highly optimized modules for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your PyTorch code. ‣ A preview of Torch-TensorRT (1.4.0dev0) is now NGC. ‣ SSD300 v1.1 model: This model is based on the SSD: Single Shot MultiBox Detector paper. The main difference between this model and the model described in the paper is in the backbone. Specifically highly optimized modules for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your PyTorch code. PyTorch Release 23.06 PyTorch RN-08516-001_v23

..................................................... 55 Keras v Functional API .................................................................................................. techniques to make high level neural network API easier and more performant. It supports the following features:  Consistent, simple and extensible API.  Minimal structure - easy to achieve the learning library used for numerical computational tasks developed by Google. Keras is a high level API built on top of TensorFlow or Theano. We know already how to install TensorFlow using pip. If it

下，我们将展示如何使用 vLLM 构建一个与 OpenAI API 兼容的 API 服务。 首先，确保你已经安装 vLLM>=0.3.0 ： pip 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 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) 1.2. 快速开始 5 Qwen (接上页) api_key=openai_api_key, b base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen1.5-7B-Chat", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content":

感谢 keras-team 所做的中文翻译工作，本文档制作基于此处。 严正声明：本文档可免费用于学习和科学研究，可自由传播，但切勿擅自用于商业用途，由 此引发一切后果贡献者概不负责。 The main reason of organizing PDF version based the Chinese Keras Markdown is that it is easy to read locally LSTM 模型 . . . . . . . . . . . . 15 3.2 函数式 API 指引 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.1 开始使用 Keras 函数式 API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2 Sequential 顺序模型 API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.1 Sequential 顺序模型 API . . . . . . . . . . . . . . . . . . . . . . . . .

loss:{loss}, w:{w}, b:{b}") return [b, w] # 返回最后一次的 w,b 主训练函数实现如下： 预览版202112 2.4 线性回归 9 def main(): # 加载训练集数据，这些数据是通过真实模型添加观测误差采样得到的 lr = 0.01 # 学习率 initial_b = 0 # 初始化 b 为 0 2017 年开始， Keras 的大部分组件被整合到 TensorFlow 框架中。2019 年，在 TensorFlow 2 版本中，Keras 被正式确定为 TensorFlow 的高层唯一接口 API，取代了 TensorFlow 1 版本中自带的 tf.layers 等高层接口。也就是说，现在只能使用 Keras 的接口来完成 TensorFlow 层方式的 模型搭建与训练。在 TensorFlow TensorFlow 中，Keras 被实现在 tf.keras 子模块中。 Keras 与 tf.keras 有什么区别与联系呢？其实 Keras 可以理解为一套搭建与训练神经网 络的高层 API 协议，Keras 本身已经实现了此协议，安装标准的 Keras 库就可以方便地调用 TensorFlow、CNTK 等后端完成加速计算；在 TensorFlow 中，也实现了一套 Keras 协议， 即 tf

software9 where GPT-3 is used for auto-completing code snippets with an IDE. End-users can also use GPT-3 API10 to build their own applications. Given the large number of possible uses for such models, the high Budget." ACL Anthology, Nov. 2021, pp. 10644-52, doi:10.18653/v1/2021.emnlp-main.831. 10 OpenAI GPT-3 API https://openai.com/api/ 9 GitHub Copilot: https://github.com/features/copilot import tensorflow_datasets try out SAM on your models, which returns a model that will now minimize the new SAM objective. The API looks as follows: sam_model = tf.keras.models.experimental.SharpnessAwareMinimization( original_model

figure 2-6. Get the image using this command: !wget https://github.com/reddragon/book-codelabs/raw/main/pia23378-16.jpeg Solution: First, we will interpret the image in the form of a 2D matrix having values processors as well as on specialized DSPs like the Qualcomm Hexagon. We started out this section with two main objectives. The first one was to reduce the model size which is fulfilled using the quantization techniques Unoptimized Model We are all set to start training our model. Tensorflow provides a user-friendly API to train the model. All we need is to invoke the fit() method on the model object. It takes in the

production. BERT is used in Google Search to improve relevance of results, and GPT-3 is available as an API for interested users to consume. Having demonstrated the rapid growth of deep learning models, let recover the data. An example could be reading the summary of a book. You can get an idea of the book’s main points, but you will lose the finer details. We cover these in more detail in Chapter 2. (Figure

Release)、Beta 版本、原型版本（Prototype）。 其中稳定版本长期支持维护没有明显的性能问题与缺陷，理论 上支持向后兼容的版本；Beta 版本是基于用户反馈的改动版 本，可能有 API/SDK 函数改动，性能有进一步需要提升的空间； 原型版本是新功能还不可以，需要开发不能通过 pip 方式直接 安装。 1.1.2 Pytorch 的模块与功能 Pytorch 当前支持绝大数的深度学习常见的算子操作，基于相 择文件 (File)-> 设置 (Setting) 选项： 图 1-7（设置选项） 图 1-8（设置系统 Python 解释器） 完成之后，在项目中创建一个空的 python 文件命名为 main. py，然后直接输入下面两行测试代码： import torch print(torch.__version__) 执行测试（作者笔记本）： 1.9.0+cu102 这样我们就完成了

networks performed at par with the SOTA networks at the time. However, this controller design had two main drawbacks. First, the architecture of the child network is tied closely to the controller. To add recorded and the oldest model from is discarded. This process is repeated for cycles. There are two main mutations used in the mutation step: hidden state mutation and the operation mutation. One of these past_accuracies)) reward = accuracy - rolling_accuracy return reward, accuracy The get_rewards() method is the main entrypoint into the ChildManager class. It is called by the controller to obtain rewards for a sampled