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

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

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

location of the master # Default is localhost:6379 # #masterAddress=localhost:6379 # # For Sentinel model, define the name of the master # Default is myMaster # #masterName=myMaster # # For Cluster mode string to pass to the server when making requests # client.id # The compression type for all data generated by the producer # compression.type=none # The number of acknowledgments the producer requires the client on the Kafka broker. • the compression.type defines if the messages have to be compressed on the Kafka broker. Default value is none meaning no compression. • the acks defines the acknowledgement

Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 13.3.1 The Security Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 13.3.2 Secure Configuration of Virtual that it is running on Apple hardware. Most DVDs that accompany Apple hardware check for the exact model. These restrictions are not circumvented by Oracle VM VirtualBox and continue to apply. • Only CPUs If you fill in all fields, on the other hand, the filter will only apply to a particular device model from a particular vendor, and not even to other devices of the same type with a different revision

........................................................................ 508 13.3.1 The Security Model.................................................................................................. that it is running on Apple hardware. Most DVDs that accompany Apple hardware check for the exact model. These restrictions are not circumvented by Oracle VirtualBox and continue to apply. • Only CPUs that If you fill in all fields, on the other hand, the filter will only apply to a particular device model from a particular vendor, and not even to other devices of the same type with a different revision

sqlite SciPy 0.19.0 Miscellaneous statistical functions XLsxWriter 0.9.8 Excel writing blosc Compression for msgpack fastparquet 0.2.1 Parquet reading / writing gcsfs 0.2.2 Google Cloud Storage access on linux xlrd 1.1.0 Excel reading xlwt 1.2.0 Excel writing xsel Clipboard I/O on linux zlib Compression for msgpack Optional dependencies for parsing HTML One of the following combinations of libraries ============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Sat, 02 Nov 2019 Prob