Accelerate 文件
示例集
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示例集
下面包含一個非詳盡的教程和指令碼列表，展示了 Accelerate 的用法。
官方 Accelerate 示例：
基礎示例
這些示例展示了 Accelerate 的基本功能，是很好的入門起點。
- 基礎 NLP 示例
- Jupyter Notebook 中的基礎分散式 NLP 示例
- 基礎計算機視覺示例
- Jupyter Notebook 中的基礎分散式計算機視覺示例
- 在 Kaggle 中使用 Accelerate
特定功能示例
這些示例展示了 Accelerate 框架提供的特定功能
完整示例
這些示例一次性展示了“特定功能示例”中介紹的所有 Accelerate 功能
- 完整的 NLP 示例
- 完整的計算機視覺示例
- 一個非常完整且可擴充套件的視覺示例，展示了 SLURM、hydra 以及框架的高度可擴充套件用法
- 因果語言模型微調示例
- 掩碼語言模型微調示例
- 語音預訓練示例
- 翻譯微調示例
- 文字分類微調示例
- 語義分割微調示例
- 問答微調示例
- 集束搜尋問答微調示例
- 多項選擇問答微調示例
- 命名實體識別微調示例
- 影像分類微調示例
- 摘要微調示例
- 關於如何使用 AWS SageMaker 整合 Accelerate 的端到端示例
- 用於各種 NLP 任務的 Megatron-LM 示例
整合示例
這些是與 Accelerate 整合的庫的教程
沒有找到你的整合？提交一個 PR 將它包含進來！
Amphion
Catalyst
DALLE2-pytorch
Diffusers
fastai
GradsFlow
imagen-pytorch
Kornia
PyTorch Accelerated
PyTorch3D
Stable-Dreamfusion
Tez
trlx
Comfy-UI
在科學研究中
下面包含一個使用 Accelerate 的非詳盡論文列表。
沒有找到你的論文？提交一個 PR 將它包含進來！
- 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: “Offline RL for Natural Language Generation with Implicit Language Q Learning”, 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.