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

标准化模型库  标准化解决方案 1.方案复杂 图像 搜索 推荐 语音 视频理解 NLP 广告 CNN RNN GNN MLP Tensorflow PyTorch Parameter Server MPI TreeModel SQL MapReduce Blink  场景丰富: 图像/视频/推荐/搜索  大数据+大模型: Model Zoo  跨场景+跨模态  开箱即用: 卡证OCR • 人脸检测 • 活体检测 •人脸比对 Mobile SDK API + customer 示例: e-Know Your Customer eKYC eKYC Server eKYC SDK/API  多语言、国际化  多种证件版式  准确率领先同类产品  集成方便 标准化: Standard Solutions 智能推荐解决方案: 推荐请求 PAI-Studio–建模平台 Graph优化 [User Graph去重] 内存Allocate优化 ParallelStringOp [split/type conversion] Sequence Feature [side info] Op Fusion [hash + embedding] Overlap Execution [FG OP化] Item Feature增量更新 3.工程优化复 杂 4

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

download the online machine learning data for training purposes. It fetches the data from online server, process the data and return the data as training and test set. Let us check the data provided by keras dataset module.  Line 3 calls the load_data function, which will fetch the data from online server and return the data as 2 tuples, First tuple, (x_train, y_train) represent the Keras accuracy is 98.28%. We have created a best model to identify the handwriting digits. On the positive side, we can still scope to improve our model. Model Prediction Prediction is the final step and our

as the default process group via: torch.distributed.init_process_group(backend="ucc", kwargs) or a side process group with any default via: torch.distributed.init_process_group(backend=any_backend, default_pg_kwargs) 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）分布式支持，可快速处理海量数据 37 4.LightGBM LightGBM 的主要改进 LightGBM与XGBoost相比，主要有以下几个改进： • 基于梯度的单边采样算法（Gradient-based One-Side Sampling, GOSS）； • 互斥特征捆绑算法（Exclusive Feature Bundling, EFB）； • 直方图算法（ Histogram ）； • 基于最大深度的 Leaf-wise 的垂直生长算法； LightGBM = XGBoost + GOSS + EFB+ Histogram 38 4.LightGBM 基于梯度的单边采样算法（Gradient-based One-Side Sampling, GOSS） 主要思想是通过对样本采样的方法来减少计算目标函数增益时候的复杂度。 GOSS 算法保留了梯度大的样本，并对梯度小的样本进行随机抽样，为了不改 变样本的数据分 如果一个样本的梯度很小，说明该样本的训练误差很小，或者说该样本已经得 到了很好的训练(well-trained)。 39 4.LightGBM 基于梯度的单边采样算法（Gradient-based One-Side Sampling, GOSS） 输入：训练数据，迭代步数d，大梯度数据的采样率a，小梯度数据的采样率b，损失函数和若 学习器的类型（一般为决策树） 输出：训练好的强学习器 （1）根据样本点的梯度的绝对值对它们进行降序排序；

local minimas. The left hand side image shows a loss landscape that has a sharp and steep minima because the loss suddenly drops to a very low value. On the right hand side shows the loss landscape that its predictions over the same classes as the teacher. We now have a loss function on the student side, where the labels are the subclass logits from the teacher, and the predictions are the subclass

Why we need Fashion MNIST Benchmark on original MNIST Benchmark on Fashion MNIST Benchmark Side-by-side Fashion MNIST dataset 使用 TensorFlow 2 训练分类网络 Get Fashion MNIST dataset from tensorflow I mport

is vertically shifted by 50px as shown in the top middle image in figure 3-6. Such a shift has two side-effects. First, a part of the image “falls off” the top edge. That information will be lost. And the This transformation causes the whale fin to visually “stand out”. This transformation has a clipping side effect. An RGB image has a channel value range . Any channel values that exceed 255 after the 2x

Problems Predict tomorrow’s stock market price given current market conditions and other possible side information Predict the age of a viewer watching a given video on YouTube Predict the location in