 0
## What have you tried to solve it?
Compared results between a Titan V (Volta) card and a Titan X (Pascal card). Tried with and without half precision set for the train_cifar10.py example on resenet, one epoch, 110 layers and 512 batch size.
Results for Volta (Titan V) with dtype float16 flag set:
INFO:root:start with arguments Namespace(batch_size=512, benchmark=0, data_nthreads=4, data_train='data/cifar10_train.rec', data_train_idx='', data_val='data/cifar10_val.rec', data_val_idx='', disp_batches=20, dtype='float16', gc_threshold=0.5, gc_type='none', gpus='0', image_shape='3,28,28', kv_store='device', load_epoch=None, lr=0.05, lr_factor=0.1, lr_step_epochs='200,250', max_random_aspect_ratio=0, max_random_h=36, max_random_l=50, max_random_rotate_angle=0, max_random_s=50, max_random_scale=1, max_random_shear_ratio=0, min_random_scale=1, model_prefix=None, mom=0.9, monitor=0, network='resnet', num_classes=10, num_epochs=1, num_examples=50000, num_layers=110, optimizer='sgd', pad_size=4, random_crop=1, random_mirror=1, rgb_mean='123.68,116.779,103.939', test_io=0, top_k=0, wd=0.0001)
[11:33:43] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar10_train.rec, use 4 threads for decoding..
[11:33:47] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar10_val.rec, use 4 threads for decoding..
[11:33:48] src/operator/././cudnn_algoreg-inl.h:107: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
INFO:root:Epoch[0] Batch [20] Speed: 2734.21 samples/sec accuracy=0.142020
INFO:root:Epoch[0] Batch [40] Speed: 2709.23 samples/sec accuracy=0.202832
INFO:root:Epoch[0] Batch [60] Speed: 2724.39 samples/sec accuracy=0.233984
INFO:root:Epoch[0] Batch [80] Speed: 2751.87 samples/sec accuracy=0.268652
INFO:root:Epoch[0] Train-accuracy=0.303653
INFO:root:Epoch[0] Time cost=18.777
INFO:root:Epoch[0] Validation-accuracy=0.314453
real 0m26.451s
user 0m36.516s
sys 0m9.708s
Results for Volta (Titan V) without half-precision flag set:
time python2 train_cifar10.py --network resnet --num-epochs 1 --num-layers 110 --batch-size 512 --gpus 0
INFO:root:start with arguments Namespace(batch_size=512, benchmark=0, data_nthreads=4, data_train='data/cifar10_train.rec', data_train_idx='', data_val='data/cifar10_val.rec', data_val_idx='', disp_batches=20, dtype='float32', gc_threshold=0.5, gc_type='none', gpus='0', image_shape='3,28,28', kv_store='device', load_epoch=None, lr=0.05, lr_factor=0.1, lr_step_epochs='200,250', max_random_aspect_ratio=0, max_random_h=36, max_random_l=50, max_random_rotate_angle=0, max_random_s=50, max_random_scale=1, max_random_shear_ratio=0, min_random_scale=1, model_prefix=None, mom=0.9, monitor=0, network='resnet', num_classes=10, num_epochs=1, num_examples=50000, num_layers=110, optimizer='sgd', pad_size=4, random_crop=1, random_mirror=1, rgb_mean='123.68,116.779,103.939', test_io=0, top_k=0, wd=0.0001)
[11:30:53] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar10_train.rec, use 4 threads for decoding..
[11:30:56] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar10_val.rec, use 4 threads for decoding..
[11:30:58] src/operator/././cudnn_algoreg-inl.h:107: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
INFO:root:Epoch[0] Batch [20] Speed: 2855.89 samples/sec accuracy=0.121931
INFO:root:Epoch[0] Batch [40] Speed: 2933.23 samples/sec accuracy=0.191406
INFO:root:Epoch[0] Batch [60] Speed: 2944.27 samples/sec accuracy=0.239551
INFO:root:Epoch[0] Batch [80] Speed: 2871.48 samples/sec accuracy=0.271289
INFO:root:Epoch[0] Train-accuracy=0.301356
INFO:root:Epoch[0] Time cost=17.768
INFO:root:Epoch[0] Validation-accuracy=0.340820
real 0m25.560s
user 0m34.052s
sys 0m9.416s
Results for Pascal (Titan X) with dtype float16 flag set:
time python2 train_cifar10.py --dtype float16 --network resnet --num-epochs 1 --num-layers 110 --batch-size 512 --gpus 0
INFO:root:start with arguments Namespace(batch_size=512, benchmark=0, data_nthreads=4, data_train='data/cifar10_train.rec', data_train_idx='', data_val='data/cifar10_val.rec', data_val_idx='', disp_batches=20, dtype='float16', gc_threshold=0.5, gc_type='none', gpus='0', image_shape='3,28,28', kv_store='device', load_epoch=None, lr=0.05, lr_factor=0.1, lr_step_epochs='200,250', max_random_aspect_ratio=0, max_random_h=36, max_random_l=50, max_random_rotate_angle=0, max_random_s=50, max_random_scale=1, max_random_shear_ratio=0, min_random_scale=1, model_prefix=None, mom=0.9, monitor=0, network='resnet', num_classes=10, num_epochs=1, num_examples=50000, num_layers=110, optimizer='sgd', pad_size=4, random_crop=1, random_mirror=1, rgb_mean='123.68,116.779,103.939', test_io=0, top_k=0, wd=0.0001)
[11:33:43] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar10_train.rec, use 4 threads for decoding..
[11:33:47] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar10_val.rec, use 4 threads for decoding..
[11:33:48] src/operator/././cudnn_algoreg-inl.h:107: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
INFO:root:Epoch[0] Batch [20] Speed: 2734.21 samples/sec accuracy=0.142020
INFO:root:Epoch[0] Batch [40] Speed: 2709.23 samples/sec accuracy=0.202832
INFO:root:Epoch[0] Batch [60] Speed: 2724.39 samples/sec accuracy=0.233984
INFO:root:Epoch[0] Batch [80] Speed: 2751.87 samples/sec accuracy=0.268652
INFO:root:Epoch[0] Train-accuracy=0.303653
INFO:root:Epoch[0] Time cost=18.777
INFO:root:Epoch[0] Validation-accuracy=0.314453
real 0m26.451s
user 0m36.516s
sys 0m9.708s
Results for Pascal (Titan X) without half-precision flag set:
time python2 train_cifar10.py --network resnet --num-epochs 1 --num-layers 110 --batch-size 512 --gpus 2
INFO:root:start with arguments Namespace(batch_size=512, benchmark=0, data_nthreads=4, data_train='data/cifar10_train.rec', data_train_idx='', data_val='data/cifar10_val.rec', data_val_idx='', disp_batches=20, dtype='float32', gc_threshold=0.5, gc_type='none', gpus='2', image_shape='3,28,28', kv_store='device', load_epoch=None, lr=0.05, lr_factor=0.1, lr_step_epochs='200,250', max_random_aspect_ratio=0, max_random_h=36, max_random_l=50, max_random_rotate_angle=0, max_random_s=50, max_random_scale=1, max_random_shear_ratio=0, min_random_scale=1, model_prefix=None, mom=0.9, monitor=0, network='resnet', num_classes=10, num_epochs=1, num_examples=50000, num_layers=110, optimizer='sgd', pad_size=4, random_crop=1, random_mirror=1, rgb_mean='123.68,116.779,103.939', test_io=0, top_k=0, wd=0.0001)
[11:32:37] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar10_train.rec, use 4 threads for decoding..
[11:32:41] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar10_val.rec, use 4 threads for decoding..
[11:32:42] src/operator/././cudnn_algoreg-inl.h:107: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
INFO:root:Epoch[0] Batch [20] Speed: 2228.10 samples/sec accuracy=0.141927
INFO:root:Epoch[0] Batch [40] Speed: 2234.42 samples/sec accuracy=0.199609
INFO:root:Epoch[0] Batch [60] Speed: 2258.77 samples/sec accuracy=0.235449
INFO:root:Epoch[0] Batch [80] Speed: 2237.78 samples/sec accuracy=0.266992
INFO:root:Epoch[0] Train-accuracy=0.286880
INFO:root:Epoch[0] Time cost=22.809
INFO:root:Epoch[0] Validation-accuracy=0.343164
real 0m31.823s
user 0m41.688s
sys 0m11.076s
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