‣ Torch-TensorRT 1.5.0.dev0 ‣ NVIDIA DALI® 1.27.0 ‣ MAGMA 2.6.2 ‣ JupyterLab 2.3.2 including Jupyter-TensorBoard ‣ TransformerEngine 0.10.0+96ed6fc ‣ PyTorch quantization wheel 2.1.2 PyTorch Release ‣ Torch-TensorRT 1.5.0.dev0 ‣ NVIDIA DALI® 1.26.0 ‣ MAGMA 2.6.2 ‣ JupyterLab 2.3.2 including Jupyter-TensorBoard ‣ TransformerEngine 0.9.0 ‣ PyTorch quantization wheel 2.1.2 PyTorch Release 23.06 ‣ Torch-TensorRT 1.4.0.dev0 ‣ NVIDIA DALI® 1.25.0 ‣ MAGMA 2.6.2 ‣ JupyterLab 2.3.2 including Jupyter-TensorBoard ‣ TransformerEngine 0.8 ‣ PyTorch quantization wheel 2.1.2 PyTorch Release 23.05

install jupyter • ???????????? Run on your computer • jupyter notebook • ???????????? Run on Princeton CS server • Pick any 4-digit number, say 1234 • ???????????? hostname -s • ???????????? jupyter notebook username, second is hostname Jupyter Notebook VS Code • Install the Python extension. • ???????????? Install the Remote Development extension. • Python files can be run like Jupyter notebooks by delimiting PyTorch code is just like debugging any other Python code: see Piazza @108 for info. Also try Jupyter Lab! Why talk about libraries? • Advantage of various deep learning frameworks • Quick to develop

s#CPUs_with_AVX ��������� TensorFlow Jupyter Notebook ������� (venv) $ pip install jupyter (venv) $ python –m ipykernel install --user --name=venv � Jupyter Notebook ��� TensorFlow “Hello TensorFlow” tensorflow/tensorflow:nightly-jupyter 4. Start a TensorFlow Docker container $ docker run -it -p 8888:8888 -v $(notebook-examples-path):/tf/notebooks tensorflow/tensorflow:nightly-jupyter “Hello TensorFlow”

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738 16 附录：深度学习工具 741 16.1 使用Jupyter Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 xiv 16 安装库以运行代码 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757 16.3.4 远程运行Jupyter笔记本 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757 16.3.5 关闭未使用的实例 . . . 载的PDF访问，也可以作为网站在互联网上访问。目前还没有完全适合这些需求的工具和工作流程，所以我 们不得不自行组装。我们在 16.5节 中详细描述了我们的方法。我们选择GitHub来共享源代码并允许编辑，选 择Jupyter记事本来混合代码、公式和文本，选择Sphinx作为渲染引擎来生成多个输出，并为论坛提供讨论。 虽然我们的体系尚不完善，但这些选择在相互冲突的问题之间提供了一个很好的妥协。我们相信，这可能是 第一本使用这种集成工作流程出版的书。

the gzip python module for demonstrating compression. The code for this exercise is available as a Jupyter notebook here. %%capture import gzip import operator, random import numpy as np import tensorflow to the original segmentation project in chapter four. The code for this project is available as a Jupyter notebook here. def create_model_for_pruning(m, prunables, info=True): def apply_pruning_to_conv_blocks(block): k-means clustering with a real example. The code for the next few exercises is available here as a Jupyter notebook. Using clustering to compress a 1-D tensor. Let us first implement the Within-Cluster-Sum-of-Squares

?) = ?????(?, ?) (3) ???(?1 + ?2, ?) = ???(?1, ?) + ???(?2, ?) 50 Python 的环境的安装 ⚫Anaconda ⚫Jupyter notebook ⚫Pycharm 详细教程：https://zhuanlan.zhihu.com/p/59027692 3. 机器学习的背景知识-Python基础 51 Python com/distribution/ 通常选3.7版本，64位 可以用默认安装，右图两个选择框都勾上 52 Python 的环境的安装 ⚫Jupyter notebook 在cmd环境下，切换到代码的 目录，输入命令： jupyter notebook之后就可以 启动jupyter botebook编辑器 ，启动之后会自动打开浏览器 ，并访问http://localhost:8088 ，默认跳转到 http

?) = ?????(?, ?) (3) ???(?1 + ?2, ?) = ???(?1, ?) + ???(?2, ?) 51 Python 的环境的安装 ⚫Anaconda ⚫Jupyter notebook ⚫Pycharm 详细教程：https://zhuanlan.zhihu.com/p/59027692 3. 机器学习的背景知识-Python基础 52 Python com/distribution/ 通常选64位 可以用默认安装，右图两个选择框都勾上 53 Python 的环境的安装 ⚫Jupyter notebook 在cmd环境下，切换到代码的 目录，输入命令： jupyter notebook之后就可以 启动jupyter botebook编辑器 ，启动之后会自动打开浏览器 ，并访问http://localhost:8088 ，默认跳转到 http

Argo • Serve trained models for inference with TF Serving • Rapid prototyping with self-service Jupyter notebook from JupyterHub Simplified ML Workflow/Pipeline What is DevOps? • “A cross-disciplinary config • Tensorflow, PyTorch, MXNet, Chainer, and more • JupyterHub to create and manage interactive Jupyter notebooks • Model serving – serve exported models with TF Serving or Seldon • Additional components cost Demo: Create End to End ML Pipelines with Argo Demo: Rapid prototyping with self-service Jupyter notebook from JupyterHub What’s Next? one) Solution is Kubernetes: • Highly Scalable • Easy to

GPU support) • Raspbian 9.0 or later 使用 pip3 安装 TensorFlow 2 在 Jupyter Lab 中使用 TensorFlow 2 在 Jupyter Lab 中使用 TensorFlow 2 在 Jupyter Lab 中使用 TensorFlow 2 Docker 容器 与 虚拟机 虚拟机 Docker 容器 在 Docker 中使用

repository. The code examples for all the projects are available in the repository in the form of Jupyter notebooks. You can run the notebooks in Google’s Colab environment which provides free access to to CPU, GPU, and TPU resources. You can also run this locally on your machine using the Jupyter framework or with other cloud services. The solution to this specific exercise is in this notebook. Solution: dataset. Loading and Processing the MNIST Dataset Before we start, the code is available as a Jupyter notebook here. Now let’s take a look at the load_data() function in the following code snippet.