34%
04.11.2011
.
Listing 1: Running Many Commands at Once
01 $ cat commands
02 date
03 hostname
04 echo foo | md5sum
05
06 $ cat commands | parallel
07 Thu Jul 8 10:37:20 EST 2010
08 desktop‑machine
09 d3b07384d113edec49
34%
27.08.2014
.07% (4.61 GiB)
"84K READ" 2.05% (8.79 GiB)
"128K READ" 96.66% (414.41 GiB)
This heatmap can help you 'see' hot spots. It is adjusted to terminal size, so each square = 10.00 MiB
The
34%
11.06.2014
://munin-monitoring.org
Cacti: http://www.cacti.net
Ganglia: http://ganglia.sourceforge.net
Zabbix: http://www.zabbix.com
Zenoss Community: http://community.zenoss.org/
Observium: http://www.observium.org/
GKrellM
33%
02.06.2020
install https://dl.google.com/coral/python/tflite_runtime-2.1.0-cp37-cp37m-linux_armv7l.whl
Then, executing the model and making a prediction with the Python API is quite easy. Because I am working
33%
14.05.2013
log in for terminal access:
ps –aef |grep ssh
This command shows the output:
root 571 1 0 Mar26 ? 00:00:00 /usr/sbin/sshd -D
Now I can SSH into the target box with my new user account and have
33%
07.11.2023
the last command as an example, an option to specify the extent size would be:
# vgcreate -s 8M vg0 /dev/sdb1 /dev/sdd
The -s 8M
option specifies a PE size of 8MB when the VG is created. You can change
33%
30.11.2025
, $pdq::CEN, $pdq::FCFS);
27
28 # Time basis in seconds " expressed in milliseconds
29 pdq::SetDemand($node1, $work, 8.0 * 1e-3 * ($users ** 0.085));
30 pdq::SetDemand($node2, $work, 2.8 * 1e-3);
31 pdq
33%
03.02.2024
468G 136G 309G 31% /
tmpfs 126G 0 126G 0% /dev/shm
tmpfs 5.0M 8.0K 5.0M 1% /run/lock
/dev/nvme1n1p1
33%
30.11.2025
.mcwrite.net
Starting Nmap 5.00 ( http://nmap.org ) at 2011-03-06 19:35 CST
Interesting ports on blogs.typepad.com (204.9.177.195):
Not shown: 998 filtered ports
PORT STATE SERVICE
80/tcp open http
| robots
33%
27.09.2021
08 integer :: counter
09 integer :: counter_limit
10 integer :: ierr
11
12 type(rec) :: my_record
13
14 counter_limit = 2000
15
16 ierr = -1
17 open(unit=8,file
