number-crunching at the heart of deep learning. AlexNet1 was one of the earliest models to rely on Graphics Processing Units (GPUs) for training, which could 1 Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105. do linear algebra operations such as multiplying two matrices together models over time. (Data Source) We have seen a similar effect in the world of Natural Language Processing (NLP) (see Figure 1-2), where the Transformer architecture significantly beat previous benchmarks

Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems, 33, 22243-22255. 17 A head is a trainable sub-network that takes in the output of the Network. The image on the left shows a recurrent cell processing the input sequence element at time step t. The image on the right explains the processing of the entire input sequence across n time steps. (2015). 22 Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017). Mathematically, we are given a pair of sequences and with shapes (n, d) and

入参数 tensor_parallel_size ，来使用张量并行来运行 Qwen1.5-72B-Chat 模型： from vllm import LLM, SamplingParams llm = LLM(model="Qwen/Qwen1.5-72B-Chat", tensor_parallel_size=4) 您可以通过传递参数 --tensor-parallel-size 来运行多 GPU GPU 服务： python -m vllm.entrypoints.api_server \ --model Qwen/Qwen1.5-72B-Chat \ --tensor-parallel-size 4 1.10.5 部署量化模型 vLLM 支持多种类型的量化模型，例如 AWQ、GPTQ、SqueezeLLM 等。这里我们将展示如何部署 AWQ 和 GPTQ 模型。使用方法与上述基本 They are capable of generating human-like␣ �→text and are used in a variety of natural language processing tasks..." } ], "source": "unknown" } { "type": "chatml", "messages": [ { "role": "system"

cuda.is_available() ● Multiple GPUs: specify ‘cuda:0’, ‘cuda:1’, ‘cuda:2’, ... ● Why use GPUs? ○ Parallel computing with more cores for arithmetic calculations ○ See What is a GPU and do you need one in model.load_state_dict(ckpt) More About PyTorch ● torchaudio ○ speech/audio processing ● torchtext ○ natural language processing ● torchvision ○ computer vision ● skorch ○ scikit-learn + pyTorch More

12 GiB 内存 (内存存取带宽达到240 GB/秒), 以及 2,496 个并行处理核心 Instance Name GPU Count vCPU Count Memory Parallel Processing Cores GPU Memory Network Performance p2.xlarge 1 4 61 GiB 2,496 12 GiB High p2.8xlarge

the best results. The trials are independent of each other which makes them a good candidate for parallel execution. For example, the trial set for two hyperparameters and where and is Figure 7-2 (a) idea. Neural Architectures are composed of layers stacked on top of each other with a given layer processing the output of the previous layers. However, HPO techniques are insufficient to model this ordered

algorithm, which will best fit for the type of learning process (e.g image classification, text processing, etc.,) and the available input data. Algorithm is represented by Model in Keras. Algorithm includes  Text processing: Provides functions to convert text into NumPy array suitable for machine learning. We can use it in data preparation phase of machine learning.  Image processing: Provides machine learning. We can use it in data preparation phase of machine learning.  Sequence processing: Provides functions to generate time based data from the given input data. We can use it in data

昂的许多线性代 数层传递数据。这也是为什么在20世纪90年代至21世纪初，优化凸目标的简单算法是研究人员的首选。然而， 用GPU训练神经网络改变了这一格局。图形处理器（Graphics Processing Unit，GPU）早年用来加速图形处 理，使电脑游戏玩家受益。GPU可优化高吞吐量的4 × 4矩阵和向量乘法，从而服务于基本的图形任务。幸运 的是，这些数学运算与卷积层的计算惊人地相似 优化gpu，甚至把它们作为通用GPU（general‐purpose GPUs，GPGPU）来销售。 那么GPU比CPU强在哪里呢？ 首先，我们深度理解一下中央处理器（Central Processing Unit，CPU）的核心。CPU的每个核心都拥有高时 钟频率的运行能力，和高达数MB的三级缓存（L3Cache）。它们非常适合执行各种指令，具有分支预测器、深 层流水线和其他使CPU能 机的存储在数量和速度上都能根据用户需要进行动态分 配。建议用户在延迟太高时（例如，在训练期间存在许多小记录时）增加IOPs的配置数。 12.4.4 CPU 中央处理器（central processing unit，CPU）是任何计算机的核心。它们由许多关键组件组成：处理器核心 （processor cores）用于执行机器代码的；总线（bus）用于连接不同组件（注意，总线会因为处理器型号、

import multi_gpu_model # 将 `model` 复制到 8 个 GPU 上。 # 假定你的机器有 8 个可用的 GPU。 parallel_model = multi_gpu_model(model, gpus=8) parallel_model.compile(loss='categorical_crossentropy', optimizer='rmsprop') # # 这个 `fit` 调用将分布在 8 个 GPU 上。 # 由于 batch size 为 256，每个 GPU 将处理 32 个样本。 parallel_model.fit(x, y, epochs=20, batch_size=256) 3.3.4.2 设备并行 设备并行性包括在不同设备上运行同一模型的不同部分。对于具有并行体系结构的模型，例如 有两个分支的模型，这种方式很合适。 这种并行可以通过使用 classes=num_classes) 工具 241 # 将模型复制到 8 个 GPU 上。 # 这假定你的机器有 8 个可用的 GPU。 parallel_model = multi_gpu_model(model, gpus=8) parallel_model.compile(loss='categorical_crossentropy', optimizer='rmsprop') #

maximized at point (x0, y0) where they have common tangent line such that the gradient vectors are parallel ∇f (x0, y0) = λ∇g(x0, y0) ? ?, ? = 0 How about higher dimension? Feng Li (SDU) GDA, NB and EM perpendicular to the surface Since ∇g |q is also perpendicular to the surface, we have proved ∇fq is parallel to ∇g |q Feng Li (SDU) GDA, NB and EM September 27, 2023 59 / 122 Lagrange Multiplier (Contd.)