Example Zoo

Below contains a non-exhaustive list of tutorials and scripts showcasing Accelerate.

Official Accelerate Examples:

Basic Examples

These examples showcase the base features of Accelerate and are a great starting point

• Barebones NLP example
• Barebones distributed NLP example in a Jupyter Notebook
• Barebones computer vision example
• Barebones distributed computer vision example in a Jupyter Notebook
• Using Accelerate in Kaggle

Feature Specific Examples

These examples showcase specific features that the Accelerate framework offers

• Automatic memory-aware gradient accumulation
• Checkpointing states
• Cross validation
• DeepSpeed
• Fully Sharded Data Parallelism
• Gradient accumulation
• Memory-aware batch size finder
• Metric Computation
• Using Trackers
• Using Megatron-LM

Full Examples

These examples showcase every feature in Accelerate at once that was shown in “Feature Specific Examples”

• Complete NLP example
• Complete computer vision example
• Very complete and extensible vision example showcasing SLURM, hydra, and a very extensible usage of the framework
• Causal language model fine-tuning example
• Masked language model fine-tuning example
• Speech pretraining example
• Translation fine-tuning example
• Text classification fine-tuning example
• Semantic segmentation fine-tuning example
• Question answering fine-tuning example
• Beam search question answering fine-tuning example
• Multiple choice question answering fine-tuning example
• Named entity recognition fine-tuning example
• Image classification fine-tuning example
• Summarization fine-tuning example
• End-to-end examples on how to use AWS SageMaker integration of Accelerate
• Megatron-LM examples for various NLp tasks

Integration Examples

These are tutorials from libraries that integrate with Accelerate:

Don’t find your integration here? Make a PR to include it!

Amphion

• Training Text-to-Speech Models with Amphion
• Training Singing Voice Conversion Models with Amphion
• Training Vocoders with Amphion

Catalyst

• Distributed training tutorial with Catalyst

DALLE2-pytorch

• Fine-tuning DALLE2

Diffusers

• Performing textual inversion with diffusers
• Training DreamBooth with diffusers

fastai

• Distributed training from Jupyter Notebooks with fastai
• Basic distributed training examples with fastai

GradsFlow

• Auto Image Classification with GradsFlow

imagen-pytorch

• Fine-tuning Imagen

Kornia

• Fine-tuning vision models with Kornia’s Trainer

PyTorch Accelerated

• Quickstart distributed training tutorial with PyTorch Accelerated

PyTorch3D

• Perform Deep Learning with 3D data

Stable-Dreamfusion

• Training with Stable-Dreamfusion to convert text to a 3D model

Tez

• Leaf disease detection with Tez and Accelerate

trlx

• How to implement a sentiment learning task with trlx

Comfy-UI

• Enabling using large Stable Diffusion Models in low-vram settings using Accelerate

In Science

Below contains a non-exhaustive list of papers utilizing Accelerate.

Don’t find your paper here? Make a PR to include it!

• 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.

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