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

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.)

Sequnce nn.Modelist forward Model() Loss() torch.autograd. backward Torch.optims .step parallel init nn.ModuleDict 定义网络层 构建网络 前向传播 反向传播 优化参数 3. 神经网络 30 3. 神经网络 神经网络的典型训练过程如下: • 定义神经网络模型