Knowledge Graph meet Python Yongpan Sheng 目录 CONTENTS The Pipeline of Knowledge Graph Construction by Data- driven manner Python Tools for Graph Data Management Domain-specific Knowledge Graph Construction relation, object>  Mapping from natural questions to structured queries executable on knowledge graph （机器的潜台词：“我”会推理，so easy ！）。 所以，通俗的来说，在AI system中：要么从原有的知识体系中直接提取信息来使用，要 么进行推理。 将知识融合在机器中，使机器能够利 BigKE将显著提升机器的认知水平。 Preliminaries 本页PPT借鉴于复旦大学肖仰华老师《大数据时代的知识工程与知识管理》 Knowledge Graph – KG引领KE复兴  Knowledge graph is a large-scale semantic network consisting of entities and concepts as well as

tf.float16) 低精度浮点数的优点 1.节约内存/显存的使用（FP16为原来的1/2，int8为原来的1/4） 2.特殊的硬件专门用于低精度浮点数的计算加速（TensorCore） Model Speedup BERT Q&A 3.3X speedup GNMT 1.7X speedup NCF 2.6X speedup ResNet-50-v1.5 3.3X speedup tf.train.AdamOptimizer() # Modify optimizer in graph, copy between fp32 weight and fp16 weight opt = tf.train.experimental.enable_mixed_precision_graph_rewrite(opt) train_op = opt.miminize(loss) AMP的白名单/灰名单/黑名单 input_saved_model_dir=input_saved_model_dir) converter.convert() converter.save(output_saved_model_dir) with tf.Session() as sess: # First load the SavedModel into the session tf.saved_model.loader.load(

Contents: Installing and Testing Installing with git Running tests Optional dependencies Quickstart Model Definition Storing data Retrieving Data Closing the database Working with existing databases What example Diving into the code More examples Contributing Patches Bugs Questions Query Examples Model Definitions Schema Creation Basic Exercises Joins and Subqueries Modifying Data Aggregation Recursion Database Errors Logging queries Adding a new Database Driver Models and Fields Fields Creating model tables Model options and table metadata Indexes and Constraints Non-integer Primary Keys, Composite Keys

Installing with git Running tests Optional dependencies Skip Compilation of SQLite Extensions Quickstart Model Definition Storing data Retrieving Data Closing the database Working with existing databases What Database Errors Logging queries Adding a new Database Driver Models and Fields Fields Creating model tables Model options and table metadata Indexes and Constraints Non-integer Primary Keys, Composite Keys Helper Security and SQL Injection API Documentation Database Query-builder Fields Schema Manager Model Query-builder Internals Constants and Helpers SQLite Extensions Getting started APIs Additional

Installing with git Running tests Optional dependencies Skip Compilation of SQLite Extensions Quickstart Model Definition Storing data Retrieving Data Closing the database Working with existing databases What example Diving into the code More examples Contributing Patches Bugs Questions Query Examples Model Definitions Schema Creation Basic Exercises Joins and Subqueries Modifying Data Aggregation Recursion queries Adding a new Database Driver Models and Fields Fields Field-naming conflicts Creating model tables Model options and table metadata Indexes and Constraints Non-integer Primary Keys, Composite Keys

document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: • Model Definition 4 Chapter 1. Contents: peewee Documentation, Release 3.5.0 • Storing data • Retrieving 1.2.1 Model Definition Model classes, fields and model instances all map to database concepts: Object Corresponds to. . . Model class Database table Field instance Column on a table Model instance typically best to begin with your data model, by defining one or more Model classes: from peewee import * db = SqliteDatabase('people.db') class Person(Model): name = CharField() birthday = DateField()

document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: • Model Definition 4 Chapter 1. Contents: peewee Documentation, Release 3.4.0 • Storing data • Retrieving 1.2.1 Model Definition Model classes, fields and model instances all map to database concepts: Object Corresponds to. . . Model class Database table Field instance Column on a table Model instance typically best to begin with your data model, by defining one or more Model classes: from peewee import * db = SqliteDatabase('people.db') class Person(Model): name = CharField() birthday = DateField()

document presents a brief, high-level overview of Peewee’s primary features. This guide will cover: • Model Definition 4 Chapter 1. Contents: peewee Documentation, Release 3.3.0 • Storing data • Retrieving 1.2.1 Model Definition Model classes, fields and model instances all map to database concepts: Object Corresponds to. . . Model class Database table Field instance Column on a table Model instance typically best to begin with your data model, by defining one or more Model classes: from peewee import * db = SqliteDatabase('people.db') class Person(Model): name = CharField() birthday = DateField()

Contents: Installing and Testing Installing with git Running tests Optional dependencies Quickstart Model Definition Storing data Retrieving Data Closing the database Working with existing databases What example Diving into the code More examples Contributing Patches Bugs Questions Query Examples Model Definitions Schema Creation Basic Exercises Joins and Subqueries Modifying Data Aggregation Recursion Database Errors Logging queries Adding a new Database Driver Models and Fields Fields Creating model tables Model options and table metadata Indexes and Constraints Non-integer Primary Keys, Composite Keys

additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to then it must be the tasks responsibility to assert that, not the callers. Another gotcha is Django model objects. They shouldn’t be passed on as arguments to tasks. It’s almost always better to re-fetch an article and a task that automatically expands some abbreviations in it: class Article(models.Model): title = models.CharField() body = models.TextField() @app.task def expand_abbreviations(article):