[tile] DB libraries, and large-scale graph analytics. Open-source software projects resulting from his work include the Matrix Template Library, the Boost Graph Library and Open MPI. 。Phil Ratzloff *。Distinguished relationships between elements of a data set *，Without regard to what the data set actually is *。 Graph theoretical (abstract) results can be applied to many different practical (concrete) problems -theory cvMuNKCATIOWS 有二有 灿瑟必帮 和一是/第 手太则光大 A，训四 昱 站 -本| 厅 是，主 半一司、|全之体 ~ 对 /人-看 奋才全 和 全原 和 刁曾 The Future ls Big Graph: on meetalThngs soneaiadman MD] Basic Principles *。The C++ standard library (nee STL) provides

Selection ✓ Order by ❏ Group by ✓ Query ✓ Limit ✓ Expression Compatibility ✓ Join ✓ Subquery ✓ Graph traverse ✓ Out direction ✓ In direction ✓ Bidirection ✓ Edge traverse ✓ Distinct Index 01.

Scott, Principal Engineer at CMU SEI Graph/ML/AI algorithms for large- and small- scale parallel systems. Working on GBTL, a linear algebra-based C++ library for graph analytics.[DISTRIBUTION STATEMENT GraphBLAS community, C API Overview of our draft C++ API How might this interoperate with standard C++, graph library proposal? 4[DISTRIBUTION STATEMENT A] This material has been approved for public release GraphBLAS community, C API Overview of our draft C++ API How might this interoperate with standard C++, graph library proposal? 5[DISTRIBUTION STATEMENT A] This material has been approved for public release

Using the Microsoft Graph API to get Office 365 data in your mobile apps Alex Ziskind Technical Director @digitalix www.nuvious.com Hi, I’m Alex From + 750 Million 50 Million per month Problems

top-down task graph What is Task Graph-based Programming System (TGPS) Code 4• TGPS encapsulates function calls and their dependencies in a top-down task graph What is Task Graph-based Programming task graph What is Task Graph-based Programming System (TGPS) Code A B C D B A C D 6• TGPS encapsulates function calls and their dependencies in a top-down task graph What is Task Graph-based precede(task_d); 19 20 sched.schedule(); 21 sched.wait(); B A C D 1. Easy to write and express a task graph 2. Allow to implement irregular parallel decomposition strategies 8Existing TGPSs on Heterogenous

(Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/28: Graph Streaming ??? Vasiliki Kalavri | Boston University 2020 Modeling the world as a graph 2 Social networks friend follows The web Actor-movie results for the search term “graph” ??? Vasiliki Kalavri | Boston University 2020 Basics 1 5 4 3 2 “node” or “vertex” “edge” 1 5 4 3 2 undirected graph directed graph 4 ??? Vasiliki Kalavri Kalavri | Boston University 2020 Graph streams Graph streams model interactions as events that update an underlying graph structure 5 Edge events: A purchase, a movie rating, a like on an online post

All rights reserved. Amazon Trademark Deploy GluonCV Models GluonCV Models MXNet Computational Graph Json Acyclic Graph Export As-is Optimize with TVM© 2019, Amazon Web Services, Inc. or its Affiliates its Affiliates. All rights reserved. Amazon Trademark Like GluonCV? Go build! https://gluon-cv.mxnet.io https://github.com/dmlc/gluon-cv© 2019, Amazon Web Services, Inc. or its Affiliates. All rights

methodologies. Deep learning is supported by various libraries such as Theano, TensorFlow, Caffe, Mxnet etc., Keras is one of the most powerful and easy to use python library, which is built on top of one. TensorFlow TensorFlow is an open source machine learning library used for numerical computational tasks developed by Google. Keras is a high level API built on top of TensorFlow or Theano. We three important layers:  Convolution layer: It is the primary building block and perform computational tasks based on convolution function.  Pooling layer: It is arranged next to convolution layer

can run any models Your compiler (TVM) supports multiple frontends (e.g., TensorFlow, PyTorch, MXNet) Non Maximum Suppression ResNet-50 Dense Your Chip Your Chip© 2019, Amazon Web Services, Inc System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Mark supported operators or subgraphs 1. Implement an operator-level annotator, OR 2. Implement a graph-level annotator© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Option 1:

再将它载入另一种后端中（比 如为了发布）。支持的后端有： • 谷歌的 TensorFlow 后端 • 微软的 CNTK 后端 • Theano 后端 亚马逊也正在为 Keras 开发 MXNet 后端。 如此一来，你的 Keras 模型可以在 CPU 之外的不同硬件平台上训练： • NVIDIA GPU。 • Google TPU，通过 TensorFlow 后端和 Google Cloud。 Keras 的开发主要由谷歌支持，Keras API 以 tf.keras 的形式包装在 TensorFlow 中。此外， 微软维护着 Keras 的 CNTK 后端。亚马逊 AWS 正在开发 MXNet 支持。其他提供支持的公司包 括 NVIDIA、优步、苹果（通过 CoreML）等。 快速开始 8 3 快速开始 3.1 Sequential 顺序模型指引 3.1.1 开始使用 Keras tensorflow.org/api_docs/python/tf/set_random_seed tf.set_random_seed(1234) sess = tf.Session(graph=tf.get_default_graph(), config=session_conf) K.set_session(sess) # 剩余代码 ... 3.3.21 如何在 Keras 中安装 HDF5 或 h5py