8-bit floating point (FP8) precision on Hopper GPUs which provides better training and inference performance with lower memory utilization. Transformer Engine also includes a collection of highly optimized Core Examples The tensor core examples provided in GitHub and NGC focus on achieving the best performance and convergence from NVIDIA Volta™ tensor cores by using the latest deep learning example networks This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. ‣ ResNeXt101-32x4d model: This model was introduced in the Aggregated Residual Transformations

the footprint of the model (size, latency, etc). And as we have described earlier, some of these improved quality metrics can be traded off for a smaller footprint as desired. Continuing with the theme a new task: 1. Data Efficiency: It relies heavily on labeled data, and hence achieving a high performance on a new task requires a large number of labels. 2. Compute Efficiency: Training for new tasks likely wasteful. Regarding the first limitation, we know that model quality can usually be naively improved by acquiring more labels (though the rate of improvement eventually plateaus). However, acquiring

more places you'll go.” ― Dr. Seuss Model quality is an important benchmark to evaluate the performance of a deep learning model. A language translation application that uses a low quality model would samples including repeats seen by the model to reach the desired performance threshold (in terms of accuracy, precision, recall or other performance metrics). We designate a new model training setup to be more more sample efficient, if it achieves similar or better performance with fewer data samples when compared to the baseline. Think of it as teaching a child to recognize common household objects such as a

learning problems relies on the presence of sufficient labeled data. With deep learning models, the performance of the model scaled well with the number of labeled examples, since the network had a large number models also often have billions (or trillions) of parameters. At the same time, the incredible performance of these models also drives the demand for applying them on new tasks which were earlier bottlenecked ● Can the model fit in memory? ● How much data would the model need to achieve the desired performance on the given task that the model is solving? For example, when a model is trained to predict if

sequences and temporal data. These breakthroughs contributed to bigger and bigger models. Although they improved the quality of the solutions, the bigger models posed deployment challenges. What good is a model Naturally, increasing d will increase the quality of the embeddings which might lead to better performance in downstream tasks, but it will also increase the size of the embedding table. Size of the vocabulary the feature hashing or the hashing trick. It helps to reduce the vocabulary with little or no performance trade-off. The core idea of the hashing trick is as follows: 1. Choose the desired vocabulary

的参数更新 k 次再对 G的参数更新 1 次. 2. GAN的理论与实现模型 17 GAN的衍生模型 GAN的理论与实现模型 CGAN EBGAN Info GAN DCGAN Improved GAN WGAN ...... 2. GAN的理论与实现模型 18 GAN的衍生模型 GAN的理论与实现模型 （1）CGAN--条件生成对抗网络，为了防止训练崩塌将前置条件加入输入数据。 GAN的理论与实现模型 生成模型 z ~x X 自然输入 编码 判别模型 解码 均方误差 能量 生成输入 随机噪声 23 GAN的衍生模型 GAN的理论与实现模型 （6） Improved GAN--改进生成式对抗网络，提出了使模型训练稳定的五条 经验。 a.特征匹配（feature matching） b.最小批量判断（minibatch

BradburyJames, ChananGregory, . . . ChintalaSoumith. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. 出处 WallachH., LarochelleH., BeygelzimerA., d\textquotesingle Alch é-BucF Curran Associates, Inc. 检索来源: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep- learning-library.pdf 预览版202112 第4章 PyTorch 基础 我设想在未来，我们可能就相当于机器人的宠物狗， 到那时我也会支持机器人的。−克劳德·香农 Sydney, Australia, 2017. [6] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin 和 A. C. Courville, “Improved Training of Wasserstein GANs,” 出处 Advances in Neural Information Processing Systems 30, I. Guyon

= [2/i for i in timer.times] print(f'performance in Gigaflops: element {gigaflops[0]:.3f}, ' f'column {gigaflops[1]:.3f}, full {gigaflops[2]:.3f}') performance in Gigaflops: element 1.204, column 88 A[:, j:j+64] = torch.mm(B, C[:, j:j+64]) timer.stop() print(f'performance in Gigaflops: block {2 / timer.times[3]:.3f}') performance in Gigaflops: block 2056.535 显而易见，小批量上的计算基本上与完整矩阵一样有效。需要注意的是，在 7 Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: surpassing human‐ level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision

results. For example, between quantization and clustering, which one is preferable? What is the performance impact when both are used together? We have four options: none, quantization, clustering, and both past few years, we have seen newer architectures, techniques and training procedures pushing the performance benchmarks higher. Figure 7-1 shows some of the choices we face when working on a deep learning process of learning are called hyperparameters to differentiate them from model parameters. The performance of deep learning relies on a set of good hyperparameters. Some of the commonly tuned hyperparameters

optimally prune the network connections, remove extraneous nodes, etc. while retaining the model’s performance? In this chapter we introduce the intuition behind sparsity, different possible methods of picking 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 using clustering. Weight sharing, and in as 50% of the connections (weights) from a large network could be safely removed with minimal performance deterioration. A random removal could work for removing a few weights. However, when pruning a