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

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

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

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

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

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

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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://hub.docker.com/r/deepspeed/deepspeed
DeepSpeedhttps://www.deepspeed.ai/
Turing-NLGhttps://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft
AI at Scalehttps://www.microsoft.com/en-us/research/project/ai-at-scale/
herehttps://innovation.microsoft.com/en-us/exploring-ai-at-scale
deepspeed.aihttps://www.deepspeed.ai/
https://github.com/ghosthamlet/DeepSpeed#news
ZeRO-3 Offload: Scale your models to trillion parameters without code changes while leveraging both CPUs & GPUshttps://www.deepspeed.ai/news/2021/03/07/zero3-offload.html
Simplified install, JIT compiled ops, PyPI releases, and reduced dependencieshttps://github.com/ghosthamlet/DeepSpeed#installation
Efficient and robust compressed training through progressive layer droppinghttps://www.deepspeed.ai/news/2020/10/28/progressive-layer-dropping-news.html
DeepSpeed v0.3: Extreme-scale model training for everyonehttps://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/
Powering 10x longer sequences and 6x faster execution through DeepSpeed Sparse Attentionhttps://www.deepspeed.ai/news/2020/09/08/sparse-attention-news.html
Training a trillion parameters with pipeline parallelismhttps://www.deepspeed.ai/news/2020/09/08/pipeline-parallelism.html
Up to 5x less communication and 3.4x faster training through 1-bit Adamhttps://www.deepspeed.ai/news/2020/09/08/onebit-adam-news.html
10x bigger model training on a single GPU with ZeRO-Offloadhttps://www.deepspeed.ai/news/2020/09/08/ZeRO-Offload.html
DeepSpeed Microsoft Research Webinarhttps://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html
https://github.com/ghosthamlet/DeepSpeed#table-of-contents
Why DeepSpeed?https://github.com/ghosthamlet/DeepSpeed#why-deepspeed
Installhttps://github.com/ghosthamlet/DeepSpeed#installation
Featureshttps://github.com/ghosthamlet/DeepSpeed#features
Further Readinghttps://github.com/ghosthamlet/DeepSpeed#further-reading
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https://github.com/ghosthamlet/DeepSpeed#why-deepspeed
https://github.com/ghosthamlet/DeepSpeed#installation
torch's JIT C++ extension loader that relies on ninjahttps://pytorch.org/docs/stable/cpp_extension.html
PyTorchhttps://pytorch.org/
advanced installation instructionshttps://www.deepspeed.ai/tutorials/advanced-install/
https://github.com/ghosthamlet/DeepSpeed#features
feature overviewhttps://www.deepspeed.ai/features/
Distributed Training with Mixed Precisionhttps://www.deepspeed.ai/features/#distributed-training-with-mixed-precision
Model Parallelismhttps://www.deepspeed.ai/features/#model-parallelism
Pipeline Parallelismhttps://www.deepspeed.ai/tutorials/pipeline/
The Zero Redundancy Optimizer (ZeRO)https://www.deepspeed.ai/tutorials/zero/
ZeRO-Offloadhttps://www.deepspeed.ai/tutorials/zero-offload/
Ultra-fast dense transformer kernelshttps://www.deepspeed.ai/news/2020/05/18/bert-record.html
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1-bit Adamhttps://www.deepspeed.ai/news/2020/09/08/onebit-adam-blog-post.html
Additional Memory and Bandwidth Optimizationshttps://www.deepspeed.ai/features/#additional-memory-and-bandwidth-optimizations
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https://github.com/ghosthamlet/DeepSpeed#further-reading
deepspeed.aihttps://www.deepspeed.ai/
DeepSpeed Featureshttps://www.deepspeed.ai/features/
Getting Startedhttps://www.deepspeed.ai/getting-started/
DeepSpeed JSON Configurationhttps://www.deepspeed.ai/docs/config-json/
API Documentationhttps://deepspeed.readthedocs.io/en/latest/
CIFAR-10 Tutorialhttps://www.deepspeed.ai/tutorials/cifar-10
Megatron-LM Tutorialhttps://www.deepspeed.ai/tutorials/megatron/
BERT Pre-training Tutorialhttps://www.deepspeed.ai/tutorials/bert-pretraining/
Learning Rate Range Test Tutorialhttps://www.deepspeed.ai/tutorials/lrrt/
1Cycle Tutorialhttps://www.deepspeed.ai/tutorials/1Cycle/
https://github.com/ghosthamlet/DeepSpeed#contributing
contributinghttps://github.com/ghosthamlet/DeepSpeed/blob/master/CONTRIBUTING.md
https://github.com/ghosthamlet/DeepSpeed#contributor-license-agreement
https://cla.opensource.microsoft.comhttps://cla.opensource.microsoft.com
https://github.com/ghosthamlet/DeepSpeed#code-of-conduct
Microsoft Open Source Code of Conducthttps://opensource.microsoft.com/codeofconduct/
Code of Conduct FAQhttps://opensource.microsoft.com/codeofconduct/faq/
https://github.com/ghosthamlet/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
arXiv:2102.02888https://arxiv.org/abs/2102.02888
https://github.com/ghosthamlet/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
www.deepspeed.ai/https://www.deepspeed.ai/
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