投稿時間:2020-12-12 02:40:41 RSSフィード2020-12-12 02:00 分まとめ(41件)

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AWS	AWS for SAP	Mark Your Calendars for SAP on AWS at re:Invent Sessions and Watch Parties	https://aws.amazon.com/blogs/awsforsap/mark-your-calendars-for-sap-on-aws-at-reinvent-sessions-and-watch-parties/	Mark Your Calendars for SAP on AWS at re Invent Sessions and Watch PartiesAWS re Invent is in full swing and the SAP on AWS team will take the virtual stage on December to share the latest developments on modernizing SAP environments how AWS and SAP on AWS Partners are creating solutions that go beyond infrastructure as well as customer success stories Register for the free virtual …	2020-12-11 16:03:49
AWS	AWS Security Blog	Detecting sensitive data in DynamoDB with Macie	https://aws.amazon.com/blogs/security/detecting-sensitive-data-in-dynamodb-with-macie/	Detecting sensitive data in DynamoDB with MacieAmazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect your sensitive data in Amazon Web Services AWS It gives you the ability to automatically scan for sensitive data and get an inventory of your Amazon Simple Storage Service Amazon S buckets …	2020-12-11 16:39:05
AWS	AWS Security Blog	Detecting sensitive data in DynamoDB with Macie	https://aws.amazon.com/blogs/security/detecting-sensitive-data-in-dynamodb-with-macie/	Detecting sensitive data in DynamoDB with MacieAmazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect your sensitive data in Amazon Web Services AWS It gives you the ability to automatically scan for sensitive data and get an inventory of your Amazon Simple Storage Service Amazon S buckets …	2020-12-11 16:39:05
python	Pythonタグが付けられた新着投稿 - Qiita	Pythonの__getattr__()を使ってObserverデザインパターンを実装	https://qiita.com/Philosophistoria/items/11806bb90d2d3240a9c0		2020-12-12 01:16:00
js	JavaScriptタグが付けられた新着投稿 - Qiita	Nuxt.jsでスムーススクロールする	https://qiita.com/oblivion/items/8b458cb441aa34fb9ec1		2020-12-12 01:49:06
js	JavaScriptタグが付けられた新着投稿 - Qiita	【React】一定時間で消える表示を作る	https://qiita.com/HashibamiAkira/items/09e636a2f46ad3ecbc54	setTimeoutを使ってstateを変更する前回、stateの値がtrueかfalseかでメッセージを表示させる、という風に作成しました。	2020-12-12 01:42:59
js	JavaScriptタグが付けられた新着投稿 - Qiita	Nuxt.jsでスクロールに応じてフェード出現	https://qiita.com/oblivion/items/08d49949216bb89f5143		2020-12-12 01:36:13
js	JavaScriptタグが付けられた新着投稿 - Qiita	Nuxt.jsでLoadingページを設定する	https://qiita.com/oblivion/items/0f0b1722fbf2b52d9eae		2020-12-12 01:29:07
js	JavaScriptタグが付けられた新着投稿 - Qiita	Nuxt.jsでエラー用ページを作る	https://qiita.com/oblivion/items/c6d0a0a356c3f3a020ab		2020-12-12 01:26:02
js	JavaScriptタグが付けられた新着投稿 - Qiita	Nuxt.jsにgtm入れる	https://qiita.com/oblivion/items/56175bd59dae00260692	nuxtjs	2020-12-12 01:22:27
js	JavaScriptタグが付けられた新着投稿 - Qiita	Nuxt.jsでデフォルトで使うメタ情報を設定する	https://qiita.com/oblivion/items/2d173f7fd1f9980a7717		2020-12-12 01:18:34
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	MySQL:whereしたテーブル同士のjoinでインデックスが使用されず高速化できない	https://teratail.com/questions/309710?rss=all	MySQLwhereしたテーブル同士のjoinでインデックスが使用されず高速化できないwhereしたつのテーブルをinnernbspjoinしたいのですがインデックスが使用できません。	2020-12-12 01:37:02
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	c#でリストボックスの内容を選択した後ボタンを押すと選択した内容が勝手に変わる	https://teratail.com/questions/309709?rss=all	cでリストボックスの内容を選択した後ボタンを押すと選択した内容が勝手に変わる前提・実現したいこと私は現在NETnbspFrameworkを使用してcで簡単なサウンドプレイヤーを作成しています。	2020-12-12 01:35:57
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	al-agentsで学習が実行できない	https://teratail.com/questions/309708?rss=all		2020-12-12 01:24:04
AWS	AWSタグが付けられた新着投稿 - Qiita	スイッチロールを使用している場合のクロスアカウントでS3バケット同期(aws s3 sync)	https://qiita.com/shiro01/items/dc4f7be84bb07407fd94	プロファイルスイッチロールを使用している場合、プロファイルは以下のように設定し、ltスイッチ後プロファイル名gtをprofileに指定します。	2020-12-12 01:10:19
Docker	dockerタグが付けられた新着投稿 - Qiita	Docker Desktop Mac 3.0.0 にアップデートで起動しなくなって対応した後、mysqlが動かない時の対応	https://qiita.com/shibomb/items/34fe78233a2bd0bf7338	今回現象が出ている環境はで新規作成した環境ですから、初めて出くわしたのですね・・・埋蔵している過去のプロジェクトを復活することになった時、これが発生するのか・・・対応方法stackoverflowの記事のつ目の回答の方法はダメでした。	2020-12-12 01:28:59
Ruby	Railsタグが付けられた新着投稿 - Qiita	RailsのHTTPステータス	https://qiita.com/shimadama/items/0f34df52ceaf6e1fc0c1		2020-12-12 01:43:27
Ruby	Railsタグが付けられた新着投稿 - Qiita	【React】Railsでsaveされた時に、Reactでサクセスメッセージを表示	https://qiita.com/HashibamiAkira/items/d12d58f7c20719330abb	流れとしては、stateを作るcreateが呼ばれsaveされたらstateを変更するstateをコンポーネントに渡してメッセージを表示させるこんな感じです。	2020-12-12 01:05:58
Apple	AppleInsider - Frontpage News	How to make the most of notifications in macOS Big Sur	https://appleinsider.com/articles/20/12/11/how-to-make-the-most-of-notifications-in-macos-big-sur	How to make the most of notifications in macOS Big SurThe new style Notification Center in macOS Big Sur come with many hidden options that make using your Mac quicker and more convenient There s so much more to Notification Center than widgetsNext time you open Notification Center on your Mac ignore all the useful widgets Even though they are the most visibly improved part there s a reason the feature is called Notification Center Read more	2020-12-11 17:00:18
Apple	AppleInsider - Frontpage News	AppleInsider's 2020 HomeKit holiday gift guide	https://appleinsider.com/articles/20/12/11/appleinsiders-2020-homekit-holiday-gift-guide	AppleInsider x s HomeKit holiday gift guideThrill your friends and family this holiday with one of our recommended smart home items on our HomeKit gift guide including lights cameras speakers and more Level Lock Level Lock TouchOf all the HomeKit smart locks out there Level Lock and the Level Lock Touch top our list Billed as the invisible smart lock the Level Lock is able to hide completely within your door adding smart functionality without having to replace your external hardware It is easy to install and worked phenomenal during our testing Read more	2020-12-11 16:30:41
海外TECH	Engadget	This week's best deals: $110 AirPods, $278 Sony headphones and more	https://www.engadget.com/weekly-deals-apple-airpods-sony-wh-1000xm4-headphones-last-minute-holiday-deals-164122700.html	This week x s best deals AirPods Sony headphones and moreAs the holidays inch closer we re still seeing solid deals on some of the latest gadgets Even though most of the flagship deals have come and gone now that Black Friday and Cyber Monday have passed you re still able to save a lot of money on speake	2020-12-11 16:41:22
海外TECH	Engadget	'Dune' director slams AT&T's decision to release films on HBO Max	https://www.engadget.com/dune-director-denis-villeneuve-slam-att-warner-hbo-max-162021581.html	x Dune x director slams AT amp T x s decision to release films on HBO MaxFollowing the news that all of Warner Bros films in will be released on HBO Max the same day they hit theaters Dune director Denis Villeneuve has written an essay published on Variety slamming AT amp amp T for the move Warner Bros is owned by	2020-12-11 16:20:21
海外TECH	Engadget	Boeing's tanker drone completes first flight with refueling pod	https://www.engadget.com/boeing-mq-25-tanker-drone-flies-with-refueling-pod-160152881.html	Boeing x s tanker drone completes first flight with refueling podHumans might not have much involvement in mid air refueling before long Boeing has flown a test version of its MQ tanker drone with a refueling pod attached for the first time taking it one step closer to topping up military aircraft The ho	2020-12-11 16:01:52
海外科学	NYT > Science	Saving Corpse Flowers From Being Inbred to Extinction	https://www.nytimes.com/2020/12/11/science/corpse-flowers-inbreeding-studbooks.html	breeders	2020-12-11 16:14:30
金融	金融庁ホームページ	中国財務局が高病原性鳥インフルエンザ疑似患畜の確認を踏まえ、金融上の対応について要請しました。	https://www.fsa.go.jp/news/r2/ginkou/20201211.html	中国財務局	2020-12-11 17:10:00
金融	金融庁ホームページ	「破綻金融機関の処理のために講じた措置の内容等に関する報告」について公表しました。	https://www.fsa.go.jp/news/r2/ginkou/20201211/20201211.html	金融機関	2020-12-11 17:00:00
金融	金融庁ホームページ	「マネー・ローンダリング及びテロ資金供与対策に関するガイドライン」の一部改正（案）について公表しました。	https://www.fsa.go.jp/news/r2/2020amlcft/2020amlcft.html	資金供与	2020-12-11 17:00:00
金融	金融庁ホームページ	金融審議会「最良執行のあり方等に関するタスクフォース」（第１回）を開催します。	https://www.fsa.go.jp/news/r2/singi/20201211.html	最良執行	2020-12-11 17:00:00
金融	金融庁ホームページ	バーゼル銀行監督委員会による「銀行の外部監査についての補足ノート ― 予想信用損失の監査」について掲載しました。	https://www.fsa.go.jp/inter/bis/20201211/20201211.html	外部監査	2020-12-11 17:00:00
海外ニュース	Japan Times latest articles	Kansai International Airport trials device to disinfect luggage carts amid pandemic	https://www.japantimes.co.jp/news/2020/12/11/national/kansai-international-airport-ultraviolet-light-trial/	Kansai International Airport trials device to disinfect luggage carts amid pandemicIn laboratory experiments the device now being used at Kansai International Airport proved effective in inactivating the novel coronavirus	2020-12-12 02:45:56
海外ニュース	Japan Times latest articles	U.S. crackdown on Huawei hands Japan a 5G lifeline	https://www.japantimes.co.jp/news/2020/12/11/business/us-huawei-japan-5g/	U S crackdown on Huawei hands Japan a G lifelineWith U S partners seeking out suppliers from friendlier nations vendors in the close U S ally suddenly seem a lot more attractive to carriers around the	2020-12-12 02:37:33
ニュース	BBC News - Home	Coronavirus self-isolation reduced to 10 days	https://www.bbc.co.uk/news/health-55274147	cases	2020-12-11 16:11:55
ニュース	BBC News - Home	Reynhard Sinaga: Serial rapist 'abused 206 men'	https://www.bbc.co.uk/news/uk-england-manchester-55276209	prolific	2020-12-11 16:02:33
ニュース	BBC News - Home	'Offensive' Empire honours titles must go, says Labour's Kate Green	https://www.bbc.co.uk/news/uk-politics-55278454	empire	2020-12-11 16:14:22
ニュース	BBC News - Home	Barbara Windsor, who has died aged 83, was an entertainer from the age of 13	https://www.bbc.co.uk/news/entertainment-arts-55278940	barbara	2020-12-11 16:35:20
ニュース	BBC News - Home	Covid: Genes hold clues to why some people get severely ill	https://www.bbc.co.uk/news/health-54832563	covid	2020-12-11 16:21:50
ニュース	BBC News - Home	Deschamps defamation case against Cantona ruled void	https://www.bbc.co.uk/news/world-europe-55279298	selection	2020-12-11 16:16:49
ニュース	BBC News - Home	Royal Marines team row 3,000 miles across Atlantic	https://www.bbc.co.uk/news/uk-england-devon-55280120	atlantic	2020-12-11 16:17:13
北海道	北海道新聞	東京五輪マラソン、５月５日にテスト大会　札幌の五輪コース使いハーフで　市民参加１０キロも	https://www.hokkaido-np.co.jp/article/490871/	東京五輪	2020-12-12 01:15:57
GCP	Cloud Blog	Improve the data science experience using scalable Python data processing	https://cloud.google.com/blog/products/data-analytics/improve-data-science-experience-using-scalable-python-data-processing/	Improve the data science experience using scalable Python data processingPython has quickly solidified itself as one of the top languages for data scientists looking to prep process and analyze data for analytics and machine learning related use cases Dask is a Python library for parallel computing with similar APIs to the most popular Python data science libraries such as Pandas NumPy and scikit learn Dask s parallel processing leads to greater efficiencies and lower latency for machine learning and data processing tasks Today we re excited to announce Dask support for Dataproc Google Cloud s fully managed Apache Hadoop and Apache Spark service via a new Dask initialization action With this Dataproc initialization action we ve made it even easier for data scientists to get Dask up and running on a Dataproc cluster  Today Dask is the most commonly used parallelism framework within the PyData and SciPy communities Dask is designed to scale from parallelizing workloads on the CPUs in your laptop to thousands of nodes in a cloud cluster In conjunction with the RAPIDS framework developed by NVIDIA you can utilize the parallel processing power of both CPUs and NVIDIA GPUs  Dask is built for the Python data science communityDask is built on top of NumPy Pandas Scikit Learn and other popular Python data science libraries As such the APIs are deliberately designed to help you seamlessly transition from these core libraries to the scalable Dask versions of each The Dask documentation shows some excellent examples of how some of these libraries translate to Dask which you can find here How Dask is usedDask is being used by data science teams working on a wide range of problems including high performance computing climate science banking and imaging problems Additionally Dask is also well suited for business intelligence problems See here for a list of problems that teams have made progress using Dask Why use Dask on DataprocDask provides a fast and easy way to run data transformation jobs on your big data With Dask Yarn a Skein based tool for running Dask applications on Yarn the task scheduling is relegated to the YARN scheduler freeing you from needing to manage another set of software on your cluster Yarn takes care of allocating the resource management necessary to finish processing your jobs Additionally you get access to the full set of features offered by the Dataproc service including Autoscaling Jupyter component and component gateway for submitting jobs via a Jupyter Notebook  Dask supports data loads from many different sources such as GCS and HDFS and many different data types such as CSV parquet and avro These are supported by different projects such PyArrow GCSFS FastParquet and FastAvro all of which are included with Dataproc Additionally you can also configure Dask on Dataproc to utilize Dask with its native scheduler as opposed to Yarn  Create a Dataproc cluster with DaskYou can create a Dataproc cluster by selecting a region with the Dask initialization action Jupyter optional component and component gateway enabled with the following command You can alternatively create a cluster by changing the dask runtime metadata parameter to standalone Interacting with your clusterWith Dask configured in your environment you can now run Dask jobs by either using a notebook environment such as Jupyter or SSHing into the master node of your cluster and executing a Dask script  SSH into your cluster s master node with the following command You can then use the Python base environment to submit jobs Copy the following into a Python file on your cluster dask job py which will create a YarnCluster object with which you can interact with your Dask cluster add the ability for Dask to scale resources as needed and sum an array Submit the job Your output should be a floating point number You can also use a notebook for executing your Dask jobs Using a notebook comes with some extra added bonuses such as viewing graphical representations of your data structures Additionally Dask with Jupyter notebooks provide a graphical interface for managing resources on your Dask cluster in addition to doing so via the API  Monitoring Dask workloadsYou can take advantage of multiple Web UIs for monitoring your Dask applications With Dask Yarn you can take advantage of the Dataproc console s cluster monitor to view metrics such as YARN memory and YARN pending memory  You can also access the Skein Web UI when using Dask Yarn You can find this as your application s Application Master within the YARN ResourceManager which you can access with component gateway If using Dask s standalone scheduler you can access the Dask Web UI via an SSH tunnel Combining Dask CPU parallelism with NVIDIA RAPIDS GPU parallelism You can combine the CPU parallelism capabilities of Dask with the NVIDIA GPU parallelism capabilities of the RAPIDS open source data science framework which Dataproc also supports You can create a Dataproc cluster with Dask RAPIDS NVIDIA GPUs and the necessary drivers with the following command For more information about the RAPIDS and Dask ecosystem including crossover libraries such as dask cudf check out the RAPIDS documentation here ConclusionDask is an exciting framework that has seen tremendous growth over the past few years We look forward to seeing what you re able to accomplish with it on Dataproc For more information on how to get started with Dask on Dataproc check out this quickstart in the official Dask documentation You can also get started using Dask on Dataproc with Google Cloud Platform s credit for new customers	2020-12-11 16:30:00
GCP	Cloud Blog	Faster machine learning on Dataproc with new initialization action	https://cloud.google.com/blog/products/data-analytics/faster-machine-learning-dataproc-new-initialization-action/	Faster machine learning on Dataproc with new initialization actionApache Hadoop and Apache Spark are established and standard frameworks for distributed storage and data processing Google Cloud s Dataproc is a fast easy to use fully managed cloud service for running Apache Spark and Apache Hadoop clusters in a simple cost efficient way For users looking to build machine learning models you might use Dataproc for preprocessing your data using Apache Spark and then use that same Spark cluster to power your Notebook for machine learning We created the machine learning initialization action to provide a set of commonly used libraries to reduce the time spent configuring your cluster for machine learning  In this blog you will learn how you can get started working with this initialization action on a Dataproc cluster This will provide you with an environment that lets you leverage the latest and greatest in open source machine learning including Python packages such as TensorFlow PyTorch MxNet Scikit learn and KerasR packages including XGBoost Caret randomForest sparklyrSpark BigQuery ConnectorDask and Dask YarnRAPIDS on Spark optionally GPUs and drivers optionally Plus you can augment your experience of using Dataproc with Jupyter and Zeppelin via Optional Components and Component Gateway  The machine learning initialization action relies on using other initialization actions for installing certain components such as RAPIDS Dask and GPU drivers As such you have access to the full functionality and configurations that these components provide Data preprocessing and machine learning training in one placeThe machine learning initialization action for Dataproc provides a environment for running production Spark Dask or other ETL jobs on your data while also being able to build your machine learning models with your choice of machine learning libraries all in the same place By adding GPUs to your cluster configuration you can also decrease the training time for machine learning models with TensorFlow or RAPIDS as well as use the RAPIDS SQL Accelerator to further improve the efficiencies of model training Configure your Google Cloud Platform projectYou ll need a Google Cloud Platform GCP project Use your own or create a new one following the instructions here The machine learning initialization action relies on using other initialization actions for parts of its installation You can make a copy of these scripts to effectively “pin the versions of the scripts that you re using To do so create a Cloud Storage bucket and copy the scripts into this bucket Create a Dataproc cluster with the machine learning initialization actionYou ll now proceed with the creation of your cluster First define a region and a cluster name Next run the following command This will create a cluster configured with the machine learning initialization action configured The cluster will contain master node worker nodes and NVIDIA T GPUs available to each node The cluster s Jupyter optional component will also be enabled along with component gateway to access the cluster using Jupyter Notebooks or JupyterLab The configuration shown above will create a Dataproc cluster equipped with NVIDIA graphics cards and their respective drivers installed You can then take advantage of GPU accelerated data processing using frameworks such as NVIDIA RAPIDS or TensorFlow In this configuration we re including the init actions repo metadata flag to tell the machine learning initialization action where to locate the necessary other installation scripts Additionally we re including the include gpus true and gpu driver provider NVIDIA flags to tell the script that we want to install GPU drivers and that the drivers should come from NVIDIA You can optionally run the cluster without any GPUs attached or drivers included Alternatively you can also equip the cluster with NVIDIA RAPIDS Spark Jars or NVIDIA RAPIDS for Dask You can do so with the rapids runtime metadata flag and assign this to be DASK or RAPIDS Use the spark tensorflow distributor to run distributed TensorFlow jobsYou can run distributed TensorFlow jobs on your Spark cluster with the spark tensorflow distributor included in the machine learning initialization action This library is a wrapper for the TensorFlow distributed library Copy the following code into a file spark tf dist py This code uses the MirroredStrategyRunner to submit a TensorFlow training job to your Spark cluster The Spark config provided will ensure your cluster is able to best utilize the GPUs on your cluster for training You can then submit a TensorFlow code as a PySpark job to your cluster Dataproc HubThe machine learning initialization action is great to use in a notebook environment One way to do this is using the Jupyter Optional Componentwith Dataproc and more information for this can be found in this blog post Another excellent way is to use Dataproc Hub Dataproc s managed JupyterLab service This service allows IT administrators to provide preconfigured environments optimized for security and resources allocation to their data scientists while giving the data scientists the flexibility to customize the packages and libraries available on the cluster You can configure your cluster with the machine learning initialization action by following the instructions here and using the following YAML configuration For more Information on Dataproc Hub check out this announcement blog Next stepsThe machine learning initialization action is a great place to run both your ETL processing jobs as well as train machine learning models You can also customize your experience with any of our other open source initialization actions Additionally you can create a custom Dataproc image for convenience and faster cluster creation times For more information on getting started with the machine learning initialization action check out the documentation here  You can also get started using Dask on Dataproc with Google Cloud Platform s credit for new customers	2020-12-11 16:30:00