投稿時間:2022-01-05 08:39:20 RSSフィード2022-01-05 08:00 分まとめ(50件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
IT	気になる、記になる…	Apple、今年後半にオーディオブックの定額制サービスを提供か	https://taisy0.com/2022/01/05/150402.html	apple	2022-01-04 22:43:25
IT	気になる、記になる…	米Apple、現地時間1月27日に同社2022年第1四半期の業績を発表へ	https://taisy0.com/2022/01/05/150400.html	apple	2022-01-04 22:33:08
IT	気になる、記になる…	Tapbots、｢Tweetbot 6.8｣をリリース − 新しいウィジェット追加など	https://taisy0.com/2022/01/05/150398.html	tapbots	2022-01-04 22:28:58
TECH	Engadget Japanese	目に温かさを感じながら仕事ができる。さっと塗るだけ、新感覚のホットアイマスク【Eye Hot】	https://japanese.engadget.com/eye-hot-224027194.html	最近は穴が空いていて目を開けながらケアできる製品も出てきましたが集中している時に、目の周りにあるのも違和感があって集中できない。	2022-01-04 22:40:27
TECH	Engadget Japanese	レア装備を揃えてダンジョン制覇！放置で楽しめるハクスラアクション『グロウナイト』：発掘！スマホゲーム	https://japanese.engadget.com/glow-knights-221045308.html	話題	2022-01-04 22:10:45
TECH	Engadget Japanese	「小さなアイデアでも集めれば企業は強くなる」UHKBのクラウドファンディングを終えて感じたこと：PC広報風雲伝（第26回）	https://japanese.engadget.com/publicrelations26-220027057.html		2022-01-04 22:00:27
IT	ITmedia 総合記事一覧	[ITmedia エグゼクティブ] 第1回 なぜ今「学び」が求められているのか	https://mag.executive.itmedia.co.jp/executive/articles/2201/05/news022.html	itmedia	2022-01-05 07:02:00
IT	ITmedia 総合記事一覧	[ITmedia エグゼクティブ] 2022年に注目したい日本映画	https://mag.executive.itmedia.co.jp/executive/articles/2201/05/news035.html	itmedia	2022-01-05 07:02:00
IT	ITmedia 総合記事一覧	[ITmedia News] QualcommとMicrosoft、メタバースに向けてARメガネ用チップ開発で提携	https://www.itmedia.co.jp/news/articles/2201/05/news057.html	itmedianewsqualcomm	2022-01-05 07:01:00
AWS	AWS	How can I troubleshoot issues with using a custom SSL certificate for my CloudFront distribution?	https://www.youtube.com/watch?v=E5Iler8QaSQ	How can I troubleshoot issues with using a custom SSL certificate for my CloudFront distribution For more details see the Knowledge Center article with this video Eric shows you how to troubleshoot issues with using a custom SSL certificate for my CloudFront distribution Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWS AmazonWebServices CloudComputing	2022-01-04 22:06:51
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	リスト内の要素をcsv化したい	https://teratail.com/questions/376658?rss=all	counter	2022-01-05 07:48:38
技術ブログ	Developers.IO	【2022年】AWS全サービスまとめ	https://dev.classmethod.jp/articles/aws-summary-2022/	毎年	2022-01-04 22:00:40
海外TECH	Ars Technica	Nevermind baby’s child-porn lawsuit against Nirvana dismissed by judge	https://arstechnica.com/?p=1823686	statute	2022-01-04 22:36:56
海外TECH	Ars Technica	Coming to a laptop near you: A new type of security chip from Microsoft	https://arstechnica.com/?p=1823688	microsoft	2022-01-04 22:15:27
海外TECH	MakeUseOf	9 Websites to Easily Create and Send Cards	https://www.makeuseof.com/websites-create-send-cards/	cardswhether	2022-01-04 22:30:12
海外TECH	MakeUseOf	How to Search Reddit Effectively: 4 Useful Tips and Tricks to Know	https://www.makeuseof.com/tag/right-way-search-reddit/	relevant	2022-01-04 22:19:46
海外TECH	DEV Community	The CodeNewbie Challenge Is Returning for 2022 with Exciting New Updates	https://dev.to/devteam/the-codenewbie-challenge-is-returning-for-2022-with-exciting-new-updates-2b32	The CodeNewbie Challenge Is Returning for with Exciting New Updates CodeNewbie Challenge begins on January st Register for Cohort by January th In early DEV acquired an incredible supportive community for early career software developers and people learning to code called CodeNewbie Since then we ve embarked on many exciting adventures together including two entirely virtual CodeLand conferences and the creation of CodeNewbie Community the official home of CodeNewbie built on Forem In we relaunched the CodeNewbie Challenge together This collection of email based challenges including Start Coding Code More Write More and Get a Job existed long before DEV and CodeNewbie joined forces but together we updated it and added in additional support options and a central hub on CodeNewbie Community Those who participated benefitted from structure public accountability inspiration and ample learning opportunities to reach their goals We re thrilled to share that we re updating the CodeNewbie Challenge for CNC CNC returns on January st This time we re making a few changes including A pre registration cohort based model that allows you to stay in tighter communication with others participating in your chosen challengeUpdated resourcesA brand new Learn in Public challenge that will give you the guidance you need to turn your technical learning journey into a tool for othersIf you want to make a change in your career or coding journey we highly encourage you to sign up for Cohort of CNC Learn more pick your challenge and sign up for Cohort here by January th The challenge officially kicks off on January st Cheers to starting to code coding more writing more getting a job and learning in public with the CodeNewbie Challenge in ️	2022-01-04 22:21:22
海外TECH	DEV Community	Profiling and Analyzing Performance of Python Programs	https://dev.to/martinheinz/profiling-and-analyzing-performance-of-python-programs-5c9c	Profiling and Analyzing Performance of Python ProgramsProfiling is integral to any code and performance optimization Any experience and skill in performance optimization that you might already have will not be very useful if you don t know where to apply it Therefore finding bottlenecks in your applications can help you solve performance issues quickly with very little overall effort In this article we will look at the tools and techniques that can help us narrow down our focus and find bottlenecks both for CPU and memory consumption as well as how to implement easy almost zero effort solutions to performance issues in cases where even well targeted code changes won t help anymore Identify BottlenecksIt s good to be lazy when it comes to performance optimization Instead of trying to figure out which part of a codebase is making an application slow we can just use profiling tools to find the areas that need attention or further digging The most common tool for this task used by Python developers is cProfile It s a builtin module that can measure execution time of each function in our code Let s consider the following function that slowly computes e to the power of X some code pyfrom decimal import def exp x getcontext prec i lasts s fact num while s lasts lasts s i fact i num x s num fact getcontext prec return sexp Decimal Now let s run cProfile against the above slow code python m cProfile s cumulative some code py function calls primitive calls in seconds Ordered by cumulative timek ncalls tottime percall cumtime percall filename lineno function built in method builtins exec some code py lt module gt some code py exp lt frozen importlib bootstrap gt find and load lt frozen importlib bootstrap gt find and load unlocked lt frozen importlib bootstrap gt load unlocked lt frozen importlib bootstrap external gt exec module lt frozen importlib bootstrap gt call with frames removed decimal py lt module gt Here we used s cumulative to sort the output by cumulative time spend in each function making it easier to find the problematic areas of code in the output We can see that pretty much all time sec was spent in the exp function during the single invocation This kind of profiling can be useful but unfortunately might not be always sufficient cProfile only gives information about function calls not about individual lines of code if you call some particular function such as append in different places then it will be all aggregated into single line in cProfile output Same goes for scripts like the one we used above it contains single function that gets called just once so there s not much for cProfile to report Sometimes we don t have the luxury of being able to analyze the troublesome code locally or we might need to analyze a performance issue on the fly when it arises in production environment In these situations we can make use of py spy which is a profiler that can introspect already running program for example an application in production environment or on any remote system pip install py spypython some code py amp ps A o pid cmd grep python python some code py grep pythonsudo env PATH PATH py spy top pid In the above snippet we first install py spy and then run our long running Python program in background This will show PID number automatically but if we didn t know it then we could use the ps command to look it up Finally we run py spy in top mode passing in the PID This will produce terminal view that mimics the auto updating output of Linux top utility similar to the screenshot below This doesn t really give us that much info because this script has just one long running function but in real world scenarios you would most likely see many functions sharing the CPU time which might help shed some light on ongoing performance issues of the program Digging DeeperThe above profilers should help you find function s that are causing performance issues but if that doesn t narrow down the focus area enough for you to know what to fix then we can turn to profilers that operate on more granular level First of those is line profiler which as the name suggests can be used to drill down on time spend on each individual line of code pip install line profilerkernprof l v some code py This might take a while Wrote profile results to some code py lprofTimer unit e sTotal time sFile some code pyFunction exp at line Line Hits Time Per Hit Time Line Contents profile def exp x getcontext prec i lasts s fact num while s lasts lasts s i fact i num x s num fact getcontext prec return sline profiler library is distributed together with kernprof CLI named after Robert Kern used to effectively analyze results of test runs By running this utility against our code we produce a lprof file with code analysis as well as the above output when v is used This output clearly shows where in the function we spend the most amount of time which greatly helps with finding and fixing the performance issue In the output you will also notice the profile decorator was added to the exp function that s necessary so that line profiler knows which function in the file we want to analyze Even when running analysis on per line basis it still might not be clear what is the culprit of performance issues Example of this could be while or if conditionals composed of multiple expression In cases like these it would make sense to rewrite the specific line into multiple ones to get more comprehensive analysis results If you re truly lazy developer as me and reading CLI text output is too much to ask then another option is to use pyheat This is a profiler based on pprofile another line by line profiler inspired by line profiler which generates a heat map of code lines areas that are taking the most amount of time pip install py heatpyheat some code py out image file pngConsidering the simplicity of our sample code we used the CLI output from kernprof earlier was already pretty clear but the above heat map makes the bottleneck in the function even more obvious So far we ve spoken about CPU profiling but CPU usage might not always be what we care about RAM is cheap so we don t usually think about its usage well at least until we run out of it Even if you re not running out of RAM it still makes sense to profile your application for memory usage to find out whether you can optimize code to save memory or whether you can add more memory to improve performance of your code To analyze memory usage we can use tool called memory profiler which mimics the behaviour of earlier shown line profiler pip install memory profiler psutil psutil is needed for better memory profiler performancepython m memory profiler some code pyFilename some code pyLine Mem usage Increment Occurrences Line Contents MiB MiB profile def memory intensive MiB MiB small list None MiB MiB big list None MiB MiB del big list MiB MiB return small listFor this test we chose a little different piece of code The memory intensive function creates and deletes large Python lists to clearly demonstrate how memory profiler can be helpful in analyzing memory usage Same as with kernprof profiling here we also have to tack on profile to function under text for memory profiler to recognize which part of code we want to profile This shows that upwards of MiB were allocated just for a simple list of None values Bear in mind though that this output doesn t show true usage of memory but rather how much memory was allocated by function call on each line In this case that means that the list variables aren t actually storing as much memory just that Python list is likely to over allocate memory to accommodate for the expected growth of the variable As we can see from the above Python lists can oftentimes consume hundreds of megabytes or even gigabytes of memory one quick optimization can be switching to plain array objects instead which stores primitive datatypes such as int or float more efficiently Additionally you can also limit memory usage by choosing lower precision type using typecode parameter use help array to see table of valid options and their sizes If even these more specific and granular tools aren t sufficient at finding bottlenecks in your code then you can try disassembling it and see the actual bytecode used by Python interpreter Even if the disassembly doesn t help you solve the problem at hand it will still be useful at getting better idea and understanding of which operations are performed by Python in the background each time you invoke some particular function Keeping these facts in mind might help you write more performant code in the future Code disassembly can be generated using the builtin dis module by passing a function code module to dis dis This generates and prints a list of bytecode instructions performed by the function from math import edef exp x return e x math exp x import disdis dis exp Throughout whole article we ve been using very slow implementation of e to power of X so above we defined trivial function that does it fast so that we can compare their disassemblies Trying to disassemble both of them will lend a wildly different outputs that makes it even more obvious why one is significantly slower than the other The fast one LOAD GLOBAL e LOAD FAST x BINARY POWER RETURN VALUEThe old slow version LOAD GLOBAL getcontext CALL FUNCTION DUP TOP LOAD ATTR prec LOAD CONST INPLACE ADD ROT TWO STORE ATTR prec LOAD CONST UNPACK SEQUENCE STORE FAST i STORE FAST lasts STORE FAST s STORE FAST fact STORE FAST num gt gt LOAD FAST s LOAD FAST lasts COMPARE OP POP JUMP IF FALSE RETURN VALUETo better understand what is actually happening in the above output I recommend reading this StackOverflow answer which explains all columns in the output The SolutionsAt some point making additional tweaks to your code and algorithms will start providing diminishing returns At that time it s a good idea to turn your attention to external tools to provide some additional performance boost A sure way to get speed improvement in your code is to compile it to C That can be done using various tools for example PyPy or Cython The former is a JIT Just In Time compiler which can be used as drop in replacement for CPython It can provide a significant performance boost with zero effort giving you an easy quick win All you need to do is download the archive untar it and run your code Download from tar xjf pypy v linux tar bzcd pypy v linux bin pypy some code pyAnd just to prove to you that we will get immediate performance improvement with zero effort let s just quickly check the run time of the script with CPython and PyPy time python some code pyreal msuser mssys mstime pypy some code pyreal msuser mssys msIn addition to the above mentioned benefits PyPy also doesn t require any changes to code and supports all builtin modules and functions This all sounds amazing but there are some trade offs that come with using PyPy It supports projects that require C bindings such as numpy but that creates big overhead making the libraries significantly slower effectively negating any other performance gains It will also not solve your performance issues in situations where you use external libraries or when interacting with databases Similarly you can t expect a lot of performance gains in I O bound programs If PyPy isn t cutting it then you can try using Cython a compiler which uses C like type annotation not Python type hints to create compiled Python extension modules Cython also uses AOT ahead of time compilation which can bring bigger performance gains by avoiding application cold start Using it however requires you to rewrite you code to work with Cython syntax which in turn increases complexity of your code If you don t mind switching to a little different syntax of Python then you also might want to take a look at prometeo an embedded domain specific language based on Python specifically aimed at scientific computing Prometeo programs transpile to pure C code and its performance can be comparable with hand written C code If none of the above solutions satisfy the performance requirements then you might have to write your optimized code in C or Fortran a use Foreign Function Interfaces FFI to call the code from Python Examples of libraries that can help you with that would be ctypes or cffi for C code and fpy for Fortran Closing ThoughtsThe first rule of optimization is to not do it If you really have to though then optimize where appropriate Use the above profiling tools to find bottlenecks so you don t waste time optimizing some inconsequential piece of code It s also useful to create a reproducible benchmark for the piece of code you re trying to optimize so that you can measure the actual improvement This article should help you find the culprit of performance issue Fixing the issue is however completely different topic some easy way to make you Python code significantly faster can be found in my previous article Making Python Programs Blazingly Fast	2022-01-04 22:01:32
Apple	AppleInsider - Frontpage News	Apple to announce first quarter earnings on Jan. 27	https://appleinsider.com/articles/22/01/04/apple-to-announce-first-quarter-earnings-call-on-jan-27?utm_medium=rss	Apple to announce first quarter earnings on Jan Apple on Tuesday announced that it will detail earnings for the first fiscal quarter of on January with an ensuing investor conference call expected to include a discussion from CEO Tim Cook and CFO Luca Maestri Apple will hold an investor conference call following its first fiscal quarter earnings release on Thursday January the company said in an update to its investor webpage The call is scheduled to take place at p m Pacific p m Eastern As usual Cook and Maestri are expected to deliver word on Apple s overall financial health and break out segment details for the three month period ending in December Read more	2022-01-04 22:49:02
海外TECH	Engadget	Watch LG's CES 2022 event in under five minutes	https://www.engadget.com/lg-ces-2022-supercut-220755632.html?src=rss	Watch LG x s CES event in under five minutesAfter many companies dropped out of CES over health and safety fears related to the new omicron coronavirus strain LG was one of the first to host a keynote before the trade show s official start on Wednesday Thankfully you don t have to watch the entire event to see everything the company showed off We ve compiled all of LG s major announcements into a video that clocks in at under five minutes Expect to see its OLED TV lineup make an appearance Oh and make sure to stick around until the end to see an adorable delivery robot Follow all of the latest news from CES right here	2022-01-04 22:07:55
海外科学	NYT > Science	Biden ‘Over-Promised and Under-Delivered’ on Climate. Now, Trouble Looms in 2022.	https://www.nytimes.com/2022/01/04/climate/biden-climate-change.html	agenda	2022-01-04 22:26:27
海外TECH	WIRED	Join WIRED HQ at CES (Virtually)	https://www.wired.com/story/ces-2022-wiredhq	showcases	2022-01-04 22:45:00
海外TECH	WIRED	CES 2022 Liveblog: The Latest News From Tech’s Big Show	https://www.wired.com/story/ces-2022-liveblog	companies	2022-01-04 22:18:00
金融	金融総合：経済レポート一覧	トルコ政府の「奇策」は反ってリラ相場に火を注いでしまった可能性も～預金満期にかけては「攻防戦」が強まる可能性もあり、トルコを巡る状況は厳しさを増すと見込まれる：Asia Trends	http://www3.keizaireport.com/report.php/RID/480324/?rss	asiatrends	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	CSRを巡る動き：「インパクト」を測定し、そして管理する	http://www3.keizaireport.com/report.php/RID/480325/?rss	日本総合研究所	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	FX Daily（1月3日）～ドル円、115円台前半で強含み	http://www3.keizaireport.com/report.php/RID/480326/?rss	fxdaily	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	日経平均4万円への道 ～寅千里を走り、卯跳ねる。でも寅だってジャンプすることあるよね	http://www3.keizaireport.com/report.php/RID/480340/?rss	岡三証券	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	信金中金月報 2022年1月号～自己の認識と顧客の評価 / 市街地信用組合制度（信用金庫制度の前身）の確立に貢献した4人の英傑 / 信用金庫の個人ローン残高の動向...	http://www3.keizaireport.com/report.php/RID/480341/?rss	中小企業	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	J-REIT不動産価格指数（2021年12月分）	http://www3.keizaireport.com/report.php/RID/480345/?rss	jreit	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	週間市場レポート（2021年12月27日～12月31日）～日本の株式・債券市場、米国の株式市場、外国為替市場	http://www3.keizaireport.com/report.php/RID/480349/?rss	債券市場	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	ウィークリーレポート 2022年1月4日号～NYダウとS＆P500指数は続伸、一時過去最高値を更新。	http://www3.keizaireport.com/report.php/RID/480350/?rss	三井住友トラスト	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	KAMIYAMA Seconds!：2022年を展望する	http://www3.keizaireport.com/report.php/RID/480351/?rss	kamiyamaseconds	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	2021年の株式市場の回顧と2022年の展望：フォローアップ・メモ	http://www3.keizaireport.com/report.php/RID/480352/?rss	日興アセットマネジメント	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	楽読 Vol.1778～2022年1月の金融政策、政治・経済イベント	http://www3.keizaireport.com/report.php/RID/480353/?rss	日興アセットマネジメント	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	ウィークリー・マーケット 2022年1月第1週号	http://www3.keizaireport.com/report.php/RID/480354/?rss	日興アセットマネジメント	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	よく分かる！経済のツボ『求められる資産形成～学校でも金融経済教育を開始～』	http://www3.keizaireport.com/report.php/RID/480355/?rss	第一生命経済研究所	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	マーケット見通し『向こう1年間の市場予想』(2022年1月号)（12月1日時点）	http://www3.keizaireport.com/report.php/RID/480360/?rss	市場予想	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	甦る自動車生産　サプライチェーンは快方へ：Market Flash	http://www3.keizaireport.com/report.php/RID/480362/?rss	marketflash	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	地方銀行の越境融資に伴うリスクとビジネス機会：リサーチ・アイ No.2021-060	http://www3.keizaireport.com/report.php/RID/480363/?rss	地方銀行	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	コロナ禍対応で特例免除54万件、標準報酬特例改定50万人～年金改革ウォッチ 2022年1月号：保険・年金フォーカス	http://www3.keizaireport.com/report.php/RID/480365/?rss	標準報酬	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	2021年（年間）及び12月の売買状況について～日本取引所グループの現物市場とデリバティブ市場における売買状況...	http://www3.keizaireport.com/report.php/RID/480385/?rss	日本取引所グループ	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	【石黒英之のMarket Navi】2022年年始相場の注目点	http://www3.keizaireport.com/report.php/RID/480386/?rss	marketnavi	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	政治・経済スケジュール 2022年1月号	http://www3.keizaireport.com/report.php/RID/480387/?rss	三菱ufj	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	【注目検索キーワード】脱炭素燃料	http://search.keizaireport.com/search.php/-/keyword=脱炭素燃料/?rss	検索キーワード	2022-01-05 00:00:00
金融	金融総合：経済レポート一覧	【お薦め書籍】サイコロジー・オブ・マネー 一生お金に困らない「富」のマインドセット	https://www.amazon.co.jp/exec/obidos/ASIN/4478114137/keizaireport-22/	貧乏	2022-01-05 00:00:00
ニュース	BBC News - Home	Covid: Critical incidents declared over staff shortages	https://www.bbc.co.uk/news/uk-england-59875679?at_medium=RSS&at_campaign=KARANGA	trusts	2022-01-04 22:06:31
ニュース	BBC News - Home	Kane 'totally involved' in Spurs project - Conte	https://www.bbc.co.uk/sport/football/59875709?at_medium=RSS&at_campaign=KARANGA	Kane x totally involved x in Spurs project ConteTottenham manager Antonio Conte says Harry Kane is totally involved in his project at Spurs after it appeared the striker would leave last summer	2022-01-04 22:30:23
ニュース	BBC News - Home	Djokovic to compete at Australian Open after receiving Covid-19 vaccine medical exemption	https://www.bbc.co.uk/sport/tennis/59865959?at_medium=RSS&at_campaign=KARANGA	Djokovic to compete at Australian Open after receiving Covid vaccine medical exemptionNovak Djokovic will defend his Australian Open title after receiving a medical exemption from having a Covid vaccination	2022-01-04 22:40:37
ビジネス	ダイヤモンド・オンライン - 新着記事	デトロイト勢の供給難、EVで再燃も - WSJ発	https://diamond.jp/articles/-/292428	再燃	2022-01-05 07:23:00
北海道	北海道新聞	室蘭市、８万人割れ　８４年ぶり　人口規模道内１１番目	https://www.hokkaido-np.co.jp/article/630038/	住民基本台帳に基づく人口	2022-01-05 07:16:03