Measuring Woody: The Size of Debian 3.0∗ Juan Jos´e Amor, Gregorio Robles, and Jes´us M. Gonz´alez-Barahona December 2004 Keywords libre software, Debian, GNU/Linux, libre software engineering, lines (almost twice than Red Hat 9, released about 8 months later), showing that the Debian development model (based on the work of a large group of voluntary developers spread around the world) is at least as or Microsoft) to manage distributions of this size. It is also shown that if Debian had been developed using traditional proprietary methods, the COCOMO model estimates that its cost would be close to $6

rights reserved. Presenter: Haichen Shen, Yao Wang Amazon SageMaker Neo, Deep Engine Science Dynamic Model in TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Models with with dynamism ● Control flow (if, loop, etc) ● Dynamic shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control models© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Support dynamic model in TVM ● Support Any-dim in typing ● Use shape function to compute the type at runtime ● Virtual

Model and Operate Datacenter by Kubernetes at eBay 辛肖刚, Cloud Engineering Manager, ebay 梅岑恺, Senior Operation Manager, ebay Agenda About ebay Our fleet Kubernetes makes magic at ebay Model + Controller Controller How we model our datacenter Operation in large scale Q&A About ebay 177M Active buyers worldwide $22.7B Amount of eBay Inc. GMV $2.6B Reported revenue 62% International revenue 1.1B Kubernetes Onboard Provision Configuration Kubernetes You need onboard something from nothing! Let’s model a datacenter running Kubernetes Onboard Provision Configuration Kubernetes After you define your

std::ranges::transform_view(range, add_two); Ranges LibraryRanges Library - Have begin() and end() - Often have size() - Random access: access any element at random in constant time - Contiguous: a contiguous block algorithm(1.0f, 3, data); // Send data to proc. 1 MPI_Send(values.data(), values.size(), MPI_FLOAT, 1, 0, MPI_COMM_WORLD); // Data is now sent. Process 0 Process 1 algorithm(1.0f, 3, data); // Send data to proc. 1 MPI_Send(values.data(), values.size(), MPI_FLOAT, 1, 0, MPI_COMM_WORLD); // Data is now sent. // Allocate space for

The Future of Cloud Native Applications with Open Application Model (OAM) and Dapr @markrussinovich Application models Describes the topology of your application and its components The way developers services and data stores Programming models Distributed Application Runtime (Dapr) Open Application Model (OAM) https://oam.dev State of Cloud Native Application Platforms Kubernetes for applications can maintain the size of our team." —CTO @ Handled Cloud + Edge Separation of concerns Application focused Application focused Container infrastructure Open Application Model Service Job Namespace

Memory Model C++11 – C++23About Me: alex.dathskovsky@speedata.io www.linkedin.com/in/alexdathskovsky https://www.cppnext.comAlex Dathskovsky | alex.dathskovsky@speedata.io | www.linkedin.com/in/a

Efficient Mixture-of-Experts Language Model DeepSeek-AI research@deepseek.com Abstract We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models. The model checkpoints are available at h t t p s : / / g i t h u b . c o m / d e e p s e e k - a i / D e e p Work 21 A Contributions and Acknowledgments 27 B DeepSeek-V2-Lite: A 16B Model Equipped with MLA and DeepSeekMoE 29 2 B.1 Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

49 4.3.1 Model 类 API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.2 Model 的实用属性 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.3 Model 类模型方法 . . . . . . . . . . . . . . . 239 20.8 plot_model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 20.9 multi_gpu_model . . . . . . . . . . . . . . . . . . . . . . Keras 的核心数据结构是 model，一种组织网络层的方式。最简单的模型是 Sequential 顺 序模型，它是由多个网络层线性堆叠的栈。对于更复杂的结构，你应该使用 Keras 函数式 API， 它允许构建任意的神经网络图。 Sequential 顺序模型如下所示： from keras.models import Sequential model = Sequential()

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 methods of picking the connections and nodes to prune, 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 learn about these techniques together! Model Compression Using Sparsity Sparsity or Pruning refers to the technique of removing (pruning) weights during the model training to achieve smaller models. Such

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