Fedora release 32 Kernel5.6.0-0.rc1 Update

投稿日: 2020年2月22日 作成者: chibi

Fedora release 32 Kernelが5.6.0-0.rc1にUpdateされました。

[root@f32 ~]# uname -r
5.6.0-0.rc1.git0.1.fc32.x86_64
[root@f32 ~]# php -v
PHP 7.4.3RC1 (cli) (built: Feb  4 2020 09:48:25) ( NTS )
Copyright (c) The PHP Group
Zend Engine v3.4.0, Copyright (c) Zend Technologies
with Zend OPcache v7.4.3RC1, Copyright (c), by Zend Technologies
[root@f32 ~]# curl -V
curl 7.68.0 (x86_64-redhat-linux-gnu) libcurl/7.68.0 OpenSSL/1.1.1d-fips zlib/1.2.11 brotli/1.0.7 libidn2/2.3.0 libpsl/0.21.0 (+libidn2/2.3.0) libssh/0.9.3/openssl/zlib nghttp2/1.40.0
Release-Date: 2020-01-08
Protocols: dict file ftp ftps gopher http https imap imaps ldap ldaps pop3 pop3s rtsp scp sftp smb smbs smtp smtps telnet tftp
Features: AsynchDNS brotli GSS-API HTTP2 HTTPS-proxy IDN IPv6 Kerberos Largefile libz Metalink NTLM NTLM_WB PSL SPNEGO SSL TLS-SRP UnixSockets
[root@f32 ~]# samba -V
Version 4.12.0rc2
[root@f32 ~]# cat /etc/os-release
NAME=Fedora
VERSION=”32 (Server Edition)”
ID=fedora
VERSION_ID=32
VERSION_CODENAME=””
PLATFORM_ID=”platform:f32″
PRETTY_NAME=”Fedora 32 (Server Edition)”
ANSI_COLOR=”0;34″
LOGO=fedora-logo-icon
CPE_NAME=”cpe:/o:fedoraproject:fedora:32″
HOME_URL=“https://fedoraproject.org/”
DOCUMENTATION_URL=“https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/”
SUPPORT_URL=“https://fedoraproject.org/wiki/Communicating_and_getting_help”
BUG_REPORT_URL=“https://bugzilla.redhat.com/”
REDHAT_BUGZILLA_PRODUCT=”Fedora”
REDHAT_BUGZILLA_PRODUCT_VERSION=rawhide
REDHAT_SUPPORT_PRODUCT=”Fedora”
REDHAT_SUPPORT_PRODUCT_VERSION=rawhide
PRIVACY_POLICY_URL=“https://fedoraproject.org/wiki/Legal:PrivacyPolicy”
VARIANT=”Server Edition”
VARIANT_ID=server
[root@f32 ~]# cat /etc/redhat-release
Fedora release 32 (Rawhide)
[root@f32 ~]#

カテゴリー: fedora | コメントする

Samba 4.12.0rc3 Available for Download

投稿日: 2020年2月21日 作成者: chibi

Samba 4.12.0rc3が公開されました。ReleaseNoteはこちらです。

カテゴリー: samba4 | コメントする

PHP 7.4.3が公開

投稿日: 2020年2月21日 作成者: chibi

PHP 7.4.3が公開されました。関連記事はこちらとこちらです。ChangeLogはこちらです。

カテゴリー: Linux | コメントする

CentOS Linux release 7.7.1908 TITAN V x2 CUDA 10.1 Samples nbody を単精度 倍精度で動作させてみた 17596.655 6569.545 GFLOP s

投稿日: 2020年2月21日 作成者: chibi

[chibi@centos7 nbody]$ cat /etc/redhat-release
CentOS Linux release 7.7.1908 (Core)
[chibi@centos7 nbody]$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
[chibi@centos7 nbody]$ sudo hddtemp /dev/sda
[sudo] chibi password:
/dev/sda: ST2000LX001-1RG174: 18°C
[chibi@centos7 nbody]$ ./nbody –benchmark –numbodies=256000 -numdevices=2
Run “nbody -benchmark [-numbodies=<numBodies>]” to measure performance.
-fullscreen (run n-body simulation in fullscreen mode)
-fp64 (use double precision floating point values for simulation)
-hostmem (stores simulation data in host memory)
-benchmark (run benchmark to measure performance)
-numbodies=<N> (number of bodies (>= 1) to run in simulation)
-device=<d> (where d=0,1,2…. for the CUDA device to use)
-numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation)
-compare (compares simulation results running once on the default GPU and once on the CPU)
-cpu (run n-body simulation on the CPU)
-tipsy=<file.bin> (load a tipsy model file for simulation)

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

number of CUDA devices = 2
> Windowed mode
> Simulation data stored in system memory
> Single precision floating point simulation
> 2 Devices used for simulation
GPU Device 0: “TITAN V” with compute capability 7.0

> Compute 7.0 CUDA device: [TITAN V]
> Compute 7.0 CUDA device: [TITAN V]
number of bodies = 256000
256000 bodies, total time for 10 iterations: 744.869 ms
= 879.833 billion interactions per second
= 17596.655 single-precision GFLOP/s at 20 flops per interaction
[chibi@centos7 nbody]$ ./nbody –benchmark -fp64 –numbodies=256000 -numdevices=
2
Run “nbody -benchmark [-numbodies=<numBodies>]” to measure performance.
-fullscreen (run n-body simulation in fullscreen mode)
-fp64 (use double precision floating point values for simulation)
-hostmem (stores simulation data in host memory)
-benchmark (run benchmark to measure performance)
-numbodies=<N> (number of bodies (>= 1) to run in simulation)
-device=<d> (where d=0,1,2…. for the CUDA device to use)
-numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation)
-compare (compares simulation results running once on the default GPU and once on the CPU)
-cpu (run n-body simulation on the CPU)
-tipsy=<file.bin> (load a tipsy model file for simulation)

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

number of CUDA devices = 2
> Windowed mode
> Simulation data stored in system memory
> Double precision floating point simulation
> 2 Devices used for simulation
GPU Device 0: “TITAN V” with compute capability 7.0

Compute 7.0 CUDA device: [TITAN V]
> Compute 7.0 CUDA device: [TITAN V]
number of bodies = 256000
256000 bodies, total time for 10 iterations: 2992.719 ms
= 218.985 billion interactions per second
= 6569.545 double-precision GFLOP/s at 30 flops per interaction

データ詳細　CentOS Linux release 7.7.1908 TITAN V x2 CUDA 10.1 Samples nbody 単精度 倍精度 17596.655 6569.545 GFLOP s

GPU温度推移 CentOS Linux release 7.7.1908 TITAN V x2 CUDA 10.1 Samples nbody 単精度 倍精度 17596.655 6569.545 GFLOP s nvidia-smi

カテゴリー: centos7, nvidia | コメントする

Caffeを使ってCougarの画像識別率を確認してみた 99.3%

投稿日: 2020年2月20日 作成者: chibi

選んだ画像

画像識別手順詳細 cougar_face 33 99.3

画像識別結果 99.3%

user0034@10-111-34-14:~/caffe$ python show_result.py data/ilsvrc12/synset_words.txt result.npy
#1 | n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor | 99.3%
#2 | n02114712 red wolf, maned wolf, Canis rufus, Canis niger | 0.1%
#3 | n02129165 lion, king of beasts, Panthera leo | 0.1%
user0034@10-111-34-14:~/caffe$

カテゴリー: nvidia, ubuntu | コメントする