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

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

graph directed graph 4 ??? Vasiliki Kalavri | Boston University 2020 Graph streams Graph streams model interactions as events that update an underlying graph structure 5 Edge events: A purchase 2020 8 Some algorithms model graph streams a sequence of vertex events. A vertex stream consists of events that contain a vertex and all of its neighbors. Although this model can enable a theoretical theoretical analysis of streaming algorithms, it cannot adequately model real-world unbounded streams, as the neighbors cannot be known in advance. Vertex streams (not today) ??? Vasiliki Kalavri | Boston

--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™,

........................................................................................... 17 Model ................................................................................................. ............................................................................... 58 10. Keras ― Model Compilation ..................................................................................... ..... 61 Compile the model ........................................................................................................................................ 62 Model Training ..............

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

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

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()

Qwen Team 2024 年 05 月 11 日 快速开始 1 文档 3 i ii Qwen Qwen is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Now the large language models have been upgraded AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-7B-Chat") # Instead of using model.chat(), we directly use model.generate() # But you need to use tokenizer.apply_chat_template() to format your inputs

compression techniques. Compression techniques aim to reduce the model footprint (size, latency, memory etc.). We can reduce the model footprint by reducing the number of trainable parameters. However 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 In this chapter, we introduce Quantization, a model compression technique that addresses both these issues. We’ll start with a gentle introduction to the idea of compression. Details of quantization and