Put Deep Learning to Work: Accelerate Deep Learning through Amazon SageMaker and ML Services

被引:1
|
作者
Ye, Wenming [1 ]
Hu, Rachel [2 ]
Enev, Miro [3 ]
机构
[1] Amazon Web Serv, Redmond, WA 98052 USA
[2] Amazon Web Serv, Palo Alto, CA USA
[3] Nvidia Corp, Edmonds, WA USA
关键词
Deep Learning; BERT; NLP; AWS Inferentia; GPU; AWS; Deployment; Distributed training;
D O I
10.1145/3394486.3406698
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
引用
收藏
页码:3496 / 3496
页数:1
相关论文
共 50 条
  • [1] A lightweight performance proxy for deep-learning model training on Amazon SageMaker
    Tesser, Rafael Keller
    Marques, Alvaro
    Borin, Edson
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2024, 36 (14):
  • [2] Selecting efficient VM types to train deep learning models on Amazon SageMaker
    Tesser, Rafael Keller
    Marques, Alvaro
    Borin, Edson
    2021 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING WORKSHOPS (SBAC-PADW 2021), 2021, : 20 - 27
  • [3] Profiling Deep LearningWorkloads at Scale using Amazon SageMaker
    Rauschmayr, Nathalie
    Kama, Sami
    Kim, Muhyun
    Choi, Miyoung
    Kenthapadi, Krishnaram
    PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 3801 - 3809
  • [4] Elastic Machine Learning Algorithms in Amazon SageMaker
    Liberty, Edo
    Karnin, Zohar
    Xiang, Bing
    Rouesnel, Laurence
    Coskun, Baris
    Nallapati, Ramesh
    Delgado, Julio
    Sadoughi, Amir
    Astashonok, Yury
    Das, Piali
    Balioglu, Can
    Chakravarty, Saswata
    Jha, Madhav
    Gautier, Philip
    Arpin, David
    Januschowski, Tim
    Flunkert, Valentin
    Wang, Yuyang
    Gasthaus, Jan
    Stella, Lorenzo
    Rangapuram, Syama
    Salinas, David
    Schelter, Sebastian
    Smola, Alex
    SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2020, : 731 - 737
  • [5] Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer
    Petryshyn, Bohdan
    Postupaiev, Serhii
    Ben Bari, Soufiane
    Ostreika, Armantas
    INFORMATION, 2024, 15 (02)
  • [6] Exascale Deep Learning to Accelerate Cancer Research
    Patton, Robert M.
    Johnston, J. Travis
    Young, Steven R.
    Schuman, Catherine D.
    Potok, Thomas E.
    Rose, Derek C.
    Lim, Seung-Hwan
    Chae, Junghoon
    Hou, Le
    Abousamra, Shahira
    Samaras, Dimitris
    Saltz, Joel
    2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2019, : 1488 - 1496
  • [7] Deep learning tools to accelerate antibiotic discovery
    Cesaro, Angela
    Bagheri, Mojtaba
    Torres, Marcelo
    Wan, Fangping
    de la Fuente-Nunez, Cesar
    EXPERT OPINION ON DRUG DISCOVERY, 2023, : 1245 - 1257
  • [8] Exploiting Typical Values to Accelerate Deep Learning
    Moshovos, Andreas
    Albericio, Jorge
    Judd, Patrick
    Lascorz, Alberto Delmas
    Sharify, Sayeh
    Poulos, Zissis
    Hetherington, Tayler
    Aamodt, Tor
    Jerger, Natalie Enright
    COMPUTER, 2018, 51 (05) : 18 - 30
  • [9] Neural Group Testing to Accelerate Deep Learning
    Liang, Weixin
    Zou, James
    2021 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2021, : 958 - 963
  • [10] Accelerate Learning of Deep Hashing With Gradient Attention
    Huang, Long-Kai
    Chen, Jianda
    Pan, Sinno Jialin
    2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, : 5270 - 5279