ã¯ããã« ããã«ã¡ã¯ãã¯ã©ã¦ãã¨ã¼ã¹ ãã¼ã¿ã½ãªã¥ã¼ã·ã§ã³é¨ã®æ¾æ¬ã§ãã æ®æ®µã¯ãã¼ã¿åºç¤ã MLOps ã®æ§ç¯ãããããGoogle Cloud èªå®ãã¬ã¼ãã¼ã¨ãã¦ãã¬ã¼ãã³ã°ãæä¾ãã¦ããã¾ããã¾ããæ¨å¹´ã¯ Google Cloud Partner Top Engineer 2024 ã«é¸åºããã¾ãããä»å¹´ã Goodle Cloud çéãçãä¸ãã¦ãããããé å¼µã£ã¦ããããã¨æãã¾ãã ã¯ã©ã¦ãã¨ã¼ã¹ ãã¼ã¿ã½ãªã¥ã¼ã·ã§ã³é¨ ã«ã¤ã㦠ã¯ã©ã¦ãã¨ã¼ã¹ã®ITã¨ã³ã¸ãã¢ãªã³ã°ãæ ã ã·ã¹ãã éçºçµ±æ¬é¨ ã®ä¸ã§ãç¹ã«ãã¼ã¿åºç¤æ§ç¯ã»åæåºç¤æ§ç¯ãããã¼ã¿åæã¾ã§ãå«ãä¸è²«ãããã¼ã¿èª²é¡ã®è§£æ±ºãå°éã¨ããã®ã ãã¼ã¿ã½ãªã¥ã¼ã·ã§ã³é¨ ã§ãã å¼ç¤¾ã§ã¯ãæ°ãã«ä»²éã«å ãã£ã¦ãã ããæ¹ãåéãã¦ãã¾ããããããèå³ãããã° ã¨ã³ããªã¼ ããå¾ ã¡ãã¦ããã¾ãï¼ ä»åã¯ã次ä¸ä»£ãã¼ã¿åºç¤ã§ããã
Transcript Danilov: We'll talk about AWS Lambda, how it's built, how it works, and why it's so cool. My name is Mike Danilov. I'm a Senior Principal Engineer at AWS Serverless. A decade ago, I joined EC2 networking team, and it was a fantastic ride. Then, five years back, I heard about Lambda. I really liked the simplicity of the idea. We run your code in the cloud, no servers needed, so I joined
Previous ToC Next PDF çã¯ãã¡ã é«æ©ã¡ã½ããç(2006/1/13 天æå°ã§ã®ãã¬ã¼ã³ãã¼ã·ã§ã³)ã¯ãã¡ã Contents 0. åãã« 1. IBM BG/L ã®æ¹å (2005/12/5) 2. Cray XT3 --- MPPã¯å¾©æ´»ãããï¼(2005/12/6) 3. NEC SX-8 --- ãã¯ãã«ã®ãããã (2005/12/7) 4. å¯å£«é NWT/VPP-500 --- æ¥æ¬çºã®ãé©å½ã (2005/12/8) 5. ä½æ ã¹ã¼ãã¼ã³ã³ãã¥ã¼ã¿ã¼ã¯å£²ããªããªã£ãã (2005/12/13) 6. x86 以å¤ã®ããã»ããµã«ãªã«ãèµ·ãã£ãã®ãï¼ (2005/12/14) 7. SSE ã®éç(2005/12/23) 8. å°ç¨è¨ç®æ©ã¯ï¼(2005/12/25) 9. ä»ã®å¯è½æ§ã¯ï¼ --- I. FPGA ã¨åæ§æå¯è½è¨ç® (2005/12/28)
å¤æ©ãã¥ã¼ã¿ã¦ã³ãã§ãã¦50年以ä¸ãç·é¢ç©ç´3000haãè¨ç»äººå£34ä¸äººã¨ããæ¥æ¬æ大ã®ãã¥ã¼ã¿ã¦ã³è¨ç»ã ã£ããããã«ãã第åã®å±±ã®æããããé¸ã®å¤å³¶ãããªã¼ã«ãã¿ã¦ã³ãã¾ã§ããããæªããä¸éã®æ³¨ç®ãæµ´ã³ç¶ãã¦ããè¡ã ã ç¾ä»£ã®æ±äº¬ã«ä½ãã§ããã¨å½ããåã®åå¨ã«ãªã£ã¦ããããã巨大ãªå®é¨é½å¸ãã¨ãè¨ãããããã«ãå®ã¯æ¥æ¬å²ä¸ã§ãäºåº¦ã¨ãããããªããè²´éãªå ´æãªã®ãããããªãã 建é ç©ã¯50å¹´ãã¤ã¨æå財ã®ä»²éå ¥ãã§ããã¨ããããã©ãä¸æ¹ã§å¤æ©ãã¥ã¼ã¿ã¦ã³ã¯çããè¡ã§ãããæ±äº¬é½ã¯2040年代ãè¦æ®ããé½å¸è¨ç»ãç«ã¦ã¦ãããããã å¤æ©ãã¥ã¼ã¿ã¦ã³ã®éå»ããæªæ¥ã¸ã ãããæ©ã«ããã¥ã¼ã¿ã¦ã³ä»¥åã®å¤æ©ä¸éµã®é¢å½±ãå¤æ©ãã¥ã¼ã¿ã¦ã³é»ææãããã«ï½å¹³æã®å¤æ©ãã¥ã¼ã¿ã¦ã³ãããã¦æªæ¥ã®å¤æ©ãã¥ã¼ã¿ã¦ã³ã«ã¤ãã¦â¦åä¸ä»£ã«ããã¦ãå®éã«æ©ãã¦ã¿ããã å¤æ©ãã¥ã¼ã¿ã¦ã³ã®ãªã«ããããã®ã 1971å¹´ã
My name is Zhenzhong Xu. I joined Netflix in 2015 as a founding engineer on the Real-time Data Infrastructure team and later led the Stream Processing Engines team. I developed an interest in real-time data in the early 2010s, and ever since believe there is much value yet to be uncovered. Netflix was a fantastic place to be surrounded by many amazing colleagues. I canât be more proud of everyone
ããã«ã¡ã¯ãNTTç 究æã®å±±å£ã§ãã ååã®è¨äºã§A100ã®MIGã«ã¤ãã¦è§¦ãã¦ãã¾ãããMIGãæ´»ç¨ããéã®ã¢ããã¼ã·ã§ã³ã¨ãã¦ã1ã¤ã®GPUä¸ã§å¹ççã«è¤æ°ããã»ã¹ãå®è¡ããããè¤æ°ã¦ã¼ã¶ã§å©ç¨ã§ããããã«ãããã¨ããç®çãæãããããã¨æãã¾ããæ¬è¨äºã§ã¯1ã¤ã®GPUãªã½ã¼ã¹ãå¹ççã«å©ç¨ããããã®æè¡ã¨ãã¦ãMulti Process Service(MPS), Virtual GPU(vGPU), Multi Instance GPU(MIG)ã¨ããä¸ã¤ã®NVIDIA社ã®æè¡ã«ã¤ãã¦ã¾ã¨ãã¾ãã MPS: GPUä¸ã§ä¸¦åå¦çãå¹ççã«è¡ããã¨ãã§ããvGPU: GPUãä»®æ³åãã¦VMã«å¯¾ãã¦ãªã½ã¼ã¹ãå²ãå½ã¦ããã¨ãå¯è½ãåå²ãããGPUã¯ãVMå ã§åã ã«GPUã¨ãã¦èªèå¯è½ã§ããããè¤æ°ã¦ã¼ã¶ã«å¯¾ãã¦æä¾ããããMIG: ææ°ã®ã¢ã¼ããã¯ãã£ã§ããAmpereã§ããå©ç¨ã§ããªã
ã¾ããã åãã¾ãã¦ãR&Dãã¼ã ã®å®®ï¨ã§ãã趣å³ã¯FPSããã£ã¦ã¾ãã150æéãã£ã¦ã¾ããæªã ã«ãã³ãã¤ãé£ã¹ããã¦ãã¾ãããæ®æ®µã¯æ·±å±¤å¦ç¿ã®ãã³ããã¼ã¯ãåã£ãããã¦ãã¾ãã å®ã¯ã²ã¨æåãããã£ã深層å¦ç¿ãè¿å¹´å®ç¨æ§ãå¢ãã¦ä¸å¤§ãã¼ã ã¨ãªã£ã¦ãã¾ããå®ç¨çã«ãªã£ãèæ¯ã®ä¸ã¤ã¨ãã¦GPUãç¨ããé«éåã«ãã深層å¦ç¿ã®å¦ç¿åã³æ¨è«ãå®ç¨çãªæéã§å®è¡ã§ããããã«ãªã£ããã¨ãããã¾ããNVIDIAããæä¾ãããCUDAãç¨ããã¨Cè¨èªã«æ¡å¼µãå ããå½¢å¼ã§CPU+GPUã®ãããã¸ãã¢ã¹ã³ã³ãã¥ã¼ãã£ã³ã°ãè¨è¿°ã§ãã¾ãã CUDAã使ç¨ããã«ã¯CPU+GPUã®ç°å¢ãã©ã®ãããªãã¼ãã¦ã§ã¢æ§æããã¦ããããã®ä¸ã§CUDAãã©ã®ãããªã·ã¹ãã ãæ§ç¯ãã¦ããã®ããç解ããå¿ è¦ãããã¾ãããã®ç解ãªãã«æ¼«ç¶ã¨ãµã³ãã«ã³ã¼ããçä¼¼ããã ãã§ã¯æå³éãã®ããã©ã¼ãã³ã¹ãåºãªãã£ãããããããAPIã®æå³
Photo by Thalia Tran on UnsplashKafka is a top-notch industry platform for streaming data processing at scale. No surprise that first-class citizens of Kafka world are 24/7-running producer/consumer applications (e.g. classical servers, k8s-pods, etc.). But what about the rapidly rising world of AWS Serverless ecosystem? Image credit: AuthorThe diagram above is a collection of workflows: Propagate
Distributed systems provide a particular challenge to program. They often require us to have multiple copies of data, which need to keep synchronized. Yet we cannot rely on processing nodes working reliably, and network delays can easily lead to inconsistencies. Despite this, many organizations rely on a range of core distributed software handling data storage, messaging, system management, and co
â»ãã®æ稿ã¯ç±³å½æé 2019 å¹´ 6 æ 20 æ¥ã« Google Cloud blog ã«æ稿ããããã®ã®æ訳ã§ãã ç§ãã¡ Google Cloud ã¯ãGoogle Cloud Platformï¼GCPï¼ä¸ã«ç§»è¡ãããã¯æ§ç¯ãããã¢ããªã±ã¼ã·ã§ã³ã®æçµç®æ¨ã¨ãã¦ããããã¯ã©ã¦ããã¤ãã£ã ã¢ã¼ããã¯ãã£ãã¨ããè¨èã使ãã¾ããã§ã¯ãã¯ã©ã¦ããã¤ãã£ãã¨ã¯æ£ç¢ºã«ã¯ã©ãããæå³ãªã®ã§ãããããããã¦ããã®ãããªã·ã¹ãã ã¯ã©ãããã°è¨è¨ã§ããã®ã§ããããã 大ã¾ãã«è¨ãã°ãã¯ã©ã¦ããã¤ãã£ãã¨ã¯ãã¯ã©ã¦ãã«ãã£ã¦ãããããããå¾æ¥ã®ãªã³ãã¬ãã¹ã«ã¯ãªãæ°ããå¯è½æ§ã«é©å¿ãããã¨ãæå³ãã¾ãï¼ã¢ã¼ããã¯ãã£ä¸ã®å¶ç´ãå¾æ¥ã¨ã¯å¤§ããç°ãªããããããã«ãé©å¿ï¼ãã½ããã¦ã§ã¢ ã¢ã¼ããã¯ãã¨ãã¦ç§ãã¡ãèæ ®ããããè¨ç·´ãåãã¦ããé«ã¬ãã«ã®è¦ç´ ã«ã¤ãã¦èãã¦ã¿ã¾ãããã ã·ã¹ãã ã®æ©è½è¦ç´ ï¼ä½ã
EngineeringPeloton: Uberâs Unified Resource Scheduler for Diverse Cluster WorkloadsOctober 30, 2018 / Global Cluster management, a common software infrastructure among technology companies, aggregates compute resources from a collection of physical hosts into a shared resource pool, amplifying compute power and allowing for the flexible use of data center hardware. At Uber, cluster management prov
Architects look at thousands of buildings during their training, and study critiques of those buildings written by masters. In contrast, most software developers only ever get to know a handful of large programs wellâusually programs they wrote themselvesâand never study the great programs of history. As a result, they repeat one another's mistakes rather than building on one another's successes.
Our mission at Netflix is to deliver joy to our members by providing high-quality content, presented with a delightful experience. We are constantly innovating on our product at a rapid pace in pursuit of this mission. Our innovations span personalized title recommendations, infrastructure, and application features like downloading and customer profiles. Our growing global member base of 125 milli
å··ã§ã¯Intel, AMD, ARMãå·»ãè¾¼ãã CPUã®ãã° "Meltdown", "Spectre" ã話é¡ã§ãã ãããã®åé¡ãå 容ãèªã¿é²ãã¦ããã¨ãã³ã³ãã¥ã¼ã¿ã¢ã¼ããã¯ãã£ã«ãããéè¦ãªè¦ç´ ãå¤ãå«ãã§ãããã¨ãåãã£ã¦æ¥ã¾ããã ã¤ã¾ãããã®CPUã®ã»ãã¥ãªãã£åé¡ãèªã¿è§£ãã¦ããã¨ç¾ä»£ã®ãã¤ã¯ãããã»ããµãæã¤ãæ§è½åä¸ã®ããã®ãããªãæ©è½è¿½å ã®ä¸ç«¯ãè¦ãã¦ããã®ã§ã¯ãªããã¨æããGoogle, Intelã®æç®ãèªã¿è§£ãã¦ã¿ããã¨ã«ãã¾ããã ããç§ã¯ã»ãã¥ãªãã£ã®å°é家ã§ã¯ããã¾ããããéå»ã«ãã¹ã¯ãããPCåãã®ãããªå¤§è¦æ¨¡ãªCPUè¨è¨ã«åå ãããã¨ãããã¾ããã ããã¾ã§ã³ã³ãã¥ã¼ã¿ã¢ã¼ããã¯ãã£ã«æ¯è¼çè¿ãå ´æã«ãã人éã¨ãã¦ããã®åé¡ã®æ¬è³ªã¯ã©ãã«ããã®ããå¯è½ãªéãèªã¿è§£ãã¦ãããç¾ä»£ã®ãã¤ã¯ãããã»ããµãæã¤é«æ§è½ãã¤é«æ©è½ãªå é¨å®è£ ã«ã¤ãã¦è§£ãæããã¦ãã
Yahooã®æè¡è ãæ¸ããããã° techblog.yahoo.co.jp ãæªãæ¹åã«æå¾ ãè£åã£ã¦ãããã®ã«å¯¾ãã @kuenishi ãããã¾ã¨ã¾ã£ãæç« kuenishi.hatenadiary.jp ãæ¸ãã¦ããã®ã§ãåã2çªç ããããã§ã¾ã¨ã¾ã£ãæç« ãæ¸ãã å§ãã«æã£ã¦ããã¨ãåæ£ã·ã¹ãã ã¨ããã®ã¯ã¾ã ã¾ã äºä¾ãéãã¦ãããã§ã¼ãºãæããã£ã¦ããããä½ç³»ç«ã£ã大統ä¸çè«çãªåé¡æ³ã¯ç¢ºç«ãã¦ããªããããã«æ¸ãã®ã¯ãããã¾ã§ã®åæ£ã·ã¹ãã äºä¾ãããããã®åæ£ã·ã¹ãã äºä¾ãåé¡ãã¦ããéã«ãã®æ§è³ªãã«ãã´ã©ã¤ãºããä¸å©ã¨ãªãã°è¯ããªãç¨åº¦ã®æç« ãªã®ã§ãã¾ãçã«åããªãã§æ¬²ããã ãªãYahooã®è¨äºãæå¾ ã¯ãããªã®ã 人ã«ãã£ã¦æè¦ã¯ããã¨ã¯æãããå人çã«æããã®ã¯ä»¥ä¸ã®ï¼ã¤ã åæ£ã·ã¹ãã ã®ãã¶ã¤ã³ãã¿ã¼ã³ã¨éæã£ã¦ãããªãã並åã»ä¸¦è¡ã·ã¹ãã ã®åéã®è©±ããã¯ã©ã¦ãç°å¢ã¸ã¨ãã
ãç¥ãã
ã©ã³ãã³ã°
ã©ã³ãã³ã°
ãªãªã¼ã¹ãé害æ å ±ãªã©ã®ãµã¼ãã¹ã®ãç¥ãã
ææ°ã®äººæ°ã¨ã³ããªã¼ã®é ä¿¡
å¦çãå®è¡ä¸ã§ã
j次ã®ããã¯ãã¼ã¯
kåã®ããã¯ãã¼ã¯
lãã¨ã§èªã
eã³ã¡ã³ãä¸è¦§ãéã
oãã¼ã¸ãéã
{{#tags}}- {{label}}
{{/tags}}