投稿時間:2022-06-11 22:19:13 RSSフィード2022-06-11 22:00 分まとめ(23件)

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python	Pythonタグが付けられた新着投稿 - Qiita	週一30分づつで競馬予想を初めてみる	https://qiita.com/nico4316/items/2f0128fcc67abdcf7391	記事	2022-06-11 21:36:27
js	JavaScriptタグが付けられた新着投稿 - Qiita	【React】公式のチュートリアルをやってみる～⑦勝利判定を追加	https://qiita.com/asahina820/items/e0fdd233eeefa0d328c4	react	2022-06-11 21:39:38
js	JavaScriptタグが付けられた新着投稿 - Qiita	フロントエンドノート	https://qiita.com/WangLY/items/b55adfd77fd9eead4567	関連	2022-06-11 21:34:10
js	JavaScriptタグが付けられた新着投稿 - Qiita	私は中国のフロントエンドエンジニアです	https://qiita.com/WangLY/items/e777a43de3a700f77133	関連	2022-06-11 21:09:21
AWS	AWSタグが付けられた新着投稿 - Qiita	AZ別に提供されているEC2インスタンスタイプの一覧を出力するPowerShellスクリプト	https://qiita.com/SAITO_Keita/items/d0e0ea02b1df8b102221	powershell	2022-06-11 21:42:02
Azure	Azureタグが付けられた新着投稿 - Qiita	Azureの機能廃止やサポート終了情報を手早く確認する方法	https://qiita.com/Hikaruru_g/items/a39ebfd6f848bd7d4dd6	azure	2022-06-11 21:51:32
海外TECH	Ars Technica	Mutations thought to be harmless turn out to cause problems	https://arstechnica.com/?p=1860267	harmless	2022-06-11 12:31:10
海外TECH	MakeUseOf	8 Cool HTML Effects Anyone Can Add to Their Websites	https://www.makeuseof.com/tag/7-cool-html-effects-that-anyone-can-add-to-their-website-nb/	awesome	2022-06-11 12:30:14
海外TECH	MakeUseOf	The 7 Best Risk Free Crypto Trading Simulators	https://www.makeuseof.com/best-risk-free-crypto-trading-simulators/	crypto	2022-06-11 12:30:14
海外TECH	MakeUseOf	5 Tools to Help Make Your Vue.js App Accessible to Everyone	https://www.makeuseof.com/vuejs-app-accessible-tools-help/	solid	2022-06-11 12:30:13
海外TECH	MakeUseOf	10 Risks When Dual Booting Operating Systems	https://www.makeuseof.com/tag/risks-dual-booting-windows-linux-operating-systems/	introduce	2022-06-11 12:15:14
海外TECH	DEV Community	Kubectl Top Pod/Node | How to get & read resource utilization metrics of K8s pods and nodes?	https://dev.to/signoz/kubectl-top-podnode-how-to-get-read-resource-utilization-metrics-of-k8s-pods-and-nodes-4o9c	Kubectl Top Pod Node How to get amp read resource utilization metrics of Ks pods and nodes Kubectl Top command can be used to retrieve snapshots of resource utilization of pods or nodes in your Kubernetes cluster Resource utilization is an important thing to monitor for Kubernetes cluster owners In order to monitor resource utilization you can keep track of things like CPU memory and storage In this article we will see how to use kubectl Top command to get and read metrics about pods and nodes We will also breakdown the output to understand what it means But before we get down to learn about Kubectl Top command let s have a brief overview of a few concepts in Kubernetes What is kubectl kubectl is the Kubernetes command line tool and it allows you to run commands against your Kubernetes cluster kubectl lets you interact with your Kubernetes cluster for day to day management For example kubectl get nodes lets you retrieve details about nodes running in your cluster or namespace Under the hood kubectl interacts with the API server The API server is responsible for all communication between Kubernetes components including both the internal components of your Kubernetes cluster and external components kubectl sends out POST commands to the API server endpoint in order to execute its commands What is a pod in Kubernetes Kubernetes is meant to orchestrate the management of containers Pods are the first level of abstraction that it provides over containers Pods are groups of one or more containers with shared resources like storage and networks They are used as units of replication in cases where applications need to be scaled up or down What is a node in Kubernetes Nodes are where Kubernetes pods run Nodes can be virtual machines bare metal servers in a data center or instances in a private or public cloud Kubernetes uses nodes to handle on demand scaling of resources A single node can have multiple pods separated by namespaces What is kubectl Top command As mentioned before kubectl pronounced Kube c t l is a CLI for running commands that can help you interact with a Kubernetes interface or resources in a k cluster These resources include pods deploy replica set etc A kubectl top is a command used to list all the running nodes and pods along with their resource utilization It provides you a snapshot of resource utilization metrics like CPU memory and storage on each running node Each node in Kubernetes comes with cAdvisor which is an open source agent that monitors resource usage about containers kubectl command gets resource utilization metrics from cAdvisor via the metrics server To obtain these metrics you need to run the kubectl top command which shows the CPU memory and network utilization for the containers pods or nodes For the kubectl top command to work you need to have metrics API installed You can find instructions to install metrics API here Now that you have a brief understanding of the concepts let s see how the kubectl top command operates Using kubectl top nodeRunning the kubectl top node command lists metrics of the current node which would look like this kubectl top nodeNAME CPU cores CPU MEMORY bytes MEMORY kind control plane m Mi How to read the output from kubectl top The output from kubectl top node gives you information about CPU cores CPU memory and memory Let s see what these terms mean CPU cores m means millicpu m is equal to CPU hence m means of CPU CPU It is displayed only for nodes and it stands for the total CPU usage percentage of that node MemoryMemory being used by that nodeMemory It is also displayed only for nodes and it stands for total memory usage percentage of that node Using kubectl top pod commandRunning the kubectl top pod command displays the metrics about pods from the default namespace which looks like this NAME CPU Cores MEMORY Bytes nginx m Minginx cbsd gce m Minginx cbsd cae m MiHere Mi under memory stands for mebibytes Running the kubectl top pod command with all namespaces lists down pods from all namespaces in your ks cluster For example below is a snapshot from SigNoz ks cluster kubectl top pod all namespacesNAMESPACE NAME CPU cores MEMORY bytes kube system coredns bdddb kmfl m Mi kube system coredns bdddb qwrrk m Mi kube system etcd kind control plane m Mi kube system kindnet trm m Mi kube system kube apiserver kind control plane m Mi kube system kube controller manager kind control plane m Mi kube system kube proxy nt m Mi kube system kube scheduler kind control plane m Mi kube system metrics server bfdb bhrwl m Mi local path storage local path provisioner fdff tjnqb m Mi platform chi signoz cluster m Mi platform clickhouse operator cff hggdm m Mi platform my release signoz alertmanager m Mi platform my release signoz frontend ff wjf m Mi platform my release signoz otel collector cbff twr m Mi platform my release signoz otel collector metrics dcbc bgpt m Mi platform my release signoz query service m Mi platform my release zookeeper m MiYou can also use the namespace flag to get information about pods from a particular namespace For example in the below snapshot we can see details about pods from the platform namespace kubectl top pod namespace platformNAME CPU cores MEMORY bytes chi signoz cluster m Mi clickhouse operator cff hggdm m Mi my release signoz alertmanager m Mi my release signoz frontend ff wjf m Mi my release signoz otel collector cbff twr m Mi my release signoz otel collector metrics dcbc bgpt m Mi my release signoz query service m Mi my release zookeeper m Mi ConclusionResource utilization metrics are key to understanding the health of your Kubernetes cluster From the article you learned how to get resource utilization snapshots using the kubectl top command Though the kubectl top command gives you basic metrics about resource utilization it is very convenient to inspect your nodes and pods at any time For example if you see that there is a sudden spike in your resource utilization you can check which pod is consuming the most resources Kubernetes provides us with a smarter way to manage our resources for scaling cloud native applications on demand But you also need to monitor your Kubernetes resources effectively If you want to dive deeper into Kubernetes monitoring you can check out SigNoz Kubernetes node metrics monitoring with SigNozSigNoz is a full stack open source APM tool that can help you monitor your Kubernetes cluster It uses OpenTelemetry to collect metrics from your Ks cluster for monitoring OpenTelemetry is becoming the world standard for instrumentation of cloud native applications and it is backed by CNCF foundation the same foundation under which Kubernetes graduated If you wish to learn more about how to monitor your Kubernetes cluster with OpenTelemetry and SigNoz follow this blog Kubernetes monitoring with OpenTelemetry and SigNozIf you wish to learn more about SigNoz follow this blog SigNoz an open source alternative to DataDog	2022-06-11 12:20:27
海外TECH	DEV Community	Hand Digit Prediction using CNN and Tensorflow	https://dev.to/ndrohith/hand-digit-prediction-using-cnn-and-tensorflow-23d1	Hand Digit Prediction using CNN and TensorflowIn this blog I ll show you how to train a neural network model using the MNIST dataset as well as how to predict the digits in them using images Make sure you have the Tensorflow package installed as it will be utilised as a Deep Learning library here Let s import all packages which are necessary for usimport os import cv import matplotlib pyplot as plt import tensorflow as tf import numpy as npAfter we ve imported all of the essential packages we ll need to import the MNIST dataset which will let us train our model We ll split the dataset into training and testing after it s imported mnist tf keras datasets mnist x train y train x test y test mnist load data x train tf keras utils normalize x train axis x test tf keras utils normalize x test axis It s now time to put our neural network model together Let s make our Network model with Keras We ll use a Flatten layer to reshape our image s dimensions to a single dimension A Dense layer of neurons neurons and neurons will constitute the second third and fourth layers respectively The final output layer is the Dense layer which has neurons because our MNIST dataset has ten different classes model tf keras Sequential model add tf keras layers Flatten input shape model add tf keras layers Dense activation relu model add tf keras layers Dense activation relu model add tf keras layers Dense activation relu model add tf keras layers Dense activation softmax Later let s compile the network model by specifying adam for optimizer accuracy for metrices and sparse categorical crossentropy for lossmodel compile optimizer adam loss sparse categorical crossentropy metrics accuracy Finally we ll use the fit function to train our model We must test our model after it has been trained We ll be providing input images that were generated with figma Let s make a folder called digits and then put our digit images within it as digit png digit png and so on def predict img model image number while os path isfile f digits digit image number png img cv imread f digits digit image number png img cv resize img img np invert np array img prediction model predict img print f This digit is probably a np argmax prediction plt imshow img cmap plt cm binary plt show image number Let s use the model as a parameter to call this function predict img model As a result you ll get a series of photos like the ones belowNow let s try in a different approach by saving our model as hand digit h and using that model to make our predictions model save hand digit h loaded model tf keras models load model hand digit h loss accuracy model evaluate x test y test print loss print accuracy predict img loaded model As a result our final output will look like thisAs a result I ve developed other networks and worked on them The jupyter file can be found on my github repository Github Repo Hope i think this blog helps you to create your own CNN model for Hand Digit Recognition	2022-06-11 12:07:33
Apple	AppleInsider - Frontpage News	Daily deals June 11: $225 off WD Black 2TB NVMe SSD, $200 50-inch JVC 4K Smart TV, $90 Donner Speakers, more	https://appleinsider.com/articles/22/06/11/daily-deals-june-11-225-off-wd-black-2tb-nvme-ssd-200-50-inch-jvc-4k-smart-tv-90-donner-speakers-more?utm_medium=rss	Daily deals June off WD Black TB NVMe SSD inch JVC K Smart TV Donner Speakers moreSaturday s best deals include off SanDisk s GB Ultra Dual Drive Go the Wyze Robot Vacuum with LiDAR and much more Best deals for June AppleInsider checks online stores to track down discounts on Apple hardware smart devices accessories toys and other products The best ones are put together into our daily deals post Read more	2022-06-11 12:06:16
北海道	北海道新聞	侵攻「世界で起こりうる」　ゼレンスキー氏、抑止訴え	https://www.hokkaido-np.co.jp/article/692433/	抑止	2022-06-11 21:22:00
北海道	北海道新聞	オホーツク管内７５人感染　新型コロナ	https://www.hokkaido-np.co.jp/article/692431/	新型コロナウイルス	2022-06-11 21:20:00
北海道	北海道新聞	上川管内１０２人感染　新型コロナ	https://www.hokkaido-np.co.jp/article/692371/	上川管内	2022-06-11 21:19:07
北海道	北海道新聞	名古屋、福岡など８強入り　ルヴァン杯プレーオフ	https://www.hokkaido-np.co.jp/article/692413/	福岡	2022-06-11 21:03:14
北海道	北海道新聞	北朝鮮のミサイル費用８７０億円　ワクチン全国民分と韓国試算	https://www.hokkaido-np.co.jp/article/692424/	韓国	2022-06-11 21:03:14
北海道	北海道新聞	釧根管内１９人感染　新型コロナ	https://www.hokkaido-np.co.jp/article/692429/	根室管内	2022-06-11 21:16:00
北海道	北海道新聞	道南在住７４人感染　函館は５１人　新型コロナ	https://www.hokkaido-np.co.jp/article/692361/	新型コロナウイルス	2022-06-11 21:12:35
北海道	北海道新聞	十勝管内６２人感染　新型コロナ	https://www.hokkaido-np.co.jp/article/692426/	十勝管内	2022-06-11 21:10:00
北海道	北海道新聞	函館ゆかり「蟹工船の記憶」出版　富山の地域史研究家	https://www.hokkaido-np.co.jp/article/692425/	関係	2022-06-11 21:08:06