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X-WR-CALDESC:第3回 Designing Deep Learning Systems 読書会
X-WR-CALNAME:第3回 Designing Deep Learning Systems 読書会
X-WR-TIMEZONE:Asia/Tokyo
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DTSTART:19700101T000000
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TZOFFSETTO:+0900
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UID:954809@techplay.jp
SUMMARY:第3回 Designing Deep Learning Systems 読書会
DTSTART;TZID=Asia/Tokyo:20240921T130000
DTEND;TZID=Asia/Tokyo:20240921T180000
DTSTAMP:20260406T175939Z
CREATED:20240817T140636Z
DESCRIPTION:イベント詳細はこちら\nhttps://techplay.jp/event/95480
 9?utm_medium=referral&utm_source=ics&utm_campaign=ics\n\n内容\n\n\nDesi
 gning Deep Learning Systems 読書会\n\n今回は2.3 Open source approac
 hes から読み進めます\n\n\n\n進め方\n\n書籍の内容を翻訳
 および要約し、それを勉強会で共有して、参加者で議
 論します\n次回分の要約は希望者を募って行います。
 要約を行わずに参加を継続するだけでも大丈夫です！\
 nラジオのように視聴される方も歓迎です！\n声を発す
 るのに抵抗がある方向け\n録音はしていません\n\n\n\n\n\
 n書籍は以下のサイトより入手できます\n\nO'reilly\nManning
 \nAmazon\n\n\n\n主催 : JavaEE勉強会 創立 2004年 (Java EE勉強会
 )\n\nJ2EE(Java EE)に関連した話題を議論する勉強会\n2004年8
 月から月1回のペースで定期的に開催を続けています(
 第211回目)\n\n\n\nDiscordサーバーへの参加\n\n以下の招待UR
 Lから Discord サーバー javee-study-jp へ参加し、#designing-dee
 p-learning-systems のチャンネルに参加してください。\nhttp
 s://discord.gg/4qtpMbsdJz\n勉強会中は主に、VOICE CHANNELS → Gen
 eralにて画面共有、音声チャットでやりとりします。\n\
 n準備\n\n特に準備するものなどは不要です。\n\n進行\n\n
 \n\n時間\n内容\n\n\n\n\n～13:00\n入室，準備\n\n\n13:00～13:10\
 n開始の挨拶\n\n\n13:10～14:00\n自己紹介\n\n\n14:10～15:00\n読
 書会\n\n\n15:00～15:20\n長休憩\n\n\n15:20～17:50\n読書会\n\n\n1
 7:50～18:00\nふりかえり，退室\n\n\n18:00～\n雑談、飲み会 
 (自由参加)\n\n\n\n目次\n\n\n\n目次\n\n\n\n\nfront matter\n\n\n1 A
 n introduction to deep learning systems\n\n\n1.1 The deep learning develo
 pment cycle\n\n\n1.2 Deep learning system design overview\n\n\n1.3 Buildi
 ng a deep learning system vs. developing a model\n\n\nChapter 1 Summary\n
 \n\n2 Dataset management service\n\n\n2.1 Understanding dataset managemen
 t service\n\n\n2.2 Touring a sample dataset management service\n\n\n2.3 O
 pen source approaches\n\n\nChapter 2 Summary\n\n\n3 Model training servic
 e\n\n\n3.1 Model training service: Design overview\n\n\n3.2 Deep learning
  training code pattern\n\n\n3.3 A sample model training service\n\n\n3.4 
 Kubeflow training operators: An open source approach\n\n\n3.5 When to use
  the public cloud\n\n\nChapter 3 Summary\n\n\n4 Distributed training\n\n\
 n4.1 Types of distributed training methods\n\n\n4.2 Data parallelism\n\n\
 n4.3 A sample service supporting data parallel–distributed training\n\n
 \n4.4 Training large models that can’t load on one GPU\n\n\nChapter 4 S
 ummary\n\n\n5 Hyperparameter optimization service\n\n\n5.1 Understanding 
 hyperparameters\n\n\n5.2 Understanding hyperparameter optimization\n\n\n5
 .3 Designing an HPO service\n\n\n5.4 Open source HPO libraries\n\n\nChapt
 er 5 Summary\n\n\n6 Model serving design\n\n\n6.1 Explaining model servin
 g\n\n\n6.2 Common model serving strategies\n\n\n6.3 Designing a predictio
 n service\n\n\nChapter 6 Summary\n\n\n7 Model serving in practice\n\n\n7.
 1 A model service sample\n\n\n7.2 TorchServe model server sample\n\n\n7.3
  Model server vs. model service\n\n\n7.4 Touring open source model servin
 g tools\n\n\n7.5 Releasing models\n\n\n7.6 Postproduction model monitorin
 g\n\n\nChapter 7 Summary\n\n\n8 Metadata and artifact store\n\n\n8.1 Intr
 oducing artifacts\n\n\n8.2 Metadata in a deep learning context\n\n\n8.3 D
 esigning a metadata and artifacts store\n\n\n8.4 Open source solutions\n\
 n\nChapter 8 Summary\n\n\n9 Workflow orchestration\n\n\n9.1 Introducing w
 orkflow orchestration\n\n\n9.2 Designing a workflow orchestration system\
 n\n\n9.3 Touring open source workflow orchestration systems\n\n\nChapter 
 9 Summary\n\n\n10 Path to production\n\n\n10.1 Preparing for productioniz
 ation\n\n\n10.2 Model productionization\n\n\n10.3 Model deployment strate
 gies\n\n\nChapter 10 Summary\n\n\nAppendix A. A “hello world” deep le
 arning system\n\n\nAppendix B. Survey of existing solutions\n\n\nAppendix
  C. Creating an HPO service with Kubeflow Katib\n\n\n
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