accelerate/pr_4091/en/_app/immutable/nodes/59.jTSlUlFH.js

import"../chunks/DsnmJJEf.js";import{i as X,h as P,C as Y,H as a,E as H,s as I}from"../chunks/ClLAY5C0.js";import{p as G,o as R,s as e,f as F,a as k,b as B,c as M,n as K}from"../chunks/BhevU81Y.js";const Z='{"title":"Example Zoo","local":"example-zoo","sections":[{"title":"Official Accelerate Examples:","local":"official-accelerate-examples","sections":[{"title":"Basic Examples","local":"basic-examples","sections":[],"depth":3},{"title":"Feature Specific Examples","local":"feature-specific-examples","sections":[],"depth":3},{"title":"Full Examples","local":"full-examples","sections":[],"depth":3}],"depth":2},{"title":"Integration Examples","local":"integration-examples","sections":[{"title":"Amphion","local":"amphion","sections":[],"depth":3},{"title":"Catalyst","local":"catalyst","sections":[],"depth":3},{"title":"DALLE2-pytorch","local":"dalle2-pytorch","sections":[],"depth":3},{"title":"Diffusers","local":"diffusers","sections":[],"depth":3},{"title":"fastai","local":"fastai","sections":[],"depth":3},{"title":"GradsFlow","local":"gradsflow","sections":[],"depth":3},{"title":"imagen-pytorch","local":"imagen-pytorch","sections":[],"depth":3},{"title":"Kornia","local":"kornia","sections":[],"depth":3},{"title":"PyTorch Accelerated","local":"pytorch-accelerated","sections":[],"depth":3},{"title":"PyTorch3D","local":"pytorch3d","sections":[],"depth":3},{"title":"Stable-Dreamfusion","local":"stable-dreamfusion","sections":[],"depth":3},{"title":"Tez","local":"tez","sections":[],"depth":3},{"title":"trlx","local":"trlx","sections":[],"depth":3},{"title":"Comfy-UI","local":"comfy-ui","sections":[],"depth":3}],"depth":2},{"title":"In Science","local":"in-science","sections":[],"depth":2}],"depth":1}';var z=M('<meta name="hf:doc:metadata"/>'),J=M('<p></p> <!> <!> <p>Below contains a non-exhaustive list of tutorials and scripts showcasing Accelerate.</p> <!> <!> <p>These examples showcase the base features of Accelerate and are a great starting point</p> <ul><li><a href="https://github.com/huggingface/accelerate/blob/main/examples/nlp_example.py" rel="nofollow">Barebones NLP example</a></li> <li><a href="https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb" rel="nofollow">Barebones distributed NLP example in a Jupyter Notebook</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/cv_example.py" rel="nofollow">Barebones computer vision example</a></li> <li><a href="https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_cv_example.ipynb" rel="nofollow">Barebones distributed computer vision example in a Jupyter Notebook</a></li> <li><a href="https://www.kaggle.com/code/muellerzr/multi-gpu-and-accelerate" rel="nofollow">Using Accelerate in Kaggle</a></li></ul> <!> <p>These examples showcase specific features that the Accelerate framework offers</p> <ul><li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/automatic_gradient_accumulation.py" rel="nofollow">Automatic memory-aware gradient accumulation</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/checkpointing.py" rel="nofollow">Checkpointing states</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/cross_validation.py" rel="nofollow">Cross validation</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/deepspeed_with_config_support.py" rel="nofollow">DeepSpeed</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/fsdp_with_peak_mem_tracking.py" rel="nofollow">Fully Sharded Data Parallelism</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/gradient_accumulation.py" rel="nofollow">Gradient accumulation</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/memory.py" rel="nofollow">Memory-aware batch size finder</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/multi_process_metrics.py" rel="nofollow">Metric Computation</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/tracking.py" rel="nofollow">Using Trackers</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/by_feature/megatron_lm_gpt_pretraining.py" rel="nofollow">Using Megatron-LM</a></li></ul> <!> <p>These examples showcase every feature in Accelerate at once that was shown in “Feature Specific Examples”</p> <ul><li><a href="https://github.com/huggingface/accelerate/blob/main/examples/complete_nlp_example.py" rel="nofollow">Complete NLP example</a></li> <li><a href="https://github.com/huggingface/accelerate/blob/main/examples/complete_cv_example.py" rel="nofollow">Complete computer vision example</a></li> <li><a href="https://github.com/yuvalkirstain/PickScore" rel="nofollow">Very complete and extensible vision example showcasing SLURM, hydra, and a very extensible usage of the framework</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm_no_trainer.py" rel="nofollow">Causal language model fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm_no_trainer.py" rel="nofollow">Masked language model fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py" rel="nofollow">Speech pretraining example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py" rel="nofollow">Translation fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue_no_trainer.py" rel="nofollow">Text classification fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py" rel="nofollow">Semantic segmentation fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_no_trainer.py" rel="nofollow">Question answering fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py" rel="nofollow">Beam search question answering fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/multiple-choice/run_swag_no_trainer.py" rel="nofollow">Multiple choice question answering fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/token-classification/run_ner_no_trainer.py" rel="nofollow">Named entity recognition fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification_no_trainer.py" rel="nofollow">Image classification fine-tuning example</a></li> <li><a href="https://github.com/huggingface/transformers/blob/main/examples/pytorch/summarization/run_summarization_no_trainer.py" rel="nofollow">Summarization fine-tuning example</a></li> <li><a href="https://github.com/huggingface/notebooks/blob/main/sagemaker/22_accelerate_sagemaker_examples/README.md" rel="nofollow">End-to-end examples on how to use AWS SageMaker integration of Accelerate</a></li> <li><a href="https://github.com/pacman100/accelerate-megatron-test" rel="nofollow">Megatron-LM examples for various NLp tasks</a></li></ul> <!> <p>These are tutorials from libraries that integrate with Accelerate:</p> <blockquote><p>Don’t find your integration here? Make a PR to include it!</p></blockquote> <!> <ul><li><a href="https://github.com/open-mmlab/Amphion/blob/main/egs/tts/README.md" rel="nofollow">Training Text-to-Speech Models with Amphion</a></li> <li><a href="https://github.com/open-mmlab/Amphion/blob/main/egs/svc/README.md" rel="nofollow">Training Singing Voice Conversion Models with Amphion</a></li> <li><a href="https://github.com/open-mmlab/Amphion/blob/main/egs/vocoder/README.md" rel="nofollow">Training Vocoders with Amphion</a></li></ul> <!> <ul><li><a href="https://catalyst-team.github.io/catalyst/tutorials/ddp.html" rel="nofollow">Distributed training tutorial with Catalyst</a></li></ul> <!> <ul><li><a href="https://github.com/lucidrains/DALLE2-pytorch#usage" rel="nofollow">Fine-tuning DALLE2</a></li></ul> <!> <ul><li><a href="https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion" rel="nofollow">Performing textual inversion with diffusers</a></li> <li><a href="https://github.com/huggingface/diffusers/tree/main/examples/dreambooth" rel="nofollow">Training DreamBooth with diffusers</a></li></ul> <!> <ul><li><a href="https://docs.fast.ai/tutorial.distributed.html" rel="nofollow">Distributed training from Jupyter Notebooks with fastai</a></li> <li><a href="https://docs.fast.ai/examples/distributed_app_examples.html" rel="nofollow">Basic distributed training examples with fastai</a></li></ul> <!> <ul><li><a href="https://docs.gradsflow.com/en/latest/examples/nbs/01-ImageClassification/" rel="nofollow">Auto Image Classification with GradsFlow</a></li></ul> <!> <ul><li><a href="https://github.com/lucidrains/imagen-pytorch#usage" rel="nofollow">Fine-tuning Imagen</a></li></ul> <!> <ul><li><a href="https://kornia.readthedocs.io/en/latest/get-started/training.html" rel="nofollow">Fine-tuning vision models with Kornia’s Trainer</a></li></ul> <!> <ul><li><a href="https://pytorch-accelerated.readthedocs.io/en/latest/quickstart.html" rel="nofollow">Quickstart distributed training tutorial with PyTorch Accelerated</a></li></ul> <!> <ul><li><a href="https://pytorch3d.org/tutorials/" rel="nofollow">Perform Deep Learning with 3D data</a></li></ul> <!> <ul><li><a href="https://colab.research.google.com/drive/1MXT3yfOFvO0ooKEfiUUvTKwUkrrlCHpF?usp=sharing" rel="nofollow">Training with Stable-Dreamfusion to convert text to a 3D model</a></li></ul> <!> <ul><li><a href="https://www.kaggle.com/code/abhishek/tez-faster-and-easier-training-for-leaf-detection/notebook" rel="nofollow">Leaf disease detection with Tez and Accelerate</a></li></ul> <!> <ul><li><a href="https://github.com/CarperAI/trlx#example-how-to-add-a-task" rel="nofollow">How to implement a sentiment learning task with trlx</a></li></ul> <!> <ul><li><a href="https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/model_management.py#L291-L296" rel="nofollow">Enabling using large Stable Diffusion Models in low-vram settings using Accelerate</a></li></ul> <!> <p>Below contains a non-exhaustive list of papers utilizing Accelerate.</p> <blockquote><p>Don’t find your paper here? Make a PR to include it!</p></blockquote> <ul><li>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; <a href="http://huggingface.co/papers/2305.01569" rel="nofollow">arXiv:2305.01569</a>.</li> <li>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; <a href="http://huggingface.co/papers/2305.04091" rel="nofollow">arXiv:2305.04091</a>.</li> <li>Arthur Câmara, Claudia Hauff: “Moving Stuff Around: A study on efficiency of moving documents into memory for Neural IR models”, 2022; <a href="http://huggingface.co/papers/2205.08343" rel="nofollow">arXiv:2205.08343</a>.</li> <li>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; <a href="http://huggingface.co/papers/2303.06865" rel="nofollow">arXiv:2303.06865</a>.</li> <li>Peter Melchior, Yan Liang, ChangHoon Hahn, Andy Goulding: “Autoencoding Galaxy Spectra I: Architecture”, 2022; <a href="http://huggingface.co/papers/2211.07890" rel="nofollow">arXiv:2211.07890</a>.</li> <li>Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang: “A Cheaper and Better Diffusion Language Model with Soft-Masked Noise”, 2023; <a href="http://huggingface.co/papers/2304.04746" rel="nofollow">arXiv:2304.04746</a>.</li> <li>Ayaan Haque, Matthew Tancik, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa: “Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions”, 2023; <a href="http://huggingface.co/papers/2303.12789" rel="nofollow">arXiv:2303.12789</a>.</li> <li>Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi: “RealFusion: 360° Reconstruction of Any Object from a Single Image”, 2023; <a href="http://huggingface.co/papers/2302.10663" rel="nofollow">arXiv:2302.10663</a>.</li> <li>Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, Hongsheng Li: “Better Aligning Text-to-Image Models with Human Preference”, 2023; <a href="http://huggingface.co/papers/2303.14420" rel="nofollow">arXiv:2303.14420</a>.</li> <li>Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang: “HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace”, 2023; <a href="http://huggingface.co/papers/2303.17580" rel="nofollow">arXiv:2303.17580</a>.</li> <li>Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, Jianshu Chen: “Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination”, 2022; <a href="http://huggingface.co/papers/2210.12261" rel="nofollow">arXiv:2210.12261</a>.</li> <li>Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho: “How to Backdoor Diffusion Models?”, 2022; <a href="http://huggingface.co/papers/2212.05400" rel="nofollow">arXiv:2212.05400</a>.</li> <li>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; <a href="http://huggingface.co/papers/2303.07937" rel="nofollow">arXiv:2303.07937</a>.</li> <li>Or Patashnik, Daniel Garibi, Idan Azuri, Hadar Averbuch-Elor, Daniel Cohen-Or: “Localizing Object-level Shape Variations with Text-to-Image Diffusion Models”, 2023; <a href="http://huggingface.co/papers/2303.11306" rel="nofollow">arXiv:2303.11306</a>.</li> <li>Dídac Surís, Sachit Menon, Carl Vondrick: “ViperGPT: Visual Inference via Python Execution for Reasoning”, 2023; <a href="http://huggingface.co/papers/2303.08128" rel="nofollow">arXiv:2303.08128</a>.</li> <li>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; <a href="http://huggingface.co/papers/2303.09535" rel="nofollow">arXiv:2303.09535</a>.</li> <li>Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi: “NaturalProver: Grounded Mathematical Proof Generation with Language Models”, 2022; <a href="http://huggingface.co/papers/2205.12910" rel="nofollow">arXiv:2205.12910</a>.</li> <li>Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; <a href="http://huggingface.co/papers/2302.01721" rel="nofollow">arXiv:2302.01721</a>.</li> <li>Puijin Cheng, Li Lin, Yijin Huang, Huaqing He, Wenhan Luo, Xiaoying Tang: “Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement”, 2023; <a href="http://huggingface.co/papers/2303.04603" rel="nofollow">arXiv:2303.04603</a>.</li> <li>Shun Shao, Yftah Ziser, Shay Cohen: “Erasure of Unaligned Attributes from Neural Representations”, 2023; <a href="http://huggingface.co/papers/2302.02997" rel="nofollow">arXiv:2302.02997</a>.</li> <li>Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun, Yireun Kim, Minjoon Seo: “In-Context Instruction Learning”, 2023; <a href="http://huggingface.co/papers/2302.14691" rel="nofollow">arXiv:2302.14691</a>.</li> <li>Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar: “Prismer: A Vision-Language Model with An Ensemble of Experts”, 2023; <a href="http://huggingface.co/papers/2303.02506" rel="nofollow">arXiv:2303.02506</a>.</li> <li>Haoyu Chen, Zhihua Wang, Yang Yang, Qilin Sun, Kede Ma: “Learning a Deep Color Difference Metric for Photographic Images”, 2023; <a href="http://huggingface.co/papers/2303.14964" rel="nofollow">arXiv:2303.14964</a>.</li> <li>Van-Hoang Le, Hongyu Zhang: “Log Parsing with Prompt-based Few-shot Learning”, 2023; <a href="http://huggingface.co/papers/2302.07435" rel="nofollow">arXiv:2302.07435</a>.</li> <li>Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui: “Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?”, 2023; <a href="http://huggingface.co/papers/2302.07866" rel="nofollow">arXiv:2302.07866</a>.</li> <li>Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu: “Behavior Cloned Transformers are Neurosymbolic Reasoners”, 2022; <a href="http://huggingface.co/papers/2210.07382" rel="nofollow">arXiv:2210.07382</a>.</li> <li>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; <a href="http://huggingface.co/papers/2304.13148" rel="nofollow">arXiv:2304.13148</a>. DOI: [https://dx.doi.org/10.1145/3539618.3591882 10.1145/3539618.3591882].</li> <li>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; <a href="http://huggingface.co/papers/2301.13826" rel="nofollow">arXiv:2301.13826</a>.</li> <li>Marcio Fonseca, Yftah Ziser, Shay B. Cohen: “Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents”, 2022; <a href="http://huggingface.co/papers/2205.12486" rel="nofollow">arXiv:2205.12486</a>.</li> <li>Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; <a href="http://huggingface.co/papers/2302.01721" rel="nofollow">arXiv:2302.01721</a>.</li> <li>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; <a href="http://huggingface.co/papers/2212.10020" rel="nofollow">arXiv:2212.10020</a>.</li> <li>Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham: “In-Context Retrieval-Augmented Language Models”, 2023; <a href="http://huggingface.co/papers/2302.00083" rel="nofollow">arXiv:2302.00083</a>.</li> <li>Dacheng Li, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, Hao Zhang: “MPCFormer: fast, performant and private Transformer inference with MPC”, 2022; <a href="http://huggingface.co/papers/2211.01452" rel="nofollow">arXiv:2211.01452</a>.</li> <li>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; <a href="http://huggingface.co/papers/2206.11309" rel="nofollow">arXiv:2206.11309</a>.</li> <li>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; <a href="http://huggingface.co/papers/2304.14241" rel="nofollow">arXiv:2304.14241</a>.</li> <li>Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, Sergey Levine: “Offline RL for Natural Language Generation with Implicit Language Q Learning”, 2022; <a href="http://huggingface.co/papers/2206.11871" rel="nofollow">arXiv:2206.11871</a>.</li> <li>Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig: “Execution-Based Evaluation for Open-Domain Code Generation”, 2022; <a href="http://huggingface.co/papers/2212.10481" rel="nofollow">arXiv:2212.10481</a>.</li> <li>Minh-Long Luu, Zeyi Huang, Eric P. 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