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

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

X Title: GitHub - BoxiangW/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. - BoxiangW/DeepSpeed

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

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

Mail addresses
opencode@microsoft.com

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

X: @github

direct link

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Links:

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License	https://github.com/BoxiangW/DeepSpeed
Security	https://github.com/BoxiangW/DeepSpeed
	https://github.com/Microsoft/DeepSpeed/blob/master/LICENSE
	https://pypi.org/project/deepspeed/
	https://pepy.tech/project/deepspeed
	https://github.com/BoxiangW/DeepSpeed#build-pipeline-status
	https://github.com/BoxiangW/DeepSpeed/blob/master/docs/assets/images/DeepSpeed_light.svg#gh-light-mode-only
	https://github.com/BoxiangW/DeepSpeed/blob/master/docs/assets/images/DeepSpeed_dark_transparent.svg#gh-dark-mode-only
	https://github.com/BoxiangW/DeepSpeed#latest-news
MT-530B	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/
BLOOM	https://huggingface.co/blog/bloom-megatron-deepspeed
learn how	https://www.deepspeed.ai/tutorials/large-models-w-deepspeed/
DeepSpeed-MII: instant speedup on 24,000+ open-source DL models with up to 40x cheaper inference	https://www.deepspeed.ai/2022/10/10/mii.html
ZeRO-Inference: Democratizing massive model inference	https://www.deepspeed.ai/2022/09/09/zero-inference.html
Azure and DeepSpeed empower easy-to-use and high-performance model training	https://azure.microsoft.com/en-us/blog/azure-empowers-easytouse-highperformance-and-hyperscale-model-training-using-deepspeed/
DeepSpeed Compression: A composable library for extreme compression	https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/
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/
	https://github.com/BoxiangW/DeepSpeed#extreme-speed-and-scale-for-dl-training-and-inference
DeepSpeed	https://www.deepspeed.ai/
	https://github.com/BoxiangW/DeepSpeed#deepspeeds-three-innovation-pillars
	https://github.com/BoxiangW/DeepSpeed/blob/master/docs/assets/images/3pillars.png
	https://github.com/BoxiangW/DeepSpeed#deepspeed-training
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	https://github.com/BoxiangW/DeepSpeed#deepspeed-inference
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	https://github.com/BoxiangW/DeepSpeed#deepspeed-compression
DeepSpeed-Compression	https://www.deepspeed.ai/compression
	https://github.com/BoxiangW/DeepSpeed#deepspeed-software-suite
	https://github.com/BoxiangW/DeepSpeed#deepspeed-library
DeepSpeed	https://github.com/microsoft/deepspeed
DeepSpeed Adoption	https://github.com/BoxiangW/DeepSpeed#deepspeed-adoption
	https://github.com/BoxiangW/DeepSpeed#model-implementations-for-inference-mii
Model Implementations for Inference (MII)	https://github.com/microsoft/deepspeed-mii
	https://github.com/BoxiangW/DeepSpeed#deepspeed-on-azure
recipes	https://github.com/Azure/azureml-examples/tree/main/python-sdk/workflows/train/deepspeed
here	https://github.com/microsoft/Megatron-DeepSpeed/tree/main/examples/azureml
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	https://github.com/BoxiangW/DeepSpeed#deepspeed-adoption
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
Megatron-Turing NLG (530B)	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/
Jurassic-1 (178B)	https://uploads-ssl.webflow.com/60fd4503684b466578c0d307/61138924626a6981ee09caf6_jurassic_tech_paper.pdf
BLOOM (176B)	https://huggingface.co/blog/bloom-megatron-deepspeed
GLM (130B)	https://github.com/THUDM/GLM-130B
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/BoxiangW/DeepSpeed/blob/master/docs/assets/images/transformers-light.png#gh-light-mode-only
	https://github.com/BoxiangW/DeepSpeed/blob/master/docs/assets/images/transformers-dark.png#gh-dark-mode-only
Transformers with DeepSpeed	https://huggingface.co/docs/transformers/main/main_classes/deepspeed
	https://github.com/BoxiangW/DeepSpeed/blob/master/docs/assets/images/accelerate-light.png#gh-light-mode-only
	https://github.com/BoxiangW/DeepSpeed/blob/master/docs/assets/images/accelerate-dark.png#gh-dark-mode-only
Accelerate with DeepSpeed	https://huggingface.co/docs/accelerate/main/en/deepspeed
	https://github.com/BoxiangW/DeepSpeed/blob/master/docs/assets/images/lightning-light.svg#gh-light-mode-only
	https://github.com/BoxiangW/DeepSpeed/blob/master/docs/assets/images/lightning-dark.svg#gh-dark-mode-only
Lightning with DeepSpeed	https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.strategies.DeepSpeedStrategy.html
	https://github.com/BoxiangW/DeepSpeed/blob/master/docs/assets/images/mosaicml.svg
MosaicML with DeepSpeed	https://docs.mosaicml.com/en/latest/trainer/using_the_trainer.html?highlight=deepspeed#deepspeed-integration
	https://github.com/BoxiangW/DeepSpeed#build-pipeline-status
	https://github.com/microsoft/DeepSpeed/actions/workflows/nv-torch12-p40.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/nv-torch18-v100.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/nv-torch-latest-v100.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/nv-inference.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/amd.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/nv-transformers-v100.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/nv-lightning-v100.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/nv-accelerate-v100.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/formatting.yml
	https://github.com/microsoft/DeepSpeed/actions/workflows/pages/pages-build-deployment
	https://deepspeed.readthedocs.io/en/latest/?badge=latest
	https://github.com/BoxiangW/DeepSpeed#installation
torch's JIT C++ extension loader that relies on ninja	https://pytorch.org/docs/stable/cpp_extension.html
	https://github.com/BoxiangW/DeepSpeed#requirements
PyTorch	https://pytorch.org/
	https://github.com/BoxiangW/DeepSpeed#pypi
PyPI	https://pypi.org/project/deepspeed/
advanced installation instructions	https://www.deepspeed.ai/tutorials/advanced-install/
	https://github.com/BoxiangW/DeepSpeed#windows
	https://github.com/BoxiangW/DeepSpeed#features
DeepSpeed-Training	https://www.deepspeed.ai/training
DeepSpeed-Inference	https://www.deepspeed.ai/inference
DeepSpeed-Compression	https://www.deepspeed.ai/compression
	https://github.com/BoxiangW/DeepSpeed#further-reading
deepspeed.ai	https://www.deepspeed.ai/
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/
Tutorials	https://www.deepspeed.ai/tutorials/
Blogs	https://www.deepspeed.ai/posts/
	https://github.com/BoxiangW/DeepSpeed#contributing
contributing	https://github.com/BoxiangW/DeepSpeed/blob/master/CONTRIBUTING.md
	https://github.com/BoxiangW/DeepSpeed#contributor-license-agreement
https://cla.opensource.microsoft.com	https://cla.opensource.microsoft.com
	https://github.com/BoxiangW/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/BoxiangW/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
arXiv:2201.11990	https://arxiv.org/abs/2201.11990
arXiv:2206.01859	https://arxiv.org/abs/2206.01859
arXiv:2206.01861	https://arxiv.org/abs/2206.01861
arXiv:2207.00032	https://arxiv.org/abs/2207.00032
	https://github.com/BoxiangW/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 (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/BoxiangW/DeepSpeed#readme-ov-file
MIT license	https://github.com/BoxiangW/DeepSpeed#MIT-1-ov-file
Code of conduct	https://github.com/BoxiangW/DeepSpeed#coc-ov-file
Contributing	https://github.com/BoxiangW/DeepSpeed#contributing-ov-file
Security policy	https://github.com/BoxiangW/DeepSpeed#security-ov-file
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Releases	https://github.com/BoxiangW/DeepSpeed/releases
Packages 0	https://github.com/users/BoxiangW/packages?repo_name=DeepSpeed
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