Ubuntu 16.04 下编译安装Faiss CPU版本

环境：unbuntu 16.04

安装anaconda2

#下载
wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda2-4.3.0-Linux-x86_64.sh #权限
chmod +x Anaconda2-4.3.0-Linux-x86_64.sh
#安装
bash Anaconda2-4.3.0-Linux-x86_64.sh

安装过程anaconda会问要不要加入环境变量选是不行就手动添加
#编辑
subl $HOME/.bashrc
#添加
export PATH="$HOME/anaconda2/bin:$PATH"
#启用
source $HOME/.bashrc

然后检查一下是不是能用conda list

装openblas

#安装openblas

conda install openblas

#创建软链

sudo ln -s $HOME/anaconda2/lib/libopenblas.so.0 /usr/lib/libopenblas.so.0

下Faiss

# 下载FAISS源码
git clone https://github.com/facebookresearch/faiss.git # 进入FAISS源码目录
cd faiss
# 根据系统配置编译环境
cp example_makefiles/makefile.inc.Linux ./makefile.inc
这里需要进到 faiss目录下的makefile.inc里修改linux的版本我这里把centos注释掉，取消ubuntu16的注释
cd
cd faiss/
subl makefile.inc

下面是修改的部分
# 2. Openblas
#
# The library contains both BLAS and Lapack. About 30% slower than MKL. Please see
# https://github.com/facebookresearch/faiss/wiki/Troubleshooting#slow-brute-force-search-with-openblas # to fix performance problemes with OpenBLAS
BLASCFLAGS=-DFINTEGER=int
# This is for Centos:
#BLASLDFLAGS?=/usr/lib64/libopenblas.so.0
# for Ubuntu 16:
sudo apt-get install libopenblas-dev python-numpy python-dev
BLASLDFLAGS?=/usr/lib/libopenblas.so.0
# 编译 &测试BLAS案例
make tests/test_blas
./tests/test_blas

C++环境

#编译安装
make
#下载数据集
wget ftp://ftp.irisa.fr/local/texmex/corpus/sift.tar.gz tar -xzvf sift.tar.gz
# 转移数据集。
mv sift sift1M
# 编译运行
make tests/demo_sift1M
./tests/demo_sift1M

python环境

# 更改配置文件
subl makefile.inc
大概118行 PYTHONCFLAGS 选项，替换如下：
PYTHONCFLAGS=-I/home/JIAO/anaconda2/include/python2.7/ -I/home/JIAO/anaconda2/lib/python2.7/site-packages/numpy/core/include/
# 编译.
make py
#如果出现以下，说明上面的改PYTHONCFLAGS没有改对
python/swigfaiss_wrap.cxx:154:21: fatal error: Python.h: 没有那个文件或目录compilation terminated.
# 检验
python -c "import faiss"
Failed to load GPU Faiss: No module named swigfaiss_gpuFaiss falling back to CPU-only.Traceback (most recent call last):  File "<string>", line 1, in <module>  File "faiss.py", line 28, in <module>    from swigfaiss import *  File "swigfaiss.py", line 28, in <module>    _swigfaiss = swig_import_helper()  File "swigfaiss.py", line 24, in swig_import_helper    _mod = imp.load_module('_swigfaiss', fp, pathname, description)ImportError: /home/JIAO/anaconda2/bin/../lib/libgomp.so.1: version `GOMP_4.0' not found (required by ./_swigfaiss.so)
conda install libgcc
#查看anaconda2 GOMP版本
strings/home/JIAO/anaconda2/bin/../lib/libgomp.so.1 | grep GOMP
#会看到GOMP 1.0 GOMP2.0 GOMP3.0 没有GOMP4.0
#查看linux自带的GOMP版本有GOMP4.0
sudofind / -name libgomp.so.1*
#将anaconda2的gomp删除链接到系统自带的gomp4.0
sudo rm -rf /home/JIAO/anaconda2/bin/../lib/libgomp.so.1
sudo ln -s /usr/lib/x86_64-linux-gnu/libgomp.so.1/home/JIAO/anaconda2/bin/..libgomp.so.1
#有时候会出现swigfaiss failed
make clean
cd faiss/python
git checkout swigfaiss_gpu_wrap.cxx swigfaiss_gpu.py swigfaiss_wrap.cxx swigfaiss.py
cd faiss/
make py
# 6.1、简单测试.
python -c "import faiss, numpy
#下面这行错误不用管
Failed to load GPU Faiss: No module named swigfaiss_gpuFaiss falling back to CPU-only.
faiss.Kmeans(10, 20).train(numpy.random.rand(1000, 10).astype('float32'))"
#复杂测试.
export PYTHONPATH=.
mkdir tmp
python tests/demo_auto_tune.py

下面是调试的完整过程

JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ cd 下载
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ ls
Anaconda3-5.0.1-Linux-x86_64.sh libcudnn.so.6
cuda sublime_text_3_build_3143_x64.tar.bz2
cuda_8.0.61_375.26_linux.run teamviewer
cudnn-8.0-linux-x64-v6.0.tgz teamviewer_13.0.6634_amd64.deb
libcudnn.so
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda2-4.3.0-Linux-x86_64.sh --2018-02-08 14:07:21-- https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda2-4.3.0-Linux-x86_64.sh 正在解析主机 mirrors.tuna.tsinghua.edu.cn (mirrors.tuna.tsinghua.edu.cn)... 101.6.6.177, 2402:f000:1:416:101:6:6:178
正在连接 mirrors.tuna.tsinghua.edu.cn (mirrors.tuna.tsinghua.edu.cn)|101.6.6.177|:443... 已连接。
已发出 HTTP 请求，正在等待回应... 200 OK
长度： 483530594 (461M) [application/octet-stream]
正在保存至: “Anaconda2-4.3.0-Linux-x86_64.sh”

Anaconda2-4.3.0-Lin 100%[===================>] 461.13M 10.3MB/s in 44s

2018-02-08 14:08:06 (10.6 MB/s) - 已保存 “Anaconda2-4.3.0-Linux-x86_64.sh” [483530594/483530594])

JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ chmod +x Anaconda2-4.3.0-Linux-x86_64.sh
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ bash Anaconda2-4.3.0-Linux-x86_64.sh

Welcome to Anaconda2 4.3.0 (by Continuum Analytics, Inc.)

In order to continue the installation process, please review the license
agreement.
Please, press ENTER to continue
>>>
================
Anaconda License
================

Copyright 2016, Continuum Analytics, Inc.

All rights reserved under the 3-clause BSD License:

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.

* Neither the name of Continuum Analytics, Inc. nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"

Do you approve the license terms? [yes|no]
>>> yes

Anaconda2 will now be installed into this location:
/home/JIAO/anaconda2

- Press ENTER to confirm the location
- Press CTRL-C to abort the installation
- Or specify a different location below

[/home/JIAO/anaconda2] >>>
PREFIX=/home/JIAO/anaconda2
installing: python-2.7.13-0 ...
installing: _license-1.1-py27_1 ...
installing: alabaster-0.7.9-py27_0 ...
installing: anaconda-client-1.6.0-py27_0 ...
installing: anaconda-navigator-1.4.3-py27_0 ...
installing: argcomplete-1.0.0-py27_1 ...
installing: astroid-1.4.9-py27_0 ...
installing: astropy-1.3-np111py27_0 ...
installing: babel-2.3.4-py27_0 ...
installing: backports-1.0-py27_0 ...
installing: backports_abc-0.5-py27_0 ...
installing: beautifulsoup4-4.5.3-py27_0 ...
installing: bitarray-0.8.1-py27_0 ...
installing: blaze-0.10.1-py27_0 ...
installing: bokeh-0.12.4-py27_0 ...
installing: boto-2.45.0-py27_0 ...
installing: bottleneck-1.2.0-np111py27_0
4000
...
installing: cairo-1.14.8-0 ...
installing: cdecimal-2.3-py27_2 ...
installing: cffi-1.9.1-py27_0 ...
installing: chardet-2.3.0-py27_0 ...
installing: chest-0.2.3-py27_0 ...
installing: click-6.7-py27_0 ...
installing: cloudpickle-0.2.2-py27_0 ...
installing: clyent-1.2.2-py27_0 ...
installing: colorama-0.3.7-py27_0 ...
installing: configobj-5.0.6-py27_0 ...
installing: configparser-3.5.0-py27_0 ...
installing: contextlib2-0.5.4-py27_0 ...
installing: cryptography-1.7.1-py27_0 ...
installing: curl-7.52.1-0 ...
installing: cycler-0.10.0-py27_0 ...
installing: cython-0.25.2-py27_0 ...
installing: cytoolz-0.8.2-py27_0 ...
installing: dask-0.13.0-py27_0 ...
installing: datashape-0.5.4-py27_0 ...
installing: dbus-1.10.10-0 ...
installing: decorator-4.0.11-py27_0 ...
installing: dill-0.2.5-py27_0 ...
installing: docutils-0.13.1-py27_0 ...
installing: entrypoints-0.2.2-py27_0 ...
installing: enum34-1.1.6-py27_0 ...
installing: et_xmlfile-1.0.1-py27_0 ...
installing: expat-2.1.0-0 ...
installing: fastcache-1.0.2-py27_1 ...
installing: flask-0.12-py27_0 ...
installing: flask-cors-3.0.2-py27_0 ...
installing: fontconfig-2.12.1-2 ...
installing: freetype-2.5.5-2 ...
installing: funcsigs-1.0.2-py27_0 ...
installing: functools32-3.2.3.2-py27_0 ...
installing: futures-3.0.5-py27_0 ...
installing: get_terminal_size-1.0.0-py27_0 ...
installing: gevent-1.2.1-py27_0 ...
installing: glib-2.50.2-1 ...
installing: greenlet-0.4.11-py27_0 ...
installing: grin-1.2.1-py27_3 ...
installing: gst-plugins-base-1.8.0-0 ...
installing: gstreamer-1.8.0-0 ...
installing: h5py-2.6.0-np111py27_2 ...
installing: harfbuzz-0.9.39-2 ...
installing: hdf5-1.8.17-1 ...
installing: heapdict-1.0.0-py27_1 ...
installing: icu-54.1-0 ...
installing: idna-2.2-py27_0 ...
installing: imagesize-0.7.1-py27_0 ...
installing: ipaddress-1.0.18-py27_0 ...
installing: ipykernel-4.5.2-py27_0 ...
installing: ipython-5.1.0-py27_0 ...
installing: ipython_genutils-0.1.0-py27_0 ...
installing: ipywidgets-5.2.2-py27_1 ...
installing: isort-4.2.5-py27_0 ...
installing: itsdangerous-0.24-py27_0 ...
installing: jbig-2.1-0 ...
installing: jdcal-1.3-py27_0 ...
installing: jedi-0.9.0-py27_1 ...
installing: jinja2-2.9.4-py27_0 ...
installing: jpeg-9b-0 ...
installing: jsonschema-2.5.1-py27_0 ...
installing: jupyter-1.0.0-py27_3 ...
installing: jupyter_client-4.4.0-py27_0 ...
installing: jupyter_console-5.0.0-py27_0 ...
installing: jupyter_core-4.2.1-py27_0 ...
installing: lazy-object-proxy-1.2.2-py27_0 ...
installing: libffi-3.2.1-1 ...
installing: libgcc-4.8.5-2 ...
installing: libgfortran-3.0.0-1 ...
installing: libiconv-1.14-0 ...
installing: libpng-1.6.27-0 ...
installing: libsodium-1.0.10-0 ...
installing: libtiff-4.0.6-3 ...
installing: libxcb-1.12-1 ...
installing: libxml2-2.9.4-0 ...
installing: libxslt-1.1.29-0 ...
installing: llvmlite-0.15.0-py27_0 ...
installing: locket-0.2.0-py27_1 ...
installing: lxml-3.7.2-py27_0 ...
installing: markupsafe-0.23-py27_2 ...
installing: matplotlib-2.0.0-np111py27_0 ...
installing: mistune-0.7.3-py27_0 ...
installing: mkl-2017.0.1-0 ...
installing: mkl-service-1.1.2-py27_3 ...
installing: mpmath-0.19-py27_1 ...
installing: multipledispatch-0.4.9-py27_0 ...
installing: nbconvert-4.2.0-py27_0 ...
installing: nbformat-4.2.0-py27_0 ...
installing: networkx-1.11-py27_0 ...
installing: nltk-3.2.2-py27_0 ...
installing: nose-1.3.7-py27_1 ...
installing: notebook-4.3.1-py27_0 ...
installing: numba-0.30.1-np111py27_0 ...
installing: numexpr-2.6.1-np111py27_2 ...
installing: numpy-1.11.3-py27_0 ...
installing: numpydoc-0.6.0-py27_0 ...
installing: odo-0.5.0-py27_1 ...
installing: openpyxl-2.4.1-py27_0 ...
installing: openssl-1.0.2k-0 ...
installing: pandas-0.19.2-np111py27_1 ...
installing: partd-0.3.7-py27_0 ...
installing: path.py-10.0-py27_0 ...
installing: pathlib2-2.2.0-py27_0 ...
installing: patsy-0.4.1-py27_0 ...
installing: pcre-8.39-1 ...
installing: pep8-1.7.0-py27_0 ...
installing: pexpect-4.2.1-py27_0 ...
installing: pickleshare-0.7.4-py27_0 ...
installing: pillow-4.0.0-py27_0 ...
installing: pip-9.0.1-py27_1 ...
installing: pixman-0.34.0-0 ...
installing: ply-3.9-py27_0 ...
installing: prompt_toolkit-1.0.9-py27_0 ...
installing: psutil-5.0.1-py27_0 ...
installing: ptyprocess-0.5.1-py27_0 ...
installing: py-1.4.32-py27_0 ...
installing: pyasn1-0.1.9-py27_0 ...
installing: pycairo-1.10.0-py27_0 ...
installing: pycosat-0.6.1-py27_1 ...
installing: pycparser-2.17-py27_0 ...
installing: pycrypto-2.6.1-py27_4 ...
installing: pycurl-7.43.0-py27_2 ...
installing: pyflakes-1.5.0-py27_0 ...
installing: pygments-2.1.3-py27_0 ...
installing: pylint-1.6.4-py27_1 ...
installing: pyopenssl-16.2.0-py27_0 ...
installing: pyparsing-2.1.4-py27_0 ...
installing: pyqt-5.6.0-py27_2 ...
installing: pytables-3.3.0-np111py27_0 ...
installing: pytest-3.0.5-py27_0 ...
installing: python-dateutil-2.6.0-py27_0 ...
installing: pytz-2016.10-py27_0 ...
installing: pyyaml-3.12-py27_0 ...
installing: pyzmq-16.0.2-py27_0 ...
installing: qt-5.6.2-3 ...
installing: qtawesome-0.4.3-py27_0 ...
installing: qtconsole-4.2.1-py27_1 ...
installing: qtpy-1.2.1-py27_0 ...
installing: readline-6.2-2 ...
installing: redis-3.2.0-0 ...
installing: redis-py-2.10.5-py27_0 ...
installing: requests-2.12.4-py27_0 ...
installing: rope-0.9.4-py27_1 ...
installing: scandir-1.4-py27_0 ...
installing: scikit-image-0.12.3-np111py27_1 ...
installing: scikit-learn-0.18.1-np111py27_1 ...
installing: scipy-0.18.1-np111py27_1 ...
installing: seaborn-0.7.1-py27_0 ...
installing: setuptools-27.2.0-py27_0 ...
installing: simplegeneric-0.8.1-py27_1 ...
installing: singledispatch-3.4.0.3-py27_0 ...
installing: sip-4.18-py27_0 ...
installing: six-1.10.0-py27_0 ...
installing: snowballstemmer-1.2.1-py27_0 ...
installing: sockjs-tornado-1.0.3-py27_0 ...
installing: sphinx-1.5.1-py27_0 ...
installing: spyder-3.1.2-py27_0 ...
installing: sqlalchemy-1.1.5-py27_0 ...
installing: sqlite-3.13.0-0 ...
installing: ssl_match_hostname-3.4.0.2-py27_1 ...
installing: statsmodels-0.6.1-np111py27_1 ...
installing: subprocess32-3.2.7-py27_0 ...
installing: sympy-1.0-py27_0 ...
installing: terminado-0.6-py27_0 ...
installing: tk-8.5.18-0 ...
installing: toolz-0.8.2-py27_0 ...
installing: tornado-4.4.2-py27_0 ...
installing: traitlets-4.3.1-py27_0 ...
installing: unicodecsv-0.14.1-py27_0 ...
installing: wcwidth-0.1.7-py27_0 ...
installing: werkzeug-0.11.15-py27_0 ...
installing: wheel-0.29.0-py27_0 ...
installing: widgetsnbextension-1.2.6-py27_0 ...
installing: wrapt-1.10.8-py27_0 ...
installing: xlrd-1.0.0-py27_0 ...
installing: xlsxwriter-0.9.6-py27_0 ...
installing: xlwt-1.2.0-py27_0 ...
installing: xz-5.2.2-1 ...
installing: yaml-0.1.6-0 ...
installing: zeromq-4.1.5-0 ...
installing: zlib-1.2.8-3 ...
installing: anaconda-4.3.0-np111py27_0 ...
installing: ruamel_yaml-0.11.14-py27_1 ...
installing: conda-4.3.8-py27_0 ...
installing: conda-env-2.6.0-0 ...
Python 2.7.13 :: Continuum Analytics, Inc.
creating default environment...
installation finished.
WARNING:
You currently have a PYTHONPATH environment variable set. This may cause
unexpected behavior when running the Python interpreter in Anaconda2.
For best results, please verify that your PYTHONPATH only points to
directories of packages that are compatible with the Python interpreter
in Anaconda2: /home/JIAO/anaconda2
Do you wish the installer to prepend the Anaconda2 install location
to PATH in your /home/JIAO/.bashrc ? [yes|no]
[no] >>> yes

Prepending PATH=/home/JIAO/anaconda2/bin to PATH in /home/JIAO/.bashrc
A backup will be made to: /home/JIAO/.bashrc-anaconda2.bak

For this change to become active, you have to open a new terminal.

Thank you for installing Anaconda2!

Share your notebooks and packages on Anaconda Cloud!
Sign up for free: https://anaconda.org
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ subl $HOME/.bashrc
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ source $HOME/.bashrc
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ conda list
# packages in environment at /home/JIAO/anaconda2:
#
_license 1.1 py27_1
alabaster 0.7.9 py27_0
anaconda 4.3.0 np111py27_0
anaconda-client 1.6.0 py27_0
anaconda-navigator 1.4.3 py27_0
argcomplete 1.0.0 py27_1
astroid 1.4.9 py27_0
astropy 1.3 np111py27_0
babel 2.3.4 py27_0
backports 1.0 py27_0
backports_abc 0.5 py27_0
beautifulsoup4 4.5.3 py27_0
bitarray 0.8.1 py27_0
blaze 0.10.1 py27_0
bokeh 0.12.4 py27_0
boto 2.45.0 py27_0
bottleneck 1.2.0 np111py27_0
cairo 1.14.8 0
cdecimal 2.3 py27_2
cffi 1.9.1 py27_0
chardet 2.3.0 py27_0
chest 0.2.3 py27_0
click 6.7 py27_0
cloudpickle 0.2.2 py27_0
clyent 1.2.2 py27_0
colorama 0.3.7 py27_0
conda 4.3.8 py27_0
conda-env 2.6.0 0
configobj 5.0.6 py27_0
configparser 3.5.0 py27_0
contextlib2 0.5.4 py27_0
cryptography 1.7.1 py27_0
curl 7.52.1 0
cycler 0.10.0 py27_0
cython 0.25.2 py27_0
cytoolz 0.8.2 py27_0
dask 0.13.0 py27_0
datashape 0.5.4 py27_0
dbus 1.10.10 0
decorator 4.0.11 py27_0
dill 0.2.5 py27_0
docutils 0.13.1 py27_0
entrypoints 0.2.2 py27_0
enum34 1.1.6 py27_0
et_xmlfile 1.0.1 py27_0
expat 2.1.0 0
fastcache 1.0.2 py27_1
flask 0.12 py27_0
flask-cors 3.0.2 py27_0
fontconfig 2.12.1 2
freetype 2.5.5 2
funcsigs 1.0.2 py27_0
functools32 3.2.3.2 py27_0
futures 3.0.5 py27_0
get_terminal_size 1.0.0 py27_0
gevent 1.2.1 py27_0
glib 2.50.2 1
greenlet 0.4.11 py27_0
grin 1.2.1 py27_3
gst-plugins-base 1.8.0 0
gstreamer 1.8.0 0
h5py 2.6.0 np111py27_2
harfbuzz 0.9.39 2
hdf5 1.8.17 1
heapdict 1.0.0 py27_1
icu 54.1 0
idna 2.2 py27_0
imagesize 0.7.1 py27_0
ipaddress 1.0.18 py27_0
ipykernel 4.5.2 py27_0
ipython 5.1.0 py27_0
ipython_genutils 0.1.0 py27_0
ipywidgets 5.2.2 py27_1
isort 4.2.5 py27_0
itsdangerous 0.24 py27_0
jbig 2.1 0
jdcal 1.3 py27_0
jedi 0.9.0 py27_1
jinja2 2.9.4 py27_0
jpeg 9b 0
jsonschema 2.5.1 py27_0
jupyter 1.0.0 py27_3
jupyter_client 4.4.0 py27_0
jupyter_console 5.0.0 py27_0
jupyter_core 4.2.1 py27_0
lazy-object-proxy 1.2.2 py27_0
libffi 3.2.1 1
libgcc 4.8.5 2
libgfortran 3.0.0 1
libiconv 1.14 0
libpng 1.6.27 0
libsodium 1.0.10 0
libtiff 4.0.6 3
libxcb 1.12 1
libxml2 2.9.4 0
libxslt 1.1.29 0
llvmlite 0.15.0 py27_0
locket 0.2.0 py27_1
lxml 3.7.2 py27_0
markupsafe 0.23 py27_2
matplotlib 2.0.0 np111py27_0
mistune 0.7.3 py27_0
mkl 2017.0.1 0
mkl-service 1.1.2 py27_3
mpmath 0.19 py27_1
multipledispatch 0.4.9 py27_0
nbconvert 4.2.0 py27_0
nbformat 4.2.0 py27_0
networkx 1.11 py27_0
nltk 3.2.2 py27_0
nose 1.3.7 py27_1
notebook 4.3.1 py27_0
numba 0.30.1 np111py27_0
numexpr 2.6.1 np111py27_2
numpy 1.11.3 py27_0
numpydoc 0.6.0 py27_0
odo 0.5.0 py27_1
openpyxl 2.4.1 py27_0
openssl 1.0.2k 0
pandas 0.19.2 np111py27_1
partd 0.3.7 py27_0
path.py 10.0 py27_0
pathlib2 2.2.0 py27_0
patsy 0.4.1 py27_0
pcre 8.39 1
pep8 1.7.0 py27_0
pexpect 4.2.1 py27_0
pickleshare 0.7.4 py27_0
pillow 4.0.0 py27_0
pip 9.0.1 py27_1
pixman 0.34.0 0
ply 3.9 py27_0
prompt_toolkit 1.0.9 py27_0
psutil 5.0.1 py27_0
ptyprocess 0.5.1 py27_0
py 1.4.32 py27_0
pyasn1 0.1.9 py27_0
p
24000
ycairo 1.10.0 py27_0
pycosat 0.6.1 py27_1
pycparser 2.17 py27_0
pycrypto 2.6.1 py27_4
pycurl 7.43.0 py27_2
pyflakes 1.5.0 py27_0
pygments 2.1.3 py27_0
pylint 1.6.4 py27_1
pyopenssl 16.2.0 py27_0
pyparsing 2.1.4 py27_0
pyqt 5.6.0 py27_2
pytables 3.3.0 np111py27_0
pytest 3.0.5 py27_0
python 2.7.13 0
python-dateutil 2.6.0 py27_0
pytz 2016.10 py27_0
pyyaml 3.12 py27_0
pyzmq 16.0.2 py27_0
qt 5.6.2 3
qtawesome 0.4.3 py27_0
qtconsole 4.2.1 py27_1
qtpy 1.2.1 py27_0
readline 6.2 2
redis 3.2.0 0
redis-py 2.10.5 py27_0
requests 2.12.4 py27_0
rope 0.9.4 py27_1
ruamel_yaml 0.11.14 py27_1
scandir 1.4 py27_0
scikit-image 0.12.3 np111py27_1
scikit-learn 0.18.1 np111py27_1
scipy 0.18.1 np111py27_1
seaborn 0.7.1 py27_0
setuptools 27.2.0 py27_0
simplegeneric 0.8.1 py27_1
singledispatch 3.4.0.3 py27_0
sip 4.18 py27_0
six 1.10.0 py27_0
snowballstemmer 1.2.1 py27_0
sockjs-tornado 1.0.3 py27_0
sphinx 1.5.1 py27_0
spyder 3.1.2 py27_0
sqlalchemy 1.1.5 py27_0
sqlite 3.13.0 0
ssl_match_hostname 3.4.0.2 py27_1
statsmodels 0.6.1 np111py27_1
subprocess32 3.2.7 py27_0
sympy 1.0 py27_0
terminado 0.6 py27_0
tk 8.5.18 0
toolz 0.8.2 py27_0
tornado 4.4.2 py27_0
traitlets 4.3.1 py27_0
unicodecsv 0.14.1 py27_0
wcwidth 0.1.7 py27_0
werkzeug 0.11.15 py27_0
wheel 0.29.0 py27_0
widgetsnbextension 1.2.6 py27_0
wrapt 1.10.8 py27_0
xlrd 1.0.0 py27_0
xlsxwriter 0.9.6 py27_0
xlwt 1.2.0 py27_0
xz 5.2.2 1
yaml 0.1.6 0
zeromq 4.1.5 0
zlib 1.2.8 3
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ python
Python 2.7.13 |Anaconda 4.3.0 (64-bit)| (default, Dec 20 2016, 23:09:15)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org >>>
[1]+ 已停止 python
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ python --version
Python 2.7.13 :: Anaconda 4.3.0 (64-bit)
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ conda install openblas
Fetching package metadata ...
......
Solving package specifications: .

Package plan for installation in environment /home/JIAO/anaconda2:

The following NEW packages will be INSTALLED:

openblas: 0.2.19-0

The following packages will be UPDATED:

conda: 4.3.8-py27_0 --> 4.3.30-py27h6ae6dc7_0

Proceed ([y]/n)?
openblas-0.2.1 100% |################################| Time: 0:00:07 446.81 kB/s
conda-4.3.30-p 100% |################################| Time: 0:00:00 997.60 kB/s
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ ln -s $HOME/anaconda2/lib/libopenblas.so.0 /usr/lib64/libopenblas.so.0
ln: 无法创建符号链接'/usr/lib64/libopenblas.so.0': 没有那个文件或目录
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ ln -s $HOME/anaconda2/lib/libopenblas.so.0 /usr/lib/libopenblas.so.0
ln: 无法创建符号链接'/usr/lib/libopenblas.so.0': 文件已存在
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/下载$ cd
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ git clone https://github.com/facebookresearch/faiss.git 正克隆到 'faiss'...
remote: Counting objects: 4924, done.
remote: Compressing objects: 100% (7/7), done.
remote: Total 4924 (delta 0), reused 1 (delta 0), pack-reused 4917
接收对象中: 100% (4924/4924), 8.17 MiB | 181.00 KiB/s, 完成.
处理 delta 中: 100% (4234/4234), 完成.
检查连接... 完成。
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ cd faiss/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ cp example_makefiles/makefile.inc.Linux ./makefile.inc
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make tests/test_blas
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp tests/test_blas.cpp -o tests/test_blas -g -fPIC -fopenmp /usr/lib64/libopenblas.so.0 -DFINTEGER=int
g++: error: /usr/lib64/libopenblas.so.0: 没有那个文件或目录
Makefile:59: recipe for target 'tests/test_blas' failed
make: *** [tests/test_blas] Error 1
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make tests/test_blas
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp tests/test_blas.cpp -o tests/test_blas -g -fPIC -fopenmp /usr/lib/libopenblas.so.0 -DFINTEGER=int
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ ./tests/test_blas
BLAS test
errors=
0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 -0.000 0.000 0.000 0.000 0.000 0.000 -0.000 0.000 0.000 0.000 0.000 -0.000 0.000 0.000 -0.000 -0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 -0.000 0.000 -0.000 -0.000 0.000 0.000 0.000 0.000 -0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 -0.000 0.000 -0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 -0.000 0.000 -0.000 0.000 -0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.000
0.000 0.000 0.000 0.000 0.000 -0.000 0.000 0.000 0.000 -0.000 -0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.000 0.000
0.000 0.000 -0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.000 0.000 0.000 0.000 -0.000 0.000 0.000 0.000 0.000 -0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.000 -0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 -0.000 0.000 -0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.000 -0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 -0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.000 0.000 0.000 0.000 0.000 0.000
Intentional Lapack error (appears only for 64-bit INTEGER):
info=0000064b00000000
Lapack uses 32-bit integers
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c hamming.cpp -o hamming.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c utils.cpp -o utils.o -DFINTEGER=int
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexFlat.cpp -o IndexFlat.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexIVF.cpp -o IndexIVF.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexLSH.cpp -o IndexLSH.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexPQ.cpp -o IndexPQ.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexIVFPQ.cpp -o IndexIVFPQ.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c Clustering.cpp -o Clustering.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c Heap.cpp -o Heap.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c VectorTransform.cpp -o VectorTransform.o -DFINTEGER=int
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c index_io.cpp -o index_io.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c PolysemousTraining.cpp -o PolysemousTraining.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c MetaIndexes.cpp -o MetaIndexes.o
MetaIndexes.cpp:286:6: warning: ‘void faiss::{anonymous}::translate_labels(long int, faiss::{anonymous}::idx_t*, long int)’ defined but not used [-Wunused-function]
void translate_labels (long n, idx_t *labels, long translation)
^
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c Index.cpp -o Index.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c ProductQuantizer.cpp -o ProductQuantizer.o -DFINTEGER=int
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c AutoTune.cpp -o AutoTune.o
AutoTune.cpp: In member function ‘virtual void faiss::ParameterSpace::initialize(const faiss::Index*)’:
AutoTune.cpp:309:22: warning: unused variable ‘ix’ [-Wunused-variable]
const classname *ix = dynamic_cast<const classname *>(index)
^
AutoTune.cpp:389:9: note: in expansion of macro ‘DC’
if (DC (IndexIVFPQR)) {
^
AutoTune.cpp: In function ‘faiss::Index* faiss::index_factory(int, const char*, faiss::MetricType)’:
AutoTune.cpp:706:40: warning: unused variable ‘pq_m’ [-Wunused-variable]
int d_out, opq_M, nbit, M, M2, pq_m, ncent;
^
AutoTune.cpp:706:46: warning: unused variable ‘ncent’ [-Wunused-variable]
int d_out, opq_M, nbit, M, M2, pq_m, ncent;
^
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c AuxIndexStructures.cpp -o AuxIndexStructures.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexScalarQuantizer.cpp -o IndexScalarQuantizer.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c FaissException.cpp -o FaissException.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexHNSW.cpp -o IndexHNSW.o -DFINTEGER=int
IndexHNSW.cpp: In member function ‘void faiss::ReconstructFromNeighbors::estimate_code(const float*, faiss::ReconstructFromNeighbors::storage_idx_t, uint8_t*) const’:
IndexHNSW.cpp:1545:13: warning: unused variable ‘d1’ [-Wunused-variable]
int d1 = d0 + dsub;
^
IndexHNSW.cpp: In function ‘int faiss::{anonymous}::search_from_candidates_2(const faiss::HNSW&, faiss::{anonymous}::DistanceComputer&, int, faiss::{anonymous}::idx_t*, float*, faiss::{anonymous}::MinimaxHeap&, faiss::VisitedTable&, int, int)’:
IndexHNSW.cpp:2001:15: warning: unused variable ‘d’ [-Wunused-variable]
float d = candidates.dis[i];
^
ar r libfaiss.a hamming.o utils.o IndexFlat.o IndexIVF.o IndexLSH.o IndexPQ.o IndexIVFPQ.o Clustering.o Heap.o VectorTransform.o index_io.o PolysemousTraining.o MetaIndexes.o Index.o ProductQuantizer.o AutoTune.o AuxIndexStructures.o IndexScalarQuantizer.o FaissException.o IndexHNSW.o
ar: 正在创建 libfaiss.a
g++ -o tests/demo_ivfpq_indexing -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp tests/demo_ivfpq_indexing.cpp libfaiss.a -g -fPIC -fopenmp /usr/lib/libopenblas.so.0
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ ./tests/demo_ivfpq_indexing
[0.000 s] Generating 100000 vectors in 128D for training
[0.773 s] Training the index
Training level-1 quantizer
Training level-1 quantizer on 100000 vectors in 128D
Training IVF residual
Input training set too big (max size is 65536), sampling 65536 / 100000 vectors
computing residuals
training 4x256 product quantizer on 65536 vectors in 128D
Training PQ slice 0/4
Clustering 65536 points in 32D to 256 clusters, redo 1 times, 25 iterations
Preprocessing in 0.01 s
Iteration 0 (0.40 s, search 0.37 s): objective=151134 imbalance=2.083 nsplit=0 Iteration 1 (0.83 s, search 0.75 s): objective=116917 imbalance=1.060 nsplit=0 Iteration 2 (1.27 s, search 1.16 s): objective=115683 imbalance=1.017 nsplit=0 Iteration 3 (1.60 s, search 1.46 s): objective=115138 imbalance=1.010 nsplit=0 Iteration 4 (2.16 s, search 2.00 s): objective=114789 imbalance=1.008 nsplit=0 Iteration 5 (2.57 s, search 2.40 s): objective=114542 imbalance=1.007 nsplit=0 Iteration 6 (3.04 s, search 2.86 s): objective=114347 imbalance=1.006 nsplit=0 Iteration 7 (3.50 s, search 3.30 s): objective=114192 imbalance=1.005 nsplit=0 Iteration 8 (3.94 s, search 3.71 s): objective=114064 imbalance=1.005 nsplit=0 Iteration 9 (4.46 s, search 4.21 s): objective=113960 imbalance=1.005 nsplit=0 Iteration 10 (4.97 s, search 4.71 s): objective=113873 imbalance=1.005 nsplit= Iteration 11 (5.34 s, search 5.05 s): objective=113797 imbalance=1.005 nsplit= Iteration 12 (5.86 s, search 5.54 s): objective=113731 imbalance=1.005 nsplit= Iteration 13 (6.40 s, search 6.06 s): objective=113676 imbalance=1.005 nsplit= Iteration 14 (6.90 s, search 6.55 s): objective=113624 imbalance=1.005 nsplit= Iteration 15 (7.41 s, search 7.04 s): objective=113579 imbalance=1.005 nsplit= Iteration 16 (7.96 s, search 7.56 s): objective=113537 imbalance=1.005 nsplit= Iteration 17 (8.35 s, search 7.91 s): objective=113504 imbalance=1.005 nsplit= Iteration 18 (8.81 s, search 8.34 s): objective=113474 imbalance=1.005 nsplit= Iteration 19 (9.37 s, search 8.88 s): objective=113446 imbalance=1.004 nsplit= Iteration 20 (9.85 s, search 9.33 s): objective=113420 imbalance=1.004 nsplit= Iteration 21 (10.35 s, search 9.79 s): objective=113399 imbalance=1.004 nsplit Iteration 22 (10.87 s, search 10.27 s): objective=113378 imbalance=1.004 nspli Iteration 23 (11.26 s, search 10.63 s): objective=113358 imbalance=1.004 nspli Iteration 24 (11.74 s, search 11.09 s): objective=113338 imbalance=1.004 nsplit=0
Training PQ slice 1/4
Clustering 65536 points in 32D to 256 clusters, redo 1 times, 25 iterations
Preprocessing in 0.00 s
Iteration 0 (0.33 s, search 0.30 s): objective=149183 imbalance=2.952 nsplit=0 Iteration 1 (0.69 s, search 0.62 s): objective=116843 imbalance=1.067 nsplit=0 Iteration 2 (1.15 s, search 1.06 s): objective=115476 imbalance=1.016 nsplit=0 Iteration 3 (1.66 s, search 1.55 s): objective=114921 imbalance=1.009 nsplit=0 Iteration 4 (1.99 s, search 1.85 s): objective=114560 imbalance=1.006 nsplit=0 Iteration 5 (2.54 s, search 2.38 s): objective=114312 imbalance=1.006 nsplit=0 Iteration 6 (3.11 s, search 2.94 s): objective=114126 imbalance=1.005 nsplit=0 Iteration 7 (3.58 s, search 3.37 s): objective=113985 imbalance=1.005 nsplit=0 Iteration 8 (4.13 s, search 3.91 s): objective=113872 imbalance=1.005 nsplit=0 Iteration 9 (4.67 s, search 4.43 s): objective=113781 imbalance=1.005 nsplit=0 Iteration 10 (5.20 s, search 4.93 s): objective=113701 imbalance=1.005 nsplit= Iteration 11 (5.74 s, search 5.43 s): objective=113630 imbalance=1.004 nsplit= Iteration 12 (6.17 s, search 5.84 s): objective=113570 imbalance=1.004 nsplit= Iteration 13 (6.70 s, search 6.35 s): objective=113517 imbalance=1.004 nsplit= Iteration 14 (7.05 s, search 6.66 s): objective=113469 imbalance=1.004 nsplit= Iteration 15 (7.57 s, search 7.16 s): objective=113423 imbalance=1.004 nsplit= Iteration 16 (8.04 s, search 7.62 s): objective=113385 imbalance=1.004 nsplit= Iteration 17 (8.55 s, search 8.09 s): objective=113351 imbalance=1.004 nsplit= Iteration 18 (9.05 s, search 8.58 s): objective=113323 imbalance=1.004 nsplit= Iteration 19 (9.56 s, search 9.05 s): objective=113297 imbalance=1.004 nsplit= Iteration 20 (10.08 s, search 9.56 s): objective=113273 imbalance=1.004 nsplit Iteration 21 (10.61 s, search 10.05 s): objective=113248 imbalance=1.004 nspli Iteration 22 (11.05 s, search 10.47 s): objective=113226 imbalance=1.004 nspli Iteration 23 (11.63 s, search 11.02 s): objective=113207 imbalance=1.004 nspli Iteration 24 (12.13 s, search 11.49 s): objective=113188 imbalance=1.004 nsplit=0
Training PQ slice 2/4
Clustering 65536 points in 32D to 256 clusters, redo 1 times, 25 iterations
Preprocessing in 0.00 s
Iteration 0 (0.56 s, search 0.53 s): objective=152236 imbalance=2.501 nsplit=0 Iteration 1 (0.96 s, search 0.89 s): objective=116683 imbalance=1.055 nsplit=0 Iteration 2 (1.53 s, search 1.43 s): objective=115451 imbalance=1.018 nsplit=0 Iteration 3 (2.03 s, search 1.91 s): objective=114919 imbalance=1.012 nsplit=0 Iteration 4 (2.36 s, search 2.22 s): objective=114592 imbalance=1.011 nsplit=0 Iteration 5 (2.90 s, search 2.73 s): objective=114358 imbalance=1.010 nsplit=0 Iteration 6 (3.41 s, search 3.23 s): objective=114184 imbalance=1.009 nsplit=0 Iteration 7 (3.81 s, search 3.61 s): objective=114042 imbalance=1.009 nsplit=0 Iteration 8 (4.31 s, search 4.08 s): objective=113925 imbalance=1.009 nsplit=0 Iteration 9 (4.83 s, search 4.55 s): objective=113826 imbalance=1.009 nsplit=0 Iteration 10 (5.40 s, search 5.09 s): objective=113746 imbalance=1.009 nsplit= Iteration 11 (5.79 s, search 5.45 s): objective=113681 imbalance=1.009 nsplit= Iteration 12 (6.29 s, search 5.91 s): objective=113621 imbalance=1.009 nsplit= Iteration 13 (6.85 s, search 6.44 s): objective=113570 imbalance=1.009 nsplit= Iteration 14 (7.35 s, search 6.93 s): objective=113522 imbalance=1.008 nsplit= Iteration 15 (7.71 s, search 7.25 s): objective=113480 imbalance=1.008 nsplit= Iteration 16 (8.20 s, search 7.72 s): objective=113439 imbalance=1.008 nsplit= Iteration 17 (8.62 s, search 8.13 s): objective=113402 imbalance=1.008 nsplit= Iteration 18 (9.13 s, search 8.62 s): objective=113368 imbalance=1.009 nsplit= Iteration 19 (9.62 s, search 9.07 s): objective=113342 imbalance=1.009 nsplit= Iteration 20 (9.99 s, search 9.41 s): objective=113315 imbalance=1.009 nsplit= Iteration 21 (10.51 s, search 9.91 s): objective=113293 imbalance=1.009 nsplit Iteration 22 (11.01 s, search 10.39 s): objective=113269 imbalance=1.008 nspli Iteration 23 (11.47 s, search 10.84 s): objective=113248 imbalance=1.008 nspli Iteration 24 (12.00 s, search 11.35 s): objective=113227 imbalance=1.009 nsplit=0
Training PQ slice 3/4
Clustering 65536 points in 32D to 256 clusters, redo 1 times, 25 iterations
Preprocessing in 0.00 s
Iteration 0 (0.52 s, search 0.49 s): objective=150530 imbalance=2.238 nsplit=0 Iteration 1 (1.02 s, search 0.96 s): objective=116725 imbalance=1.062 nsplit=0 Iteration 2 (1.54 s, search 1.45 s): objective=115446 imbalance=1.015 nsplit=0 Iteration 3 (2.03 s, search 1.92 s): objective=114878 imbalance=1.008 nsplit=0 Iteration 4 (2.54 s, search 2.41 s): objective=114517 imbalance=1.006 nsplit=0 Iteration 5 (3.06 s, search 2.91 s): objective=114254 imbalance=1.006 nsplit=0 Iteration 6 (3.58 s, search 3.42 s): objective=114056 imbalance=1.005 nsplit=0 Iteration 7 (4.03 s, search 3.83 s): objective=113899 imbalance=1.005 nsplit=0 Iteration 8 (4.49 s, search 4.27 s): objective=113769 imbalance=1.004 nsplit=0 Iteration 9 (5.02 s, search 4.78 s): objective=113664 imbalance=1.004 nsplit=0 Iteration 10 (5.55 s, search 5.30 s): objective=113574 imbalance=1.004 nsplit= Iteration 11 (6.07 s, search 5.79 s): objective=113496 imbalance=1.004 nsplit= Iteration 12 (6.60 s, search 6.29 s): objective=113431 imbalance=1.004 nsplit= Iteration 13 (7.13 s, search 6.80 s): objective=113375 imbalance=1.004 nsplit= Iteration 14 (7.69 s, search 7.33 s): objective=113326 imbalance=1.004 nsplit= Iteration 15 (8.22 s, search 7.83 s): objective=113283 imbalance=1.004 nsplit= Iteration 16 (8.78 s, search 8.37 s): objective=113243 imbalance=1.004 nsplit= Iteration 17 (9.11 s, search 8.67 s): objective=113210 imbalance=1.004 nsplit= Iteration 18 (9.63 s, search 9.16 s): objective=113181 imbalance=1.004 nsplit= Iteration 19 (10.14 s, search 9.65 s): objective=113154 imbalance=1.004 nsplit Iteration 20 (10.66 s, search 10.15 s): objective=113130 imbalance=1.004 nspli Iteration 21 (11.15 s, search 10.61 s): objective=113110 imbalance=1.004 nspli Iteration 22 (11.69 s, search 11.13 s): objective=113093 imbalance=1.004 nspli Iteration 23 (12.06 s, search 11.47 s): objective=113076 imbalance=1.004 nspli Iteration 24 (12.60 s, search 11.98 s): objective=113061 imbalance=1.004 nsplit=0
precomputing IVFPQ tables type 1
[64.371 s] storing the pre-trained index to /tmp/index_trained.faissindex
[64.372 s] Building a dataset of 200000 vectors to index
[64.570 s] Adding the vectors to the index
IndexIVFPQ::add_core_o: adding 0:32768 / 200000
add_core times: 653.988 316.309 2.517
IndexIVFPQ::add_core_o: adding 32768:65536 / 200000
add_core times: 539.748 234.671 1.499
IndexIVFPQ::add_core_o: adding 65536:98304 / 200000
add_core times: 456.647 202.689 1.470
IndexIVFPQ::add_core_o: adding 98304:131072 / 200000
add_core times: 490.088 262.767 1.160
IndexIVFPQ::add_core_o: adding 131072:163840 / 200000
add_core times: 584.106 292.540 1.238
IndexIVFPQ::add_core_o: adding 163840:196608 / 200000
add_core times: 248.710 245.927 1.318
IndexIVFPQ::add_core_o: adding 196608:200000 / 200000
add_core times: 92.542 243.516 0.176
[69.445 s] imbalance factor: 1.23893
[69.449 s] Searching the 5 nearest neighbors of 9 vectors in the index
[69.489 s] Query results (vector ids, then distances):
query 0: 1234 11667 163213 13439 83335
dis: 7.08558 9.57216 9.67245 9.83403 9.83821
query 1: 1235 56663 76478 28798 117320
dis: 7.78506 9.70143 10.1121 10.5115 10.6679
query 2: 1236 80604 189820 185497 81842
dis: 7.52846 11.0073 11.0746 11.1844 11.3107
query 3: 1237 116226 85618 187787 36842
dis: 8.04249 10.4582 10.6636 10.749 10.7599
query 4: 1238 91514 52306 86365 148470
dis: 7.36588 10.1087 10.144 10.2007 10.2791
query 5: 1239 143840 77650 2444 70772
dis: 7.19399 9.78232 9.94378 10.3445 10.4876
query 6: 1240 72767 170354 122756 61561
dis: 7.33832 9.7697 9.9693 10.0013 10.2586
query 7: 1241 30037 162441 194151 196333
dis: 8.14279 11.3589 11.6361 11.6927 11.7009
query 8: 1242 66419 10135 10268 3851
dis: 8.18552 10.7842 11.0947 11.2108 11.2265
note that the nearest neighbor is not at distance 0 due to quantization errors
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ ls
AutoTune.cpp Heap.o INSTALL.md
AutoTune.h Index.cpp libfaiss.a
AutoTune.o IndexFlat.cpp LICENSE
AuxIndexStructures.cpp IndexFlat.h Makefile
AuxIndexStructures.h IndexFlat.o makefile.inc
AuxIndexStructures.o Index.h MetaIndexes.cpp
benchs IndexHNSW.cpp MetaIndexes.h
Clustering.cpp IndexHNSW.h MetaIndexes.o
Clustering.h IndexHNSW.o PATENTS
Clustering.o index_io.cpp PolysemousTraining.cpp
cmake index_io.h PolysemousTraining.h
CMakeLists.txt index_io.o PolysemousTraining.o
CONTRIBUTING.md IndexIVF.cpp ProductQuantizer.cpp
Dockerfile IndexIVF.h ProductQuantizer.h
docs IndexIVF.o ProductQuantizer.o
example_makefiles IndexIVFPQ.cpp python
FaissAssert.h IndexIVFPQ.h README.md
FaissException.cpp IndexIVFPQ.o swigfaiss.swig
FaissException.h IndexLSH.cpp tests
FaissException.o IndexLSH.h tutorial
faiss.h IndexLSH.o utils.cpp
faiss.py Index.o utils.h
gpu IndexPQ.cpp utils.o
hamming.cpp IndexPQ.h VectorTransform.cpp
hamming.h IndexPQ.o VectorTransform.h
hamming.o IndexScalarQuantizer.cpp VectorTransform.o
Heap.cpp IndexScalarQuantizer.h
Heap.h IndexScalarQuantizer.o
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ wget ftp://ftp.irisa.fr/local/texmex/corpus/sift.tar.gz --2018-02-08 14:32:13-- ftp://ftp.irisa.fr/local/texmex/corpus/sift.tar.gz => “sift.tar.gz”
正在解析主机 ftp.irisa.fr (ftp.irisa.fr)... 131.254.254.45, 2001:660:7303:254::45
正在连接 ftp.irisa.fr (ftp.irisa.fr)|131.254.254.45|:21... 已连接。
正在以 anonymous 登录 ... 登录成功！
==> SYST ... 完成。 ==> PWD ... 完成。
==> TYPE I ... 完成。 ==> CWD (1) /local/texmex/corpus ... 完成。
==> SIZE sift.tar.gz ... 168280445
==> PASV ... 完成。 ==> RETR sift.tar.gz ... 完成。
长度：168280445 (160M) (非正式数据)

sift.tar.gz 100%[===================>] 160.48M 10.1MB/s in 17s

2018-02-08 14:32:34 (9.21 MB/s) - “sift.tar.gz” 已保存 [168280445]

JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ tar -xzvf sift.tar.gz
sift/
sift/sift_base.fvecs
sift/sift_groundtruth.ivecs
sift/sift_learn.fvecs
sift/sift_query.fvecs
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ mv sift sift1M
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make tests/demo_sift1M
g++ -o tests/demo_sift1M -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp tests/demo_sift1M.cpp libfaiss.a -g -fPIC -fopenmp /usr/lib/libopenblas.so.0
tests/demo_sift1M.cpp: In function ‘float* fvecs_read(const char*, size_t*, size_t*)’:
tests/demo_sift1M.cpp:50:33: warning: ignoring return value of ‘size_t fread(void*, size_t, size_t, FILE*)’, declared with attribute warn_unused_result [-Wunused-result]
fread(&d, 1, sizeof(int), f);
^
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ ./tests/demo_sift1M
[0.000 s] Loading train set
[0.043 s] Preparing index "IVF4096,Flat" d=128
[0.043 s] Training on 100000 vectors
WARNING clustering 100000 points to 4096 centroids: please provide at least 159744 training points
[24.819 s] Loading database
[25.063 s] Indexing database, size 1000000*128
[50.138 s] Loading queries
[50.139 s] Loading ground truth for 10000 queries
[50.143 s] Preparing auto-tune criterion 1-recall at 1 criterion, with k=100 nq=10000
[50.146 s] Preparing auto-tune parameters
[50.146 s] Auto-tuning over 1 parameters (12 combinations)
0/12: cno=0 nprobe=1 bounds [perf<=1.000 t>=0.000] perf 0.428 t 0.407 *
1/12: cno=11 nprobe=2048 bounds [perf<=1.000 t>=0.407] perf 0.992 t 66.673 *
2/12: cno=4 nprobe=16 bounds [perf<=0.992 t>=0.407] perf 0.887 t 1.267 *
3/12: cno=8 nprobe=256 bounds [perf<=0.992 t>=1.267] perf 0.992 t 10.369 *
4/12: cno=1 nprobe=2 bounds [perf<=0.887 t>=0.407] perf 0.560 t 0.553 *
5/12: cno=5 nprobe=32 bounds [perf<=0.992 t>=1.267] perf 0.948 t 1.859 *
6/12: cno=2 nprobe=4 bounds [perf<=0.887 t>=0.553] perf 0.685 t 0.709 *
7/12: cno=6 nprobe=64 bounds [perf<=0.992 t>=1.859] perf 0.976 t 3.167 *
8/12: cno=3 nprobe=8 bounds [perf<=0.887 t>=0.709] perf 0.799 t 0.891 *
9/12: cno=9 nprobe=512 bounds [perf<=0.992 t>=10.369] perf 0.993 t 19.099 *
10/12: cno=10 nprobe=1024 bounds [perf<=0.992 t>=19.099] skip
11/12: cno=7 nprobe=128 bounds [perf<=0.992 t>=3.167] perf 0.988 t 5.747 *
[160.904 s] Found the following operating points:
Tested 11 operating points, 11 ones are optimal:
cno=-1 key= perf=0.0000 t=0.000
cno=0 key=nprobe=1 perf=0.4285 t=0.407
cno=1 key=nprobe=2 perf=0.5603 t=0.553
cno=2 key=nprobe=4 perf=0.6851 t=0.709
cno=3 key=nprobe=8 perf=0.7993 t=0.891
cno=4 key=nprobe=16 perf=0.8869 t=1.267
cno=5 key=nprobe=32 perf=0.9478 t=1.859
cno=6 key=nprobe=64 perf=0.9764 t=3.167
cno=7 key=nprobe=128 perf=0.9876 t=5.747
cno=8 key=nprobe=256 perf=0.9918 t=10.369
cno=9 key=nprobe=512 perf=0.9927 t=19.099
[160.906 s] Setting parameter configuration "nprobe=2" on index
[160.906 s] Perform a search on 10000 queries
[161.445 s] Compute recalls
R@1 = 0.5603
R@10 = 0.5636
R@100 = 0.5636
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ ./tests/test_blas^C
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make py
python -c++ -Doverride= -o python/swigfaiss_wrap.cxx swigfaiss.swig
File "<string>", line 1
++
^
SyntaxError: invalid syntax
Makefile:79: recipe for target 'python/swigfaiss_wrap.cxx' failed
make: [python/swigfaiss_wrap.cxx] Error 1 (ignored)
g++ -I. -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -g -fPIC -fopenmp -I/home/JIAO/anaconda2/include/python2.7/ -I/home/JIAO/anaconda2/lib/python2.7/site-packages/numpy/core/include/ -shared \
-o python/_swigfaiss.so python/swigfaiss_wrap.cxx libfaiss.a /usr/lib/libopenblas.so.0
cp python/_swigfaiss.so python/swigfaiss.py .
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make py
python -c++ -Doverride= -o python/swigfaiss_wrap.cxx swigfaiss.swig
File "<string>", line 1
++
^
SyntaxError: invalid syntax
Makefile:79: recipe for target 'python/swigfaiss_wrap.cxx' failed
make: [python/swigfaiss_wrap.cxx] Error 1 (ignored)
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make py
python -c++ -Doverride= -o python/swigfaiss_wrap.cxx swigfaiss.swig
File "<string>", line 1
++
^
SyntaxError: invalid syntax
Makefile:79: recipe for target 'python/swigfaiss_wrap.cxx' failed
make: [python/swigfaiss_wrap.cxx] Error 1 (ignored)
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make clean
rm -f libfaiss.a libfaiss.so* *.o \
lua/swigfaiss.so lua/swigfaiss_wrap.cxx \
python/_swigfaiss.so python/swigfaiss_wrap.cxx \
python/swigfaiss.py _swigfaiss.so swigfaiss.py
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c hamming.cpp -o hamming.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c utils.cpp -o utils.o -DFINTEGER=int
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexFlat.cpp -o IndexFlat.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexIVF.cpp -o IndexIVF.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexLSH.cpp -o IndexLSH.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexPQ.cpp -o IndexPQ.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexIVFPQ.cpp -o IndexIVFPQ.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c Clustering.cpp -o Clustering.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c Heap.cpp -o Heap.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c VectorTransform.cpp -o VectorTransform.o -DFINTEGER=int
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c index_io.cpp -o index_io.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c PolysemousTraining.cpp -o PolysemousTraining.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c MetaIndexes.cpp -o MetaIndexes.o
MetaIndexes.cpp:286:6: warning: ‘void faiss::{anonymous}::translate_labels(long int, faiss::{anonymous}::idx_t*, long int)’ defined but not used [-Wunused-function]
void translate_labels (long n, idx_t *labels, long translation)
^
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c Index.cpp -o Index.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c ProductQuantizer.cpp -o ProductQuantizer.o -DFINTEGER=int
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c AutoTune.cpp -o AutoTune.o
AutoTune.cpp: In member function ‘virtual void faiss::ParameterSpace::initialize(const faiss::Index*)’:
AutoTune.cpp:309:22: warning: unused variable ‘ix’ [-Wunused-variable]
const classname *ix = dynamic_cast<const classname *>(index)
^
AutoTune.cpp:389:9: note: in expansion of macro ‘DC’
if (DC (IndexIVFPQR)) {
^
AutoTune.cpp: In function ‘faiss::Index* faiss::index_factory(int, const char*, faiss::MetricType)’:
AutoTune.cpp:706:40: warning: unused variable ‘pq_m’ [-Wunused-variable]
int d_out, opq_M, nbit, M, M2, pq_m, ncent;
^
AutoTune.cpp:706:46: warning: unused variable ‘ncent’ [-Wunused-variable]
int d_out, opq_M, nbit, M, M2, pq_m, ncent;
^
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c AuxIndexStructures.cpp -o AuxIndexStructures.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexScalarQuantizer.cpp -o IndexScalarQuantizer.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c FaissException.cpp -o FaissException.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexHNSW.cpp -o IndexHNSW.o -DFINTEGER=int
IndexHNSW.cpp: In member function ‘void faiss::ReconstructFromNeighbors::estimate_code(const float*, faiss::ReconstructFromNeighbors::storage_idx_t, uint8_t*) const’:
IndexHNSW.cpp:1545:13: warning: unused variable ‘d1’ [-Wunused-variable]
int d1 = d0 + dsub;
^
IndexHNSW.cpp: In function ‘int faiss::{anonymous}::search_from_candidates_2(const faiss::HNSW&, faiss::{anonymous}::DistanceComputer&, int, faiss::{anonymous}::idx_t*, float*, faiss::{anonymous}::MinimaxHeap&, faiss::VisitedTable&, int, int)’:
IndexHNSW.cpp:2001:15: warning: unused variable ‘d’ [-Wunused-variable]
float d = candidates.dis[i];
^
ar r libfaiss.a hamming.o utils.o IndexFlat.o IndexIVF.o IndexLSH.o IndexPQ.o IndexIVFPQ.o Clustering.o Heap.o VectorTransform.o index_io.o PolysemousTraining.o MetaIndexes.o Index.o ProductQuantizer.o AutoTune.o AuxIndexStructures.o IndexScalarQuantizer.o FaissException.o IndexHNSW.o
ar: 正在创建 libfaiss.a
g++ -o tests/demo_ivfpq_indexing -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp tests/demo_ivfpq_indexing.cpp libfaiss.a -g -fPIC -fopenmp /usr/lib/libopenblas.so.0
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ sudo apt-get install libopenblas-dev python-numpy python-dev
[sudo] JIAO 的密码：
对不起，请重试。
[sudo] JIAO 的密码：
正在读取软件包列表... 完成
正在分析软件包的依赖关系树
正在读取状态信息... 完成
python-dev 已经是最新版 (2.7.11-1)。
python-numpy 已经是最新版 (1:1.11.0-1ubuntu1)。
libopenblas-dev 已经是最新版 (0.2.18-1ubuntu1)。
升级了 0 个软件包，新安装了 0 个软件包，要卸载 0 个软件包，有 26 个软件包未被升级。
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make py
python -c++ -Doverride= -o python/swigfaiss_wrap.cxx swigfaiss.swig
File "<string>", line 1
++
^
SyntaxError: invalid syntax
Makefile:79: recipe for target 'python/swigfaiss_wrap.cxx' failed
make: [python/swigfaiss_wrap.cxx] Error 1 (ignored)
g++ -I. -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -g -fPIC -fopenmp -IOME/anaconda2/include/python2.7/ -IOME/anaconda2/lib/python2.7/site-packages/numpy/core/include/ -shared \
-o python/_swigfaiss.so python/swigfaiss_wrap.cxx libfaiss.a /usr/lib/libopenblas.so.0
g++: error: python/swigfaiss_wrap.cxx: 没有那个文件或目录
Makefile:84: recipe for target 'python/_swigfaiss.so' failed
make: *** [python/_swigfaiss.so] Error 1
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ cd python/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/python$ ls
swigfaiss_gpu.py swigfaiss_gpu_wrap.cxx
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/python$ git checkout swigfaiss_gpu_wrap.cxx swigfaiss_gpu.py swigfaiss_wrap.cxx swigfaiss.py
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/python$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/python$ cd
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ cd faiss/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make py
g++ -I. -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -g -fPIC -fopenmp -IOME/anaconda2/include/python2.7/ -IOME/anaconda2/lib/python2.7/site-packages/numpy/core/include/ -shared \
-o python/_swigfaiss.so python/swigfaiss_wrap.cxx libfaiss.a /usr/lib/libopenblas.so.0
python/swigfaiss_wrap.cxx:154:21: fatal error: Python.h: 没有那个文件或目录
compilation terminated.
Makefile:84: recipe for target 'python/_swigfaiss.so' failed
make: *** [python/_swigfaiss.so] Error 1
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ cd python/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/python$ ls
swigfaiss_gpu.py swigfaiss_gpu_wrap.cxx swigfaiss.py swigfaiss_wrap.cxx
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/python$ cd
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ cd faiss/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make clean
rm -f libfaiss.a libfaiss.so* *.o \
lua/swigfaiss.so lua/swigfaiss_wrap.cxx \
python/_swigfaiss.so python/swigfaiss_wrap.cxx \
python/swigfaiss.py _swigfaiss.so swigfaiss.py
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ cd python/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/python$ git checkout swigfaiss_gpu_wrap.cxx swigfaiss_gpu.py swigfaiss_wrap.cxx swigfaiss.py
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/python$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/python$ cd
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ cd faiss/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ makep y
未找到 'makep' 命令，您要输入的是否是：
命令 'make' 来自于包 'make' (main)
命令 'make' 来自于包 'make-guile' (universe)
命令 'makeg' 来自于包 'xutils-dev' (universe)
命令 'makepp' 来自于包 'makepp' (universe)
makep：未找到命令
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make py
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c hamming.cpp -o hamming.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c utils.cpp -o utils.o -DFINTEGER=int
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexFlat.cpp -o IndexFlat.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexIVF.cpp -o IndexIVF.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexLSH.cpp -o IndexLSH.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexPQ.cpp -o IndexPQ.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexIVFPQ.cpp -o IndexIVFPQ.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c Clustering.cpp -o Clustering.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c Heap.cpp -o Heap.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c VectorTransform.cpp -o VectorTransform.o -DFINTEGER=int
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c index_io.cpp -o index_io.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c PolysemousTraining.cpp -o PolysemousTraining.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c MetaIndexes.cpp -o MetaIndexes.o
MetaIndexes.cpp:286:6: warning: ‘void faiss::{anonymous}::translate_labels(long int, faiss::{anonymous}::idx_t*, long int)’ defined but not used [-Wunused-function]
void translate_labels (long n, idx_t *labels, long translation)
^
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c Index.cpp -o Index.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c ProductQuantizer.cpp -o ProductQuantizer.o -DFINTEGER=int
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c AutoTune.cpp -o AutoTune.o
AutoTune.cpp: In member function ‘virtual void faiss::ParameterSpace::initialize(const faiss::Index*)’:
AutoTune.cpp:309:22: warning: unused variable ‘ix’ [-Wunused-variable]
const classname *ix = dynamic_cast<const classname *>(index)
^
AutoTune.cpp:389:9: note: in expansion of macro ‘DC’
if (DC (IndexIVFPQR)) {
^
AutoTune.cpp: In function ‘faiss::Index* faiss::index_factory(int, const char*, faiss::MetricType)’:
AutoTune.cpp:706:40: warning: unused variable ‘pq_m’ [-Wunused-variable]
int d_out, opq_M, nbit, M, M2, pq_m, ncent;
^
AutoTune.cpp:706:46: warning: unused variable ‘ncent’ [-Wunused-variable]
int d_out, opq_M, nbit, M, M2, pq_m, ncent;
^
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c AuxIndexStructures.cpp -o AuxIndexStructures.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexScalarQuantizer.cpp -o IndexScalarQuantizer.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c FaissException.cpp -o FaissException.o
g++ -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -c IndexHNSW.cpp -o IndexHNSW.o -DFINTEGER=int
IndexHNSW.cpp: In member function ‘void faiss::ReconstructFromNeighbors::estimate_code(const float*, faiss::ReconstructFromNeighbors::storage_idx_t, uint8_t*) const’:
IndexHNSW.cpp:1545:13: warning: unused variable ‘d1’ [-Wunused-variable]
int d1 = d0 + dsub;
^
IndexHNSW.cpp: In function ‘int faiss::{anonymous}::search_from_candidates_2(const faiss::HNSW&, faiss::{anonymous}::DistanceComputer&, int, faiss::{anonymous}::idx_t*, float*, faiss::{anonymous}::MinimaxHeap&, faiss::VisitedTable&, int, int)’:
IndexHNSW.cpp:2001:15: warning: unused variable ‘d’ [-Wunused-variable]
float d = candidates.dis[i];
^
ar r libfaiss.a hamming.o utils.o IndexFlat.o IndexIVF.o IndexLSH.o IndexPQ.o IndexIVFPQ.o Clustering.o Heap.o VectorTransform.o index_io.o PolysemousTraining.o MetaIndexes.o Index.o ProductQuantizer.o AutoTune.o AuxIndexStructures.o IndexScalarQuantizer.o FaissException.o IndexHNSW.o
ar: 正在创建 libfaiss.a
g++ -I. -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -g -fPIC -fopenmp -IOME/anaconda2/include/python2.7/ -IOME/anaconda2/lib/python2.7/site-packages/numpy/core/include/ -shared \
-o python/_swigfaiss.so python/swigfaiss_wrap.cxx libfaiss.a /usr/lib/libopenblas.so.0
python/swigfaiss_wrap.cxx:154:21: fatal error: Python.h: 没有那个文件或目录
compilation terminated.
Makefile:84: recipe for target 'python/_swigfaiss.so' failed
make: *** [python/_swigfaiss.so] Error 1
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ cp example_makefiles/makefile.inc.Linux ./makefile.inc
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ python -c "import distutils.sysconfig; print distutils.sysconfig.get_python_inc()"
/home/JIAO/anaconda2/include/python2.7
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ python -c "import numpy ; print numpy.get_include()"
/home/JIAO/.local/lib/python2.7/site-packages/numpy/core/include
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make py
g++ -I. -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -g -fPIC -fopenmp -IOME/anaconda2/include/python2.7/ -IOME/anaconda2/lib/python2.7/site-packages/numpy/core/include/ -shared \
-o python/_swigfaiss.so python/swigfaiss_wrap.cxx libfaiss.a /usr/lib/libopenblas.so.0
python/swigfaiss_wrap.cxx:154:21: fatal error: Python.h: 没有那个文件或目录
compilation terminated.
Makefile:84: recipe for target 'python/_swigfaiss.so' failed
make: *** [python/_swigfaiss.so] Error 1
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ make py
g++ -I. -fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp -g -fPIC -fopenmp -I/home/JIAO/anaconda2/include/python2.7/ -I/home/JIAO/anaconda2/lib/python2.7/site-packages/numpy/core/include/ -shared \
-o python/_swigfaiss.so python/swigfaiss_wrap.cxx libfaiss.a /usr/lib/libopenblas.so.0
cp python/_swigfaiss.so python/swigfaiss.py .
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ python -c "import faiss"
Failed to load GPU Faiss: No module named swigfaiss_gpu
Faiss falling back to CPU-only.
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "faiss.py", line 28, in <module>
from swigfaiss import *
File "swigfaiss.py", line 28, in <module>
_swigfaiss = swig_import_helper()
File "swigfaiss.py", line 24, in swig_import_helper
_mod = imp.load_module('_swigfaiss', fp, pathname, description)
ImportError: /home/JIAO/anaconda2/bin/../lib/libgomp.so.1: version `GOMP_4.0' not found (required by ./_swigfaiss.so)
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ libstdc++.so.6 | grep GLIBCXX
libstdc++.so.6：未找到命令
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ cd
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ cd anaconda2/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/anaconda2$ cd bin/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/anaconda2/bin$ libstdc++.so.6 | grep GLIBCXX
libstdc++.so.6：未找到命令
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/anaconda2/bin$ .
bash: .: 需要文件名参数
.: 用法: . 文件名 [参数]
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/anaconda2/bin$ libstdc++.so.6 | grep GLIBCXX
libstdc++.so.6：未找到命令
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/anaconda2/bin$ ls
2to3 jupyter-notebook
activate jupyter-qtconsole
activate-global-python-argcomplete jupyter-serverextension
anaconda jupyter-troubleshoot
anaconda-navigator jupyter-trust
asadmin kill_instance
assistant launch_instance
binstar lconvert
blaze-server libpng16-config
bokeh libpng-config
bundle_image linguist
cairo-trace list_instances
cfadmin lrelease
chardetect lss3
cjpeg lupdate
conda makeconv
conda-env moc
conda-server mturk
cq nosetests
c_rehash numba
curl odo
curl-config openssl
curve_keygen pcre-config
cwutil pcregrep
cygdb pcretest
cython pep8
cythonize pip
dbus-cleanup-sockets pixeltool
dbus-daemon pkgdata
dbus-launch pngfix
dbus-monitor png-fix-itxt
dbus-run-session pt2to3
dbus-send ptdump
dbus-test-tool ptrepack
dbus-update-activation-environment pttree
dbus-uuidgen pyami_sendmail
deactivate pybabel
derb pycc
designer pydoc
djpeg pyflakes
dynamodb_dump pygmentize
dynamodb_load pylint
easy_install pylint-gui
easy_install-2.7 pylupdate5
elbadmin pyrcc5
epylint pyreverse
f2py pytest
fc-cache py.test
fc-cat python
fc-list python2
fc-match python2.7
fc-pattern python-argcomplete-check-easy-install-script
fc-query python-config
fc-scan pyuic5
fc-validate qcollectiongenerator
fetch_file qdbus
fits2bitmap qdbuscpp2xml
fitscheck qdbusviewer
fitsdiff qdbusxml2cpp
fitsheader qdoc
fitsinfo qhelpconverter
fixqt4headers.pl qhelpgenerator
flask qlalr
freetype-config qmake
gapplication qml
gdbus qmleasing
gdbus-codegen qmlimportscanner
genbrk qmllint
gencfu qmlmin
gencnval qmlplugindump
gendict qmlprofiler
genrb qmlscene
get_objgraph.py qmltestrunner
gif2h5 qt.conf
gio qtdiag
gio-querymodules qtpaths
glacier qtplugininfo
glib-compile-resources rcc
glib-compile-schemas rdjpgcom
glib-genmarshal redis-benchmark
glib-gettextize redis-check-aof
glib-mkenums redis-check-rdb
gobject-query redis-cli
gresource redis-sentinel
grin redis-server
grind register-python-argcomplete
gsettings route53
gst-device-monitor-1.0 rst2html5.py
gst-discoverer-1.0 rst2html.py
gst-inspect-1.0 rst2latex.py
gst-launch-1.0 rst2man.py
gst-play-1.0 rst2odt_prepstyles.py
gst-stats-1.0 rst2odt.py
gst-typefind-1.0 rst2pseudoxml.py
gtester rst2s5.py
gtester-report rst2xetex.py
h52gif rst2xml.py
h5c++ rstpep2html.py
h5cc runxlrd.py
h5copy s3put
h5debug samp_hub
h5diff sdbadmin
h5dump sip
h5import skivi
h5jam smtpd.py
h5ls sphinx-apidoc
h5mkgrp sphinx-autogen
h5perf_serial sphinx-build
h5redeploy sphinx-quickstart
h5repack spyder
h5repart sqlite3
h5stat symilar
h5unjam syncqt.pl
hb-ot-shape-closure taskadmin
hb-shape tclsh8.5
hb-view uconv
iconv uic
icu-config unpickle.py
icuinfo unxz
idle vba_extract.py
instance_events volint
iptest wcslint
iptest2 wheel
ipython wish8.5
ipython2 wrjpgcom
isort xml2-config
isympy xmlcatalog
jpegtran xmllint
jsonschema xmlpatterns
jupyter xmlpatternsvalidator
jupyter-console xmlwf
jupyter-kernelspec xslt-config
jupyter-migrate xsltproc
jupyter-nbconvert xz
jupyter-nbextension
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/anaconda2/bin$ cd
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ conda install libgcc
Fetching package metadata ...........
Solving package specifications: .

Package plan for installation in environment /home/JIAO/anaconda2:

The following NEW packages will be INSTALLED:

libgcc-ng: 7.2.0-h7cc24e2_2
libstdcxx-ng: 7.2.0-h7a57d05_2

The following packages will be UPDATED:

anaconda: 4.3.0-np111py27_0 --> custom-py27h4a00acb_0
conda: 4.3.30-py27h6ae6dc7_0 --> 4.4.9-py27_0
conda-env: 2.6.0-0 --> 2.6.0-h36134e3_1
libgcc: 4.8.5-2 --> 7.2.0-h69d50b8_2
pycosat: 0.6.1-py27_1 --> 0.6.3-py27ha4109ae_0

Proceed ([y]/n)? y

conda-env-2.6. 100% |###################################| Time: 0:00:00 1.97 MB/s
libgcc-ng-7.2. 100% |###################################| Time: 0:00:02 2.34 MB/s
libstdcxx-ng-7 100% |###################################| Time: 0:00:00 4.46 MB/s
libgcc-7.2.0-h 100% |###################################| Time: 0:00:00 6.07 MB/s
anaconda-custo 100% |###################################| Time: 0:00:00 6.99 MB/s
pycosat-0.6.3- 100% |###################################| Time: 0:00:00 6.57 MB/s
conda-4.4.9-py 100% |###################################| Time: 0:00:00 4.92 MB/s
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ strings /home/JIAO/anaconda2/bin/../lib/libgomp.so.1 | grep GOMP
GOMP_atomic_start
GOMP_atomic_end
GOMP_barrier
GOMP_barrier_cancel
GOMP_critical_start
GOMP_critical_end
GOMP_critical_name_start
GOMP_critical_name_end
GOMP_loop_static_start
GOMP_loop_nonmonotonic_dynamic_start
GOMP_loop_dynamic_start
GOMP_loop_nonmonotonic_guided_start
GOMP_loop_guided_start
GOMP_loop_runtime_start
GOMP_loop_ordered_static_start
GOMP_loop_ordered_dynamic_start
GOMP_loop_ordered_guided_start
GOMP_loop_ordered_runtime_start
GOMP_loop_doacross_static_start
GOMP_loop_doacross_dynamic_start
GOMP_loop_doacross_guided_start
GOMP_loop_doacross_runtime_start
GOMP_loop_static_next
GOMP_loop_nonmonotonic_dynamic_next
GOMP_loop_dynamic_next
GOMP_loop_nonmonotonic_guided_next
GOMP_loop_guided_next
GOMP_loop_runtime_next
GOMP_loop_ordered_static_next
GOMP_loop_ordered_dynamic_next
GOMP_loop_ordered_guided_next
GOMP_loop_ordered_runtime_next
GOMP_parallel_loop_static_start
GOMP_parallel_loop_dynamic_start
GOMP_parallel_loop_guided_start
GOMP_parallel_loop_runtime_start
GOMP_parallel_loop_static
GOMP_parallel_loop_dynamic
GOMP_parallel_loop_nonmonotonic_dynamic
GOMP_parallel_loop_guided
GOMP_parallel_loop_nonmonotonic_guided
GOMP_parallel_loop_runtime
GOMP_loop_end
GOMP_loop_end_cancel
GOMP_loop_end_nowait
GOMP_loop_ull_static_start
GOMP_loop_ull_nonmonotonic_dynamic_start
GOMP_loop_ull_dynamic_start
GOMP_loop_ull_nonmonotonic_guided_start
GOMP_loop_ull_guided_start
GOMP_loop_ull_runtime_start
GOMP_loop_ull_ordered_static_start
GOMP_loop_ull_ordered_dynamic_start
GOMP_loop_ull_ordered_guided_start
GOMP_loop_ull_ordered_runtime_start
GOMP_loop_ull_doacross_static_start
GOMP_loop_ull_doacross_dynamic_start
GOMP_loop_ull_doacross_guided_start
GOMP_loop_ull_doacross_runtime_start
GOMP_loop_ull_static_next
GOMP_loop_ull_nonmonotonic_dynamic_next
GOMP_loop_ull_dynamic_next
GOMP_loop_ull_nonmonotonic_guided_next
GOMP_loop_ull_guided_next
GOMP_loop_ull_runtime_next
GOMP_loop_ull_ordered_static_next
GOMP_loop_ull_ordered_dynamic_next
GOMP_loop_ull_ordered_guided_next
GOMP_loop_ull_ordered_runtime_next
GOMP_ordered_start
GOMP_ordered_end
GOMP_doacross_post
GOMP_doacross_wait
GOMP_doacross_ull_post
GOMP_doacross_ull_wait
GOMP_parallel_start
GOMP_parallel_end
GOMP_parallel
GOMP_cancellation_point
GOMP_cancel
GOMP_sections_start
GOMP_sections_next
GOMP_parallel_sections_start
GOMP_parallel_sections
GOMP_sections_end
GOMP_sections_end_cancel
GOMP_sections_end_nowait
GOMP_single_start
GOMP_single_copy_start
GOMP_single_copy_end
GOMP_PLUGIN_target_task_completion
GOMP_taskwait
GOMP_task
GOMP_taskyield
GOMP_taskgroup_start
GOMP_taskgroup_end
GOMP_taskloop
GOMP_taskloop_ull
GOMP_offload_register_ver
GOMP_offload_register
GOMP_offload_unregister_ver
GOMP_offload_unregister
GOMP_target
GOMP_target_ext
GOMP_target_data
GOMP_target_data_ext
GOMP_target_end_data
GOMP_target_update
GOMP_target_update_ext
GOMP_target_enter_exit_data
GOMP_teams
GOMP_PLUGIN_malloc
GOMP_PLUGIN_malloc_cleared
GOMP_PLUGIN_realloc
GOMP_PLUGIN_debug
GOMP_PLUGIN_error
GOMP_PLUGIN_fatal
GOMP_PLUGIN_async_unmap_vars
GOMP_PLUGIN_acc_thread
GOMP_1.0
GOMP_2.0
GOMP_3.0
GOMP_4.0
GOMP_4.0.1
GOMP_4.5
GOMP_PLUGIN_1.0
GOMP_PLUGIN_1.1
GOMP_DEBUG
Invalid value for enviroment variable GOMP_CPU_AFFINITY
GOMP_SPINCOUNT
GOMP_STACKSIZE
GOMP_CPU_AFFINITY = ''
GOMP_STACKSIZE = '%lu'
GOMP_SPINCOUNT = '%lu'
GOMP_target_enter_exit_data unhandled kind 0x%.2x
GOMP_TASK_ASYNC_RUNNING
GOMP_TASK_WAITING
GOMP_TASK_TIED
GOMP_TASK_IMPLICIT
GOMP_TASK_UNDEFERRED
GOMP_DEVICE_INITIALIZED
GOMP_DEVICE_FINALIZED
GOMP_DEVICE_UNINITIALIZED
GOMP_atomic_end
GOMP_atomic_start
GOMP_barrier_cancel
GOMP_barrier
GOMP_critical_name_end
GOMP_critical_end
GOMP_critical_name_start
GOMP_critical_start
GOMP_loop_runtime_start
GOMP_parallel_loop_guided
GOMP_loop_ordered_runtime_next
GOMP_loop_runtime_next
GOMP_parallel_loop_dynamic
GOMP_loop_end_cancel
GOMP_parallel_loop_static_start
GOMP_parallel_loop_runtime
GOMP_parallel_loop_guided_start
GOMP_parallel_loop_runtime_start
gomp_ialias_GOMP_parallel_end
GOMP_loop_end
GOMP_loop_doacross_runtime_start
GOMP_parallel_loop_static
GOMP_loop_end_nowait
GOMP_loop_ordered_runtime_start
GOMP_parallel_loop_dynamic_start
GOMP_loop_ull_runtime_next
GOMP_loop_ull_runtime_start
GOMP_loop_ull_doacross_runtime_start
GOMP_loop_ull_ordered_runtime_next
GOMP_loop_ull_ordered_runtime_start
GOMP_doacross_post
GOMP_doacross_ull_wait
GOMP_doacross_wait
GOMP_ordered_end
GOMP_doacross_ull_post
GOMP_cancel
GOMP_parallel_start
GOMP_cancellation_point
GOMP_CANCEL_TASKGROUP
GOMP_CANCEL_SECTIONS
GOMP_CANCEL_FOR
GOMP_CANCEL_PARALLEL
GOMP_parallel
GOMP_CANCEL_DO
GOMP_CANCEL_LOOP
GOMP_sections_end_cancel
GOMP_sections_end
GOMP_parallel_sections
GOMP_sections_end_nowait
GOMP_sections_start
GOMP_parallel_sections_start
GOMP_sections_next
GOMP_single_copy_start
GOMP_single_copy_end
GOMP_single_start
GOMP_MAP_FORCE_DEVICEPTR
GOMP_MAP_FORCE_TOFROM
GOMP_MAP_FIRSTPRIVATE_POINTER
GOMP_TARGET_TASK_READY_TO_RUN
GOMP_MAP_ALWAYS_FROM
GOMP_MAP_FROM
GOMP_MAP_ZERO_LEN_ARRAY_SECTION
GOMP_taskloop
GOMP_TARGET_TASK_FALLBACK
GOMP_MAP_TO_PSET
GOMP_MAP_ALWAYS_TO
GOMP_MAP_STRUCT
GOMP_MAP_LINK
GOMP_MAP_FIRSTPRIVATE
GOMP_MAP_TO
GOMP_MAP_TOFROM
GOMP_MAP_FIRSTPRIVATE_REFERENCE
GOMP_MAP_FIRSTPRIVATE_INT
GOMP_TARGET_TASK_BEFORE_MAP
GOMP_MAP_USE_DEVICE_PTR
GOMP_task
GOMP_MAP_POINTER
GOMP_taskgroup_start
GOMP_MAP_RELEASE
GOMP_TARGET_TASK_DATA
GOMP_MAP_FORCE_PRESENT
GOMP_PLUGIN_target_task_completion
GOMP_TARGET_TASK_RUNNING
GOMP_taskwait
GOMP_TARGET_TASK_FINISHED
GOMP_MAP_DELETE
GOMP_MAP_DELETE_ZERO_LEN_ARRAY_SECTION
GOMP_MAP_FORCE_TO
GOMP_MAP_FORCE_ALLOC
GOMP_taskgroup_end
GOMP_MAP_ALWAYS_POINTER
GOMP_MAP_DEVICE_RESIDENT
GOMP_MAP_FORCE_FROM
GOMP_MAP_ALLOC
GOMP_MAP_ALWAYS_TOFROM
GOMP_taskloop_ull
GOMP_taskyield
GOMP_offload_unregister_ver
GOMP_target_ext
GOMP_target_data_ext
GOMP_offload_register_ver
GOMP_target_end_data
GOMP_target_update
GOMP_MAP_VARS_ENTER_DATA
GOMP_MAP_VARS_TARGET
GOMP_offload_unregister
GOMP_MAP_VARS_DATA
GOMP_target_enter_exit_data
GOMP_teams
GOMP_offload_register
GOMP_target
GOMP_target_update_ext
GOMP_target_data
GOMP_MAP_VARS_OPENACC
GOMP_PLUGIN_debug
GOMP_PLUGIN_error
GOMP_PLUGIN_malloc
GOMP_PLUGIN_realloc
GOMP_PLUGIN_malloc_cleared
GOMP_PLUGIN_fatal
GOMP_PLUGIN_async_unmap_vars
GOMP_PLUGIN_acc_thread
gomp_ialias_GOMP_cancellation_point
gomp_ialias_GOMP_parallel_end
gomp_ialias_GOMP_taskgroup_start
gomp_ialias_GOMP_taskgroup_end
GOMP_PLUGIN_error
GOMP_loop_ordered_static_next
GOMP_PLUGIN_1.0
GOMP_loop_guided_start
GOMP_taskloop
GOMP_loop_ordered_dynamic_next
GOMP_PLUGIN_async_unmap_vars
GOMP_4.5
GOMP_loop_ull_nonmonotonic_dynamic_start
GOMP_loop_runtime_next
GOMP_single_copy_end
GOMP_PLUGIN_fatal
GOMP_loop_doacross_static_start
GOMP_parallel_loop_guided
GOMP_loop_ull_guided_start
GOMP_doacross_ull_post
GOMP_loop_ull_nonmonotonic_guided_start
GOMP_loop_ull_doacross_runtime_start
GOMP_PLUGIN_malloc
GOMP_loop_ull_ordered_runtime_next
GOMP_loop_ull_nonmonotonic_guided_next
GOMP_target_data
GOMP_loop_end
GOMP_sections_end_cancel
GOMP_parallel_loop_nonmonotonic_guided
GOMP_ordered_start
GOMP_critical_end
GOMP_offload_register
GOMP_parallel_sections_start
GOMP_sections_end_nowait
GOMP_doacross_ull_wait
GOMP_loop_ull_runtime_start
GOMP_loop_ull_nonmonotonic_dynamic_next
GOMP_taskloop_ull
GOMP_loop_ull_dynamic_next
GOMP_parallel_loop_static_start
GOMP_parallel_sections
GOMP_parallel_loop_nonmonotonic_dynamic
GOMP_offload_unregister
GOMP_loop_doacross_runtime_start
GOMP_doacross_post
GOMP_loop_ull_static_start
GOMP_parallel_loop_static
GOMP_loop_ordered_dynamic_start
GOMP_loop_ordered_guided_next
GOMP_loop_ull_static_next
GOMP_target_update_ext
GOMP_atomic_end
GOMP_parallel_loop_runtime
GOMP_barrier_cancel
GOMP_sections_start
GOMP_PLUGIN_realloc
GOMP_loop_end_cancel
GOMP_target_ext
GOMP_PLUGIN_malloc_cleared
GOMP_loop_nonmonotonic_dynamic_start
GOMP_loop_doacross_guided_start
GOMP_loop_end_nowait
GOMP_PLUGIN_1.1
GOMP_barrier
GOMP_loop_ull_dynamic_start
GOMP_loop_static_start
GOMP_4.0
GOMP_parallel_loop_dynamic
GOMP_1.0
GOMP_loop_ull_ordered_guided_start
GOMP_sections_next
GOMP_target_end_data
GOMP_teams
GOMP_cancel
GOMP_loop_static_next
GOMP_taskyield
GOMP_parallel_start
GOMP_loop_ordered_runtime_start
GOMP_offload_unregister_ver
GOMP_loop_ull_doacross_guided_start
GOMP_loop_ull_ordered_guided_next
GOMP_PLUGIN_acc_thread
GOMP_parallel
GOMP_parallel_loop_dynamic_start
GOMP_loop_runtime_start
GOMP_atomic_start
GOMP_loop_ull_ordered_static_start
GOMP_PLUGIN_debug
GOMP_loop_ull_runtime_next
GOMP_critical_name_end
GOMP_loop_ull_guided_next
GOMP_offload_register_ver
GOMP_doacross_wait
GOMP_parallel_loop_guided_start
GOMP_target_update
GOMP_target
GOMP_loop_dynamic_next
GOMP_loop_guided_next
GOMP_parallel_loop_runtime_start
GOMP_loop_ull_doacross_dynamic_start
GOMP_loop_nonmonotonic_dynamic_next
GOMP_single_start
GOMP_4.0.1
GOMP_loop_ull_ordered_dynamic_next
GOMP_target_data_ext
GOMP_loop_ull_ordered_runtime_start
GOMP_critical_start
GOMP_loop_nonmonotonic_guided_start
GOMP_single_copy_start
GOMP_loop_nonmonotonic_guided_next
GOMP_taskwait
GOMP_target_enter_exit_data
GOMP_loop_ordered_runtime_next
GOMP_loop_doacross_dynamic_start
GOMP_loop_ull_ordered_dynamic_start
GOMP_loop_dynamic_start
GOMP_2.0
GOMP_loop_ull_ordered_static_next
GOMP_loop_ordered_static_start
GOMP_loop_ordered_guided_start
GOMP_PLUGIN_target_task_completion
GOMP_ordered_end
GOMP_loop_ull_doacross_static_start
GOMP_sections_end
GOMP_3.0
GOMP_critical_name_start
GOMP_task
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ sudo find / -name libgomp.so.1*
[sudo] JIAO 的密码：
find: `/run/user/1000/gvfs': 权限不够
/usr/lib/x86_64-linux-gnu/libgomp.so.1
/usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0
/home/JIAO/anaconda2/lib/libgomp.so.1
/home/JIAO/anaconda2/lib/libgomp.so.1.0.0
/home/JIAO/anaconda2/x86_64-conda_cos6-linux-gnu/sysroot/lib/libgomp.so.1
/home/JIAO/anaconda2/x86_64-conda_cos6-linux-gnu/sysroot/lib/libgomp.so.1.0.0
/home/JIAO/anaconda2/pkgs/libgcc-4.8.5-2/lib/libgomp.so.1
/home/JIAO/anaconda2/pkgs/libgcc-4.8.5-2/lib/libgomp.so.1.0.0
/home/JIAO/anaconda2/pkgs/libgcc-ng-7.2.0-h7cc24e2_2/lib/libgomp.so.1
/home/JIAO/anaconda2/pkgs/libgcc-ng-7.2.0-h7cc24e2_2/lib/libgomp.so.1.0.0
/home/JIAO/anaconda2/pkgs/libgcc-ng-7.2.0-h7cc24e2_2/x86_64-conda_cos6-linux-gnu/sysroot/lib/libgomp.so.1
/home/JIAO/anaconda2/pkgs/libgcc-ng-7.2.0-h7cc24e2_2/x86_64-conda_cos6-linux-gnu/sysroot/lib/libgomp.so.1.0.0
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ cd /usr/lib/x86_64-linux-gnu/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ strings libgomp.so.1 | grep GOMP
GOMP_barrier
GOMP_barrier_cancel
GOMP_critical_start
GOMP_critical_end
GOMP_critical_name_start
GOMP_critical_name_end
GOMP_atomic_start
GOMP_atomic_end
GOMP_loop_static_start
GOMP_loop_dynamic_start
GOMP_loop_guided_start
GOMP_loop_runtime_start
GOMP_loop_ordered_static_start
GOMP_loop_ordered_dynamic_start
GOMP_loop_ordered_guided_start
GOMP_loop_ordered_runtime_start
GOMP_loop_static_next
GOMP_loop_dynamic_next
GOMP_loop_guided_next
GOMP_loop_runtime_next
GOMP_loop_ordered_static_next
GOMP_loop_ordered_dynamic_next
GOMP_loop_ordered_guided_next
GOMP_loop_ordered_runtime_next
GOMP_parallel_loop_static_start
GOMP_parallel_loop_dynamic_start
GOMP_parallel_loop_guided_start
GOMP_parallel_loop_runtime_start
GOMP_parallel_loop_static
GOMP_parallel_loop_dynamic
GOMP_parallel_loop_guided
GOMP_parallel_loop_runtime
GOMP_loop_end
GOMP_loop_end_cancel
GOMP_loop_end_nowait
GOMP_loop_ull_static_start
GOMP_loop_ull_dynamic_start
GOMP_loop_ull_guided_start
GOMP_loop_ull_runtime_start
GOMP_loop_ull_ordered_static_start
GOMP_loop_ull_ordered_dynamic_start
GOMP_loop_ull_ordered_guided_start
GOMP_loop_ull_ordered_runtime_start
GOMP_loop_ull_static_next
GOMP_loop_ull_dynamic_next
GOMP_loop_ull_guided_next
GOMP_loop_ull_runtime_next
GOMP_loop_ull_ordered_static_next
GOMP_loop_ull_ordered_dynamic_next
GOMP_loop_ull_ordered_guided_next
GOMP_loop_ull_ordered_runtime_next
GOMP_ordered_start
GOMP_ordered_end
GOMP_parallel_start
GOMP_parallel_end
GOMP_parallel
GOMP_cancellation_point
GOMP_cancel
GOMP_sections_start
GOMP_sections_next
GOMP_parallel_sections_start
GOMP_parallel_sections
GOMP_sections_end
GOMP_sections_end_cancel
GOMP_sections_end_nowait
GOMP_single_start
GOMP_single_copy_start
GOMP_single_copy_end
GOMP_task
GOMP_taskwait
GOMP_taskyield
GOMP_taskgroup_start
GOMP_taskgroup_end
GOMP_offload_register
GOMP_offload_unregister
GOMP_target
GOMP_target_data
GOMP_target_end_data
GOMP_target_update
GOMP_teams
GOMP_PLUGIN_malloc
GOMP_PLUGIN_malloc_cleared
GOMP_PLUGIN_realloc
GOMP_PLUGIN_debug
GOMP_PLUGIN_error
GOMP_PLUGIN_fatal
GOMP_PLUGIN_async_unmap_vars
GOMP_PLUGIN_acc_thread
GOMP_1.0
GOMP_2.0
GOMP_3.0
GOMP_4.0
GOMP_4.0.1
GOMP_PLUGIN_1.0
GOMP_DEBUG
GOMP_CPU_AFFINITY
GOMP_SPINCOUNT
GOMP_STACKSIZE
GOMP_CPU_AFFINITY = ''
GOMP_STACKSIZE = '%lu'
GOMP_SPINCOUNT = '%lu'
Invalid value for enviroment variable GOMP_CPU_AFFINITY
GOMP_OFFLOAD_get_name
GOMP_OFFLOAD_get_caps
GOMP_OFFLOAD_get_type
GOMP_OFFLOAD_get_num_devices
GOMP_OFFLOAD_init_device
GOMP_OFFLOAD_fini_device
GOMP_OFFLOAD_load_image
GOMP_OFFLOAD_unload_image
GOMP_OFFLOAD_alloc
GOMP_OFFLOAD_free
GOMP_OFFLOAD_dev2host
GOMP_OFFLOAD_host2dev
GOMP_OFFLOAD_run
GOMP_OFFLOAD_openacc_parallel
GOMP_OFFLOAD_openacc_register_async_cleanup
GOMP_OFFLOAD_openacc_async_test
GOMP_OFFLOAD_openacc_async_test_all
GOMP_OFFLOAD_openacc_async_wait
GOMP_OFFLOAD_openacc_async_wait_async
GOMP_OFFLOAD_openacc_async_wait_all
GOMP_OFFLOAD_openacc_async_wait_all_async
GOMP_OFFLOAD_openacc_async_set_async
GOMP_OFFLOAD_openacc_create_thread_data
GOMP_OFFLOAD_openacc_destroy_thread_data
GOMP_OFFLOAD_openacc_get_current_cuda_device
GOMP_OFFLOAD_openacc_get_current_cuda_context
GOMP_OFFLOAD_openacc_get_cuda_stream
GOMP_OFFLOAD_openacc_set_cuda_stream
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ sudo rm -rf /home/JIAO/anaconda2/bin/../lib/libgomp.so.1
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ sudo l
Display all 143 possibilities? (y or n)
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ sudo ln -s /usr/lib/x86_64-linux-gnu/libgomp.so.1 /home/JIAO/anaconda2/bin/../l
ibgomp.so.1
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ strings /home/JIAO/anaconda2/bin/../lib
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ strings /home/JIAO/anaconda2/bin/../lib/libgomp.so.1 | grep GOMP
strings: /home/JIAO/anaconda2/bin/../lib/libgomp.so.1：无此文件
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ sudo ln -s /usr/lib/x86_64-linux-gnu/libgomp.so.1 /home/JIAO/anaconda2/bin/..libgomp.so.1
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ strings /home/JIAO/anaconda2/bin/..libgomp.so.1 | grep GOMP
GOMP_barrier
GOMP_barrier_cancel
GOMP_critical_start
GOMP_critical_end
GOMP_critical_name_start
GOMP_critical_name_end
GOMP_atomic_start
GOMP_atomic_end
GOMP_loop_static_start
GOMP_loop_dynamic_start
GOMP_loop_guided_start
GOMP_loop_runtime_start
GOMP_loop_ordered_static_start
GOMP_loop_ordered_dynamic_start
GOMP_loop_ordered_guided_start
GOMP_loop_ordered_runtime_start
GOMP_loop_static_next
GOMP_loop_dynamic_next
GOMP_loop_guided_next
GOMP_loop_runtime_next
GOMP_loop_ordered_static_next
GOMP_loop_ordered_dynamic_next
GOMP_loop_ordered_guided_next
GOMP_loop_ordered_runtime_next
GOMP_parallel_loop_static_start
GOMP_parallel_loop_dynamic_start
GOMP_parallel_loop_guided_start
GOMP_parallel_loop_runtime_start
GOMP_parallel_loop_static
GOMP_parallel_loop_dynamic
GOMP_parallel_loop_guided
GOMP_parallel_loop_runtime
GOMP_loop_end
GOMP_loop_end_cancel
GOMP_loop_end_nowait
GOMP_loop_ull_static_start
GOMP_loop_ull_dynamic_start
GOMP_loop_ull_guided_start
GOMP_loop_ull_runtime_start
GOMP_loop_ull_ordered_static_start
GOMP_loop_ull_ordered_dynamic_start
GOMP_loop_ull_ordered_guided_start
GOMP_loop_ull_ordered_runtime_start
GOMP_loop_ull_static_next
GOMP_loop_ull_dynamic_next
GOMP_loop_ull_guided_next
GOMP_loop_ull_runtime_next
GOMP_loop_ull_ordered_static_next
GOMP_loop_ull_ordered_dynamic_next
GOMP_loop_ull_ordered_guided_next
GOMP_loop_ull_ordered_runtime_next
GOMP_ordered_start
GOMP_ordered_end
GOMP_parallel_start
GOMP_parallel_end
GOMP_parallel
GOMP_cancellation_point
GOMP_cancel
GOMP_sections_start
GOMP_sections_next
GOMP_parallel_sections_start
GOMP_parallel_sections
GOMP_sections_end
GOMP_sections_end_cancel
GOMP_sections_end_nowait
GOMP_single_start
GOMP_single_copy_start
GOMP_single_copy_end
GOMP_task
GOMP_taskwait
GOMP_taskyield
GOMP_taskgroup_start
GOMP_taskgroup_end
GOMP_offload_register
GOMP_offload_unregister
GOMP_target
GOMP_target_data
GOMP_target_end_data
GOMP_target_update
GOMP_teams
GOMP_PLUGIN_malloc
GOMP_PLUGIN_malloc_cleared
GOMP_PLUGIN_realloc
GOMP_PLUGIN_debug
GOMP_PLUGIN_error
GOMP_PLUGIN_fatal
GOMP_PLUGIN_async_unmap_vars
GOMP_PLUGIN_acc_thread
GOMP_1.0
GOMP_2.0
GOMP_3.0
GOMP_4.0
GOMP_4.0.1
GOMP_PLUGIN_1.0
GOMP_DEBUG
GOMP_CPU_AFFINITY
GOMP_SPINCOUNT
GOMP_STACKSIZE
GOMP_CPU_AFFINITY = ''
GOMP_STACKSIZE = '%lu'
GOMP_SPINCOUNT = '%lu'
Invalid value for enviroment variable GOMP_CPU_AFFINITY
GOMP_OFFLOAD_get_name
GOMP_OFFLOAD_get_caps
GOMP_OFFLOAD_get_type
GOMP_OFFLOAD_get_num_devices
GOMP_OFFLOAD_init_device
GOMP_OFFLOAD_fini_device
GOMP_OFFLOAD_load_image
GOMP_OFFLOAD_unload_image
GOMP_OFFLOAD_alloc
GOMP_OFFLOAD_free
GOMP_OFFLOAD_dev2host
GOMP_OFFLOAD_host2dev
GOMP_OFFLOAD_run
GOMP_OFFLOAD_openacc_parallel
GOMP_OFFLOAD_openacc_register_async_cleanup
GOMP_OFFLOAD_openacc_async_test
GOMP_OFFLOAD_openacc_async_test_all
GOMP_OFFLOAD_openacc_async_wait
GOMP_OFFLOAD_openacc_async_wait_async
GOMP_OFFLOAD_openacc_async_wait_all
GOMP_OFFLOAD_openacc_async_wait_all_async
GOMP_OFFLOAD_openacc_async_set_async
GOMP_OFFLOAD_openacc_create_thread_data
GOMP_OFFLOAD_openacc_destroy_thread_data
GOMP_OFFLOAD_openacc_get_current_cuda_device
GOMP_OFFLOAD_openacc_get_current_cuda_context
GOMP_OFFLOAD_openacc_get_cuda_stream
GOMP_OFFLOAD_openacc_set_cuda_stream
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ conda install -c salford_systems gcc-5
Solving environment: /
WARNING: The remote server could not find the noarch directory for the
requested channel with url: https://conda.anaconda.org/salford_systems
It is possible you have given conda an invalid channel. Please double-check
your conda configuration using `conda config --show`.

If the requested url is in fact a valid conda channel, please request that the
channel administrator create `noarch/repodata.json` and associated
`noarch/repodata.json.bz2` files, even if `noarch/repodata.json` is empty.
$ mkdir noarch
$ echo '{}' > noarch/repodata.json
$ bzip2 -k noarch/repodata.json
failed

PackagesNotFoundError: The following packages are not available from current channels:

- gcc-5

Current channels:

- https://conda.anaconda.org/salford_systems/linux-64 - https://conda.anaconda.org/salford_systems/noarch - https://repo.continuum.io/pkgs/main/linux-64 - https://repo.continuum.io/pkgs/main/noarch - https://repo.continuum.io/pkgs/free/linux-64 - https://repo.continuum.io/pkgs/free/noarch - https://repo.continuum.io/pkgs/r/linux-64 - https://repo.continuum.io/pkgs/r/noarch - https://repo.continuum.io/pkgs/pro/linux-64 - https://repo.continuum.io/pkgs/pro/noarch
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:/usr/lib/x86_64-linux-gnu$ cd
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~$ cd faiss/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ python
Python 2.7.13 |Anaconda custom (64-bit)| (default, Dec 20 2016, 23:09:15)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org >>> import faiss
Failed to load GPU Faiss: No module named swigfaiss_gpu
Faiss falling back to CPU-only.
>>> cd
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'cd' is not defined
>>>
[2]+ 已停止 python
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ python -c "import faiss"
Failed to load GPU Faiss: No module named swigfaiss_gpu
Faiss falling back to CPU-only.
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ spyder
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ python python/demo_auto_tune.py
python: can't open file 'python/demo_auto_tune.py': [Errno 2] No such file or directory
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ python tests/demo_auto_tune.py
Failed to load GPU Faiss: No module named swigfaiss_gpu
Faiss falling back to CPU-only.
load data
load GT
prepare criterion
============ key IMI2x10,Flat
[0.443 s] train & add
[32.610 s] explore op points
0/169: cno=0 nprobe=1,max_codes=256 bounds [perf<=1.000 t>=0.000] perf 0.147 t 0.162 *
1/169: cno=168 nprobe=4096,max_codes=inf bounds [perf<=1.000 t>=0.162] perf 0.987 t 4.130 *
2/169: cno=58 nprobe=64,max_codes=4096 bounds [perf<=0.987 t>=0.162] perf 0.702 t 0.349 *
3/169: cno=124 nprobe=128,max_codes=131072 bounds [perf<=0.987 t>=0.349] perf 0.788 t 0.425 *
4/169: cno=69 nprobe=16,max_codes=8192 bounds [perf<=0.788 t>=0.162] perf 0.506 t 0.221 *
5/169: cno=142 nprobe=4096,max_codes=262144 bounds [perf<=0.987 t>=0.425] perf 0.987 t 4.152
6/169: cno=12 nprobe=4096,max_codes=256 bounds [perf<=0.987 t>=0.162] perf 0.597 t 1.107
7/169: cno=108 nprobe=16,max_codes=65536 bounds [perf<=0.788 t>=0.221] perf 0.506 t 0.224
8/169: cno=53 nprobe=2,max_codes=4096 bounds [perf<=0.506 t>=0.162] perf 0.224 t 0.178 *
9/169: cno=114 nprobe=1024,max_codes=65536 bounds [perf<=0.987 t>=0.349] perf 0.954 t 1.501 *
10/169: cno=147 nprobe=16,max_codes=524288 bounds [perf<=0.987 t>=0.224] perf 0.506 t 0.207 *
11/169: cno=64 nprobe=4096,max_codes=4096 bounds [perf<=0.987 t>=1.107] perf 0.955 t 1.809 *
12/169: cno=96 nprobe=32,max_codes=32768 bounds [perf<=0.788 t>=0.221] perf 0.607 t 0.279 *
13/169: cno=94 nprobe=8,max_codes=32768 bounds [perf<=0.506 t>=0.178] perf 0.406 t 0.217
14/169: cno=3 nprobe=8,max_codes=256 bounds [perf<=0.406 t>=0.162] perf 0.390 t 0.182 *
15/169: cno=20 nprobe=128,max_codes=512 bounds [perf<=0.788 t>=0.182] perf 0.708 t 0.312 *
16/169: cno=50 nprobe=2048,max_codes=2048 bounds [perf<=0.955 t>=0.312] perf 0.904 t 1.019 *
17/169: cno=26 nprobe=1,max_codes=1024 bounds [perf<=0.224 t>=0.162] perf 0.147 t 0.176
18/169: cno=61 nprobe=512,max_codes=4096 bounds [perf<=0.954 t>=0.349] perf 0.913 t 0.777 *
19/169: cno=66 nprobe=2,max_codes=8192 bounds [perf<=0.406 t>=0.178] perf 0.224 t 0.191
20/169: cno=110 nprobe=64,max_codes=65536 bounds [perf<=0.788 t>=0.349] perf 0.702 t 0.336
21/169: cno=102 nprobe=2048,max_codes=32768 bounds [perf<=0.987 t>=1.019] perf 0.976 t 2.451 *
22/169: cno=136 nprobe=64,max_codes=262144 bounds [perf<=0.987 t>=0.349] perf 0.702 t 0.349
23/169: cno=2 nprobe=4,max_codes=256 bounds [perf<=0.390 t>=0.162] perf 0.303 t 0.197
24/169: cno=122 nprobe=32,max_codes=131072 bounds [perf<=0.702 t>=0.279] perf 0.607 t 0.252 *
25/169: cno=146 nprobe=8,max_codes=524288 bounds [perf<=0.506 t>=0.217] skip
26/169: cno=10 nprobe=1024,max_codes=256 bounds [perf<=0.597 t>=0.197] perf 0.597 t 0.500
27/169: cno=48 nprobe=512,max_codes=2048 bounds [perf<=0.904 t>=0.312] perf 0.892 t 0.648 *
28/169: cno=21 nprobe=256,max_codes=512 bounds [perf<=0.892 t>=0.312] perf 0.722 t 0.342 *
29/169: cno=126 nprobe=512,max_codes=131072 bounds [perf<=0.987 t>=0.777] perf 0.916 t 0.935 *
30/169: cno=60 nprobe=256,max_codes=4096 bounds [perf<=0.913 t>=0.349] perf 0.861 t 0.566 *
31/169: cno=92 nprobe=2,max_codes=32768 bounds [perf<=0.406 t>=0.191] perf 0.224 t 0.175 *
32/169: cno=130 nprobe=1,max_codes=262144 bounds [perf<=0.506 t>=0.176] perf 0.147 t 0.170
33/169: cno=74 nprobe=512,max_codes=8192 bounds [perf<=0.916 t>=0.777] perf 0.916 t 0.864 *
34/169: cno=93 nprobe=4,max_codes=32768 bounds [perf<=0.406 t>=0.197] perf 0.310 t 0.196
35/169: cno=22 nprobe=512,max_codes=512 bounds [perf<=0.892 t>=0.342] perf 0.724 t 0.421 *
36/169: cno=85 nprobe=128,max_codes=16384 bounds [perf<=0.788 t>=0.349] perf 0.788 t 0.419 *
37/169: cno=54 nprobe=4,max_codes=4096 bounds [perf<=0.310 t>=0.197] skip
38/169: cno=83 nprobe=32,max_codes=16384 bounds [perf<=0.607 t>=0.221] perf 0.607 t 0.268
39/169: cno=1 nprobe=2,max_codes=256 bounds [perf<=0.224 t>=0.162] perf 0.222 t 0.170 *
40/169: cno=151 nprobe=256,max_codes=524288 bounds [perf<=0.987 t>=0.566] perf 0.862 t 0.602 *
41/169: cno=15 nprobe=4,max_codes=512 bounds [perf<=0.310 t>=0.197] skip
42/169: cno=100 nprobe=512,max_codes=32768 bounds [perf<=0.916 t>=0.864] perf 0.916 t 0.918 *
43/169: cno=32 nprobe=64,max_codes=1024 bounds [perf<=0.702 t>=0.197] perf 0.694 t 0.290 *
44/169: cno=46 nprobe=128,max_codes=2048 bounds [perf<=0.788 t>=0.312] perf 0.785 t 0.392 *
45/169: cno=78 nprobe=1,max_codes=16384 bounds [perf<=0.147 t>=0.176] skip
46/169: cno=45 nprobe=64,max_codes=2048 bounds [perf<=0.702 t>=0.290] perf 0.700 t 0.308 *
47/169: cno=27 nprobe=2,max_codes=1024 bounds [perf<=0.224 t>=0.176] skip
48/169: cno=29 nprobe=8,max_codes=1024 bounds [perf<=0.406 t>=0.197] perf 0.405 t 0.197 *
49/169: cno=162 nprobe=64,max_codes=inf bounds [perf<=0.987 t>=0.349] perf 0.702 t 0.321
50/169: cno=113 nprobe=512,max_codes=65536 bounds [perf<=0.916 t>=0.918] skip
51/169: cno=44 nprobe=32,max_codes=2048 bounds [perf<=0.607 t>=0.197] perf 0.606 t 0.258
52/169: cno=152 nprobe=512,max_codes=524288 bounds [perf<=0.987 t>=0.935] perf 0.916 t 0.922
53/169: cno=154 nprobe=2048,max_codes=524288 bounds [perf<=0.987 t>=2.451] perf 0.976 t 2.428 *
54/169: cno=41 nprobe=4,max_codes=2048 bounds [perf<=0.310 t>=0.197] skip
55/169: cno=40 nprobe=2,max_codes=2048 bounds [perf<=0.224 t>=0.176] skip
56/169: cno=155 nprobe=4096,max_codes=524288 bounds [perf<=0.987 t>=4.152] skip
57/169: cno=98 nprobe=128,max_codes=32768 bounds [perf<=0.788 t>=0.419] skip
58/169: cno=76 nprobe=2048,max_codes=8192 bounds [perf<=0.976 t>=1.019] perf 0.974 t 1.922 *
59/169: cno=118 nprobe=2,max_codes=131072 bounds [perf<=0.506 t>=0.191] perf 0.224 t 0.187
60/169: cno=34 nprobe=256,max_codes=1024 bounds [perf<=0.861 t>=0.342] perf 0.817 t 0.429 *
61/169: cno=23 nprobe=1024,max_codes=512 bounds [perf<=0.904 t>=0.500] perf 0.724 t 0.534
62/169: cno=73 nprobe=256,max_codes=8192 bounds [perf<=0.862 t>=0.566] perf 0.862 t 0.607
63/169: cno=30 nprobe=16,max_codes=1024 bounds [perf<=0.506 t>=0.197] perf 0.504 t 0.226
64/169: cno=97 nprobe=64,max_codes=32768 bounds [perf<=0.702 t>=0.349] skip
65/169: cno=31 nprobe=32,max_codes=1024 bounds [perf<=0.606 t>=0.226] perf 0.603 t 0.259
66/169: cno=47 nprobe=256,max_codes=2048 bounds [perf<=0.861 t>=0.429] perf 0.854 t 0.501 *
67/169: cno=80 nprobe=4,max_codes=16384 bounds [perf<=0.310 t>=0.197] skip
68/169: cno=18 nprobe=32,max_codes=512 bounds [perf<=0.603 t>=0.197] perf 0.585 t 0.234 *
69/169: cno=70 nprobe=32,max_codes=8192 bounds [perf<=0.607 t>=0.259] skip
70/169: cno=143 nprobe=1,max_codes=524288 bounds [perf<=0.506 t>=0.176] perf 0.147 t 0.156 *
71/169: cno=144 nprobe=2,max_codes=524288 bounds [perf<=0.506 t>=0.191] perf 0.224 t 0.175 *
72/169: cno=38 nprobe=4096,max_codes=1024 bounds [perf<=0.955 t>=1.107] perf 0.832 t 1.217
73/169: cno=8 nprobe=256,max_codes=256 bounds [perf<=0.597 t>=0.197] perf 0.597 t 0.308
74/169: cno=5 nprobe=32,max_codes=256 bounds [perf<=0.585 t>=0.197] perf 0.541 t 0.225 *
75/169: cno=90 nprobe=4096,max_codes=16384 bounds [perf<=0.987 t>=1.922] perf 0.987 t 3.556 *
76/169: cno=16 nprobe=8,max_codes=512 bounds [perf<=0.405 t>=0.197] perf 0.401 t 0.201
77/169: cno=42 nprobe=8,max_codes=2048 bounds [perf<=0.406 t>=0.201] perf 0.406 t 0.206 *
78/169: cno=109 nprobe=32,max_codes=65536 bounds [perf<=0.607 t>=0.279] skip
79/169: cno=71 nprobe=64,max_codes=8192 bounds [perf<=0.702 t>=0.349] skip
80/169: cno=164 nprobe=256,max_codes=inf bounds [perf<=0.987 t>=0.607] perf 0.862 t 0.606
81/169: cno=25 nprobe=4096,max_codes=512 bounds [perf<=0.832 t>=1.107] skip
82/169: cno=57 nprobe=32,max_codes=4096 bounds [perf<=0.607 t>=0.259] skip
83/169: cno=11 nprobe=2048,max_codes=256 bounds [perf<=0.597 t>=0.500] skip
84/169: cno=137 nprobe=128,max_codes=262144 bounds [perf<=0.862 t>=0.425] perf 0.788 t 0.426
85/169: cno=86 nprobe=256,max_codes=16384 bounds [perf<=0.862 t>=0.607] skip
86/169: cno=79 nprobe=2,max_codes=16384 bounds [perf<=0.224 t>=0.191] skip
87/169: cno=158 nprobe=4,max_codes=inf bounds [perf<=0.702 t>=0.197] perf 0.310 t 0.210
88/169: cno=106 nprobe=4,max_codes=65536 bounds [perf<=0.310 t>=0.197] skip
89/169: cno=56 nprobe=16,max_codes=4096 bounds [perf<=0.506 t>=0.226] skip
90/169: cno=125 nprobe=256,max_codes=131072 bounds [perf<=0.862 t>=0.607] skip
91/169: cno=88 nprobe=1024,max_codes=16384 bounds [perf<=0.954 t>=0.864] perf 0.954 t 1.473 *
92/169: cno=132 nprobe=4,max_codes=262144 bounds [perf<=0.310 t>=0.197] skip
93/169: cno=67 nprobe=4,max_codes=8192 bounds [perf<=0.310 t>=0.197] skip
94/169: cno=153 nprobe=1024,max_codes=524288 bounds [perf<=0.976 t>=1.501] perf 0.954 t 1.493
95/169: cno=65 nprobe=1,max_codes=8192 bounds [perf<=0.147 t>=0.176] skip
96/169: cno=68 nprobe=8,max_codes=8192 bounds [perf<=0.406 t>=0.206] perf 0.406 t 0.221
97/169: cno=138 nprobe=256,max_codes=262144 bounds [perf<=0.862 t>=0.607] skip
98/169: cno=139 nprobe=512,max_codes=262144 bounds [perf<=0.916 t>=0.935] skip
99/169: cno=24 nprobe=2048,max_codes=512 bounds [perf<=0.832 t>=0.534] skip
100/169: cno=161 nprobe=32,max_codes=inf bounds [perf<=0.702 t>=0.279] perf 0.607 t 0.280
101/169: cno=62 nprobe=1024,max_codes=4096 bounds [perf<=0.954 t>=0.777] perf 0.945 t 1.111 *
102/169: cno=163 nprobe=128,max_codes=inf bounds [perf<=0.862 t>=0.426] perf 0.788 t 0.456
103/169: cno=95 nprobe=16,max_codes=32768 bounds [perf<=0.506 t>=0.226] skip
104/169: cno=127 nprobe=1024,max_codes=131072 bounds [perf<=0.954 t>=1.501] skip
105/169: cno=72 nprobe=128,max_codes=8192 bounds [perf<=0.788 t>=0.392] perf 0.788 t 0.432
106/169: cno=149 nprobe=64,max_codes=524288 bounds [perf<=0.702 t>=0.349] skip
107/169: cno=156 nprobe=1,max_codes=inf bounds [perf<=0.310 t>=0.176] perf 0.147 t 0.175
108/169: cno=82 nprobe=16,max_codes=16384 bounds [perf<=0.506 t>=0.226] skip
109/169: cno=166 nprobe=1024,max_codes=inf bounds [perf<=0.987 t>=1.501] perf 0.954 t 1.474
110/169: cno=43 nprobe=16,max_codes=2048 bounds [perf<=0.506 t>=0.226] skip
111/169: cno=150 nprobe=128,max_codes=524288 bounds [perf<=0.788 t>=0.432] skip
112/169: cno=140 nprobe=1024,max_codes=262144 bounds [perf<=0.954 t>=1.501] skip
113/169: cno=7 nprobe=128,max_codes=256 bounds [perf<=0.597 t>=0.225] perf 0.595 t 0.286
114/169: cno=4 nprobe=16,max_codes=256 bounds [perf<=0.504 t>=0.197] perf 0.474 t 0.224
115/169: cno=111 nprobe=128,max_codes=65536 bounds [perf<=0.788 t>=0.432] skip
116/169: cno=116 nprobe=4096,max_codes=65536 bounds [perf<=0.987 t>=3.556] skip
117/169: cno=49 nprobe=1024,max_codes=2048 bounds [perf<=0.904 t>=0.648] perf 0.903 t 0.811
118/169: cno=133 nprobe=8,max_codes=262144 bounds [perf<=0.506 t>=0.221] skip
119/169: cno=128 nprobe=2048,max_codes=131072 bounds [perf<=0.976 t>=2.451] skip
120/169: cno=84 nprobe=64,max_codes=16384 bounds [perf<=0.702 t>=0.349] skip
121/169: cno=81 nprobe=8,max_codes=16384 bounds [perf<=0.406 t>=0.221] skip
122/169: cno=120 nprobe=8,max_codes=131072 bounds [perf<=0.506 t>=0.221] skip
123/169: cno=36 nprobe=1024,max_codes=1024 bounds [perf<=0.832 t>=0.534] skip
124/169: cno=14 nprobe=2,max_codes=512 bounds [perf<=0.224 t>=0.170] perf 0.223 t 0.179
125/169: cno=119 nprobe=4,max_codes=131072 bounds [perf<=0.310 t>=0.197] skip
126/169: cno=17 nprobe=16,max_codes=512 bounds [perf<=0.504 t>=0.224] skip
127/169: cno=52 nprobe=1,max_codes=4096 bounds [perf<=0.147 t>=0.176] skip
128/169: cno=75 nprobe=1024,max_codes=8192 bounds [perf<=0.954 t>=1.111] perf 0.953 t 1.339 *
129/169: cno=107 nprobe=8,max_codes=65536 bounds [perf<=0.506 t>=0.221] skip
130/169: cno=157 nprobe=2,max_codes=inf bounds [perf<=0.310 t>=0.191] skip
131/169: cno=51 nprobe=4096,max_codes=2048 bounds [perf<=0.955 t>=1.217] perf 0.904 t 1.428
132/169: cno=141 nprobe=2048,max_codes=262144 bounds [perf<=0.976 t>=2.451] skip
133/169: cno=19 nprobe=64,max_codes=512 bounds [perf<=0.694 t>=0.234] perf 0.662 t 0.281 *
134/169: cno=112 nprobe=256,max_codes=65536 bounds [perf<=0.862 t>=0.607] skip
135/169: cno=77 nprobe=4096,max_codes=8192 bounds [perf<=0.987 t>=1.922] perf 0.981 t 2.499 *
136/169: cno=145 nprobe=4,max_codes=524288 bounds [perf<=0.310 t>=0.197] skip
137/169: cno=121 nprobe=16,max_codes=131072 bounds [perf<=0.506 t>=0.226] skip
138/169: cno=131 nprobe=2,max_codes=262144 bounds [perf<=0.224 t>=0.191] skip
139/169: cno=123 nprobe=64,max_codes=131072 bounds [perf<=0.702 t>=0.349] skip
140/169: cno=55 nprobe=8,max_codes=4096 bounds [perf<=0.406 t>=0.206] perf 0.406 t 0.210
141/169: cno=91 nprobe=1,max_codes=32768 bounds [perf<=0.147 t>=0.176] skip
142/169: cno=148 nprobe=32,max_codes=524288 bounds [perf<=0.607 t>=0.279] skip
143/169: cno=104 nprobe=1,max_codes=65536 bounds [perf<=0.147 t>=0.176] skip
144/169: cno=28 nprobe=4,max_codes=1024 bounds [perf<=0.310 t>=0.197] skip
145/169: cno=13 nprobe=1,max_codes=512 bounds [perf<=0.147 t>=0.162] skip
146/169: cno=105 nprobe=2,max_codes=65536 bounds [perf<=0.224 t>=0.191] skip
147/169: cno=59 nprobe=128,max_codes=4096 bounds [perf<=0.788 t>=0.392] perf 0.788 t 0.438
148/169: cno=129 nprobe=4096,max_codes=131072 bounds [perf<=0.987 t>=3.556] skip
149/169: cno=115 nprobe=2048,max_codes=65536 bounds [perf<=0.976 t>=2.451] skip
150/169: cno=135 nprobe=32,max_codes=262144 bounds [perf<=0.607 t>=0.279] skip
151/169: cno=134 nprobe=16,max_codes=262144 bounds [perf<=0.506 t>=0.226] skip
152/169: cno=6 nprobe=64,max_codes=256 bounds [perf<=0.595 t>=0.225] perf 0.583 t 0.243
153/169: cno=103 nprobe=4096,max_codes=32768 bounds [perf<=0.987 t>=3.556] skip
154/169: cno=99 nprobe=256,max_codes=32768 bounds [perf<=0.862 t>=0.607] skip
155/169: cno=87 nprobe=512,max_codes=16384 bounds [perf<=0.916 t>=0.864] perf 0.916 t 0.913 *
156/169: cno=39 nprobe=1,max_codes=2048 bounds [perf<=0.147 t>=0.176] skip
157/169: cno=63 nprobe=2048,max_codes=4096 bounds [perf<=0.955 t>=1.111] perf 0.955 t 1.392 *
158/169: cno=33 nprobe=128,max_codes=1024 bounds [perf<=0.785 t>=0.312] perf 0.769 t 0.348 *
159/169: cno=117 nprobe=1,max_codes=131072 bounds [perf<=0.147 t>=0.176] skip
160/169: cno=165 nprobe=512,max_codes=inf bounds [perf<=0.954 t>=0.935] perf 0.916 t 0.919
161/169: cno=37 nprobe=2048,max_codes=1024 bounds [perf<=0.832 t>=0.534] skip
162/169: cno=101 nprobe=1024,max_codes=32768 bounds [perf<=0.954 t>=1.473] skip
163/169: cno=160 nprobe=16,max_codes=inf bounds [perf<=0.607 t>=0.226] perf 0.506 t 0.229
164/169: cno=159 nprobe=8,max_codes=inf bounds [perf<=0.506 t>=0.221] skip
165/169: cno=89 nprobe=2048,max_codes=16384 bounds [perf<=0.976 t>=1.922] perf 0.976 t 2.327 *
166/169: cno=35 nprobe=512,max_codes=1024 bounds [perf<=0.832 t>=0.429] perf 0.830 t 0.515
167/169: cno=9 nprobe=512,max_codes=256 bounds [perf<=0.597 t>=0.308] skip
168/169: cno=167 nprobe=2048,max_codes=inf bounds [perf<=0.987 t>=2.451] perf 0.976 t 2.471
[102.575 s] result operating points:
Tested 96 operating points, 35 ones are optimal:
cno=-1 key= perf=0.0000 t=0.000
cno=143 key=nprobe=1,max_codes=524288 perf=0.1475 t=0.156
cno=1 key=nprobe=2,max_codes=256 perf=0.2220 t=0.170
cno=144 key=nprobe=2,max_codes=524288 perf=0.2244 t=0.175
cno=3 key=nprobe=8,max_codes=256 perf=0.3904 t=0.182
cno=29 key=nprobe=8,max_codes=1024 perf=0.4046 t=0.197
cno=42 key=nprobe=8,max_codes=2048 perf=0.4060 t=0.206
cno=147 key=nprobe=16,max_codes=524288 perf=0.5063 t=0.207
cno=5 key=nprobe=32,max_codes=256 perf=0.5411 t=0.225
cno=18 key=nprobe=32,max_codes=512 perf=0.5853 t=0.234
cno=122 key=nprobe=32,max_codes=131072 perf=0.6065 t=0.252
cno=19 key=nprobe=64,max_codes=512 perf=0.6619 t=0.281
cno=32 key=nprobe=64,max_codes=1024 perf=0.6941 t=0.290
cno=45 key=nprobe=64,max_codes=2048 perf=0.7003 t=0.308
cno=20 key=nprobe=128,max_codes=512 perf=0.7075 t=0.312
cno=21 key=nprobe=256,max_codes=512 perf=0.7220 t=0.342
cno=33 key=nprobe=128,max_codes=1024 perf=0.7694 t=0.348
cno=46 key=nprobe=128,max_codes=2048 perf=0.7850 t=0.392
cno=85 key=nprobe=128,max_codes=16384 perf=0.7885 t=0.419
cno=34 key=nprobe=256,max_codes=1024 perf=0.8168 t=0.429
cno=47 key=nprobe=256,max_codes=2048 perf=0.8537 t=0.501
cno=60 key=nprobe=256,max_codes=4096 perf=0.8610 t=0.566
cno=151 key=nprobe=256,max_codes=524288 perf=0.8622 t=0.602
cno=48 key=nprobe=512,max_codes=2048 perf=0.8921 t=0.648
cno=61 key=nprobe=512,max_codes=4096 perf=0.9130 t=0.777
cno=74 key=nprobe=512,max_codes=8192 perf=0.9158 t=0.864
cno=87 key=nprobe=512,max_codes=16384 perf=0.9159 t=0.913
cno=62 key=nprobe=1024,max_codes=4096 perf=0.9450 t=1.111
cno=75 key=nprobe=1024,max_codes=8192 perf=0.9532 t=1.339
cno=63 key=nprobe=2048,max_codes=4096 perf=0.9551 t=1.392
cno=64 key=nprobe=4096,max_codes=4096 perf=0.9553 t=1.809
cno=76 key=nprobe=2048,max_codes=8192 perf=0.9744 t=1.922
cno=89 key=nprobe=2048,max_codes=16384 perf=0.9757 t=2.327
cno=77 key=nprobe=4096,max_codes=8192 perf=0.9808 t=2.499
cno=90 key=nprobe=4096,max_codes=16384 perf=0.9872 t=3.556
Traceback (most recent call last):
File "tests/demo_auto_tune.py", line 170, in <module>
fig.savefig('tmp/demo_auto_tune.png')
File "/home/JIAO/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1572, in savefig
self.canvas.print_figure(*args, **kwargs)
File "/home/JIAO/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2244, in print_figure
**kwargs)
File "/home/JIAO/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 550, in print_png
filename_or_obj = open(filename_or_obj, 'wb')
IOError: [Errno 2] No such file or directory: 'tmp/demo_auto_tune.png'
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ python -c "import faiss, numpy
>
> faiss.Kmeans(10, 20).train(numpy.random.rand(1000, 10).astype('float32'))"
Failed to load GPU Faiss: No module named swigfaiss_gpu
Faiss falling back to CPU-only.
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ export PYTHONPATH=.
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ mkdir tmp
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ python tests/demo_auto_tune.py
Failed to load GPU Faiss: No module named swigfaiss_gpu
Faiss falling back to CPU-only.
load data
load GT
prepare criterion
============ key IMI2x10,Flat
[0.417 s] train & add
[35.935 s] explore op points
0/169: cno=0 nprobe=1,max_codes=256 bounds [perf<=1.000 t>=0.000] perf 0.147 t 0.207 *
1/169: cno=168 nprobe=4096,max_codes=inf bounds [perf<=1.000 t>=0.207] perf 0.987 t 4.261 *
2/169: cno=58 nprobe=64,max_codes=4096 bounds [perf<=0.987 t>=0.207] perf 0.702 t 0.335 *
3/169: cno=124 nprobe=128,max_codes=131072 bounds [perf<=0.987 t>=0.335] perf 0.788 t 0.460 *
4/169: cno=69 nprobe=16,max_codes=8192 bounds [perf<=0.788 t>=0.207] perf 0.506 t 0.222 *
5/169: cno=142 nprobe=4096,max_codes=262144 bounds [perf<=0.987 t>=0.460] perf 0.987 t 4.355
6/169: cno=12 nprobe=4096,max_codes=256 bounds [perf<=0.987 t>=0.207] perf 0.597 t 1.146
7/169: cno=108 nprobe=16,max_codes=65536 bounds [perf<=0.788 t>=0.222] perf 0.506 t 0.226
8/169: cno=53 nprobe=2,max_codes=4096 bounds [perf<=0.506 t>=0.207] perf 0.224 t 0.190 *
9/169: cno=114 nprobe=1024,max_codes=65536 bounds [perf<=0.987 t>=0.335] perf 0.954 t 1.575 *
10/169: cno=147 nprobe=16,max_codes=524288 bounds [perf<=0.987 t>=0.226] perf 0.506 t 0.225
11/169: cno=64 nprobe=4096,max_codes=4096 bounds [perf<=0.987 t>=1.146] perf 0.955 t 1.921 *
12/169: cno=96 nprobe=32,max_codes=32768 bounds [perf<=0.788 t>=0.222] perf 0.607 t 0.262 *
13/169: cno=94 nprobe=8,max_codes=32768 bounds [perf<=0.506 t>=0.207] perf 0.406 t 0.194 *
14/169: cno=3 nprobe=8,max_codes=256 bounds [perf<=0.406 t>=0.207] skip
15/169: cno=20 nprobe=128,max_codes=512 bounds [perf<=0.788 t>=0.207] perf 0.708 t 0.306 *
16/169: cno=50 nprobe=2048,max_codes=2048 bounds [perf<=0.955 t>=0.306] perf 0.904 t 1.032 *
17/169: cno=26 nprobe=1,max_codes=1024 bounds [perf<=0.224 t>=0.207] skip
18/169: cno=61 nprobe=512,max_codes=4096 bounds [perf<=0.954 t>=0.335] perf 0.913 t 0.985 *
19/169: cno=66 nprobe=2,max_codes=8192 bounds [perf<=0.406 t>=0.207] skip
20/169: cno=110 nprobe=64,max_codes=65536 bounds [perf<=0.788 t>=0.335] perf 0.702 t 0.390
21/169: cno=102 nprobe=2048,max_codes=32768 bounds [perf<=0.987 t>=1.032] perf 0.976 t 2.513 *
22/169: cno=136 nprobe=64,max_codes=262144 bounds [perf<=0.987 t>=0.390] perf 0.702 t 0.333
23/169: cno=2 nprobe=4,max_codes=256 bounds [perf<=0.406 t>=0.207] skip
24/169: cno=122 nprobe=32,max_codes=131072 bounds [perf<=0.702 t>=0.262] perf 0.607 t 0.278
25/169: cno=146 nprobe=8,max_codes=524288 bounds [perf<=0.506 t>=0.207] perf 0.406 t 0.204
26/169: cno=10 nprobe=1024,max_codes=256 bounds [perf<=0.597 t>=0.207] perf 0.597 t 0.517
27/169: cno=48 nprobe=512,max_codes=2048 bounds [perf<=0.904 t>=0.306] perf 0.892 t 0.656 *
28/169: cno=21 nprobe=256,max_codes=512 bounds [perf<=0.892 t>=0.306] perf 0.722 t 0.356 *
29/169: cno=126 nprobe=512,max_codes=131072 bounds [perf<=0.987 t>=0.985] perf 0.916 t 0.929 *
30/169: cno=60 nprobe=256,max_codes=4096 bounds [perf<=0.913 t>=0.356] perf 0.861 t 0.585 *
31/169: cno=92 nprobe=2,max_codes=32768 bounds [perf<=0.406 t>=0.207] skip
32/169: cno=130 nprobe=1,max_codes=262144 bounds [perf<=0.406 t>=0.207] skip
33/169: cno=74 nprobe=512,max_codes=8192 bounds [perf<=0.916 t>=0.985] skip
34/169: cno=93 nprobe=4,max_codes=32768 bounds [perf<=0.406 t>=0.207] skip
35/169: cno=22 nprobe=512,max_codes=512 bounds [perf<=0.892 t>=0.356] perf 0.724 t 0.430 *
36/169: cno=85 nprobe=128,max_codes=16384 bounds [perf<=0.788 t>=0.335] perf 0.788 t 0.419 *
37/169: cno=54 nprobe=4,max_codes=4096 bounds [perf<=0.406 t>=0.207] skip
38/169: cno=83 nprobe=32,max_codes=16384 bounds [perf<=0.607 t>=0.222] perf 0.607 t 0.264
39/169: cno=1 nprobe=2,max_codes=256 bounds [perf<=0.224 t>=0.207] skip
40/169: cno=151 nprobe=256,max_codes=524288 bounds [perf<=0.987 t>=0.585] perf 0.862 t 0.600 *
41/169: cno=15 nprobe=4,max_codes=512 bounds [perf<=0.406 t>=0.207] skip
42/169: cno=100 nprobe=512,max_codes=32768 bounds [perf<=0.916 t>=0.985] skip
43/169: cno=32 nprobe=64,max_codes=1024 bounds [perf<=0.702 t>=0.207] perf 0.694 t 0.303 *
44/169: cno=46 nprobe=128,max_codes=2048 bounds [perf<=0.788 t>=0.306] perf 0.785 t 0.386 *
45/169: cno=78 nprobe=1,max_codes=16384 bounds [perf<=0.406 t>=0.207] skip
46/169: cno=45 nprobe=64,max_codes=2048 bounds [perf<=0.702 t>=0.303] perf 0.700 t 0.324
47/169: cno=27 nprobe=2,max_codes=1024 bounds [perf<=0.224 t>=0.207] skip
48/169: cno=29 nprobe=8,max_codes=1024 bounds [perf<=0.406 t>=0.207] skip
49/169: cno=162 nprobe=64,max_codes=inf bounds [perf<=0.987 t>=0.390] perf 0.702 t 0.327
50/169: cno=113 nprobe=512,max_codes=65536 bounds [perf<=0.916 t>=0.985] skip
51/169: cno=44 nprobe=32,max_codes=2048 bounds [perf<=0.607 t>=0.207] perf 0.606 t 0.282
52/169: cno=152 nprobe=512,max_codes=524288 bounds [perf<=0.987 t>=0.985] perf 0.916 t 0.917 *
53/169: cno=154 nprobe=2048,max_codes=524288 bounds [perf<=0.987 t>=2.513] perf 0.976 t 2.429 *
54/169: cno=41 nprobe=4,max_codes=2048 bounds [perf<=0.406 t>=0.207] skip
55/169: cno=40 nprobe=2,max_codes=2048 bounds [perf<=0.224 t>=0.207] skip
56/169: cno=155 nprobe=4096,max_codes=524288 bounds [perf<=0.987 t>=4.355] skip
57/169: cno=98 nprobe=128,max_codes=32768 bounds [perf<=0.788 t>=0.419] skip
58/169: cno=76 nprobe=2048,max_codes=8192 bounds [perf<=0.976 t>=1.032] perf 0.974 t 1.923 *
59/169: cno=118 nprobe=2,max_codes=131072 bounds [perf<=0.406 t>=0.207] skip
60/169: cno=34 nprobe=256,max_codes=1024 bounds [perf<=0.861 t>=0.356] perf 0.817 t 0.430 *
61/169: cno=23 nprobe=1024,max_codes=512 bounds [perf<=0.904 t>=0.517] perf 0.724 t 0.550
62/169: cno=73 nprobe=256,max_codes=8192 bounds [perf<=0.862 t>=0.585] perf 0.862 t 0.614
63/169: cno=30 nprobe=16,max_codes=1024 bounds [perf<=0.506 t>=0.207] perf 0.504 t 0.218 *
64/169: cno=97 nprobe=64,max_codes=32768 bounds [perf<=0.702 t>=0.335] skip
65/169: cno=31 nprobe=32,max_codes=1024 bounds [perf<=0.606 t>=0.218] perf 0.603 t 0.241 *
66/169: cno=47 nprobe=256,max_codes=2048 bounds [perf<=0.861 t>=0.430] perf 0.854 t 0.512 *
67/169: cno=80 nprobe=4,max_codes=16384 bounds [perf<=0.406 t>=0.207] skip
68/169: cno=18 nprobe=32,max_codes=512 bounds [perf<=0.603 t>=0.207] perf 0.585 t 0.223 *
69/169: cno=70 nprobe=32,max_codes=8192 bounds [perf<=0.607 t>=0.282] skip
70/169: cno=143 nprobe=1,max_codes=524288 bounds [perf<=0.406 t>=0.207] skip
71/169: cno=144 nprobe=2,max_codes=524288 bounds [perf<=0.406 t>=0.207] skip
72/169: cno=38 nprobe=4096,max_codes=1024 bounds [perf<=0.955 t>=1.146] perf 0.832 t 1.324
73/169: cno=8 nprobe=256,max_codes=256 bounds [perf<=0.597 t>=0.207] perf 0.597 t 0.327
74/169: cno=5 nprobe=32,max_codes=256 bounds [perf<=0.585 t>=0.207] perf 0.541 t 0.234
75/169: cno=90 nprobe=4096,max_codes=16384 bounds [perf<=0.987 t>=1.923] perf 0.987 t 3.667 *
76/169: cno=16 nprobe=8,max_codes=512 bounds [perf<=0.406 t>=0.207] skip
77/169: cno=42 nprobe=8,max_codes=2048 bounds [perf<=0.406 t>=0.207] skip
78/169: cno=109 nprobe=32,max_codes=65536 bounds [perf<=0.607 t>=0.282] skip
79/169: cno=71 nprobe=64,max_codes=8192 bounds [perf<=0.702 t>=0.335] skip
80/169: cno=164 nprobe=256,max_codes=inf bounds [perf<=0.987 t>=0.614] perf 0.862 t 0.715
81/169: cno=25 nprobe=4096,max_codes=512 bounds [perf<=0.832 t>=1.146] skip
82/169: cno=57 nprobe=32,max_codes=4096 bounds [perf<=0.607 t>=0.282] skip
83/169: cno=11 nprobe=2048,max_codes=256 bounds [perf<=0.597 t>=0.517] skip
84/169: cno=137 nprobe=128,max_codes=262144 bounds [perf<=0.862 t>=0.460] perf 0.788 t 0.489
85/169: cno=86 nprobe=256,max_codes=16384 bounds [perf<=0.862 t>=0.614] skip
86/169: cno=79 nprobe=2,max_codes=16384 bounds [perf<=0.406 t>=0.207] skip
87/169: cno=158 nprobe=4,max_codes=inf bounds [perf<=0.702 t>=0.207] perf 0.310 t 0.197
88/169: cno=106 nprobe=4,max_codes=65536 bounds [perf<=0.310 t>=0.207] skip
89/169: cno=56 nprobe=16,max_codes=4096 bounds [perf<=0.506 t>=0.218] perf 0.506 t 0.220 *
90/169: cno=125 nprobe=256,max_codes=131072 bounds [perf<=0.862 t>=0.614] skip
91/169: cno=88 nprobe=1024,max_codes=16384 bounds [perf<=0.954 t>=0.985] perf 0.954 t 1.592
92/169: cno=132 nprobe=4,max_codes=262144 bounds [perf<=0.310 t>=0.207] skip
93/169: cno=67 nprobe=4,max_codes=8192 bounds [perf<=0.310 t>=0.207] skip
94/169: cno=153 nprobe=1024,max_codes=524288 bounds [perf<=0.976 t>=1.592] perf 0.954 t 1.567 *
95/169: cno=65 nprobe=1,max_codes=8192 bounds [perf<=0.310 t>=0.207] skip
96/169: cno=68 nprobe=8,max_codes=8192 bounds [perf<=0.406 t>=0.207] skip
97/169: cno=138 nprobe=256,max_codes=262144 bounds [perf<=0.862 t>=0.614] skip
98/169: cno=139 nprobe=512,max_codes=262144 bounds [perf<=0.916 t>=0.985] skip
99/169: cno=24 nprobe=2048,max_codes=512 bounds [perf<=0.832 t>=0.550] skip
100/169: cno=161 nprobe=32,max_codes=inf bounds [perf<=0.702 t>=0.282] perf 0.607 t 0.230 *
101/169: cno=62 nprobe=1024,max_codes=4096 bounds [perf<=0.954 t>=0.985] perf 0.945 t 1.081 *
102/169: cno=163 nprobe=128,max_codes=inf bounds [perf<=0.862 t>=0.489] perf 0.788 t 0.426
103/169: cno=95 nprobe=16,max_codes=32768 bounds [perf<=0.506 t>=0.222] skip
104/169: cno=127 nprobe=1024,max_codes=131072 bounds [perf<=0.954 t>=1.592] skip
105/169: cno=72 nprobe=128,max_codes=8192 bounds [perf<=0.788 t>=0.386] perf 0.788 t 0.438
106/169: cno=149 nprobe=64,max_codes=524288 bounds [perf<=0.702 t>=0.390] skip
107/169: cno=156 nprobe=1,max_codes=inf bounds [perf<=0.310 t>=0.207] skip
108/169: cno=82 nprobe=16,max_codes=16384 bounds [perf<=0.506 t>=0.222] skip
109/169: cno=166 nprobe=1024,max_codes=inf bounds [perf<=0.987 t>=1.592] perf 0.954 t 1.625
110/169: cno=43 nprobe=16,max_codes=2048 bounds [perf<=0.506 t>=0.218] perf 0.506 t 0.231
111/169: cno=150 nprobe=128,max_codes=524288 bounds [perf<=0.788 t>=0.489] skip
112/169: cno=140 nprobe=1024,max_codes=262144 bounds [perf<=0.954 t>=1.592] skip
113/169: cno=7 nprobe=128,max_codes=256 bounds [perf<=0.597 t>=0.234] skip
114/169: cno=4 nprobe=16,max_codes=256 bounds [perf<=0.504 t>=0.207] perf 0.474 t 0.203 *
115/169: cno=111 nprobe=128,max_codes=65536 bounds [perf<=0.788 t>=0.438] skip
116/169: cno=116 nprobe=4096,max_codes=65536 bounds [perf<=0.987 t>=3.667] skip
117/169: cno=49 nprobe=1024,max_codes=2048 bounds [perf<=0.904 t>=0.656] perf 0.903 t 0.793 *
118/169: cno=133 nprobe=8,max_codes=262144 bounds [perf<=0.406 t>=0.207] skip
119/169: cno=128 nprobe=2048,max_codes=131072 bounds [perf<=0.976 t>=2.513] skip
120/169: cno=84 nprobe=64,max_codes=16384 bounds [perf<=0.702 t>=0.335] skip
121/169: cno=81 nprobe=8,max_codes=16384 bounds [perf<=0.406 t>=0.207] skip
122/169: cno=120 nprobe=8,max_codes=131072 bounds [perf<=0.406 t>=0.207] skip
123/169: cno=36 nprobe=1024,max_codes=1024 bounds [perf<=0.832 t>=0.550] skip
124/169: cno=14 nprobe=2,max_codes=512 bounds [perf<=0.224 t>=0.207] skip
125/169: cno=119 nprobe=4,max_codes=131072 bounds [perf<=0.310 t>=0.207] skip
126/169: cno=17 nprobe=16,max_codes=512 bounds [perf<=0.504 t>=0.207] perf 0.495 t 0.216 *
127/169: cno=52 nprobe=1,max_codes=4096 bounds [perf<=0.224 t>=0.207] skip
128/169: cno=75 nprobe=1024,max_codes=8192 bounds [perf<=0.954 t>=1.081] perf 0.953 t 1.314 *
129/169: cno=107 nprobe=8,max_codes=65536 bounds [perf<=0.406 t>=0.207] skip
130/169: cno=157 nprobe=2,max_codes=inf bounds [perf<=0.310 t>=0.207] skip
131/169: cno=51 nprobe=4096,max_codes=2048 bounds [perf<=0.955 t>=1.324] perf 0.904 t 1.415
132/169: cno=141 nprobe=2048,max_codes=262144 bounds [perf<=0.976 t>=2.513] skip
133/169: cno=19 nprobe=64,max_codes=512 bounds [perf<=0.694 t>=0.234] perf 0.662 t 0.277 *
134/169: cno=112 nprobe=256,max_codes=65536 bounds [perf<=0.862 t>=0.614] skip
135/169: cno=77 nprobe=4096,max_codes=8192 bounds [perf<=0.987 t>=1.923] perf 0.981 t 2.625 *
136/169: cno=145 nprobe=4,max_codes=524288 bounds [perf<=0.310 t>=0.207] skip
137/169: cno=121 nprobe=16,max_codes=131072 bounds [perf<=0.506 t>=0.231] skip
138/169: cno=131 nprobe=2,max_codes=262144 bounds [perf<=0.310 t>=0.207] skip
139/169: cno=123 nprobe=64,max_codes=131072 bounds [perf<=0.702 t>=0.390] skip
140/169: cno=55 nprobe=8,max_codes=4096 bounds [perf<=0.406 t>=0.207] skip
141/169: cno=91 nprobe=1,max_codes=32768 bounds [perf<=0.310 t>=0.207] skip
142/169: cno=148 nprobe=32,max_codes=524288 bounds [perf<=0.607 t>=0.282] skip
143/169: cno=104 nprobe=1,max_codes=65536 bounds [perf<=0.310 t>=0.207] skip
144/169: cno=28 nprobe=4,max_codes=1024 bounds [perf<=0.310 t>=0.207] skip
145/169: cno=13 nprobe=1,max_codes=512 bounds [perf<=0.224 t>=0.207] skip
146/169: cno=105 nprobe=2,max_codes=65536 bounds [perf<=0.310 t>=0.207] skip
147/169: cno=59 nprobe=128,max_codes=4096 bounds [perf<=0.788 t>=0.386] perf 0.788 t 0.422
148/169: cno=129 nprobe=4096,max_codes=131072 bounds [perf<=0.987 t>=3.667] skip
149/169: cno=115 nprobe=2048,max_codes=65536 bounds [perf<=0.976 t>=2.513] skip
150/169: cno=135 nprobe=32,max_codes=262144 bounds [perf<=0.607 t>=0.282] skip
151/169: cno=134 nprobe=16,max_codes=262144 bounds [perf<=0.506 t>=0.231] skip
152/169: cno=6 nprobe=64,max_codes=256 bounds [perf<=0.597 t>=0.234] skip
153/169: cno=103 nprobe=4096,max_codes=32768 bounds [perf<=0.987 t>=3.667] skip
154/169: cno=99 nprobe=256,max_codes=32768 bounds [perf<=0.862 t>=0.614] skip
155/169: cno=87 nprobe=512,max_codes=16384 bounds [perf<=0.916 t>=0.985] skip
156/169: cno=39 nprobe=1,max_codes=2048 bounds [perf<=0.224 t>=0.207] skip
157/169: cno=63 nprobe=2048,max_codes=4096 bounds [perf<=0.955 t>=1.081] perf 0.955 t 1.355 *
158/169: cno=33 nprobe=128,max_codes=1024 bounds [perf<=0.785 t>=0.306] perf 0.769 t 0.361 *
159/169: cno=117 nprobe=1,max_codes=131072 bounds [perf<=0.310 t>=0.207] skip
160/169: cno=165 nprobe=512,max_codes=inf bounds [perf<=0.954 t>=0.985] perf 0.916 t 0.932
161/169: cno=37 nprobe=2048,max_codes=1024 bounds [perf<=0.832 t>=0.550] skip
162/169: cno=101 nprobe=1024,max_codes=32768 bounds [perf<=0.954 t>=1.592] skip
163/169: cno=160 nprobe=16,max_codes=inf bounds [perf<=0.607 t>=0.231] skip
164/169: cno=159 nprobe=8,max_codes=inf bounds [perf<=0.607 t>=0.207] perf 0.406 t 0.201
165/169: cno=89 nprobe=2048,max_codes=16384 bounds [perf<=0.976 t>=1.923] perf 0.976 t 2.303 *
166/169: cno=35 nprobe=512,max_codes=1024 bounds [perf<=0.832 t>=0.430] perf 0.830 t 0.525
167/169: cno=9 nprobe=512,max_codes=256 bounds [perf<=0.597 t>=0.327] skip
168/169: cno=167 nprobe=2048,max_codes=inf bounds [perf<=0.987 t>=2.513] perf 0.976 t 2.413
[101.786 s] result operating points:
Tested 77 operating points, 31 ones are optimal:
cno=-1 key= perf=0.0000 t=0.000
cno=53 key=nprobe=2,max_codes=4096 perf=0.2244 t=0.190
cno=94 key=nprobe=8,max_codes=32768 perf=0.4062 t=0.194
cno=4 key=nprobe=16,max_codes=256 perf=0.4743 t=0.203
cno=17 key=nprobe=16,max_codes=512 perf=0.4954 t=0.216
cno=30 key=nprobe=16,max_codes=1024 perf=0.5041 t=0.218
cno=56 key=nprobe=16,max_codes=4096 perf=0.5063 t=0.220
cno=18 key=nprobe=32,max_codes=512 perf=0.5853 t=0.223
cno=161 key=nprobe=32,max_codes=inf perf=0.6065 t=0.230
cno=19 key=nprobe=64,max_codes=512 perf=0.6619 t=0.277
cno=32 key=nprobe=64,max_codes=1024 perf=0.6941 t=0.303
cno=20 key=nprobe=128,max_codes=512 perf=0.7075 t=0.306
cno=21 key=nprobe=256,max_codes=512 perf=0.7220 t=0.356
cno=33 key=nprobe=128,max_codes=1024 perf=0.7694 t=0.361
cno=46 key=nprobe=128,max_codes=2048 perf=0.7850 t=0.386
cno=85 key=nprobe=128,max_codes=16384 perf=0.7885 t=0.419
cno=34 key=nprobe=256,max_codes=1024 perf=0.8168 t=0.430
cno=47 key=nprobe=256,max_codes=2048 perf=0.8537 t=0.512
cno=60 key=nprobe=256,max_codes=4096 perf=0.8610 t=0.585
cno=151 key=nprobe=256,max_codes=524288 perf=0.8622 t=0.600
cno=48 key=nprobe=512,max_codes=2048 perf=0.8921 t=0.656
cno=49 key=nprobe=1024,max_codes=2048 perf=0.9031 t=0.793
cno=152 key=nprobe=512,max_codes=524288 perf=0.9159 t=0.917
cno=62 key=nprobe=1024,max_codes=4096 perf=0.9450 t=1.081
cno=75 key=nprobe=1024,max_codes=8192 perf=0.9532 t=1.314
cno=63 key=nprobe=2048,max_codes=4096 perf=0.9551 t=1.355
cno=64 key=nprobe=4096,max_codes=4096 perf=0.9553 t=1.921
cno=76 key=nprobe=2048,max_codes=8192 perf=0.9744 t=1.923
cno=89 key=nprobe=2048,max_codes=16384 perf=0.9757 t=2.303
cno=77 key=nprobe=4096,max_codes=8192 perf=0.9808 t=2.625
cno=90 key=nprobe=4096,max_codes=16384 perf=0.9872 t=3.667
============ key IMI2x11,Flat
[102.288 s] train & add
[175.146 s] explore op points
0/169: cno=0 nprobe=1,max_codes=256 bounds [perf<=1.000 t>=0.000] perf 0.120 t 0.208 *
1/169: cno=168 nprobe=4096,max_codes=inf bounds [perf<=1.000 t>=0.208] perf 0.971 t 2.605 *
2/169: cno=58 nprobe=64,max_codes=4096 bounds [perf<=0.971 t>=0.208] perf 0.607 t 0.328 *
3/169: cno=124 nprobe=128,max_codes=131072 bounds [perf<=0.971 t>=0.328] perf 0.698 t 0.354 *
4/169: cno=69 nprobe=16,max_codes=8192 bounds [perf<=0.698 t>=0.208] perf 0.426 t 0.245 *
5/169: cno=142 nprobe=4096,max_codes=262144 bounds [perf<=0.971 t>=0.354] perf 0.971 t 2.543 *
6/169: cno=12 nprobe=4096,max_codes=256 bounds [perf<=0.971 t>=0.208] perf 0.685 t 1.265
7/169: cno=108 nprobe=16,max_codes=65536 bounds [perf<=0.698 t>=0.245] perf 0.426 t 0.254
8/169: cno=53 nprobe=2,max_codes=4096 bounds [perf<=0.426 t>=0.208] perf 0.182 t 0.203 *
9/169: cno=114 nprobe=1024,max_codes=65536 bounds [perf<=0.971 t>=0.328] perf 0.907 t 0.971 *
10/169: cno=147 nprobe=16,max_codes=524288 bounds [perf<=0.971 t>=0.254] perf 0.426 t 0.247
11/169: cno=64 nprobe=4096,max_codes=4096 bounds [perf<=0.971 t>=1.265] perf 0.966 t 2.035 *
12/169: cno=96 nprobe=32,max_codes=32768 bounds [perf<=0.698 t>=0.245] perf 0.517 t 0.272 *
13/169: cno=94 nprobe=8,max_codes=32768 bounds [perf<=0.426 t>=0.208] perf 0.335 t 0.230 *
14/169: cno=3 nprobe=8,max_codes=256 bounds [perf<=0.335 t>=0.208] perf 0.332 t 0.225 *
15/169: cno=20 nprobe=128,max_codes=512 bounds [perf<=0.698 t>=0.225] perf 0.683 t 0.282 *
16/169: cno=50 nprobe=2048,max_codes=2048 bounds [perf<=0.966 t>=0.282] perf 0.930 t 1.142 *
17/169: cno=26 nprobe=1,max_codes=1024 bounds [perf<=0.182 t>=0.208] skip
18/169: cno=61 nprobe=512,max_codes=4096 bounds [perf<=0.907 t>=0.328] perf 0.854 t 0.638 *
19/169: cno=66 nprobe=2,max_codes=8192 bounds [perf<=0.335 t>=0.208] perf 0.182 t 0.196 *
20/169: cno=110 nprobe=64,max_codes=65536 bounds [perf<=0.698 t>=0.328] perf 0.607 t 0.277 *
21/169: cno=102 nprobe=2048,max_codes=32768 bounds [perf<=0.971 t>=1.142] perf 0.944 t 1.541 *
22/169: cno=136 nprobe=64,max_codes=262144 bounds [perf<=0.971 t>=0.328] perf 0.607 t 0.291
23/169: cno=2 nprobe=4,max_codes=256 bounds [perf<=0.332 t>=0.208] perf 0.256 t 0.226
24/169: cno=122 nprobe=32,max_codes=131072 bounds [perf<=0.607 t>=0.272] perf 0.517 t 0.272 *
25/169: cno=146 nprobe=8,max_codes=524288 bounds [perf<=0.426 t>=0.230] perf 0.335 t 0.208 *
26/169: cno=10 nprobe=1024,max_codes=256 bounds [perf<=0.685 t>=0.226] perf 0.684 t 0.552
27/169: cno=48 nprobe=512,max_codes=2048 bounds [perf<=0.854 t>=0.282] perf 0.852 t 0.603 *
28/169: cno=21 nprobe=256,max_codes=512 bounds [perf<=0.852 t>=0.282] perf 0.749 t 0.388 *
29/169: cno=126 nprobe=512,max_codes=131072 bounds [perf<=0.971 t>=0.638] perf 0.854 t 0.638 *
30/169: cno=60 nprobe=256,max_codes=4096 bounds [perf<=0.854 t>=0.388] perf 0.781 t 0.476 *
31/169: cno=92 nprobe=2,max_codes=32768 bounds [perf<=0.335 t>=0.208] skip
32/169: cno=130 nprobe=1,max_codes=262144 bounds [perf<=0.335 t>=0.208] skip
33/169: cno=74 nprobe=512,max_codes=8192 bounds [perf<=0.854 t>=0.638] skip
34/169: cno=93 nprobe=4,max_codes=32768 bounds [perf<=0.335 t>=0.226] skip
35/169: cno=22 nprobe=512,max_codes=512 bounds [perf<=0.852 t>=0.388] perf 0.786 t 0.478 *
36/169: cno=85 nprobe=128,max_codes=16384 bounds [perf<=0.698 t>=0.328] perf 0.698 t 0.368
37/169: cno=54 nprobe=4,max_codes=4096 bounds [perf<=0.335 t>=0.226] skip
38/169: cno=83 nprobe=32,max_codes=16384 bounds [perf<=0.517 t>=0.245] perf 0.517 t 0.232 *
39/169: cno=1 nprobe=2,max_codes=256 bounds [perf<=0.182 t>=0.208] skip
40/169: cno=151 nprobe=256,max_codes=524288 bounds [perf<=0.971 t>=0.476] perf 0.781 t 0.455 *
41/169: cno=15 nprobe=4,max_codes=512 bounds [perf<=0.335 t>=0.226] skip
42/169: cno=100 nprobe=512,max_codes=32768 bounds [perf<=0.854 t>=0.638] skip
43/169: cno=32 nprobe=64,max_codes=1024 bounds [perf<=0.607 t>=0.226] perf 0.606 t 0.305
44/169: cno=46 nprobe=128,max_codes=2048 bounds [perf<=0.698 t>=0.305] perf 0.698 t 0.336 *
45/169: cno=78 nprobe=1,max_codes=16384 bounds [perf<=0.335 t>=0.208] skip
46/169: cno=45 nprobe=64,max_codes=2048 bounds [perf<=0.607 t>=0.305] skip
47/169: cno=27 nprobe=2,max_codes=1024 bounds [perf<=0.182 t>=0.208] skip
48/169: cno=29 nprobe=8,max_codes=1024 bounds [perf<=0.335 t>=0.226] skip
49/169: cno=162 nprobe=64,max_codes=inf bounds [perf<=0.971 t>=0.328] perf 0.607 t 0.266 *
50/169: cno=113 nprobe=512,max_codes=65536 bounds [perf<=0.854 t>=0.638] skip
51/169: cno=44 nprobe=32,max_codes=2048 bounds [perf<=0.517 t>=0.226] perf 0.517 t 0.259
52/169: cno=152 nprobe=512,max_codes=524288 bounds [perf<=0.971 t>=0.638] perf 0.854 t 0.637 *
53/169: cno=154 nprobe=2048,max_codes=524288 bounds [perf<=0.971 t>=1.541] perf 0.944 t 1.532 *
54/169: cno=41 nprobe=4,max_codes=2048 bounds [perf<=0.335 t>=0.226] skip
55/169: cno=40 nprobe=2,max_codes=2048 bounds [perf<=0.182 t>=0.208] skip
56/169: cno=155 nprobe=4096,max_codes=524288 bounds [perf<=0.971 t>=2.543] skip
57/169: cno=98 nprobe=128,max_codes=32768 bounds [perf<=0.698 t>=0.368] skip
58/169: cno=76 nprobe=2048,max_codes=8192 bounds [perf<=0.944 t>=1.142] perf 0.944 t 1.514 *
59/169: cno=118 nprobe=2,max_codes=131072 bounds [perf<=0.335 t>=0.208] skip
60/169: cno=34 nprobe=256,max_codes=1024 bounds [perf<=0.781 t>=0.388] perf 0.775 t 0.449 *
61/169: cno=23 nprobe=1024,max_codes=512 bounds [perf<=0.907 t>=0.552] perf 0.797 t 0.621
62/169: cno=73 nprobe=256,max_codes=8192 bounds [perf<=0.781 t>=0.476] skip
63/169: cno=30 nprobe=16,max_codes=1024 bounds [perf<=0.426 t>=0.226] perf 0.425 t 0.242
64/169: cno=97 nprobe=64,max_codes=32768 bounds [perf<=0.607 t>=0.328] skip
65/169: cno=31 nprobe=32,max_codes=1024 bounds [perf<=0.517 t>=0.242] skip
66/169: cno=47 nprobe=256,max_codes=2048 bounds [perf<=0.781 t>=0.449] perf 0.781 t 0.443 *
67/169: cno=80 nprobe=4,max_codes=16384 bounds [perf<=0.335 t>=0.226] skip
68/169: cno=18 nprobe=32,max_codes=512 bounds [perf<=0.517 t>=0.226] perf 0.513 t 0.235
69/169: cno=70 nprobe=32,max_codes=8192 bounds [perf<=0.517 t>=0.259] skip
70/169: cno=143 nprobe=1,max_codes=524288 bounds [perf<=0.335 t>=0.208] skip
71/169: cno=144 nprobe=2,max_codes=524288 bounds [perf<=0.335 t>=0.208] skip
72/169: cno=38 nprobe=4096,max_codes=1024 bounds [perf<=0.966 t>=1.265] perf 0.883 t 1.377
73/169: cno=8 nprobe=256,max_codes=256 bounds [perf<=0.684 t>=0.226] perf 0.673 t 0.361
74/169: cno=5 nprobe=32,max_codes=256 bounds [perf<=0.513 t>=0.226] perf 0.502 t 0.253
75/169: cno=90 nprobe=4096,max_codes=16384 bounds [perf<=0.971 t>=2.035] perf 0.971 t 2.539 *
76/169: cno=16 nprobe=8,max_codes=512 bounds [perf<=0.335 t>=0.226] skip
77/169: cno=42 nprobe=8,max_codes=2048 bounds [perf<=0.335 t>=0.226] skip
78/169: cno=109 nprobe=32,max_codes=65536 bounds [perf<=0.517 t>=0.272] skip
79/169: cno=71 nprobe=64,max_codes=8192 bounds [perf<=0.607 t>=0.328] skip
80/169: cno=164 nprobe=256,max_codes=inf bounds [perf<=0.971 t>=0.476] perf 0.781 t 0.498
81/169: cno=25 nprobe=4096,max_codes=512 bounds [perf<=0.883 t>=1.265] skip
82/169: cno=57 nprobe=32,max_codes=4096 bounds [perf<=0.517 t>=0.259] skip
83/169: cno=11 nprobe=2048,max_codes=256 bounds [perf<=0.685 t>=0.552] skip
84/169: cno=137 nprobe=128,max_codes=262144 bounds [perf<=0.781 t>=0.368] perf 0.698 t 0.369
85/169: cno=86 nprobe=256,max_codes=16384 bounds [perf<=0.781 t>=0.476] skip
86/169: cno=79 nprobe=2,max_codes=16384 bounds [perf<=0.335 t>=0.208] skip
87/169: cno=158 nprobe=4,max_codes=inf bounds [perf<=0.607 t>=0.226] perf 0.258 t 0.191 *
88/169: cno=106 nprobe=4,max_codes=65536 bounds [perf<=0.258 t>=0.226] skip
89/169: cno=56 nprobe=16,max_codes=4096 bounds [perf<=0.426 t>=0.242] skip
90/169: cno=125 nprobe=256,max_codes=131072 bounds [perf<=0.781 t>=0.476] skip
91/169: cno=88 nprobe=1024,max_codes=16384 bounds [perf<=0.907 t>=0.638] perf 0.907 t 0.967 *
92/169: cno=132 nprobe=4,max_codes=262144 bounds [perf<=0.258 t>=0.226] skip
93/169: cno=67 nprobe=4,max_codes=8192 bounds [perf<=0.258 t>=0.226] skip
94/169: cno=153 nprobe=1024,max_codes=524288 bounds [perf<=0.944 t>=0.971] perf 0.907 t 0.995
95/169: cno=65 nprobe=1,max_codes=8192 bounds [perf<=0.182 t>=0.208] skip
96/169: cno=68 nprobe=8,max_codes=8192 bounds [perf<=0.335 t>=0.226] skip
97/169: cno=138 nprobe=256,max_codes=262144 bounds [perf<=0.781 t>=0.476] skip
98/169: cno=139 nprobe=512,max_codes=262144 bounds [perf<=0.854 t>=0.638] skip
99/169: cno=24 nprobe=2048,max_codes=512 bounds [perf<=0.883 t>=0.621] perf 0.798 t 0.895
100/169: cno=161 nprobe=32,max_codes=inf bounds [perf<=0.607 t>=0.272] skip
101/169: cno=62 nprobe=1024,max_codes=4096 bounds [perf<=0.907 t>=0.638] perf 0.907 t 0.916 *
102/169: cno=163 nprobe=128,max_codes=inf bounds [perf<=0.781 t>=0.369] perf 0.698 t 0.362
103/169: cno=95 nprobe=16,max_codes=32768 bounds [perf<=0.426 t>=0.245] skip
104/169: cno=127 nprobe=1024,max_codes=131072 bounds [perf<=0.907 t>=0.971] skip
105/169: cno=72 nprobe=128,max_codes=8192 bounds [perf<=0.698 t>=0.336] skip
106/169: cno=149 nprobe=64,max_codes=524288 bounds [perf<=0.607 t>=0.328] skip
107/169: cno=156 nprobe=1,max_codes=inf bounds [perf<=0.258 t>=0.208] skip
108/169: cno=82 nprobe=16,max_codes=16384 bounds [perf<=0.426 t>=0.245] skip
109/169: cno=166 nprobe=1024,max_codes=inf bounds [perf<=0.971 t>=0.995] perf 0.907 t 0.952 *
110/169: cno=43 nprobe=16,max_codes=2048 bounds [perf<=0.426 t>=0.242] skip
111/169: cno=150 nprobe=128,max_codes=524288 bounds [perf<=0.698 t>=0.369] skip
112/169: cno=140 nprobe=1024,max_codes=262144 bounds [perf<=0.907 t>=0.971] skip
113/169: cno=7 nprobe=128,max_codes=256 bounds [perf<=0.673 t>=0.253] perf 0.640 t 0.330
114/169: cno=4 nprobe=16,max_codes=256 bounds [perf<=0.425 t>=0.226] perf 0.418 t 0.243
115/169: cno=111 nprobe=128,max_codes=65536 bounds [perf<=0.698 t>=0.368] skip
116/169: cno=116 nprobe=4096,max_codes=65536 bounds [perf<=0.971 t>=2.539] skip
117/169: cno=49 nprobe=1024,max_codes=2048 bounds [perf<=0.907 t>=0.621] perf 0.902 t 0.863 *
118/169: cno=133 nprobe=8,max_codes=262144 bounds [perf<=0.335 t>=0.230] skip
119/169: cno=128 nprobe=2048,max_codes=131072 bounds [perf<=0.944 t>=1.541] skip
120/169: cno=84 nprobe=64,max_codes=16384 bounds [perf<=0.607 t>=0.328] skip
121/169: cno=81 nprobe=8,max_codes=16384 bounds [perf<=0.335 t>=0.226] skip
122/169: cno=120 nprobe=8,max_codes=131072 bounds [perf<=0.335 t>=0.230] skip
123/169: cno=36 nprobe=1024,max_codes=1024 bounds [perf<=0.883 t>=0.621] perf 0.873 t 0.715 *
124/169: cno=14 nprobe=2,max_codes=512 bounds [perf<=0.182 t>=0.208] skip
125/169: cno=119 nprobe=4,max_codes=131072 bounds [perf<=0.258 t>=0.226] skip
126/169: cno=17 nprobe=16,max_codes=512 bounds [perf<=0.425 t>=0.243] skip
127/169: cno=52 nprobe=1,max_codes=4096 bounds [perf<=0.182 t>=0.208] skip
128/169: cno=75 nprobe=1024,max_codes=8192 bounds [perf<=0.907 t>=0.916] perf 0.907 t 0.964
129/169: cno=107 nprobe=8,max_codes=65536 bounds [perf<=0.335 t>=0.230] skip
130/169: cno=157 nprobe=2,max_codes=inf bounds [perf<=0.258 t>=0.208] skip
131/169: cno=51 nprobe=4096,max_codes=2048 bounds [perf<=0.966 t>=1.377] perf 0.938 t 1.693
132/169: cno=141 nprobe=2048,max_codes=262144 bounds [perf<=0.944 t>=1.541] skip
133/169: cno=19 nprobe=64,max_codes=512 bounds [perf<=0.606 t>=0.253] perf 0.600 t 0.302
134/169: cno=112 nprobe=256,max_codes=65536 bounds [perf<=0.781 t>=0.476] skip
135/169: cno=77 nprobe=4096,max_codes=8192 bounds [perf<=0.971 t>=2.035] perf 0.971 t 2.375 *
136/169: cno=145 nprobe=4,max_codes=524288 bounds [perf<=0.258 t>=0.226] skip
137/169: cno=121 nprobe=16,max_codes=131072 bounds [perf<=0.426 t>=0.254] skip
138/169: cno=131 nprobe=2,max_codes=262144 bounds [perf<=0.258 t>=0.208] skip
139/169: cno=123 nprobe=64,max_codes=131072 bounds [perf<=0.607 t>=0.328] skip
140/169: cno=55 nprobe=8,max_codes=4096 bounds [perf<=0.335 t>=0.226] skip
141/169: cno=91 nprobe=1,max_codes=32768 bounds [perf<=0.258 t>=0.208] skip
142/169: cno=148 nprobe=32,max_codes=524288 bounds [perf<=0.607 t>=0.272] skip
143/169: cno=104 nprobe=1,max_codes=65536 bounds [perf<=0.258 t>=0.208] skip
144/169: cno=28 nprobe=4,max_codes=1024 bounds [perf<=0.258 t>=0.226] skip
145/169: cno=13 nprobe=1,max_codes=512 bounds [perf<=0.182 t>=0.208] skip
146/169: cno=105 nprobe=2,max_codes=65536 bounds [perf<=0.258 t>=0.208] skip
147/169: cno=59 nprobe=128,max_codes=4096 bounds [perf<=0.698 t>=0.336] skip
148/169: cno=129 nprobe=4096,max_codes=131072 bounds [perf<=0.971 t>=2.539] skip
149/169: cno=115 nprobe=2048,max_codes=65536 bounds [perf<=0.944 t>=1.541] skip
150/169: cno=135 nprobe=32,max_codes=262144 bounds [perf<=0.607 t>=0.272] skip
151/169: cno=134 nprobe=16,max_codes=262144 bounds [perf<=0.426 t>=0.254] skip
152/169: cno=6 nprobe=64,max_codes=256 bounds [perf<=0.600 t>=0.253] perf 0.579 t 0.299
153/169: cno=103 nprobe=4096,max_codes=32768 bounds [perf<=0.971 t>=2.539] skip
154/169: cno=99 nprobe=256,max_codes=32768 bounds [perf<=0.781 t>=0.476] skip
155/169: cno=87 nprobe=512,max_codes=16384 bounds [perf<=0.854 t>=0.638] skip
156/169: cno=39 nprobe=1,max_codes=2048 bounds [perf<=0.182 t>=0.208] skip
157/169: cno=63 nprobe=2048,max_codes=4096 bounds [perf<=0.944 t>=1.142] perf 0.943 t 1.381 *
158/169: cno=33 nprobe=128,max_codes=1024 bounds [perf<=0.698 t>=0.330] perf 0.695 t 0.359
159/169: cno=117 nprobe=1,max_codes=131072 bounds [perf<=0.258 t>=0.208] skip
160/169: cno=165 nprobe=512,max_codes=inf bounds [perf<=0.907 t>=0.638] perf 0.854 t 0.628 *
161/169: cno=37 nprobe=2048,max_codes=1024 bounds [perf<=0.883 t>=0.895] skip
162/169: cno=101 nprobe=1024,max_codes=32768 bounds [perf<=0.907 t>=0.967] skip
163/169: cno=160 nprobe=16,max_codes=inf bounds [perf<=0.607 t>=0.254] perf 0.426 t 0.241
164/169: cno=159 nprobe=8,max_codes=inf bounds [perf<=0.426 t>=0.230] perf 0.335 t 0.201 *
165/169: cno=89 nprobe=2048,max_codes=16384 bounds [perf<=0.944 t>=1.514] perf 0.944 t 1.551
166/169: cno=35 nprobe=512,max_codes=1024 bounds [perf<=0.852 t>=0.478] perf 0.840 t 0.562 *
167/169: cno=9 nprobe=512,max_codes=256 bounds [perf<=0.684 t>=0.361] skip
168/169: cno=167 nprobe=2048,max_codes=inf bounds [perf<=0.971 t>=1.551] perf 0.944 t 1.542
[228.229 s] result operating points:
Tested 76 operating points, 24 ones are optimal:
cno=-1 key= perf=0.0000 t=0.000
cno=158 key=nprobe=4,max_codes=inf perf=0.2578 t=0.191
cno=159 key=nprobe=8,max_codes=inf perf=0.3354 t=0.201
cno=83 key=nprobe=32,max_codes=16384 perf=0.5170 t=0.232
cno=162 key=nprobe=64,max_codes=inf perf=0.6069 t=0.266
cno=20 key=nprobe=128,max_codes=512 perf=0.6830 t=0.282
cno=46 key=nprobe=128,max_codes=2048 perf=0.6982 t=0.336
cno=21 key=nprobe=256,max_codes=512 perf=0.7485 t=0.388
cno=47 key=nprobe=256,max_codes=2048 perf=0.7807 t=0.443
cno=151 key=nprobe=256,max_codes=524288 perf=0.7810 t=0.455
cno=22 key=nprobe=512,max_codes=512 perf=0.7865 t=0.478
cno=35 key=nprobe=512,max_codes=1024 perf=0.8396 t=0.562
cno=48 key=nprobe=512,max_codes=2048 perf=0.8522 t=0.603
cno=165 key=nprobe=512,max_codes=inf perf=0.8540 t=0.628
cno=36 key=nprobe=1024,max_codes=1024 perf=0.8726 t=0.715
cno=49 key=nprobe=1024,max_codes=2048 perf=0.9020 t=0.863
cno=62 key=nprobe=1024,max_codes=4096 perf=0.9072 t=0.916
cno=166 key=nprobe=1024,max_codes=inf perf=0.9075 t=0.952
cno=50 key=nprobe=2048,max_codes=2048 perf=0.9300 t=1.142
cno=63 key=nprobe=2048,max_codes=4096 perf=0.9432 t=1.381
cno=76 key=nprobe=2048,max_codes=8192 perf=0.9442 t=1.514
cno=154 key=nprobe=2048,max_codes=524288 perf=0.9443 t=1.532
cno=64 key=nprobe=4096,max_codes=4096 perf=0.9659 t=2.035
cno=77 key=nprobe=4096,max_codes=8192 perf=0.9708 t=2.375
============ key IVF4096,Flat
[228.663 s] train & add
WARNING clustering 100000 points to 4096 centroids: please provide at least 159744 training points
[285.896 s] explore op points
0/12: cno=0 nprobe=1 bounds [perf<=1.000 t>=0.000] perf 0.428 t 0.309 *
1/12: cno=11 nprobe=2048 bounds [perf<=1.000 t>=0.309] perf 0.992 t 66.448 *
2/12: cno=4 nprobe=16 bounds [perf<=0.992 t>=0.309] perf 0.887 t 1.278 *
3/12: cno=8 nprobe=256 bounds [perf<=0.992 t>=1.278] perf 0.992 t 10.315 *
4/12: cno=1 nprobe=2 bounds [perf<=0.887 t>=0.309] perf 0.560 t 0.636 *
5/12: cno=5 nprobe=32 bounds [perf<=0.992 t>=1.278] perf 0.948 t 1.895 *
6/12: cno=2 nprobe=4 bounds [perf<=0.887 t>=0.636] perf 0.685 t 0.730 *
7/12: cno=6 nprobe=64 bounds [perf<=0.992 t>=1.895] perf 0.976 t 3.128 *
8/12: cno=3 nprobe=8 bounds [perf<=0.887 t>=0.730] perf 0.799 t 0.836 *
9/12: cno=9 nprobe=512 bounds [perf<=0.992 t>=10.315] perf 0.993 t 19.110 *
10/12: cno=10 nprobe=1024 bounds [perf<=0.992 t>=19.110] skip
11/12: cno=7 nprobe=128 bounds [perf<=0.992 t>=3.128] perf 0.988 t 5.771 *
[396.369 s] result operating points:
Tested 11 operating points, 11 ones are optimal:
cno=-1 key= perf=0.0000 t=0.000
cno=0 key=nprobe=1 perf=0.4285 t=0.309
cno=1 key=nprobe=2 perf=0.5603 t=0.636
cno=2 key=nprobe=4 perf=0.6851 t=0.730
cno=3 key=nprobe=8 perf=0.7993 t=0.836
cno=4 key=nprobe=16 perf=0.8869 t=1.278
cno=5 key=nprobe=32 perf=0.9478 t=1.895
cno=6 key=nprobe=64 perf=0.9764 t=3.128
cno=7 key=nprobe=128 perf=0.9876 t=5.771
cno=8 key=nprobe=256 perf=0.9918 t=10.315
cno=9 key=nprobe=512 perf=0.9927 t=19.110
============ key IVF16384,Flat
[396.657 s] train & add
WARNING clustering 100000 points to 16384 centroids: please provide at least 638976 training points
[614.865 s] explore op points
0/13: cno=0 nprobe=1 bounds [perf<=1.000 t>=0.000] perf 0.409 t 1.267 *
1/13: cno=12 nprobe=4096 bounds [perf<=1.000 t>=1.267] perf 0.993 t 43.117 *
2/13: cno=6 nprobe=64 bounds [perf<=0.993 t>=1.267] perf 0.948 t 2.913 *
3/13: cno=2 nprobe=4 bounds [perf<=0.948 t>=1.267] perf 0.634 t 1.445 *
4/13: cno=1 nprobe=2 bounds [perf<=0.634 t>=1.267] perf 0.524 t 1.449
5/13: cno=10 nprobe=1024 bounds [perf<=0.993 t>=2.913] perf 0.993 t 13.883 *
6/13: cno=3 nprobe=8 bounds [perf<=0.948 t>=1.449] perf 0.736 t 1.965 *
7/13: cno=5 nprobe=32 bounds [perf<=0.948 t>=1.965] perf 0.895 t 1.744 *
8/13: cno=7 nprobe=128 bounds [perf<=0.993 t>=2.913] perf 0.977 t 3.342 *
9/13: cno=4 nprobe=16 bounds [perf<=0.895 t>=1.965] skip
10/13: cno=9 nprobe=512 bounds [perf<=0.993 t>=3.342] perf 0.992 t 8.359 *
11/13: cno=8 nprobe=256 bounds [perf<=0.992 t>=3.342] perf 0.987 t 5.541 *
12/13: cno=11 nprobe=2048 bounds [perf<=0.993 t>=13.883] perf 0.993 t 25.681 *
[725.586 s] result operating points:
Tested 12 operating points, 10 ones are optimal:
cno=-1 key= perf=0.0000 t=0.000
cno=0 key=nprobe=1 perf=0.4087 t=1.267
cno=2 key=nprobe=4 perf=0.6337 t=1.445
cno=5 key=nprobe=32 perf=0.8955 t=1.744
cno=6 key=nprobe=64 perf=0.9476 t=2.913
cno=7 key=nprobe=128 perf=0.9772 t=3.342
cno=8 key=nprobe=256 perf=0.9868 t=5.541
cno=9 key=nprobe=512 perf=0.9921 t=8.359
cno=10 key=nprobe=1024 perf=0.9927 t=13.883
cno=11 key=nprobe=2048 perf=0.9930 t=25.681
============ key PCA64,IMI2x10,Flat
[725.890 s] train & add
[771.046 s] explore op points
0/169: cno=0 nprobe=1,max_codes=256 bounds [perf<=1.000 t>=0.000] perf 0.117 t 0.227 *
1/169: cno=168 nprobe=4096,max_codes=inf bounds [perf<=1.000 t>=0.227] perf 0.651 t 1.878 *
2/169: cno=58 nprobe=64,max_codes=4096 bounds [perf<=0.651 t>=0.227] perf 0.382 t 0.288 *
3/169: cno=124 nprobe=128,max_codes=131072 bounds [perf<=0.651 t>=0.288] perf 0.426 t 0.373 *
4/169: cno=69 nprobe=16,max_codes=8192 bounds [perf<=0.426 t>=0.227] perf 0.291 t 0.248 *
5/169: cno=142 nprobe=4096,max_codes=262144 bounds [perf<=0.651 t>=0.373] perf 0.651 t 1.888
6/169: cno=12 nprobe=4096,max_codes=256 bounds [perf<=0.651 t>=0.227] perf 0.392 t 1.137
7/169: cno=108 nprobe=16,max_codes=65536 bounds [perf<=0.426 t>=0.248] perf 0.291 t 0.251
8/169: cno=53 nprobe=2,max_codes=4096 bounds [perf<=0.291 t>=0.227] perf 0.159 t 0.251
9/169: cno=114 nprobe=1024,max_codes=65536 bounds [perf<=0.651 t>=0.288] perf 0.569 t 0.738 *
10/169: cno=147 nprobe=16,max_codes=524288 bounds [perf<=0.651 t>=0.251] perf 0.291 t 0.260
11/169: cno=64 nprobe=4096,max_codes=4096 bounds [perf<=0.651 t>=1.137] perf 0.641 t 1.629 *
12/169: cno=96 nprobe=32,max_codes=32768 bounds [perf<=0.426 t>=0.251] perf 0.339 t 0.276 *
13/169: cno=94 nprobe=8,max_codes=32768 bounds [perf<=0.291 t>=0.251] skip
14/169: cno=3 nprobe=8,max_codes=256 bounds [perf<=0.291 t>=0.227] perf 0.235 t 0.235 *
15/169: cno=20 nprobe=128,max_codes=512 bounds [perf<=0.426 t>=0.235] perf 0.406 t 0.328 *
16/169: cno=50 nprobe=2048,max_codes=2048 bounds [perf<=0.641 t>=0.328] perf 0.600 t 0.980 *
17/169: cno=26 nprobe=1,max_codes=1024 bounds [perf<=0.159 t>=0.227] perf 0.117 t 0.223 *
18/169: cno=61 nprobe=512,max_codes=4096 bounds [perf<=0.569 t>=0.328] perf 0.513 t 0.545 *
19/169: cno=66 nprobe=2,max_codes=8192 bounds [perf<=0.291 t>=0.251] skip
20/169: cno=110 nprobe=64,max_codes=65536 bounds [perf<=0.426 t>=0.288] perf 0.382 t 0.299
21/169: cno=102 nprobe=2048,max_codes=32768 bounds [perf<=0.651 t>=0.980] perf 0.621 t 1.121 *
22/169: cno=136 nprobe=64,max_codes=262144 bounds [perf<=0.651 t>=0.299] perf 0.382 t 0.311
23/169: cno=2 nprobe=4,max_codes=256 bounds [perf<=0.235 t>=0.227] perf 0.195 t 0.220 *
24/169: cno=122 nprobe=32,max_codes=131072 bounds [perf<=0.382 t>=0.276] perf 0.339 t 0.267 *
25/169: cno=146 nprobe=8,max_codes=524288 bounds [perf<=0.291 t>=0.251] skip
26/169: cno=10 nprobe=1024,max_codes=256 bounds [perf<=0.392 t>=0.235] perf 0.392 t 0.543
27/169: cno=48 nprobe=512,max_codes=2048 bounds [perf<=0.513 t>=0.328] perf 0.512 t 0.461 *
28/169: cno=21 nprobe=256,max_codes=512 bounds [perf<=0.512 t>=0.328] perf 0.436 t 0.383 *
29/169: cno=126 nprobe=512,max_codes=131072 bounds [perf<=0.651 t>=0.545] perf 0.514 t 0.540 *
30/169: cno=60 nprobe=256,max_codes=4096 bounds [perf<=0.513 t>=0.383] perf 0.466 t 0.413 *
31/169: cno=92 nprobe=2,max_codes=32768 bounds [perf<=0.291 t>=0.251] skip
32/169: cno=130 nprobe=1,max_codes=262144 bounds [perf<=0.291 t>=0.227] perf 0.117 t 0.212 *
33/169: cno=74 nprobe=512,max_codes=8192 bounds [perf<=0.514 t>=0.545] skip
34/169: cno=93 nprobe=4,max_codes=32768 bounds [perf<=0.291 t>=0.251] skip
35/169: cno=22 nprobe=512,max_codes=512 bounds [perf<=0.512 t>=0.383] perf 0.455 t 0.466
36/169: cno=85 nprobe=128,max_codes=16384 bounds [perf<=0.426 t>=0.328] perf 0.426 t 0.345 *
37/169: cno=54 nprobe=4,max_codes=4096 bounds [perf<=0.291 t>=0.251] skip
38/169: cno=83 nprobe=32,max_codes=16384 bounds [perf<=0.339 t>=0.251] perf 0.339 t 0.268
39/169: cno=1 nprobe=2,max_codes=256 bounds [perf<=0.159 t>=0.227] skip
40/169: cno=151 nprobe=256,max_codes=524288 bounds [perf<=0.651 t>=0.413] perf 0.466 t 0.400 *
41/169: cno=15 nprobe=4,max_codes=512 bounds [perf<=0.291 t>=0.227] perf 0.197 t 0.228 *
42/169: cno=100 nprobe=512,max_codes=32768 bounds [perf<=0.514 t>=0.545] skip
43/169: cno=32 nprobe=64,max_codes=1024 bounds [perf<=0.382 t>=0.235] perf 0.378 t 0.231 *
44/169: cno=46 nprobe=128,max_codes=2048 bounds [perf<=0.426 t>=0.328] perf 0.425 t 0.292 *
45/169: cno=78 nprobe=1,max_codes=16384 bounds [perf<=0.117 t>=0.227] skip
46/169: cno=45 nprobe=64,max_codes=2048 bounds [perf<=0.382 t>=0.235] perf 0.382 t 0.285 *
47/169: cno=27 nprobe=2,max_codes=1024 bounds [perf<=0.159 t>=0.227] skip
48/169: cno=29 nprobe=8,max_codes=1024 bounds [perf<=0.291 t>=0.235] skip
49/169: cno=162 nprobe=64,max_codes=inf bounds [perf<=0.651 t>=0.311] perf 0.382 t 0.283 *
50/169: cno=113 nprobe=512,max_codes=65536 bounds [perf<=0.514 t>=0.545] skip
51/169: cno=44 nprobe=32,max_codes=2048 bounds [perf<=0.339 t>=0.235] skip
52/169: cno=152 nprobe=512,max_codes=524288 bounds [perf<=0.651 t>=0.545] perf 0.514 t 0.518 *
53/169: cno=154 nprobe=2048,max_codes=524288 bounds [perf<=0.651 t>=1.121] perf 0.621 t 1.099 *
54/169: cno=41 nprobe=4,max_codes=2048 bounds [perf<=0.291 t>=0.228] perf 0.200 t 0.229 *
55/169: cno=40 nprobe=2,max_codes=2048 bounds [perf<=0.159 t>=0.227] skip
56/169: cno=155 nprobe=4096,max_codes=524288 bounds [perf<=0.651 t>=1.888] skip
57/169: cno=98 nprobe=128,max_codes=32768 bounds [perf<=0.426 t>=0.345] skip
58/169: cno=76 nprobe=2048,max_codes=8192 bounds [perf<=0.621 t>=0.980] perf 0.621 t 1.109
59/169: cno=118 nprobe=2,max_codes=131072 bounds [perf<=0.291 t>=0.251] skip
60/169: cno=34 nprobe=256,max_codes=1024 bounds [perf<=0.466 t>=0.383] perf 0.459 t 0.365 *
61/169: cno=23 nprobe=1024,max_codes=512 bounds [perf<=0.569 t>=0.543] perf 0.457 t 0.578
62/169: cno=73 nprobe=256,max_codes=8192 bounds [perf<=0.466 t>=0.413] skip
63/169: cno=30 nprobe=16,max_codes=1024 bounds [perf<=0.291 t>=0.235] skip
64/169: cno=97 nprobe=64,max_codes=32768 bounds [perf<=0.382 t>=0.288] skip
65/169: cno=31 nprobe=32,max_codes=1024 bounds [perf<=0.339 t>=0.235] skip
66/169: cno=47 nprobe=256,max_codes=2048 bounds [perf<=0.466 t>=0.383] perf 0.465 t 0.395 *
67/169: cno=80 nprobe=4,max_codes=16384 bounds [perf<=0.291 t>=0.251] skip
68/169: cno=18 nprobe=32,max_codes=512 bounds [perf<=0.339 t>=0.235] skip
69/169: cno=70 nprobe=32,max_codes=8192 bounds [perf<=0.339 t>=0.251] skip
70/169: cno=143 nprobe=1,max_codes=524288 bounds [perf<=0.291 t>=0.227] perf 0.117 t 0.210 *
71/169: cno=144 nprobe=2,max_codes=524288 bounds [perf<=0.291 t>=0.251] skip
72/169: cno=38 nprobe=4096,max_codes=1024 bounds [perf<=0.641 t>=1.137] perf 0.532 t 1.239
73/169: cno=8 nprobe=256,max_codes=256 bounds [perf<=0.392 t>=0.235] perf 0.391 t 0.369
74/169: cno=5 nprobe=32,max_codes=256 bounds [perf<=0.339 t>=0.235] skip
75/169: cno=90 nprobe=4096,max_codes=16384 bounds [perf<=0.651 t>=1.629] perf 0.651 t 1.877 *
76/169: cno=16 nprobe=8,max_codes=512 bounds [perf<=0.291 t>=0.235] skip
77/169: cno=42 nprobe=8,max_codes=2048 bounds [perf<=0.291 t>=0.235] skip
78/169: cno=109 nprobe=32,max_codes=65536 bounds [perf<=0.339 t>=0.276] skip
79/169: cno=71 nprobe=64,max_codes=8192 bounds [perf<=0.382 t>=0.288] skip
80/169: cno=164 nprobe=256,max_codes=inf bounds [perf<=0.651 t>=0.413] perf 0.466 t 0.398 *
81/169: cno=25 nprobe=4096,max_codes=512 bounds [perf<=0.532 t>=1.137] skip
82/169: cno=57 nprobe=32,max_codes=4096 bounds [perf<=0.339 t>=0.251] skip
83/169: cno=11 nprobe=2048,max_codes=256 bounds [perf<=0.392 t>=0.543] skip
84/169: cno=137 nprobe=128,max_codes=262144 bounds [perf<=0.466 t>=0.373] perf 0.426 t 0.364
85/169: cno=86 nprobe=256,max_codes=16384 bounds [perf<=0.466 t>=0.413] skip
86/169: cno=79 nprobe=2,max_codes=16384 bounds [perf<=0.291 t>=0.251] skip
87/169: cno=158 nprobe=4,max_codes=inf bounds [perf<=0.382 t>=0.251] perf 0.200 t 0.238
88/169: cno=106 nprobe=4,max_codes=65536 bounds [perf<=0.200 t>=0.251] skip
89/169: cno=56 nprobe=16,max_codes=4096 bounds [perf<=0.291 t>=0.251] skip
90/169: cno=125 nprobe=256,max_codes=131072 bounds [perf<=0.466 t>=0.413] skip
91/169: cno=88 nprobe=1024,max_codes=16384 bounds [perf<=0.569 t>=0.578] perf 0.569 t 0.732 *
92/169: cno=132 nprobe=4,max_codes=262144 bounds [perf<=0.200 t>=0.251] skip
93/169: cno=67 nprobe=4,max_codes=8192 bounds [perf<=0.200 t>=0.251] skip
94/169: cno=153 nprobe=1024,max_codes=524288 bounds [perf<=0.621 t>=0.738] perf 0.569 t 0.706 *
95/169: cno=65 nprobe=1,max_codes=8192 bounds [perf<=0.117 t>=0.227] skip
96/169: cno=68 nprobe=8,max_codes=8192 bounds [perf<=0.291 t>=0.251] skip
97/169: cno=138 nprobe=256,max_codes=262144 bounds [perf<=0.466 t>=0.413] skip
98/169: cno=139 nprobe=512,max_codes=262144 bounds [perf<=0.514 t>=0.545] skip
99/169: cno=24 nprobe=2048,max_codes=512 bounds [perf<=0.532 t>=0.578] perf 0.457 t 0.767
100/169: cno=161 nprobe=32,max_codes=inf bounds [perf<=0.382 t>=0.276] perf 0.339 t 0.264
101/169: cno=62 nprobe=1024,max_codes=4096 bounds [perf<=0.569 t>=0.578] perf 0.568 t 0.715
102/169: cno=163 nprobe=128,max_codes=inf bounds [perf<=0.466 t>=0.373] perf 0.426 t 0.346
103/169: cno=95 nprobe=16,max_codes=32768 bounds [perf<=0.291 t>=0.251] skip
104/169: cno=127 nprobe=1024,max_codes=131072 bounds [perf<=0.569 t>=0.738] skip
105/169: cno=72 nprobe=128,max_codes=8192 bounds [perf<=0.426 t>=0.328] perf 0.426 t 0.338 *
106/169: cno=149 nprobe=64,max_codes=524288 bounds [perf<=0.382 t>=0.311] skip
107/169: cno=156 nprobe=1,max_codes=inf bounds [perf<=0.200 t>=0.227] perf 0.117 t 0.230
108/169: cno=82 nprobe=16,max_codes=16384 bounds [perf<=0.291 t>=0.251] skip
109/169: cno=166 nprobe=1024,max_codes=inf bounds [perf<=0.651 t>=0.738] perf 0.569 t 0.654 *
110/169: cno=43 nprobe=16,max_codes=
b43a
2048 bounds [perf<=0.291 t>=0.235] skip
111/169: cno=150 nprobe=128,max_codes=524288 bounds [perf<=0.426 t>=0.373] skip
112/169: cno=140 nprobe=1024,max_codes=262144 bounds [perf<=0.569 t>=0.738] skip
113/169: cno=7 nprobe=128,max_codes=256 bounds [perf<=0.391 t>=0.235] perf 0.380 t 0.325
114/169: cno=4 nprobe=16,max_codes=256 bounds [perf<=0.291 t>=0.235] skip
115/169: cno=111 nprobe=128,max_codes=65536 bounds [perf<=0.426 t>=0.345] skip
116/169: cno=116 nprobe=4096,max_codes=65536 bounds [perf<=0.651 t>=1.877] skip
117/169: cno=49 nprobe=1024,max_codes=2048 bounds [perf<=0.568 t>=0.578] perf 0.562 t 0.708
118/169: cno=133 nprobe=8,max_codes=262144 bounds [perf<=0.291 t>=0.251] skip
119/169: cno=128 nprobe=2048,max_codes=131072 bounds [perf<=0.621 t>=1.121] skip
120/169: cno=84 nprobe=64,max_codes=16384 bounds [perf<=0.382 t>=0.288] skip
121/169: cno=81 nprobe=8,max_codes=16384 bounds [perf<=0.291 t>=0.251] skip
122/169: cno=120 nprobe=8,max_codes=131072 bounds [perf<=0.291 t>=0.251] skip
123/169: cno=36 nprobe=1024,max_codes=1024 bounds [perf<=0.532 t>=0.578] perf 0.528 t 0.664
124/169: cno=14 nprobe=2,max_codes=512 bounds [perf<=0.159 t>=0.227] skip
125/169: cno=119 nprobe=4,max_codes=131072 bounds [perf<=0.200 t>=0.251] skip
126/169: cno=17 nprobe=16,max_codes=512 bounds [perf<=0.291 t>=0.235] skip
127/169: cno=52 nprobe=1,max_codes=4096 bounds [perf<=0.117 t>=0.227] skip
128/169: cno=75 nprobe=1024,max_codes=8192 bounds [perf<=0.569 t>=0.715] skip
129/169: cno=107 nprobe=8,max_codes=65536 bounds [perf<=0.291 t>=0.251] skip
130/169: cno=157 nprobe=2,max_codes=inf bounds [perf<=0.200 t>=0.251] skip
131/169: cno=51 nprobe=4096,max_codes=2048 bounds [perf<=0.641 t>=1.239] perf 0.602 t 1.395
132/169: cno=141 nprobe=2048,max_codes=262144 bounds [perf<=0.621 t>=1.121] skip
133/169: cno=19 nprobe=64,max_codes=512 bounds [perf<=0.378 t>=0.235] skip
134/169: cno=112 nprobe=256,max_codes=65536 bounds [perf<=0.466 t>=0.413] skip
135/169: cno=77 nprobe=4096,max_codes=8192 bounds [perf<=0.651 t>=1.629] perf 0.651 t 1.853 *
136/169: cno=145 nprobe=4,max_codes=524288 bounds [perf<=0.200 t>=0.251] skip
137/169: cno=121 nprobe=16,max_codes=131072 bounds [perf<=0.291 t>=0.251] skip
138/169: cno=131 nprobe=2,max_codes=262144 bounds [perf<=0.200 t>=0.251] skip
139/169: cno=123 nprobe=64,max_codes=131072 bounds [perf<=0.382 t>=0.299] skip
140/169: cno=55 nprobe=8,max_codes=4096 bounds [perf<=0.291 t>=0.251] skip
141/169: cno=91 nprobe=1,max_codes=32768 bounds [perf<=0.117 t>=0.227] skip
142/169: cno=148 nprobe=32,max_codes=524288 bounds [perf<=0.339 t>=0.276] skip
143/169: cno=104 nprobe=1,max_codes=65536 bounds [perf<=0.117 t>=0.227] skip
144/169: cno=28 nprobe=4,max_codes=1024 bounds [perf<=0.200 t>=0.228] perf 0.199 t 0.241
145/169: cno=13 nprobe=1,max_codes=512 bounds [perf<=0.117 t>=0.227] skip
146/169: cno=105 nprobe=2,max_codes=65536 bounds [perf<=0.200 t>=0.251] skip
147/169: cno=59 nprobe=128,max_codes=4096 bounds [perf<=0.426 t>=0.328] perf 0.426 t 0.341
148/169: cno=129 nprobe=4096,max_codes=131072 bounds [perf<=0.651 t>=1.877] skip
149/169: cno=115 nprobe=2048,max_codes=65536 bounds [perf<=0.621 t>=1.121] skip
150/169: cno=135 nprobe=32,max_codes=262144 bounds [perf<=0.339 t>=0.276] skip
151/169: cno=134 nprobe=16,max_codes=262144 bounds [perf<=0.291 t>=0.251] skip
152/169: cno=6 nprobe=64,max_codes=256 bounds [perf<=0.378 t>=0.235] skip
153/169: cno=103 nprobe=4096,max_codes=32768 bounds [perf<=0.651 t>=1.877] skip
154/169: cno=99 nprobe=256,max_codes=32768 bounds [perf<=0.466 t>=0.413] skip
155/169: cno=87 nprobe=512,max_codes=16384 bounds [perf<=0.514 t>=0.545] skip
156/169: cno=39 nprobe=1,max_codes=2048 bounds [perf<=0.117 t>=0.227] skip
157/169: cno=63 nprobe=2048,max_codes=4096 bounds [perf<=0.621 t>=0.980] perf 0.618 t 1.076 *
158/169: cno=33 nprobe=128,max_codes=1024 bounds [perf<=0.425 t>=0.328] skip
159/169: cno=117 nprobe=1,max_codes=131072 bounds [perf<=0.117 t>=0.227] skip
160/169: cno=165 nprobe=512,max_codes=inf bounds [perf<=0.569 t>=0.545] perf 0.514 t 0.525
161/169: cno=37 nprobe=2048,max_codes=1024 bounds [perf<=0.532 t>=0.767] skip
162/169: cno=101 nprobe=1024,max_codes=32768 bounds [perf<=0.569 t>=0.732] skip
163/169: cno=160 nprobe=16,max_codes=inf bounds [perf<=0.339 t>=0.260] skip
164/169: cno=159 nprobe=8,max_codes=inf bounds [perf<=0.339 t>=0.251] skip
165/169: cno=89 nprobe=2048,max_codes=16384 bounds [perf<=0.621 t>=1.109] skip
166/169: cno=35 nprobe=512,max_codes=1024 bounds [perf<=0.512 t>=0.466] skip
167/169: cno=9 nprobe=512,max_codes=256 bounds [perf<=0.392 t>=0.369] skip
168/169: cno=167 nprobe=2048,max_codes=inf bounds [perf<=0.651 t>=1.121] perf 0.621 t 1.122
[812.434 s] result operating points:
Tested 71 operating points, 20 ones are optimal:
cno=-1 key= perf=0.0000 t=0.000
cno=143 key=nprobe=1,max_codes=524288 perf=0.1167 t=0.210
cno=2 key=nprobe=4,max_codes=256 perf=0.1945 t=0.220
cno=15 key=nprobe=4,max_codes=512 perf=0.1973 t=0.228
cno=41 key=nprobe=4,max_codes=2048 perf=0.1999 t=0.229
cno=32 key=nprobe=64,max_codes=1024 perf=0.3783 t=0.231
cno=162 key=nprobe=64,max_codes=inf perf=0.3822 t=0.283
cno=46 key=nprobe=128,max_codes=2048 perf=0.4254 t=0.292
cno=72 key=nprobe=128,max_codes=8192 perf=0.4257 t=0.338
cno=34 key=nprobe=256,max_codes=1024 perf=0.4585 t=0.365
cno=47 key=nprobe=256,max_codes=2048 perf=0.4653 t=0.395
cno=164 key=nprobe=256,max_codes=inf perf=0.4662 t=0.398
cno=48 key=nprobe=512,max_codes=2048 perf=0.5119 t=0.461
cno=152 key=nprobe=512,max_codes=524288 perf=0.5140 t=0.518
cno=166 key=nprobe=1024,max_codes=inf perf=0.5688 t=0.654
cno=50 key=nprobe=2048,max_codes=2048 perf=0.5997 t=0.980
cno=63 key=nprobe=2048,max_codes=4096 perf=0.6184 t=1.076
cno=154 key=nprobe=2048,max_codes=524288 perf=0.6209 t=1.099
cno=64 key=nprobe=4096,max_codes=4096 perf=0.6407 t=1.629
cno=77 key=nprobe=4096,max_codes=8192 perf=0.6510 t=1.853
[812.724 s] final result:
Tested 247 operating points, 39 ones are optimal:
cno=-1 key= perf=0.0000 t=0.000
cno=53 key=IMI2x10,Flat nprobe=2,max_codes=4096 perf=0.2244 t=0.190
cno=158 key=IMI2x11,Flat nprobe=4,max_codes=inf perf=0.2578 t=0.191
cno=94 key=IMI2x10,Flat nprobe=8,max_codes=32768 perf=0.4062 t=0.194
cno=4 key=IMI2x10,Flat nprobe=16,max_codes=256 perf=0.4743 t=0.203
cno=17 key=IMI2x10,Flat nprobe=16,max_codes=512 perf=0.4954 t=0.216
cno=30 key=IMI2x10,Flat nprobe=16,max_codes=1024 perf=0.5041 t=0.218
cno=56 key=IMI2x10,Flat nprobe=16,max_codes=4096 perf=0.5063 t=0.220
cno=18 key=IMI2x10,Flat nprobe=32,max_codes=512 perf=0.5853 t=0.223
cno=161 key=IMI2x10,Flat nprobe=32,max_codes=inf perf=0.6065 t=0.230
cno=162 key=IMI2x11,Flat nprobe=64,max_codes=inf perf=0.6069 t=0.266
cno=19 key=IMI2x10,Flat nprobe=64,max_codes=512 perf=0.6619 t=0.277
cno=20 key=IMI2x11,Flat nprobe=128,max_codes=512 perf=0.6830 t=0.282
cno=32 key=IMI2x10,Flat nprobe=64,max_codes=1024 perf=0.6941 t=0.303
cno=20 key=IMI2x10,Flat nprobe=128,max_codes=512 perf=0.7075 t=0.306
cno=21 key=IMI2x10,Flat nprobe=256,max_codes=512 perf=0.7220 t=0.356
cno=33 key=IMI2x10,Flat nprobe=128,max_codes=1024 perf=0.7694 t=0.361
cno=46 key=IMI2x10,Flat nprobe=128,max_codes=2048 perf=0.7850 t=0.386
cno=85 key=IMI2x10,Flat nprobe=128,max_codes=16384 perf=0.7885 t=0.419
cno=34 key=IMI2x10,Flat nprobe=256,max_codes=1024 perf=0.8168 t=0.430
cno=47 key=IMI2x10,Flat nprobe=256,max_codes=2048 perf=0.8537 t=0.512
cno=60 key=IMI2x10,Flat nprobe=256,max_codes=4096 perf=0.8610 t=0.585
cno=151 key=IMI2x10,Flat nprobe=256,max_codes=524288 perf=0.8622 t=0.600
cno=48 key=IMI2x10,Flat nprobe=512,max_codes=2048 perf=0.8921 t=0.656
cno=49 key=IMI2x10,Flat nprobe=1024,max_codes=2048 perf=0.9031 t=0.793
cno=62 key=IMI2x11,Flat nprobe=1024,max_codes=4096 perf=0.9072 t=0.916
cno=152 key=IMI2x10,Flat nprobe=512,max_codes=524288 perf=0.9159 t=0.917
cno=62 key=IMI2x10,Flat nprobe=1024,max_codes=4096 perf=0.9450 t=1.081
cno=75 key=IMI2x10,Flat nprobe=1024,max_codes=8192 perf=0.9532 t=1.314
cno=63 key=IMI2x10,Flat nprobe=2048,max_codes=4096 perf=0.9551 t=1.355
cno=64 key=IMI2x10,Flat nprobe=4096,max_codes=4096 perf=0.9553 t=1.921
cno=76 key=IMI2x10,Flat nprobe=2048,max_codes=8192 perf=0.9744 t=1.923
cno=89 key=IMI2x10,Flat nprobe=2048,max_codes=16384 perf=0.9757 t=2.303
cno=77 key=IMI2x10,Flat nprobe=4096,max_codes=8192 perf=0.9808 t=2.625
cno=90 key=IMI2x10,Flat nprobe=4096,max_codes=16384 perf=0.9872 t=3.667
cno=7 key=IVF4096,Flat nprobe=128 perf=0.9876 t=5.771
cno=9 key=IVF16384,Flat nprobe=512 perf=0.9921 t=8.359
cno=10 key=IVF16384,Flat nprobe=1024 perf=0.9927 t=13.883
cno=11 key=IVF16384,Flat nprobe=2048 perf=0.9930 t=25.681
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ ls
AutoTune.cpp Index.cpp Makefile
AutoTune.h IndexFlat.cpp makefile.inc
AutoTune.o IndexFlat.h MetaIndexes.cpp
AuxIndexStructures.cpp IndexFlat.o MetaIndexes.h
AuxIndexStructures.h Index.h MetaIndexes.o
AuxIndexStructures.o IndexHNSW.cpp PATENTS
benchs IndexHNSW.h PolysemousTraining.cpp
Clustering.cpp IndexHNSW.o PolysemousTraining.h
Clustering.h index_io.cpp PolysemousTraining.o
Clustering.o index_io.h ProductQuantizer.cpp
cmake index_io.o ProductQuantizer.h
CMakeLists.txt IndexIVF.cpp ProductQuantizer.o
CONTRIBUTING.md IndexIVF.h python
Dockerfile IndexIVF.o README.md
docs IndexIVFPQ.cpp sift1M
example_makefiles IndexIVFPQ.h sift.tar.gz
FaissAssert.h IndexIVFPQ.o swigfaiss.py
FaissException.cpp IndexLSH.cpp swigfaiss.pyc
FaissException.h IndexLSH.h _swigfaiss.so
FaissException.o IndexLSH.o swigfaiss.swig
faiss.h Index.o tests
faiss.py IndexPQ.cpp tmp
faiss.pyc IndexPQ.h tutorial
gpu IndexPQ.o utils.cpp
hamming.cpp IndexScalarQuantizer.cpp utils.h
hamming.h IndexScalarQuantizer.h utils.o
hamming.o IndexScalarQuantizer.o VectorTransform.cpp
Heap.cpp INSTALL.md VectorTransform.h
Heap.h libfaiss.a VectorTransform.o
Heap.o LICENSE
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ ls
AutoTune.cpp Index.cpp Makefile
AutoTune.h IndexFlat.cpp makefile.inc
AutoTune.o IndexFlat.h MetaIndexes.cpp
AuxIndexStructures.cpp IndexFlat.o MetaIndexes.h
AuxIndexStructures.h Index.h MetaIndexes.o
AuxIndexStructures.o IndexHNSW.cpp PATENTS
benchs IndexHNSW.h PolysemousTraining.cpp
Clustering.cpp IndexHNSW.o PolysemousTraining.h
Clustering.h index_io.cpp PolysemousTraining.o
Clustering.o index_io.h ProductQuantizer.cpp
cmake index_io.o ProductQuantizer.h
CMakeLists.txt IndexIVF.cpp ProductQuantizer.o
CONTRIBUTING.md IndexIVF.h python
Dockerfile IndexIVF.o README.md
docs IndexIVFPQ.cpp sift1M
example_makefiles IndexIVFPQ.h sift.tar.gz
FaissAssert.h IndexIVFPQ.o swigfaiss.py
FaissException.cpp IndexLSH.cpp swigfaiss.pyc
FaissException.h IndexLSH.h _swigfaiss.so
FaissException.o IndexLSH.o swigfaiss.swig
faiss.h Index.o tests
faiss.py IndexPQ.cpp tmp
faiss.pyc IndexPQ.h tutorial
gpu IndexPQ.o utils.cpp
hamming.cpp IndexScalarQuantizer.cpp utils.h
hamming.h IndexScalarQuantizer.h utils.o
hamming.o IndexScalarQuantizer.o VectorTransform.cpp
Heap.cpp INSTALL.md VectorTransform.h
Heap.h libfaiss.a VectorTransform.o
Heap.o LICENSE
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ cd t
bash: cd: t: 没有那个文件或目录
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss$ cd tmp/
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/tmp$ ls
demo_auto_tune.png
JIAO@JIAO-MS-7883-Invalid-entry-length-16-Fixed-up-to-11:~/faiss/tmp$