The method implemented in your system depends on the DGX OS version that you installed (for DGX systems), the NGC Cloud Image that was provided by a Cloud Service Provider, or the software that you installed 15 ‣ OpenMPI 4.1.4+ ‣ GDRCopy 2.3 ‣ TensorBoard 2.9.0 ‣ Nsight Compute 2023.1.1.4 ‣ Nsight Systems 2023.2.3.1001 ‣ NVIDIA TensorRT™ 8.6.1.6 ‣ Torch-TensorRT 1.5.0.dev0 ‣ NVIDIA DALI® 1.27.0 ‣ 15 ‣ OpenMPI 4.1.4+ ‣ GDRCopy 2.3 ‣ TensorBoard 2.9.0 ‣ Nsight Compute 2023.1.1.4 ‣ Nsight Systems 2023.2.3.1001 ‣ NVIDIA TensorRT™ 8.6.1.6 ‣ Torch-TensorRT 1.5.0.dev0 ‣ NVIDIA DALI® 1.26.0 ‣

sharing. However, quantization falls behind in case the data that we are quantizing is not uniformly distributed, i.e. the data is more likely to take values in a certain range than another equally sized range John Denker, and Sara Solla. "Optimal brain damage." Advances in neural information processing systems 2 (1989). As you can deduce, the parameter changes the influence of the previous value of momentum "Deconstructing lottery tickets: Zeros, signs, and the supermask." Advances in neural information processing systems 32 (2019). 10 Liu, Zhuang, et al. "Rethinking the value of network pruning." arXiv preprint arXiv:1810

Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation [ICLR2018]Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Dense参数，每次 都⽤，快速收敛 Partitions for Memory-Efficient Recommendation Systems Twiiter [RecSys21] Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems 9 千 万 key hash1(key) hash2(key) 千 万 业界⽅案：Double

Google Brain Team, Building Intelligent Systems with Large Scale Deep Learning 1990s��������������� Jeff Dean, Google Brain Team, Building Intelligent Systems with Large Scale Deep Learning ������������������ Dean, Google Brain Team, Building Intelligent Systems with Large Scale Deep Learning ����� Google ��� Jeff Dean, Google Brain Team, Building Intelligent Systems with Large Scale Deep Learning TensorFlow • �� • �� • ... TensorFlow ����� DistBelief - Google ��������������� Jeff Dean, Large Scale Distributed Deep Networks, NIPS 2012 TensorFlow - Google ��������������� • ���������� • ����������� • ����������

Research Fellow, National University of Singapore, Singapore. Research Interests: Distributed Algorithms and Systems, Wireless Net- works, Mobile Computing, Internet of Things. Feng Li (SDU) Overview driver Feng Li (SDU) Overview September 6, 2023 11 / 57 Why Do We Need Machine Learning? Develop systems that are too difficult/expensive to construct manually because they require specific detailed skills skills or knowledge tuned to a specific task (knowledge engineering bottleneck) Develop systems that can automatically adapt and customize them- selves to individual users. Personalized news or mail filter

毋庸置疑，如果没有数据，那么数据科学毫无用武之地。每个数据集由一个个样本（example, sample）组成， 大多时候，它们遵循独立同分布(independently and identically distributed, i.i.d.)。样本有时也叫做数据点 （data point）或者数据实例（data instance），通常每个样本由一组称为特征（features，或协变量（covariates）） ，就会对图像中内容的推断造成极大的困难。 最重要的是，到目前为止我们默认数据都来自于某种分布，并且所有样本都是独立同分布的（independently and identically distributed，i.i.d.）。然而，大多数的数据并非如此。例如，文章中的单词是按顺序写的，如 果顺序被随机地重排，就很难理解文章原始的意思。同样，视频中的图像帧、对话中的音频信号以及网站上 的浏览行 write 94 ns UCSD Non‐Volatile Systems Lab 256MB memory ref. (remote CPU) 120 ns TinyMemBench on Broadwell E5‐2690v4 Intel Optane random read 305 ns UCSD Non‐Volatile Systems Lab Send 4KB over 100 Gbps

will be clamped to lie in this range. 2. Let us assume that the values of x will be uniformly distributed in this range. This means that all values of x are equally likely to lie in any part of the range 5 Hubara, Itay, et al. "Binarized neural networks." Advances in neural information processing systems 29 (2016). 4 Rastegari, Mohammad, et al. "Xnor-net: Imagenet classification using binary convolutional

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, "deepspeed", None) and int(os.environ.get("WORLD_SIZE", 1)) == 1 ): training_args.distributed_state.distributed_type = DistributedType.DEEPSPEED local_rank = training_args.local_rank device_map = 执行下列命令： DISTRIBUTED_ARGS=" --nproc_per_node $NPROC_PER_NODE \ --nnodes $NNODES \ --node_rank $NODE_RANK \ --master_addr $MASTER_ADDR \ --master_port $MASTER_PORT " torchrun $DISTRIBUTED_ARGS src/train_bash

distribution parameter Given: m independent and identically distributed (i.i.d.) samples of the data D = {d(i)}i=1,··· ,m Independent and Identically Distributed Given θ, each sample is independent of all other