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Title: GitHub - zh0012/DeepSpeed: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. · GitHub

Open Graph Title: GitHub - zh0012/DeepSpeed: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

X Title: GitHub - zh0012/DeepSpeed: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Description: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. - zh0012/DeepSpeed

Open Graph Description: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. - zh0012/DeepSpeed

X Description: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. - zh0012/DeepSpeed

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https://github.com/zh0012/DeepSpeed#latest-news
learn howhttps://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat
DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scaleshttps://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat
Englishhttps://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/README.md
中文https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/chinese/README.md
日本語https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/japanese/README.md
Scaling Large-Scale Generative Mixture-of-Expert Multimodal Model With VL-MoEhttps://www.deepspeed.ai/2023/03/30/multi-modal.html
Automatic Tensor Parallelism: Enables tensor parallelism by default without an injection policyhttps://www.deepspeed.ai/tutorials/automatic-tensor-parallelism/
DeepSpeed Data Efficiency: A composable library that makes better use of data, increases training efficiency, and improves model qualityhttps://www.deepspeed.ai/2022/12/11/data-efficiency.html
Stable Diffusion Image Generation under 1 second w. DeepSpeed MIIhttps://github.com/microsoft/DeepSpeed-MII/tree/main/examples/benchmark/txt2img
DeepSpeed-MII: instant speedup on 24,000+ open-source DL models with up to 40x cheaper inferencehttps://www.deepspeed.ai/2022/10/10/mii.html
ZeRO-Inference: Democratizing massive model inferencehttps://www.deepspeed.ai/2022/09/09/zero-inference.html
Azure and DeepSpeed empower easy-to-use and high-performance model traininghttps://azure.microsoft.com/en-us/blog/azure-empowers-easytouse-highperformance-and-hyperscale-model-training-using-deepspeed/
https://github.com/zh0012/DeepSpeed#extreme-speed-and-scale-for-dl-training-and-inference
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YaLM (100B)https://github.com/yandex/YaLM-100B
GPT-NeoX (20B)https://github.com/EleutherAI/gpt-neox
AlexaTM (20B)https://www.amazon.science/blog/20b-parameter-alexa-model-sets-new-marks-in-few-shot-learning
Turing NLG (17B)https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/
METRO-LM (5.4B)https://arxiv.org/pdf/2204.06644.pdf
https://github.com/zh0012/DeepSpeed/blob/master/docs/assets/images/transformers-light.png#gh-light-mode-only
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torch's JIT C++ extension loader that relies on ninjahttps://pytorch.org/docs/stable/cpp_extension.html
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PyTorchhttps://pytorch.org/
nvcchttps://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/#introduction
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https://github.com/zh0012/DeepSpeed#further-reading
deepspeed.aihttps://www.deepspeed.ai/
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DeepSpeed JSON Configurationhttps://www.deepspeed.ai/docs/config-json/
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https://cla.opensource.microsoft.comhttps://cla.opensource.microsoft.com
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Microsoft Open Source Code of Conducthttps://opensource.microsoft.com/codeofconduct/
Code of Conduct FAQhttps://opensource.microsoft.com/codeofconduct/faq/
https://github.com/zh0012/DeepSpeed#publications
arXiv:1910.02054https://arxiv.org/abs/1910.02054
In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20)https://dl.acm.org/doi/10.5555/3433701.3433727
In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '20, Tutorial)https://dl.acm.org/doi/10.1145/3394486.3406703
arXiv:2010.13369https://arxiv.org/abs/2010.13369
NeurIPS 2020https://proceedings.neurips.cc/paper/2020/hash/a1140a3d0df1c81e24ae954d935e8926-Abstract.html
arXiv:2101.06840https://arxiv.org/abs/2101.06840
USENIX ATC 2021https://www.usenix.org/conference/atc21/presentation/ren-jie
arXiv:2102.02888https://arxiv.org/abs/2102.02888
ICML 2021http://proceedings.mlr.press/v139/tang21a.html
arXiv:2104.07857https://arxiv.org/abs/2104.07857
SC 2021https://dl.acm.org/doi/abs/10.1145/3458817.3476205
arXiv:2104.06069https://arxiv.org/abs/2104.06069
HiPC 2022https://hipc.org/advance-program/
arXiv:2108.06084https://arxiv.org/abs/2108.06084
NeurIPS 2022https://openreview.net/forum?id=JpZ5du_Kdh
arXiv:2202.06009https://arxiv.org/abs/2202.06009
arXiv:2201.05596https://arxiv.org/abs/2201.05596
ICML 2022https://proceedings.mlr.press/v162/rajbhandari22a.html
arXiv:2201.11990https://arxiv.org/abs/2201.11990
arXiv:2206.01859https://arxiv.org/abs/2206.01859
NeurIPS 2022https://openreview.net/forum?id=xNeAhc2CNAl
arXiv:2206.01861https://arxiv.org/abs/2206.01861
NeurIPS 2022https://openreview.net/forum?id=f-fVCElZ-G1
arXiv:2207.00032https://arxiv.org/abs/2207.00032
SC 2022https://dl.acm.org/doi/abs/10.5555/3571885.3571946
arXiv:2211.11586https://arxiv.org/abs/2211.11586
arXiv:2212.03597https://arxiv.org/abs/2212.03597
arXiv:2301.12017https://arxiv.org/abs/2301.12017
ICLR:2023https://openreview.net/forum?id=Pgtn4l6eKjv
arXiv:2303.07226https://arxiv.org/abs/2303.07226
arXiv:2303.08374https://arxiv.org/abs/2303.08374
https://github.com/zh0012/DeepSpeed#videos
Overviewhttps://www.youtube.com/watch?v=CaseqC45DNc&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=29
ZeRO + large model traininghttps://www.youtube.com/watch?v=y4_bCiAsIAk&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=28
17B T-NLG demohttps://www.youtube.com/watch?v=9V-ZbP92drg&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=27
Fastest BERT training + RScan tuninghttps://www.youtube.com/watch?v=o1K-ZG9F6u0&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=26
part 1https://www.youtube.com/watch?v=_NOk-mBwDYg&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=92
part 2https://www.youtube.com/watch?v=sG6_c4VXLww&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=94
part 3https://www.youtube.com/watch?v=k9yPkBTayos&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=93
FAQhttps://www.youtube.com/watch?v=nsHu6vEgPew&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=24
ZeRO & Fastest BERT: Increasing the scale and speed of deep learning training in DeepSpeedhttps://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html
DeepSpeed on AzureMLhttps://youtu.be/yBVXR8G8Bg8
DeepSpeed: All the tricks to scale to gigantic models (Mark Saroufim)https://www.youtube.com/watch?v=pDGI668pNg0
Turing-NLG, DeepSpeed and the ZeRO optimizer (Yannic Kilcher)https://www.youtube.com/watch?v=tC01FRB0M7w
Ultimate Guide To Scaling ML Models (The AI Epiphany)https://www.youtube.com/watch?v=hc0u4avAkuM
www.deepspeed.ai/https://www.deepspeed.ai/
Readme https://github.com/zh0012/DeepSpeed#readme-ov-file
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