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09.08.2015
resources = Resources(cpu = 0.1, ram = 20*MB, disk = 20*MB),
09 processes = [hello_world_process])
10
11 hello_world_job = Job(
12 cluster = 'test',
13 role = os.getenv('USER'),
14 task = hello
31%
11.04.2016
-enterprise-3.8.1-ubuntu-14.04-amd64/packages/ubuntu-14.04-amd64
sudo dpkg -i pe-cloud-provisioner-libs_0.3.2-1puppet1_amd64.deb
sudo dpkg -i pe-cloud-provisioner_1.2.0-1puppet1_all.deb
vSphere Setup
After
31%
13.02.2017
-thirds, or about 0.9 seconds.
Thanks to the new -html5 option, the Javadoc tool now generates HTML with a more modern, barrier-free design and appearance. Not only a matter of a modified stylesheet, the generated
31%
05.11.2018
nodes, and make sure to do this as a user and not as root.
3. To make life easier, use shared storage between the controller and the compute nodes.
4. Make sure the UIDs and GIDs are consistent
31%
13.12.2018
In previous articles, I examined some fundamental tools for HPC systems, including pdsh [1] (parallel shells), Lmod environment modules [2], and shared storage with NFS and SSHFS [3]. One remaining
31%
30.01.2020
anything. In US East regions, for example, the S3 Standard storage class pricing (in early 2020) looks like this:
Storage price is $0.023/GB for the first 50TB.
Retrieval price is $0.005/1,000 PUT ... New S3 Services at Amazon ... New storage classes for Amazon S3
31%
28.11.2023
": "127.0.0.1"}},
06 { "type": "PORT", "params": {"hostname": "129.0.0.1", "port": 8080}},
07 { "type": "HTTP-STATUS", "params": {"url": "https://google.com"}},
08 {
09 "type": "GROUP",
10
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05.02.2023
update after appropriate testing. Image tags let you to specify only the major or minor releases (e.g., mariadb:10 or mariadb:10.8.3). However, a setup like this with a "generic" database server that many
31%
22.08.2011
:lanman passfile.txt
Loaded 2 passwords with 2 different salts (FreeBSD MD5 [32/64])
guesses: 0 time: 1:04:04:08 (3) c/s: 10927 trying: gmugoky - gmugok2
guesses: 0 time: 1:09:25:10 (3) c/s: 10929 trying
31%
02.06.2020
_function
02
03 import pymp
04
05 ex_array = pymp.shared.array((100,), dtype='uint8')
06 with pymp.Parallel(4) as p:
07 for index in p.range(0, 100):
08 ex_array[index] = 1
09 # The parallel print
