14%
23.03.2022
laytonjb laytonjb 19946519 Nov 20 2020 Lmod-8.4.15.tar.gz
31988342 drwxrwxr-x 2 laytonjb laytonjb 4096 Oct 27 14:22 mpibzip2-0.6
31988329 -rw-rw-r-- 1 laytonjb laytonjb 92160 Oct 27 14:18 mpibzip
14%
13.06.2022
.69
17.26
67.7
IS
(4 cores)
0.6
2.16
8.2
LU
(6 cores)
5.13
41.8
MG
(4 cores)
1.2
3.8
39.1
SP
(4 cores
14%
02.08.2022
.05
FT (4 cores)
1.69
17.26
67.7
IS (4 cores)
0.6
2.16
8.2
LU (6 cores)
5.13
41.8
MG (4 cores)
1.2
3.8
39
14%
09.10.2017
"SubnetMax": "10.99.0.0",
07 "Backend": {
08 "Type": "udp",
09 "Port": 7890
10 }
11 }
12 [...]
At first glance, this concept looks robust and simple
14%
10.06.2014
"ram": 2048,
07 "resolvers": ["192.168.111.254"],
08 "disks": [
09 {
10 "image_uuid": "1fc068b0-13b0-11e2-9f4e-2f3f6a96d9bc",
11 "boot": true,
12 "model": "virtio"
13 }
14
14%
26.01.2025
.add(layers.BatchNormalization())
model.add(layers.Conv2D(32, (3,3), padding='same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(0.3))
The next
14%
27.09.2021
.170
17.75
Xenial (16.04)
ami-0133407e358cc1af0
9.300
7.001
16.301
Bionic (18.04)
ami-0186d369d234b536f
12.946
5.608
18.554
Focal
14%
09.10.2023
1 loop /snap/core20/1974
loop2 7:2 0 63.5M 1 loop /snap/core20/2015
loop3 7:3 0 73.9M 1 loop /snap/core22/864
loop4 7:4 0 237.2M 1 loop /snap/firefox/3026
loop5 7:5 0
14%
17.06.2017
real :: diameter
08 real :: radius
09 real :: area
10 end type meta_data
11
12 contains
13 subroutine meta_comp(r, item)
14 type(meta_data) :: item
15 item%diameter = 2.0
14%
03.12.2024
(pool_size=(2,2)))
model.add(layers.Dropout(0.3))
The next size layers of the model (Listing 4) are the same except for some small changes:
input_shape
does not need to be specified in the first 2D
