install vllm 运行以下代码以构建 vllm 服务。此处我们以 Qwen1.5-7B-Chat 为例： python -m vllm.entrypoints.openai.api_server --model Qwen/Qwen1.5-7B-Chat 然后，您可以使用 create chat interface 来与 Qwen 进行交流： curl http://localhos 包中的 Python 客户端： from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( (续下页) safetensor.index.json │ │ ├── merges.txt │ │ ├── tokenizer_config.json │ │ └── vocab.json 随后你需要运行 python server.py 来启动你的网页服务。请点击进入 `http://localhost:7860/?__theme=dark` 然后享受使用 Qwen 的 Web UI 吧！ 1.6.2 下一步 TGW

RCNN • RCNN • Fast RCNN • Faster RCNN region proposals classification bounding-box regression region proposals classification bounding-box regression region proposals classification bounding-box Faster RCNN Faster RCNN https://github.com/rbgirshick/py-faster-rcnn Conv Layers Feature Maps Region Proposal Network Classifier © 2018 Bloomberg Finance L.P. All rights reserved. Faster RCNN © Bloomberg Finance L.P. All rights reserved. Object Detection Good Enough? Conv Layers Feature Maps Region Proposal Network Classifier © 2018 Bloomberg Finance L.P. All rights reserved. Intersection Over

oost等）。 12 1.目标检测概述 2.基于深度学习的Two Stages目标检测框架 （准确度有优势） 此类算法将检测问题分为两个阶段， 第一阶段生成大量可能含有目标的候选区域（Region Proposal），并附 加大概的位置信息； 第二个阶段对其进行分类，选出包含目标的候选区域并对其位置进行 修正（常使用R-CNN、Fast R-CNN、Faster R-CNN等算法）。 目标检测概述 37 4.Faster RCNN算法 1.Conv layers 2.Region Proposal Networks 3.Roi Pooling 4.Classification 38 4.Faster RCNN算法 39 4.Faster RCNN算法 Region Proposal Networks RPN网络的作用： RPN专门用来提取候选框，一方面RPN耗时少， Faster RCNN训练步骤 • 第一步，训练RPN，该网络用ImageNet预训练的模型初始化，并端到端微调，用于生成region proposal； • 第二步，训练Faster RCNN，由imageNet model初始化，利用第一步的RPN生成的region proposals作为输入数据，训练Fast R-CNN一个单独的检测网络，这时候两个网络还没有共享卷 积层； •

the computer vision domain. A Search Space for n parameters is a n-dimensional region such that a point in such a region is a set of well-defined values for each of those parameters. The parameters can Even though the trials in the blue region perform better than the ones in the red region, the search algorithm makes no effort to search "more" in the blue region. In other words, it doesn't learn from estimates. Take a look at the figure 7-2 (c) which shows that the BOS trials concentrated in the blue region. The surrogates are typically represented through Gaussian Processes, Random Forests or other statistical

some training examples to be misclassified, and some training examples to fall within the margin region Feng Li (SDU) SVM December 28, 2021 58 / 82 Soft-Margin SVM (Contd.) Recall that, for the separable

training examples to be misclassified, such that some training examples may fall within the margin region, as shown in Fig. 5. For the linearly separable case, the constraints are 12 Figure 6: Regularized

的梯度信息并不直接用于更新 Worker 的 Actor-Critic 网络，而是提 交到 Global Network 更新。具体地，在 Worker 类初始化阶段，获得 Global Network 传入的 server 对象和 opt 对象，分别代表了 Global Network 模型和优化器；并创建私有的 ActorCritic 网络类 client 和交互环境 env。代码如下： class Worker(threading def __init__(self, server, opt, result_queue, idx): super(Worker, self).__init__() self.result_queue = result_queue # 共享队列 self.server = server # 中央模型 self.opt # 梯度提交到 server，在 server 上更新梯度 self.opt.apply_gradients(zip(grads, self.server.trainable_weights))

sequentially from SATA SSD 2 ms ~550MB/s DC S3510 SATA SSD Read 1MB sequentially from disk 5 ms ~200MB/s server HDD Random Disk Access (seek+rotation) 10 ms Send packet CA‐>Netherlands‐>CA 150 ms 表12.4.2: NVIDIA 文 (Liu et al., 2016)中的方法。 Discussions177 13.8 区域卷积神经网络（R-CNN）系列 除了 13.7节中描述的单发多框检测之外，区域卷积神经网络（region‐based CNN或regions with CNN features， R‐CNN）(Girshick et al., 2014)也是将深度模型应用于目标检测的开创性工作之一。本节将介绍R‐CNN及其一 为了较精确地检测目标结果，Fast R‐CNN模型通常需要在选择性搜索中生成大量的提议区域。Faster R-CNN (Ren et al., 2015)提出将选择性搜索替换为区域提议网络（region proposal network），从而减少提议区域的 生成数量，并保证目标检测的精度。 图13.8.4: Faster R‐CNN 模型 图13.8.4描述了Faster R‐CNN模型。与Fast

4.6.1 ‣ Jupyter Notebook 6.0.3 ‣ JupyterLab 2.3.2, including Jupyter-TensorBoard ‣ JupyterLab Server 1.0.6 ‣ Jupyter-TensorBoard Driver Requirements Release 22.08 is based on CUDA 11.7.1, which requires 4.6.1 ‣ Jupyter Notebook 6.0.3 ‣ JupyterLab 2.3.2, including Jupyter-TensorBoard ‣ JupyterLab Server 1.0.6 ‣ Jupyter-TensorBoard Driver Requirements Release 22.07 is based on CUDA 11.7 Update 1 Preview 4.6.1 ‣ Jupyter Notebook 6.0.3 ‣ JupyterLab 2.3.2, including Jupyter-TensorBoard ‣ JupyterLab Server 1.0.6 ‣ Jupyter-TensorBoard Driver Requirements Release 22.06 is based on CUDA 11.7 Update 1 Preview

serving server server server worker Model Serving System Serving PS Traing PS Traing Model System Predict Score Sample Data worker worker worker 3 在线机器学习-参数服务器 serving serving serving server server server server server server worker worker worker PSscheduler PSserver PSserver PSserver PSagent PSagent zookeeper PSproxy PSproxy PSsubmit File System checkpoint Model Training System Model Status set/get Model delete Model Save Model Load HA Fault tolerance checkpoint Local HDFS Param Server System Model Serving System 3 在线机器学习-参数服务器 • 参数规模 • 支持百亿特征维度，千亿参数 • 模型版本 • 多模型多版本：多组实验并行执行，提高实验迭代效率