experience. In the container, see /workspace/README.md for information about customizing your PyTorch image. For more information about PyTorch, including tutorials, documentation, and examples, see: ‣ PyTorch container registry: ‣ Install Docker. ‣ For NVIDIA DGX™ users, see Preparing to use NVIDIA Containers Getting Started Guide. ‣ For non-DGX users, see NVIDIA ® GPU Cloud ™ (NGC) container registry installation documentation based on your platform. ‣ Ensure that you have access and can log in to the NGC container registry. Refer to NGC Getting Started Guide for more information. The deep learning frameworks, the NGC

(continues on next page) 目录 5 (continued from previous page) import torchvision from PIL import Image from torch import nn from torch.nn import functional as F from torch.utils import data from torchvision '__spec__', 'bernoulli', 'beta', 'biject_to', 'binomial', 'categorical', 'cauchy', 'chi2', �→'constraint_registry', 'constraints', 'continuous_bernoulli', 'dirichlet', 'distribution', 'exp_family �→', 'exponential' ImageNet数据集发布，并发起ImageNet挑战赛：要求研究人员从100万个样本中训练模型，以区分1000个不同 类别的对象。ImageNet数据集由斯坦福教授李飞飞小组的研究人员开发，利用谷歌图像搜索（Google Image Search）对每一类图像进行预筛选，并利用亚马逊众包（Amazon Mechanical Turk）来标注每张图片的相关 类别。这种规模是前所未有的。这项被称为ImageNet的挑战赛推动了计算机视觉和机器学习研究的发展，挑

code for this exercise is available as a Jupyter notebook here. %%capture import gzip import operator, random import numpy as np import tensorflow as tf from functools import reduce from matplotlib out. sparse_weights = sparsify_smallest(weights, sparsity_rate) print('Original Size:', reduce(operator.mul, weights.shape)*weights.itemsize) weights_compressed = compress_and_save(weights) print('Original fully-connected, convolutional layers and so on. 20 "Matrix Compression Operator." 17 July 2022, blog.tensorflow.org/2020/02/matrix-compression-operator-tensorflow.html. 19 X. Yu, T. Liu, X. Wang and D. Tao, "On

核心架构层 算法模型层 4 深度学习-分布式模型推理 • 推理性能优化 • 减少计算量： operator fusion/XLA/TVM/prune/float16/quantization • 加快计算速度： batching/TensorRT/MPS/SSE/AVX/Neon • operator fusion • 针对特定场景重写耗时算子 • 重构tensorflow计算引擎 •

处理特殊输入，如模糊、黑白照片 - 适配具有不同特征的数据源 - 在严肃应用中，客户追求100%准确率，算法性能提升永无止境 • 深度学习模型需要在准确率和速度上做均衡 - 使用更加精巧的模型和Operator设计 - 使用模型压缩算法，在基本保障准确率的情况下大幅提升速度 - 利用最新的硬件特性，如GPU TensorCore/int8 *示意图来自互联网 Kubernetes在异构系统调度中的挑战

58(1): 267–288. [7] TIBSHIRANI R, BICKEL P, RITOV Y, et al. Least absolute shrinkage and selection operator[J]. Software: http://www.stat.stanford.edu/ tibs/lasso.html, 1996. 33 谢 谢！

target label is a composite of the inputs that were combined. A combination of a dog with a hamster image (figure 3-5) is assigned a composite [dog, hamster] label! 2 A whale’s tail fins are called flukes problems. Figure 3-5: A mixed composite of a dog (30%) and a hamster (70%). The label assigned to this image is a composite of the two classes in the same proportion. Thus, the model would be expected to predict a dataset Nx the size? What are the constraining factors? An image transformation recomputes the pixel values. The rotation of an RGB image of 100x100 requires at least 100x100x3 (3 channels) computations

in data science fields like robotics, artificial intelligence(AI), audio & video recognition and image recognition. Artificial neural network is the core of deep learning methodologies. Deep learning keras/keras.json. keras.json { "image_data_format": "channels_last", "epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow" } Here,  image_data_format represent the data format just change the backend = theano in keras.json file. It is described below: keras.json { "image_data_format": "channels_last", "epsilon": 1e-07, "floatx": "float32", "backend": "theano"

high quality image of a cat. The cat on the right is a lower quality compressed image. Source Both the cat images in figure 2-2 might serve their purpose equally well, but the compressed image is an order smaller. Discrete Cosine Transform (DCT), is a popular algorithm which is used in the JPEG format for image compression, and the MP3 format for audio. DCT breaks down the given input data into independent components transmitting images back to earth. However, transmission costs make it infeasible to send the original image. Can we compress the transmission, and decompress it on arrival? If so, what would be the ideal tradeoff

K-Means November 27, 2018 1 Description In this exercise, you will use K-means to compress an image by reducing the number of colors it contains. To begin, download data6.zip and unpack its contents to Frank Wouters and is used with his permission. 2 Image Representation The data pack for this exercise contains a 538-pixel by 538-pixel TIFF image named bird large.tiff. It looks like the picture below below. In a straightforward 24-bit color representation of this image, each pixel is represented as three 8-bit numbers (ranging from 0 to 255) that specify red, green and blue intensity values. Our bird