Chapter 2 - Compression Techniques “I have made this longer than usual because I have not had time to make it shorter.” Blaise Pascal In the last chapter, we discussed a few ideas to improve the deep elaborate on one of those ideas, the compression techniques. Compression techniques aim to reduce the model footprint (size, latency, memory etc.). We can reduce the model footprint by reducing the number requires many trials and evaluations to reach a smaller model, if it is at all possible. Second, such an approach doesn’t generalize well because the model designs are subjective to the specific problem. In

Advanced Compression Techniques “The problem is that we attempt to solve the simplest questions cleverly, thereby rendering them unusually complex. One should seek the simple solution.” — Anton Pavlovich Pavlovich Chekhov In this chapter, we will discuss two advanced compression techniques. By ‘advanced’ we mean that these techniques are slightly more involved than quantization (as discussed in the second Can we 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

efficiency in deep learning models. We will also introduce core areas of efficiency techniques (compression techniques, learning techniques, automation, efficient models & layers, infrastructure). Our hope learning algorithms help build models, which as the name suggests is an approximate mathematical model of what outputs correspond to a given input. To illustrate, when you visit Netflix’s homepage, the might be popular with other users too. If we train a model to predict the probability based on your behavior and currently trending content, the model will assign a high probability to Seinfeld. While there

in ANALOG magazine (1991) So far, we have discussed generic techniques which are agnostic to the model architecture. These techniques can be applied in NLP, vision, speech or other domains. However, owing challenges. What good is a model that cannot be deployed in practical applications! Efficient Architectures aim to improve model deployability by proposing novel ways to reduce model footprint and improve running on mobile and edge devices. We have also set up a couple of programming projects for a hands-on model optimization experience using these efficient layers and architectures. Let’s start our journey with

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 struggle with consumer effectively with others who speak different languages. An application that employs a high quality model with a reasonable translation accuracy would garner better consumer support. In this chapter, our picked to benchmark learning techniques. It is followed by a short discussion on exchanging model quality and model footprint. An in-depth discussion of data augmentation and distillation follows right after

Communication for Distributed Deep Learning: Survey and Quantitative Evaluation [ICLR2018]Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Dense参数，每次 都⽤，快速收敛 Sparse参数，随数 Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems Twiiter [RecSys21] Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems 9 千 万 key hash1(key) Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce 端上 重排 场景1 场景X [CIKM2021] One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction � 预训练模型Bert GPT-3在CV/NLP⼤⾏其道，

Visualization: https://projector.tensorflow.org/ ▪ Taking advantages of unsupervised data ▪ Compression, denoising, super-resolution … Auto-Encoders https://towardsdatascience.com/applied-deep-le https://github.com/cryer/Variational_Auto- Encoder/blob/master/image/reconst_images_7.png Generative model https://jmetzen.github.io/2015-11-27/vae.html VAE V.S. GAN https://medium.com/@wuga/generate-a

参考文献 第 15 章 自定义数据集 15.1 精灵宝可梦数据集 15.2 自定义数据集加载流程 15.3 宝可梦数据集实战 15.4 迁移学习 15.5 Saved_model 15.6 模型部署 15.7 参考文献 预览版202112 人工智能绪论 我们需要的是一台可以从经验中学习的机器。 −阿兰·图灵 1.1 容器可以非常方便地搭建多层的网络。对于 3 层网络，我们可以通过快速 完成 3 层网络的搭建。 # 利用 Sequential 容器封装 3 个网络层，前网络层的输出默认作为下一层的输入 model = nn.Sequential( # 创建第一层，输入为 784，输出为 256 nn.Linear(28*28, 256), nn.ReLU(), # 激活函数 ) 第 1 层的输出节点数设计为 256，第 2 层设计为 128，输出层节点数设计为 10。直接调用 这个模型对象 model(x)就可以返回模型最后一层的输出?。 3.8.2 模型训练 搭建完成 3 层神经网络的对象后，给定输入?，调用 model(?)得到模型输出?后，通过 F.mse_loss 损失函数计算当前的误差ℒ： # 创建优化器，并传递需要优化的参数列表：[w1

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--shm-size= in the command line to docker run --gpus all To pull data and model descriptions from locations outside the container for use by PyTorch or save results to locations and 2X reduced memory storage for intermediates (reducing the overall memory consumption of your model). Additionally, GEMMs and convolutions with FP16 inputs can run on Tensor Cores, which provide an NVIDIA Volta™ tensor cores by using the latest deep learning example networks and model scripts for training. Each example model trains with mixed precision Tensor Cores on NVIDIA Volta and NVIDIA Turing™,