-
Change
apiVersion
from:- apiVersion: v1
(or
apiVersion: apps.openshift.io/v1
)to:
#!/usr/bin/env bash | |
# | |
# Reads AirTag data from the FindMy.app cache and converts it to a daily GPX file | |
# | |
# Rsyncs the data to a web accessible folder that can be displayed with e.g. | |
# https://gist.github.com/henrik242/84ad80dd2170385fe819df1d40224cc4 | |
# | |
# This should typically be run as a cron job | |
# |
func getAllFilenames(fs *embed.FS, path string) (out []string, err error) { | |
if len(path) == 0 { | |
path = "." | |
} | |
entries, err := fs.ReadDir(path) | |
if err != nil { | |
return nil, err | |
} | |
for _, entry := range entries { | |
fp := filepath.Join(path, entry.Name()) |
oc get crd -o=custom-columns=NAME:.metadata.name,CR_NAME:.spec.names.singular,SCOPE:.spec.scope
oc get $(oc get crd -o=custom-columns=CR_NAME:.spec.names.singular --no-headers | awk '{printf "%s%s",sep,$0; sep=","}') --ignore-not-found --all-namespaces -o=custom-columns=KIND:.kind,NAME:.metadata.name,NAMESPACE:.metadata.namespace
oc get $(oc api-resources --verbs=list -o name | awk '{printf "%s%s",sep,$0;sep=","}') --ignore-not-found --all-namespaces -o=custom-columns=KIND:.kind,NAME:.metadata.name,NAMESPACE:.metadata.namespace --sort-by='metadata.namespace'
-
Change
apiVersion
from:- apiVersion: v1
(or
apiVersion: apps.openshift.io/v1
)to:
-
Change
apiVersion
from:- apiVersion: v1
(or
apiVersion: apps.openshift.io/v1
)to:
Questions are not from any actual exam!!! | |
Q: Create a job that calculates pi to 2000 decimal points using the container with the image named perl | |
and the following commands issued to the container: ["perl", "-Mbignum=bpi", "-wle", "print bpi(2000)"] | |
Once the job has completed, check the logs to and export the result to pi-result.txt. | |
Solution: |
from pynamodb.models import Model | |
from pynamodb.constants import STRING | |
from pynamodb.attributes import UnicodeAttribute | |
ENUM = ('FOO', 'BAR', 'BAZ') | |
class EnumUnicodeAttribute(UnicodeAttribute): | |
""" | |
An enumerated unicode attribute |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman