..................................................................... 7 3. Keras ― Backend Configuration ............................................................................................. install using the below command: pip install TensorFlow Once we execute keras, we could see the configuration file is located at your home directory inside and go to .keras/keras.json. keras.json { folder name and add the above configuration inside keras.json file. We can perform some pre-defined operations to know backend functions. 3. Keras ― Backend Configuration Keras 10

features and enhancements. ‣ PyTorch container image version 23.04 is based on 2.1.0a0+fe05266f. ‣ Configuration of NCCL communicators via: https://github.com/pytorch/pytorch/ pull/97394 Announcements ‣ Transformer 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

errors. This approach is also called Configuration Selection because we are aiming to find optimal hyperparameter values. BOS is likely to reach the optimum configuration faster than Grid and Random searches configurations and adaptively allocates more resources to the promising ones. This is called Configuration Evaluation. Let's discuss it in detail in the next section. Figure 7-3: (a) Bayesian Optimization errors. (b) This plot shows the validation error as a function of resources allocated to each configuration. Promising configurations get more resources. Source: Hyperband2 2 Li, Lisha, et al. "Hyperband:

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

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

Max-Pool Conv3-32 Conv3-128 Conv3-64 Conv3-64 Max-Pool Max-Pool FC-512 Output ConvNet Configuration Stacked layers Previous input x F(x) y=F(x) Stacked layers Previous input x F(x) y=F(x)+x

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

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))