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Title: GitHub - DEVBOX10/microsoft-DeepSpeed: DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. · GitHub

Open Graph Title: GitHub - DEVBOX10/microsoft-DeepSpeed: DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

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Description: DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. - DEVBOX10/microsoft-DeepSpeed

Open Graph Description: DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. - DEVBOX10/microsoft-DeepSpeed

X Description: DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. - DEVBOX10/microsoft-DeepSpeed

Mail addresses
opencode@microsoft.com

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	https://github.com/DEVBOX10/microsoft-DeepSpeed#extreme-speed-and-scale-for-dl-training-and-inference
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	https://github.com/DEVBOX10/microsoft-DeepSpeed#deepspeeds-four-innovation-pillars
	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/images/DeepSpeed-pillars.png
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	https://github.com/DEVBOX10/microsoft-DeepSpeed#deepspeed-adoption
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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/
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Lightning with DeepSpeed	https://lightning.ai/docs/pytorch/stable/advanced/model_parallel.html#deepspeed
	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/images/mosaicml.svg
MosaicML with DeepSpeed	https://docs.mosaicml.com/projects/composer/en/latest/trainer/using_the_trainer.html?highlight=deepspeed#deepspeed-integration
	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/images/determined.svg
Determined with DeepSpeed	https://docs.determined.ai/latest/training/apis-howto/deepspeed/overview.html
	https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg
MMEngine with DeepSpeed	https://mmengine.readthedocs.io/en/latest/common_usage/large_model_training.html#deepspeed
	https://github.com/DEVBOX10/microsoft-DeepSpeed#build-pipeline-status
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-p40.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-v100.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-latest-v100.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-h100.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-inference.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-nightly.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/amd-mi200.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-torch-latest.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-inference.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/hpu-gaudi2.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/xpu-max1100.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-transformers-v100.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-lightning-v100.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-accelerate-v100.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-mii.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-sd.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/formatting.yml
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/pages/pages-build-deployment
	https://deepspeed.readthedocs.io/en/latest/?badge=latest
	https://github.com/deepspeedai/DeepSpeed/actions/workflows/python.yml
	https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml
	https://github.com/DEVBOX10/microsoft-DeepSpeed#installation
torch's JIT C++ extension loader that relies on ninja	https://pytorch.org/docs/stable/cpp_extension.html
	https://github.com/DEVBOX10/microsoft-DeepSpeed#requirements
PyTorch	https://pytorch.org/
nvcc	https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/#introduction
hipcc	https://github.com/ROCm-Developer-Tools/HIPCC
	https://github.com/DEVBOX10/microsoft-DeepSpeed#contributed-hw-support
	https://github.com/DEVBOX10/microsoft-DeepSpeed#pypi
PyPI	https://pypi.org/project/deepspeed/
advanced installation instructions	https://www.deepspeed.ai/tutorials/advanced-install/
	https://github.com/DEVBOX10/microsoft-DeepSpeed#windows
here	https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/README.md
	https://github.com/DEVBOX10/microsoft-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/DEVBOX10/microsoft-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/DEVBOX10/microsoft-DeepSpeed#contributing
contributing	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/CONTRIBUTING.md
	https://github.com/deepspeedai/DeepSpeed/graphs/contributors
	https://github.com/DEVBOX10/microsoft-DeepSpeed#contributor-license-agreement
https://cla.opensource.microsoft.com	https://cla.opensource.microsoft.com
	https://github.com/DEVBOX10/microsoft-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/DEVBOX10/microsoft-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
USENIX ATC 2021	https://www.usenix.org/conference/atc21/presentation/ren-jie
[paper]	https://arxiv.org/abs/2101.06840
[slides]	https://www.usenix.org/system/files/atc21_slides_ren-jie.pdf
[blog]	https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/
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
SC 2021	https://dl.acm.org/doi/abs/10.1145/3458817.3476205
[paper]	https://arxiv.org/abs/2104.07857
[slides]	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/files/SC21-ZeRO-Infinity.pdf
[blog]	https://www.microsoft.com/en-us/research/blog/zero-infinity-and-deepspeed-unlocking-unprecedented-model-scale-for-deep-learning-training/
arXiv:2104.06069	https://arxiv.org/abs/2104.06069
HiPC 2022	https://hipc.org/advance-program/
arXiv:2108.06084	https://arxiv.org/abs/2108.06084
NeurIPS 2022	https://openreview.net/forum?id=JpZ5du_Kdh
arXiv:2202.06009	https://arxiv.org/abs/2202.06009
arXiv:2201.05596	https://arxiv.org/abs/2201.05596
ICML 2022	https://proceedings.mlr.press/v162/rajbhandari22a.html
[pdf]	https://arxiv.org/abs/2201.05596
[slides]	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/files/ICML-5mins.pdf
[blog]	https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/
arXiv:2201.11990	https://arxiv.org/abs/2201.11990
arXiv:2206.01859	https://arxiv.org/abs/2206.01859
NeurIPS 2022	https://openreview.net/forum?id=xNeAhc2CNAl
arXiv:2206.01861	https://arxiv.org/abs/2206.01861
NeurIPS 2022	https://openreview.net/forum?id=f-fVCElZ-G1
[slides]	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf
[blog]	https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/
arXiv:2207.00032	https://arxiv.org/abs/2207.00032
SC 2022	https://dl.acm.org/doi/abs/10.5555/3571885.3571946
[paper]	https://arxiv.org/abs/2207.00032
[slides]	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/files/sc22-ds-inference.pdf
[blog]	https://www.microsoft.com/en-us/research/blog/deepspeed-accelerating-large-scale-model-inference-and-training-via-system-optimizations-and-compression/
arXiv:2211.11586	https://arxiv.org/abs/2211.11586
arXiv:2212.03597	https://arxiv.org/abs/2212.03597
ENLSP2023 Workshop at NeurIPS2023	https://neurips2023-enlsp.github.io/
arXiv:2301.12017	https://arxiv.org/abs/2301.12017
ICML2023	https://icml.cc/Conferences/2023
ICLR:2023	https://openreview.net/forum?id=Pgtn4l6eKjv
arXiv:2303.07226	https://arxiv.org/abs/2303.07226
Finding at EMNLP2023	https://2023.emnlp.org/
arXiv:2303.08374	https://arxiv.org/abs/2303.08374
arXiv:2303.06318	https://arxiv.org/abs/2303.06318
ICS 2023	https://dl.acm.org/doi/10.1145/3577193.3593704
arXiv:2306.10209	https://arxiv.org/abs/2306.10209
ML for Sys Workshop at NeurIPS2023	http://mlforsystems.org/
[blog]	https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/
arXiv:2303.08302	https://arxiv.org/abs/2303.08302
ENLSP2023 Workshop at NeurIPS2023	https://neurips2023-enlsp.github.io/
[slides]	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf
arXiv:2305.09847	https://arxiv.org/abs/2305.09847
arXiv:2308.01320	https://arxiv.org/abs/2308.01320
arXiv:2307.09782	https://arxiv.org/abs/2307.09782
ENLSP2023 Workshop at NeurIPS2023	https://neurips2023-enlsp.github.io/
[slides]	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf
arXiv:2309.14327	https://arxiv.org/pdf/2309.14327.pdf
arXiv:2310.04610	https://arxiv.org/abs/2310.04610
[blog]	https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/
arXiv:2310.17723	https://arxiv.org/abs/2310.17723
arXiv:2312.08583	https://arxiv.org/abs/2312.08583
arXiv:2401.14112	https://arxiv.org/abs/2401.14112
System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models	https://dl.acm.org/doi/10.1145/3662158.3662806
arXiv:2406.18820	https://arxiv.org/abs/2406.18820
	https://github.com/DEVBOX10/microsoft-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
Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conference	https://www.youtube.com/watch?v=cntxC3g22oU
[slides]	https://github.com/DEVBOX10/microsoft-DeepSpeed/blob/master/docs/assets/files/presentation-mlops.pdf
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/DEVBOX10/microsoft-DeepSpeed#readme-ov-file
Apache-2.0 license	https://github.com/DEVBOX10/microsoft-DeepSpeed#Apache-2.0-1-ov-file
Code of conduct	https://github.com/DEVBOX10/microsoft-DeepSpeed#coc-ov-file
Contributing	https://github.com/DEVBOX10/microsoft-DeepSpeed#contributing-ov-file
Security policy	https://github.com/DEVBOX10/microsoft-DeepSpeed#security-ov-file
Please reload this page	https://github.com/DEVBOX10/microsoft-DeepSpeed
Activity	https://github.com/DEVBOX10/microsoft-DeepSpeed/activity
0 forks	https://github.com/DEVBOX10/microsoft-DeepSpeed/forks
Report repository	https://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FDEVBOX10%2Fmicrosoft-DeepSpeed&report=DEVBOX10+%28user%29
Releases	https://github.com/DEVBOX10/microsoft-DeepSpeed/releases
5 tags	https://github.com/DEVBOX10/microsoft-DeepSpeed/tags
Packages 0	https://github.com/users/DEVBOX10/packages?repo_name=microsoft-DeepSpeed
Please reload this page	https://github.com/DEVBOX10/microsoft-DeepSpeed
Contributors	https://github.com/DEVBOX10/microsoft-DeepSpeed/graphs/contributors
Please reload this page	https://github.com/DEVBOX10/microsoft-DeepSpeed
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Terms	https://docs.github.com/site-policy/github-terms/github-terms-of-service
Privacy	https://docs.github.com/site-policy/privacy-policies/github-privacy-statement
Security	https://github.com/security
Status	https://www.githubstatus.com/
Community	https://github.community/
Docs	https://docs.github.com/
Contact	https://support.github.com?tags=dotcom-footer

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