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07.01.2024
loop /snap/core22/864
loop15 7:15 0 12.3M 1 loop /snap/snap-store/959
loop16 7:16 0 73.9M 1 loop /snap/core22/817
loop17 7:17 0 349.7M 1 loop /snap/gnome-3-38-2004/140
loop18
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20.10.2016
), has to be specified. Here is a simple example of the declaration:
INTEGER, TARGET :: a(3), b(6), c(9)
INTEGER, DIMENSION(:), POINTER :: pt2
Another quick example of multidimension arrays
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07.10.2014
.0.0.0
-c
Clusterware to use
corosync, zookeeper:
-D
Use direct I/O on the back end
n/a
-g
Work as a gateway (server without back
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07.11.2011
for [clauses ...]
for (i=0;i<N;i++) {
a[i]= i*i; /* parallelized */
}
... /* one thread */
Also, you can combine the two compiler directives, parallel
and sections
, to form a single directive
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03.12.2024
to check the version installed:
$ python3
>>> import tensorflow as tf
>>> print(tf.__version__)
It doesn’t make too much difference, but if you are curious, I used TensorFlow 2.9.2 and Keras 2.9
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21.08.2012
with the compute node:
[root@test1 ~]# pbsnodes -a
n0001
state = free
np = 3
ntype = cluster
status = rectime=1343594239,varattr=,jobs=,state=free,netload=118255091,gres=,loadave=0.02,ncpus=3
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20.03.2014
, 2)
Out: array([3, 5, 7, 9])
to generate a sequence from 3 to 10 with a step size of 2.
Basic Arithmetic Operations
NumPy allows many operations applied against all elements of an array without
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7 1 56008 loop1
06 7 2 56184 loop2
07 7 3 91264 loop3
08 259 0 244198584 nvme0n1
09 8 0 488386584 sda
10 8 1 1024 sda1
11
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05.08.2024
ago infallible_knuth
7f2d5a7b7c86 docker.io/library/ubuntu:latest /bin/bash 24 minutes ago Exited (127) 22 minutes ago wonderful_sinoussi
85b8e9efc636 docker
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25.03.2020
Vuuouf
04 aKdeFvdeY4x9tGmh7FQ51Qi6EEr9LLy2Q8qTtEuN2fJ4PnWBNRfKwhWb
05 SNQWvq1jwhsXlsAelLz7tO5kptI7TO16p2ncpnhJqfzT5mWJ4nK76YPZ
06 lu+MZlIYJOMv/OQWD2nVmsjXeO0dnsrL8MyC5wdyPy2gbksWBscsbwN2
07 34APEYO48B6sovy
