‣ 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

• 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 community 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 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 explore

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

the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be: proc print data=df(obs=5); run; 3.5. Comparison the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) – the equivalent in Stata would be: list in 1/5 Data structures General terminology analysis in Python with pandas (2016-2018) GitHub repo and Jupyter Notebook • Best practices with pandas (2018) GitHub repo and Jupyter Notebook Various tutorials • Wes McKinney’s (pandas BDFL) blog