业务落地篇：实现货架洞察 Web 应用 扫码试看/订阅 《 TensorFlow 2项目进阶实战》视频课程 • 串联 AI 流程理论：商品检测与商品识别 • 串联 AI 流程实战：商品检测与商品识别 • 展现 AI 效果理论：使用 OpenCV 可视化识别结果 • 展现 AI 效果实战：使用 OpenCV 可视化识别结果 • 搭建 AI SaaS 理论：Web 框架选型 • 搭建 AI 展现 AI 效果实战：使用 OpenCV 可视化识别结果 “Hello TensorFlow” Try it！ 搭建 AI SaaS 理论：Web 框架选型 Python Web 框架 Python Web 框架 - Flask Python Web 框架 - Flask Flask 常用扩展 Flask 项目常见目录结构 启动文件 manage.py 示例 搭建 AI SaaS 理论：数据库

(resulting in a 2X speedup for bandwidth-bound operations like most pointwise ops) and 2X reduced memory storage for intermediates (reducing the overall memory consumption of your model). Additionally, GEMMs and (resulting in a 2X speedup for bandwidth-bound operations like most pointwise ops) and 2X reduced memory storage for intermediates (reducing the overall memory consumption of your model). Additionally, GEMMs and understand improve performance of their models with visualization by using the DLProf Viewer in a web browser or by analyzing text reports. DL Prof is available on NGC or through a Python PIP wheel installation

Modelfile 完成后，你即可运行你的 ollama 模型： ollama run qwen7b 1.6 Text Generation Web UI Text Generation Web UI（简称 TGW，通常被称为“oobabooga”）是一款流行的文本生成 Web 界面工具，类似 于 AUTOMATIC1111/stable-diffusion-webui 。它拥有多个交互界面，并支持多种模型后端，包括 ├── Qwen1.5-7B-Chat │ │ ├── config.json │ │ ├── generation_config.json (续下页) 1.6. Text Generation Web UI 11 Qwen (接上页) │ │ ├── model-00001-of-00004.safetensor │ │ ├── model-00002-of-00004.safetensor 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 中包含了许多更多用途，您甚至可以在其中享受角色扮演的乐趣，并使用不同类型的量化模型。您可 以训练诸如 LoRA 这样的算法，并将 Stable Diffusion

your deep learning models. We start with sparsity. If your goal was to optimize your brain for storage, you can often trim a lot of useless trivia without it impacting your life materially. This is also picking the connections and nodes to prune, and how to prune a given deep learning model to achieve storage and latency gains with a minimal performance tradeoff. Next, the chapter goes over weight sharing Sparse compressed models achieve higher compression ratio which results in lower transmission and storage costs. Figure 5-1 visually depicts two networks. The one on the left is the original network and

metrics=['accuracy']) return model model = create_model() model.summary() Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim _ordering_tf_kernels_notop vectors. Let’s load it first. %%capture !python -m spacy download en_core_web_md import en_core_web_md nlp = en_core_web_md.load() Use the language model to transform the sentences to sequences of

mentioned. http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf 1986 https://web.stanford.edu/class/psych209a/ReadingsByDate/02_06/PDPVolIChapter8.pdf https://www.cs.cmu.edu/~epx ence/ http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf ▪ 2015 https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf AlphaZero http://www.iro.umontreal.ca/~vi

labeling. Therefore, we can simply use e-books, Wikipedia and other sources for NLU related models, and web images & videos for computer vision models. We can then construct the final dataset for the pretext again, so that the TPU doesn't complain about the # weights of the TF Hub models being on local storage. os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'UNCOMPRESSED' We first start by importing the BERT pre-processing

操作、交换维度 transpose 操作、复制数据 tile 操作等，下面将一一 介绍。 4.7.1 改变视图 在介绍改变视图 reshape 操作之前，先来认识一下张量的存储(Storage)和视图(View)的 概念。张量的视图就是人们理解张量的方式，比如 shape 为[2,3,4,4]的张量?，从逻辑上可 以理解为 2 张图片，每张图片 4 行 4 列，每个位置有 RGB 3 # 清零测量器 8.7 可视化 在网络训练的过程中，通过 Web 端远程监控网络的训练进度，可视化网络的训练结 果，对于提高开发效率和实现远程监控是非常重要的。TensorFlow 提供了一个专门的可视 化工具，叫做 TensorBoard，它通过 TensorFlow 将监控数据写入到文件系统，并利用 Web 后端监控对应的文件目录，从而可以允许用户从远程查看网络的监控数据。 在运行程序时，监控数据被写入到指定文件目录中。如果要实时远程查看、可视化这 些数据，还需要借助于浏览器和 Web 后端。首先是打开 Web 后端，通过在 cmd 终端运行 tensorboard --logdir path 指定 Web 后端监控的文件目录 path，即可打开 Web 后端监控进 程，如图 8.2 所示： 图 8.2 启动 Web 服务器 此时打开浏览器，并输入网址 http://localhost:6006

exercises, we worked out the logic to quantize a high precision vector to low precision to save storage space and the transmission bandwidth. Let’s say a receiver received this data. How would it decode in the number of quantization bits. Quantization is a useful technique in the situation where the storage space or the transmission bandwidth is expensive like deep learning models on mobile devices. Mobile stored in an N-dimensional matrix (tensor), and the weight matrix W is most expensive in terms of storage. Can we efficiently represent this weight matrix W to reduce the model size? We already have worked

存储/更新 百TB数据 分⽚训练 Feature 1: 动态空间 Feature 2.1:短时间内只有部分item和user 被命中，只有部分参数被⽤到 参数按需 获取/更新 Storage 异步训练流⽔线和多级存储：提升性能，降低内存成本 � 问题： � Learner线程中参数拉取和参数更新对性能影响⼤ � 内存成为主要资源瓶颈。由于需要等待全部参数 就绪，Parameter 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% � 异步storage线程，⽀持基于冷热数据的多级存储。内存消 耗下降30%-70% 磁盘 训练 Lookup+ pooling 算⼦融合 Unique keys Storage 近期训练 参数管理 需保持顺 序，以保证 训练效果 样本读取 样本解析 基于GPU的多级存储训练：更⾼的性价⽐