‣ 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738 16 附录：深度学习工具 741 16.1 使用Jupyter Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 xiv 16.1.1 运行和停止实例 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 16.2.4 更新Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 16.3 使用Amazon EC2实例 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 . . .

s_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”

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 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 的环境的安装 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 的环境的安装 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

Wikipedia. Our goal is to classify a given piece of text into one of the fourteen categories. The Jupyter notebook is available here for you to play with. We have already downloaded the dataset in the dbpedia_csv params: 0 We will spare you the training logs here, but you are welcome to inspect them in the notebook directly. Figure 4-12 shows that the CNN models perform better than BOW as they benefit from the compare their training efficiency and quality metrics. As always, the code is available as a Jupyter notebook here for you to experiment. Let’s get started with loading the dataset. import tensorflow

use a pre-trained ResNet50 model and fine tune it. The code for this project is available as a Jupyter notebook here. Tensorflow provides easy access to this dataset through the tensorflow-datasets package sentence. We will discuss some of them in detail in this section. The code is available here as a Jupyter notebook for you to experiment. The following code snippet sets up the modules, functions and variables Project: Distillation of a Speech Model. The code for this project is available here as a Jupyter notebook. Let’s start off with doing the regular imports: import numpy as np import tensorflow_datasets

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 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: With the logistics out of the way, 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. It uses