序也在限制规则下战胜了顶级职业队伍。 机器人(Robotics) 在真实环境中，机器人的控制也取得了一定的进展。如 UC Berkeley 实验室在机器人领域的 Imitation Learning、Meta Learning、Few-shot Learning 等方向上取得 了不少进展。美国波士顿动力公司在机器人应用中取得喜人的成就，其制造的机器人在复 杂地形行走、多智能体协作等任务上表现良好(图 1 为了方便统一格式，也将价格变化趋势表达为 shape 为 [2,60,1]的张量，其中的 1 表示特 征长度为 1。 4.5.5 四维张量 这里只讨论三、四维张量，大于四维的张量一般应用的相对较少，如在元学习(Meta Learning)、目标检测中会采用五维甚至六维的张量表示方法，理解方法与三、四维张量类 似，不再赘述。 四维张量在卷积神经网络中应用非常广泛，它用于保存特征图(Feature maps)数据，格 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

why not use MSE ▪ sigmoid + MSE ▪ gradient vanish ▪ converge slower ▪ But, sometimes ▪ e.g. meta-learning https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html Therefore Numerical

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

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

查询特征 广告特征 匹配特征 线性模型 非线性模型 Data Feature Model 线上Server CTR预估 Rank Online 特征抽取 CTR预估涉及技术 CTR预估 数据 模型 平台 MPI XgBoost Parameter Server 线性(LR) 非线性(GBDT) 深度(DNN) 实时(FTRL) 特征 训练数据 融合模型 Feature Maker One Case ALL One Hot 特征 Final CTR Bidding Server OFFLINE ONLINE OneHot Float LR Model DNN Model Retriever Server CTR Table DNN Model Feature LR Model Feature 特 征 池

内存成为主要资源瓶颈。由于需要等待全部参数 就绪，Parameter Server难以利⽤速度慢的存储 介质 样本读取 样本解析 参数拉 取 训练 参数更新 查询Sparse Table 查询Dense Tensor Reader Learner Worker 返回参数 Request Handler Parameter Server 查询Sparse Table 查询Dense Tensor 参数更新 查询Sparse Table 查询Dense Tensor Reader Learner Worker 返回参数 Request Handler Parameter Server 更新参数 � 异步参数处理流⽔线 参数 预准备 Batch⼊队列 Batch⼊队列 � 效果： � 在不影响训练效果的情况下，降低参数准备与更新耗时，提 ⾼训练速度。训练耗时下降超50%

Having such a toolbox to make our models pareto-optimal has the following benefits: Sustainable Server-Side Scaling Training and deploying large deep learning models is costly. While training is a one-time inference can be run completely on the user’s device without the need to send the input data to the server-side. New Applications Efficiency would also enable applications that couldn’t have otherwise been device. This further reduces the available resources for a single model. This could happen on the server-side where multiple models are co-located on the same machine, or could be in an app where different

install tensorboardX TensorboardX Visdom from Facebook Step 1. install Step2. run server damon Step2. run server damon install from source lines: single trace lines: multi-traces visual X 下一课时

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