正如文档所述:
对于 StatefulSet 中定义的每个 VolumeClaimTemplate 条目,每个 Pod 都会收到一个 PersistentVolumeClaim。在上面的 nginx 示例中,每个 Pod 都会收到一个具有 my-storage-class 的 StorageClass 和 1 Gib 预配置存储的 PersistentVolume。如果未指定 StorageClass,则将使用默认 StorageClass。当 Pod 被(重新)调度到节点上时,其 volumeMount 会挂载与其 PersistentVolume Claims 关联的 PersistentVolume。请注意,删除 Pod 或 StatefulSet 时,与 Pod 的 PersistentVolume Claim 关联的 PersistentVolume 不会被删除。这必须手动完成。
我感兴趣的部分是:If no StorageClassis specified, then the default StorageClass will be used
我创建了一个 StatefulSet,如下所示:
apiVersion: apps/v1
kind: StatefulSet
metadata:
namespace: ches
name: ches
spec:
serviceName: ches
replicas: 1
selector:
matchLabels:
app: ches
template:
metadata:
labels:
app: …Run Code Online (Sandbox Code Playgroud) 我创建了一个包含 30 个实例(15 个主节点/15 个节点)的 Redis 集群。通过Python代码,我连接到了这些实例,我找到了master,然后我想向它们添加一些键。
def settomasters(port, host):
r = redis.Redis( host=host, port=port )
r.set("key"+port,"value")
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错误:
redis.exceptions.ResponseError:已移动12539 127.0.0.1:30012
如果我尝试设置密钥,redis-cli -c -p portofmyinstance有时我会收到一条重定向消息,告诉您密钥存储的位置。
我知道,例如,在获取请求的情况下,需要一个智能客户端,以便将请求重定向到正确的节点(保存密钥的节点),否则会发生移动错误。是同样的情况吗?我需要捕获 redis.exceptions.ResponseError 并尝试再次设置?
while True:
try:
r.set("key","value")
break
except:
print "error"
pass
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我的第一次尝试是上面的代码,但没有解决方案。设置操作永远不会成功。
另一方面,下面的 JavaScript 代码不会抛出错误,我无法找出原因:
var redis = require('redis-stream'),
client = new redis(30001, '127.0.0.1');
// Open stream
var stream = client.stream();
// Example of setting 200 records
for(var record = 0; record <200; record++) {
var command = ['set', 'qwerty' + record, 'QWERTYUIOP']; …Run Code Online (Sandbox Code Playgroud) 我有一个这样的字典:
migration_dict = {'30005': ['key42750','key43119', 'key44103', ['key333'],
['key444'], ['keyxx']], '30003': ['key43220', 'key42244','key42230',
['keyzz'], ['kehh']]}
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我怎样才能压平每个键的值以便得到类似的东西:
migration_dict = {'30005': ['key42750','key43119', 'key44103', 'key333',
'key444', 'keyxx'], '30003': ['key43220', 'key42244','key42230',
'keyzz', 'kehh']}
Run Code Online (Sandbox Code Playgroud) 我已经训练了一个网络,并以 mynetwork.model 的形式保存了它。我想使用我自己的模型而不是 VGG16 或 ResNet 等应用 gradcam。
apply_gradcam.py
# import the necessary packages
from Grad_CAM.gradcam import GradCAM
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications import VGG16
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.applications import imagenet_utils
from tensorflow.keras.models import load_model
import numpy as np
import argparse
import imutils
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to the input image")
ap.add_argument("-m", "--model", type=str, default="vgg",
#choices=("vgg", "resnet"),
help="model to …Run Code Online (Sandbox Code Playgroud) 带有 spark-streaming 的 Kafka 抛出一个错误:
from pyspark.streaming.kafka import KafkaUtils ImportError: No module named kafka
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我已经设置了一个 kafka 代理和一个工作的 Spark 环境,一个主人和一个工人。
import os
os.environ['PYSPARK_PYTHON'] = '/usr/bin/python2.7'
import findspark
findspark.init('/usr/spark/spark-3.0.0-preview2-bin-hadoop2.7')
import pyspark
import sys
from pyspark import SparkConf,SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
if __name__=="__main__":
sc = SparkContext(appName="SparkStreamAISfromKAFKA")
sc.setLogLevel("WARN")
ssc = StreamingContext(sc,1)
kvs = KafkaUtils.createStream(ssc,"my-kafka-broker","raw-event-streaming-consumer",{'enriched_ais_messages':1})
lines = kvs.map(lambda x: x[1])
lines.count().map(lambda x: 'Messages AIS: %s' % x).pprint()
ssc.start()
ssc.awaitTermination()
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我假设错误是缺少与 kafka ans 相关的特定版本。有人能帮忙吗?
火花版本:版本 3.0.0-preview2
我执行:
/usr/spark/spark-3.0.0-preview2-bin-hadoop2.7/bin/spark-submit --packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.0.1 --jars …Run Code Online (Sandbox Code Playgroud) apache-kafka apache-spark pyspark spark-structured-streaming spark-kafka-integration
我已经使用以下命令在 k3s 集群中设置了服务:
apiVersion: v1
kind: Service
metadata:
name: myservice
namespace: mynamespace
labels:
app: myapp
spec:
type: LoadBalancer
selector:
app: myapp
ports:
- port: 9012
targetPort: 9011
protocol: TCP
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kubectl get svc -n mynamespace
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
minio ClusterIP None <none> 9011/TCP 42m
minio-service LoadBalancer 10.32.178.112 192.168.40.74,192.168.40.88,192.168.40.170 9012:32296/TCP 42m
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kubectl 描述 svc myservice -n mynamespace
Name: myservice
Namespace: mynamespace
Labels: app=myapp
Annotations: <none>
Selector: app=myapp
Type: LoadBalancer
IP Family Policy: SingleStack
IP Families: IPv4
IP: 10.32.178.112
IPs: …Run Code Online (Sandbox Code Playgroud) 我有两个清单:
alist = ['key1','key2','key3','key3','key4','key4','key5']
blist= [30001,30002,30003,30003,30004,30004,30005]
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我想合并这些列表并将它们添加到字典中。
我尝试,dict(zip(alist,blist))但这给出了:
{'key3': 30003, 'key2': 30002, 'key1': 30001, 'key5': 30005, 'key4': 30004}
The desired form of the dictionary is:
{'key1': 30001, 'key2': 30002, 'key3': 30003,'key3':30003, 'key4': 30004, 'key4': 30004, 'key5': 30005}
I want to keep the duplicates in the dictionary as well as not join the values in the same key (... key3': 30003,'key3':30003,... ).Is it possible?
Thanks in advance.
python ×4
dictionary ×2
kubernetes ×2
python-2.7 ×2
apache-kafka ×1
apache-spark ×1
flatten ×1
javascript ×1
k3s ×1
keras ×1
minio ×1
pyspark ×1
redis ×1
tensorflow ×1
yaml ×1