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

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 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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 ..............

md5 = think.md5('think_' + data.pwd); //���������������������� let result = await this.model('user').where({name: data.name, pwd: md5}).find(); //���������������������� if(think.isEmpty(result)){ js | | `-- index.js | |-- logic | | `-- doc.js | `-- model |-- view | `-- zh-CN | |-- common | | |-- error_400.html | //auto render template file index_index.html return this.display(); } } JavaScript src/home/model view www www/development.js ����������� ������ Windows �� Mac OSX �������������� Linux ����