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

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

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

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

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

Mail addresses
opencode@microsoft.com

Opengraph URL: https://github.com/zhisbug/DeepSpeed

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README	https://github.com/zhisbug/DeepSpeed
Code of conduct	https://github.com/zhisbug/DeepSpeed
Contributing	https://github.com/zhisbug/DeepSpeed
MIT license	https://github.com/zhisbug/DeepSpeed
Security	https://github.com/zhisbug/DeepSpeed
	https://github.com/microsoft/DeepSpeed/actions
	https://pypi.org/project/deepspeed/
	https://deepspeed.readthedocs.io/en/latest/?badge=latest
	https://github.com/Microsoft/DeepSpeed/blob/master/LICENSE
	https://github.com/zhisbug/DeepSpeed/blob/master/docs/assets/images/DeepSpeed_light.svg#gh-light-mode-only
	https://github.com/zhisbug/DeepSpeed/blob/master/docs/assets/images/DeepSpeed_dark_transparent.svg#gh-dark-mode-only
	https://github.com/zhisbug/DeepSpeed#latest-news
Supporting efficient large model training on AMD Instinct GPUs with DeepSpeed	https://cloudblogs.microsoft.com/opensource/2022/03/21/supporting-efficient-large-model-training-on-amd-instinct-gpus-with-deepspeed/
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam	https://www.deepspeed.ai/tutorials/zero-one-adam/
DeepSpeed: Advancing MoE inference and training to power next-generation AI scale	https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/
Mixture of Experts (MoE) for NLG tutorial	https://www.deepspeed.ai/tutorials/mixture-of-experts-nlg/
Mixture of Experts (MoE) Inference tutorial	https://www.deepspeed.ai/tutorials/moe-inference-tutorial
Autotuning: Automatically discover the optimal DeepSpeed configuration that delivers good training speed	https://www.deepspeed.ai/news/2021/11/15/autotuning.html
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, the World’s Largest and Most Powerful Generative Language Model	https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/
train large models with DeepSpeed	https://www.deepspeed.ai/tutorials/large-models-w-deepspeed/
come join us!	https://careers.microsoft.com/us/en/search-results?keywords=http:%2F%2Fdeepspeed.ai
	https://github.com/zhisbug/DeepSpeed#deepspeed-is-hiring-come-join-us
DeepSpeed	https://www.deepspeed.ai/
Turing-NLG	https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft
AI at Scale	https://www.microsoft.com/en-us/research/project/ai-at-scale/
here	https://innovation.microsoft.com/en-us/exploring-ai-at-scale
deepspeed.ai	https://www.deepspeed.ai/
	https://github.com/zhisbug/DeepSpeed#table-of-contents
Why DeepSpeed?	https://github.com/zhisbug/DeepSpeed#why-deepspeed
Install	https://github.com/zhisbug/DeepSpeed#installation
Features	https://github.com/zhisbug/DeepSpeed#features
Further Reading	https://github.com/zhisbug/DeepSpeed#further-reading
Contributing	https://github.com/zhisbug/DeepSpeed#contributing
Publications	https://github.com/zhisbug/DeepSpeed#publications
Videos	https://github.com/zhisbug/DeepSpeed#videos
	https://github.com/zhisbug/DeepSpeed#why-deepspeed
	https://github.com/zhisbug/DeepSpeed#installation
torch's JIT C++ extension loader that relies on ninja	https://pytorch.org/docs/stable/cpp_extension.html
PyTorch	https://pytorch.org/
advanced installation instructions	https://www.deepspeed.ai/tutorials/advanced-install/
	https://github.com/zhisbug/DeepSpeed#features
feature overview	https://www.deepspeed.ai/features/
Distributed Training with Mixed Precision	https://www.deepspeed.ai/features/#distributed-training-with-mixed-precision
Model Parallelism	https://www.deepspeed.ai/features/#model-parallelism
Pipeline Parallelism	https://www.deepspeed.ai/tutorials/pipeline/
The Zero Redundancy Optimizer (ZeRO)	https://www.deepspeed.ai/tutorials/zero/
ZeRO-Offload	https://www.deepspeed.ai/tutorials/zero-offload/
Ultra-fast dense transformer kernels	https://www.deepspeed.ai/2020/05/18/bert-record.html
Sparse attention	https://www.deepspeed.ai/2020/09/08/sparse-attention-news.html
1-bit Adam	https://www.deepspeed.ai/2020/09/08/onebit-adam-blog-post.html
0/1 Adam	https://www.deepspeed.ai/tutorials/zero-one-adam/
1-bit LAMB	https://www.deepspeed.ai/tutorials/onebit-lamb/
Additional Memory and Bandwidth Optimizations	https://www.deepspeed.ai/features/#additional-memory-and-bandwidth-optimizations
Training Features	https://www.deepspeed.ai/features/#training-features
Training Optimizers	https://www.deepspeed.ai/features/#training-optimizers
Training Agnostic Checkpointing	https://www.deepspeed.ai/features/#training-agnostic-checkpointing
Advanced Parameter Search	https://www.deepspeed.ai/features/#advanced-parameter-search
Simplified Data Loader	https://www.deepspeed.ai/features/#simplified-data-loader
Curriculum Learning	https://www.deepspeed.ai/tutorials/curriculum-learning/
Performance Analysis and Debugging	https://www.deepspeed.ai/features/#performance-analysis-and-debugging
Mixture of Experts (MoE)	https://www.deepspeed.ai/tutorials/mixture-of-experts/
	https://github.com/zhisbug/DeepSpeed#further-reading
deepspeed.ai	https://www.deepspeed.ai/
DeepSpeed Features	https://www.deepspeed.ai/features/
Getting Started	https://www.deepspeed.ai/getting-started/
DeepSpeed JSON Configuration	https://www.deepspeed.ai/docs/config-json/
API Documentation	https://deepspeed.readthedocs.io/en/latest/
CIFAR-10 Tutorial	https://www.deepspeed.ai/tutorials/cifar-10
Megatron-LM Tutorial	https://www.deepspeed.ai/tutorials/megatron/
BERT Pre-training Tutorial	https://www.deepspeed.ai/tutorials/bert-pretraining/
Learning Rate Range Test Tutorial	https://www.deepspeed.ai/tutorials/lrrt/
1Cycle Tutorial	https://www.deepspeed.ai/tutorials/one-cycle/
	https://github.com/zhisbug/DeepSpeed#contributing
contributing	https://github.com/zhisbug/DeepSpeed/blob/master/CONTRIBUTING.md
	https://github.com/zhisbug/DeepSpeed#contributor-license-agreement
https://cla.opensource.microsoft.com	https://cla.opensource.microsoft.com
	https://github.com/zhisbug/DeepSpeed#code-of-conduct
Microsoft Open Source Code of Conduct	https://opensource.microsoft.com/codeofconduct/
Code of Conduct FAQ	https://opensource.microsoft.com/codeofconduct/faq/
	https://github.com/zhisbug/DeepSpeed#publications
arXiv:1910.02054	https://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:2010.13369	https://arxiv.org/abs/2010.13369
NeurIPS 2020	https://proceedings.neurips.cc/paper/2020/hash/a1140a3d0df1c81e24ae954d935e8926-Abstract.html
arXiv:2101.06840	https://arxiv.org/abs/2101.06840
arXiv:2102.02888	https://arxiv.org/abs/2102.02888
ICML 2021	http://proceedings.mlr.press/v139/tang21a.html
arXiv:2104.07857	https://arxiv.org/abs/2104.07857
arXiv:2104.06069	https://arxiv.org/abs/2104.06069
arXiv:2108.06084	https://arxiv.org/abs/2108.06084
arXiv:2202.06009	https://arxiv.org/abs/2202.06009
arXiv:2201.05596	https://arxiv.org/abs/2201.05596
	https://github.com/zhisbug/DeepSpeed#videos
Overview	https://www.youtube.com/watch?v=CaseqC45DNc&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=29
ZeRO + large model training	https://www.youtube.com/watch?v=y4_bCiAsIAk&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=28
17B T-NLG demo	https://www.youtube.com/watch?v=9V-ZbP92drg&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=27
Fastest BERT training + RScan tuning	https://www.youtube.com/watch?v=o1K-ZG9F6u0&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=26
part 1	https://www.youtube.com/watch?v=_NOk-mBwDYg&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=92
part 2	https://www.youtube.com/watch?v=sG6_c4VXLww&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=94
part 3	https://www.youtube.com/watch?v=k9yPkBTayos&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=93
FAQ	https://www.youtube.com/watch?v=nsHu6vEgPew&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=24
ZeRO & Fastest BERT: Increasing the scale and speed of deep learning training in DeepSpeed	https://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html
DeepSpeed on AzureML	https://youtu.be/yBVXR8G8Bg8
DeepSpeed: All the tricks to scale to gigantic models	https://www.youtube.com/watch?v=pDGI668pNg0
Turing-NLG, DeepSpeed and the ZeRO optimizer	https://www.youtube.com/watch?v=tC01FRB0M7w
www.deepspeed.ai/	https://www.deepspeed.ai/
Readme	https://github.com/zhisbug/DeepSpeed#readme-ov-file
MIT license	https://github.com/zhisbug/DeepSpeed#MIT-1-ov-file
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