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

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

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

X Description: DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. - dataandai/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://pepy.tech/project/deepspeed
MT-530Bhttps://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/
https://github.com/dataandai/DeepSpeed#deepspeedmegatron-trained-the-worlds-most-powerful-language-model-mt-530b
come join us!https://careers.microsoft.com/us/en/search-results?keywords=deepspeed%20open%20source
https://github.com/dataandai/DeepSpeed#deepspeed-is-hiring-come-join-us
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/dataandai/DeepSpeed#news
Autotuning: Automatically discover the optimal DeepSpeed configuration that delivers good training speedhttps://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 Modelhttps://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 DeepSpeedhttps://www.deepspeed.ai/tutorials/large-models-w-deepspeed/
DeepSpeed powers 8x larger MoE model training with high performancehttps://www.microsoft.com/en-us/research/blog/deepspeed-powers-8x-larger-moe-model-training-with-high-performance/
Mixture of Experts (MoE) tutorialhttps://www.deepspeed.ai/tutorials/mixture-of-experts/
Curriculum learning: a regularization method for stable and 3.3x faster GPT-2 pre-training with 8x/4x larger batch size/learning ratehttps://www.deepspeed.ai/tutorials/curriculum-learning/
DeepSpeed: Accelerating large-scale model inference and training via system optimizations and compressionhttps://www.microsoft.com/en-us/research/blog/deepspeed-accelerating-large-scale-model-inference-and-training-via-system-optimizations-and-compression/
1-bit LAMB: up to 4.6x less communication and 2.8x faster training, together with LAMB's convergence speed at large batch sizeshttps://www.deepspeed.ai/tutorials/onebit-lamb/
ZeRO-Infinity unlocks unprecedented model scale for deep learning traininghttps://www.microsoft.com/en-us/research/blog/zero-infinity-and-deepspeed-unlocking-unprecedented-model-scale-for-deep-learning-training/
Tutorial on how to use different stages of ZeROhttps://www.deepspeed.ai/tutorials/zero/
[DeepSpeed on AzureML] Transformers and CIFAR examples are now available on AzureML GitHubhttps://github.com/Azure/azureml-examples/tree/main/python-sdk/workflows/train/deepspeed
[PyTorch Lightning Blog] Accessible Multi-Billion Parameter Model Training with PyTorch Lightning + DeepSpeedhttps://medium.com/pytorch-lightning/accessible-multi-billion-parameter-model-training-with-pytorch-lightning-deepspeed-c9333ac3bb59
1-bit Adam v2: NCCL-based implementation and morehttps://www.deepspeed.ai/tutorials/onebit-adam/
https://github.com/dataandai/DeepSpeed#table-of-contents
Why DeepSpeed?https://github.com/dataandai/DeepSpeed#why-deepspeed
Installhttps://github.com/dataandai/DeepSpeed#installation
Featureshttps://github.com/dataandai/DeepSpeed#features
Further Readinghttps://github.com/dataandai/DeepSpeed#further-reading
Contributinghttps://github.com/dataandai/DeepSpeed#contributing
Publicationshttps://github.com/dataandai/DeepSpeed#publications
Videoshttps://github.com/dataandai/DeepSpeed#videos
https://github.com/dataandai/DeepSpeed#why-deepspeed
https://github.com/dataandai/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/dataandai/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
Sparse attentionhttps://www.deepspeed.ai/news/2020/09/08/sparse-attention.html
1-bit Adamhttps://www.deepspeed.ai/news/2020/09/08/onebit-adam-blog-post.html
1-bit LAMBhttps://www.deepspeed.ai/tutorials/onebit-lamb/
Additional Memory and Bandwidth Optimizationshttps://www.deepspeed.ai/features/#additional-memory-and-bandwidth-optimizations
Training Featureshttps://www.deepspeed.ai/features/#training-features
Training Optimizershttps://www.deepspeed.ai/features/#training-optimizers
Training Agnostic Checkpointinghttps://www.deepspeed.ai/features/#training-agnostic-checkpointing
Advanced Parameter Searchhttps://www.deepspeed.ai/features/#advanced-parameter-search
Simplified Data Loaderhttps://www.deepspeed.ai/features/#simplified-data-loader
Curriculum Learninghttps://www.deepspeed.ai/tutorials/curriculum-learning/
Performance Analysis and Debugginghttps://www.deepspeed.ai/features/#performance-analysis-and-debugging
Mixture of Experts (MoE)https://www.deepspeed.ai/tutorials/mixture-of-experts/
https://github.com/dataandai/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/one-cycle/
https://github.com/dataandai/DeepSpeed#contributing
contributinghttps://github.com/dataandai/DeepSpeed/blob/master/CONTRIBUTING.md
https://github.com/dataandai/DeepSpeed#contributor-license-agreement
https://cla.opensource.microsoft.comhttps://cla.opensource.microsoft.com
https://github.com/dataandai/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/dataandai/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
ICML 2021http://proceedings.mlr.press/v139/tang21a.html
arXiv:2104.07857https://arxiv.org/abs/2104.07857
arXiv:2104.06069https://arxiv.org/abs/2104.06069
arXiv:2108.06084https://arxiv.org/abs/2108.06084
https://github.com/dataandai/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 modelshttps://www.youtube.com/watch?v=pDGI668pNg0
Turing-NLG, DeepSpeed and the ZeRO optimizerhttps://www.youtube.com/watch?v=tC01FRB0M7w
www.deepspeed.ai/https://www.deepspeed.ai/
Readme https://github.com/dataandai/DeepSpeed#readme-ov-file
MIT license https://github.com/dataandai/DeepSpeed#MIT-1-ov-file
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