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 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 input_name = "data" input_shape = [tvm.relay.Any(), 3, 224, 224] dtype = "float32" block = get_model('resnet50_v1', pretrained=True) mod, params = relay.frontend.from_mxnet(block, shape={input_name:

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

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

== 3 9 . 4/ String Compression Lets make a compressed string table https://github.com/AshleyRoll/squeeze map from enum Key to Compressed String Hu�man Coding for compression Output struct: Mapping choose an arbitrary amount of work they will allow in constexpr context Complex processing like compression will hit the limits Had to make more complex implementation to cache bit streams rather than walk

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

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 of concerns Application focused Application focused Container infrastructure Open Application Model Service Job Namespace Secret Volume Endpoint ConfigMap VolumeAttach CronJob Deployment

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 Standard Multi-Head Attention . . . . . . . . . . . . . . . . 6 2.1.2 Low-Rank Key-Value Joint Compression . . . . . . . . . . . . . . . . . . . 7 2.1.3 Decoupled Rotary Position Embedding . . . . . .

About the Portfolio Demo Use Case 6 Running the Demo Web Application 6 Exploring the Sample Data Model 7 Looking at the Schema Definitions in Cassandra-CLI 8 DataStax Community Release Notes 8 What's Releases of Cassandra 1.0.x 45 Understanding the Cassandra Data Model 45 The Cassandra Data Model 45 Comparing the Cassandra Data Model to a Relational Database 45 About Keyspaces 47 Defining Keyspaces 50 About Validators 51 About Comparators 51 About Column Family Compression 52 When to Use Compression 52 Configuring Compression on a Column Family 52 About Indexes in Cassandra 52 About Primary