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Accelerate documentation

예제 Zoo

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예제 Zoo

아래에는 Accelerate를 소개하는 튜토리얼과 스크립트의 비포괄적 목록이 포함되어 있습니다.

공식 Accelerate 예제:

기본 예제

이 예시들은 Accelerate의 기본 기능을 보여 주며, 훌륭한 시작점이 됩니다

기능별 예제

이 예제들은 Accelerate 프레임워크가 제공하는 특정 기능을 보여줍니다

전체 예제

이 예제들은 “기능별 예제”에서 보여준 Accelerate의 모든 기능을 한 번에 보여줍니다

통합 예제

다음은 Accelerate와 통합되는 라이브러리들의 튜토리얼입니다:

여기서 통합을 찾을 수 없나요? PR을 만들어 포함하세요!

Amphion

Catalyst

DALLE2-pytorch

Diffusers

fastai

GradsFlow

imagen-pytorch

Kornia

PyTorch 가속

PyTorch3D

Stable-Dreamfusion

Tez

trlx

Comfy-UI

과학 분야에서

아래에는 Accelerate를 활용한 논문의 비포괄적 목록이 포함되어 있습니다.

여기서 논문을 찾지 못하셨나요? 포함을 위해 PR을 만들어 주세요!

  • Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, Omer Levy: “Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation”, 2023; arXiv:2305.01569.
  • Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim: “Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models”, 2023; arXiv:2305.04091.
  • Arthur Câmara, Claudia Hauff: “Moving Stuff Around: A study on efficiency of moving documents into memory for Neural IR models”, 2022; arXiv:2205.08343.
  • Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang: “High-throughput Generative Inference of Large Language Models with a Single GPU”, 2023; arXiv:2303.06865.
  • Peter Melchior, Yan Liang, ChangHoon Hahn, Andy Goulding: “Autoencoding Galaxy Spectra I: Architecture”, 2022; arXiv:2211.07890.
  • Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang: “A Cheaper and Better Diffusion Language Model with Soft-Masked Noise”, 2023; arXiv:2304.04746.
  • Ayaan Haque, Matthew Tancik, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa: “Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions”, 2023; arXiv:2303.12789.
  • Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi: “RealFusion: 360° Reconstruction of Any Object from a Single Image”, 2023; arXiv:2302.10663.
  • Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, Hongsheng Li: “Better Aligning Text-to-Image Models with Human Preference”, 2023; arXiv:2303.14420.
  • Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang: “HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace”, 2023; arXiv:2303.17580.
  • Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, Jianshu Chen: “Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination”, 2022; arXiv:2210.12261.
  • Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho: “How to Backdoor Diffusion Models?”, 2022; arXiv:2212.05400.
  • Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Jaehoon Ko, Hyeonsu Kim, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, Seungryong Kim: “Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation”, 2023; arXiv:2303.07937.
  • Or Patashnik, Daniel Garibi, Idan Azuri, Hadar Averbuch-Elor, Daniel Cohen-Or: “Localizing Object-level Shape Variations with Text-to-Image Diffusion Models”, 2023; arXiv:2303.11306.
  • Dídac Surís, Sachit Menon, Carl Vondrick: “ViperGPT: Visual Inference via Python Execution for Reasoning”, 2023; arXiv:2303.08128.
  • Chenyang Qi, Xiaodong Cun, Yong Zhang, Chenyang Lei, Xintao Wang, Ying Shan, Qifeng Chen: “FateZero: Fusing Attentions for Zero-shot Text-based Video Editing”, 2023; arXiv:2303.09535.
  • Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi: “NaturalProver: Grounded Mathematical Proof Generation with Language Models”, 2022; arXiv:2205.12910.
  • Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; arXiv:2302.01721.
  • Puijin Cheng, Li Lin, Yijin Huang, Huaqing He, Wenhan Luo, Xiaoying Tang: “Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement”, 2023; arXiv:2303.04603.
  • Shun Shao, Yftah Ziser, Shay Cohen: “Erasure of Unaligned Attributes from Neural Representations”, 2023; arXiv:2302.02997.
  • Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun, Yireun Kim, Minjoon Seo: “In-Context Instruction Learning”, 2023; arXiv:2302.14691.
  • Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar: “Prismer: A Vision-Language Model with An Ensemble of Experts”, 2023; arXiv:2303.02506.
  • Haoyu Chen, Zhihua Wang, Yang Yang, Qilin Sun, Kede Ma: “Learning a Deep Color Difference Metric for Photographic Images”, 2023; arXiv:2303.14964.
  • Van-Hoang Le, Hongyu Zhang: “Log Parsing with Prompt-based Few-shot Learning”, 2023; arXiv:2302.07435.
  • Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui: “Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?”, 2023; arXiv:2302.07866.
  • Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu: “Behavior Cloned Transformers are Neurosymbolic Reasoners”, 2022; arXiv:2210.07382.
  • Martin Wessel, Tomáš Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde: “Introducing MBIB — the first Media Bias Identification Benchmark Task and Dataset Collection”, 2023; arXiv:2304.13148. DOI: [https://dx.doi.org/10.1145/3539618.3591882 10.1145/3539618.3591882].
  • Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, Daniel Cohen-Or: “Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models”, 2023; arXiv:2301.13826.
  • Marcio Fonseca, Yftah Ziser, Shay B. Cohen: “Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents”, 2022; arXiv:2205.12486.
  • Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; arXiv:2302.01721.
  • Tianxing He, Jingyu Zhang, Tianle Wang, Sachin Kumar, Kyunghyun Cho, James Glass, Yulia Tsvetkov: “On the Blind Spots of Model-Based Evaluation Metrics for Text Generation”, 2022; arXiv:2212.10020.
  • Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham: “In-Context Retrieval-Augmented Language Models”, 2023; arXiv:2302.00083.
  • Dacheng Li, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, Hao Zhang: “MPCFormer: fast, performant and private Transformer inference with MPC”, 2022; arXiv:2211.01452.
  • Baolin Peng, Michel Galley, Pengcheng He, Chris Brockett, Lars Liden, Elnaz Nouri, Zhou Yu, Bill Dolan, Jianfeng Gao: “GODEL: Large-Scale Pre-Training for Goal-Directed Dialog”, 2022; arXiv:2206.11309.
  • Egil Rønningstad, Erik Velldal, Lilja Øvrelid: “Entity-Level Sentiment Analysis (ELSA): An exploratory task survey”, 2023, Proceedings of the 29th International Conference on Computational Linguistics, 2022, pages 6773-6783; arXiv:2304.14241.
  • Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, Sergey Levine: “Implicit Language Q Learning을 활용한 자연어 생성을 위한 오프라인 RL”, 2022; arXiv:2206.11871.
  • Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig: “Execution-Based Evaluation for Open-Domain Code Generation”, 2022; arXiv:2212.10481.
  • Minh-Long Luu, Zeyi Huang, Eric P. Xing, Yong Jae Lee, Haohan Wang: “Expeditious Saliency-guided Mix-up through Random Gradient Thresholding”, 2022; arXiv:2212.04875.
  • Jun Hao Liew, Hanshu Yan, Daquan Zhou, Jiashi Feng: “MagicMix: Semantic Mixing with Diffusion Models”, 2022; arXiv:2210.16056.
  • Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao: “LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners”, 2021; arXiv:2110.06274.
Update on GitHub

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