为Python开发者带来微软的开发工具Visual Studio Code和云平台Azure上的极致开发体验。

Best development experience with VS code and Cloud platform Azure for Python developer.

探索当前Python的一些非常好用的特性，看看如何把它们和FastAPI和friends结合使用。
我们会用最佳实践的方式，去看到如何从高效简洁、少错易用等方面的角度去编写程序。其中会涉及到一些类型注释（type hints）, async/await (异步编程)的内容，同时也包括了对FastAPI、Typer等的概述。
这个演讲对参与者的技术背景没有很高门槛要求，只需要基本的Python知识就足够了。

Explore some of the great features from modern Python and how to use them with FastAPI and friends.
We will see how to make code that is less error-prone, simpler, more efficient, and have a great developer experience, all at the same time.
And all this while including best practices by default.
We will see a bit about type annotations (type hints), async/await, and an overview of FastAPI, Typer, and others.
This talk doesn't expect any technical background, just some basic knowledge of Python.

CDK8S（CDK for Kubernetes）是一个开源框架，可让您使用熟悉的语言定义 Kubernetes 应用程序和组件。在这个环节中，我将向您展示如何在不使用任何 YAML 的情况下，为您的 Kubernetes 工作负载将 k8s 资源作为 Python 中的结构进行组合和定义。我们将首先介绍高级架构，然后再进行 Python 实时编码。
看完这节课，你将学习到一种新的方法来在 Python 中完全控制和定义任何云提供商的 Kubernetes 工作负载。
cdk8s 项目地址：https://cdk8s.io/

CDK8S(CDK for Kubernetes) is an opensource framework that allows you to define Kubernetes apps and components using familiar languages. In this session, I will show you how to compose and define k8s resources as constructs in python for your Kubernetes workload without using any YAML. We will introduce the high level architecture followed by live coding in Python.
After watching this session, you will learn a new practice to fully control and define any Kubernetes workload in Python for any cloud providers.
cdk8s project - https://cdk8s.io/

Python作为一门强类型但弱语法检查的语言，加上其他语言特性，给编程者带来了极大的工程便利性和效率，但有时也增加了不少潜在BUG和失控可能。参考行业其他主流强类型语言的特点（值得一提的是TypedScript的流行），静态类型约束并非绝对枷锁。本主题分享如何用正确姿势使用Python强类型检查扩展，让你的代码开发又快又可控，覆盖常见陷阱、语言扩展、原理、工具（标准库和三方库）和最佳实践等。

As a strongly typed and weakly grammar checked language, while python with its other features has brought developer great efficiency improvement and usage convenience, sometimes it increases the potential bugs and possibility of losing control. Referring the other popular strongly typed languages ( typically the TypedScript ), static typed feature is by no means an absolute shackles.

Here in this presentation, we will see how to take full advantage of the python strongly typed extension and speed up the development while keeping the project under control. The content will cover common pitfalls, language extension, theory, tools as well as best practice.

在分布式世界中，错误无处不在，混沌工程在提高系统弹性方面起着至关重要的作用。通过执行混沌工程实验，我们可以了解系统的弱点并主动解决。我们开发了云原生混沌工程平台 Chaos Mesh，并在内部使用 Chaos Mesh 来提升分布式数据库 TiDB 的健壮性。目前 Chaos Mesh 已加入 CNCF Sandbox 项目，该平台依托于 k8s 基础设施，通过对 pod/container 进行诸如杀节点、IO 错误和延时注入、时间回退、内核分配内存失败等等来进行混沌测试。

本主题将会使用PYTHON，把操作系统中的原生线程完全重写为基于协程的版本。使现有的非异步程序，无论使用何种语言开发，都能在无需修改和重新编译的情况下直接变成协程架构下的异步程序，从而在并发处理能力上取得大幅提升。本主题将会在PYTHON的扩展与嵌入，以及协程与异步编程等方面进行较为深入地探讨。

This topic will use Python to rewrite the native thread in the operating system to it's cooperative version. This allows existing non-asynchronous programs, regardless of the programming language they use, to become coroutine-based asynchronous programs without any modifications, and makes it possible for programs to handle a large number of clients at the same time. Extension and embedding programming, cooperative multitasking, and asynchronous programming will be covered in this topic.

Analytics Zoo (https://github.com/intel-analytics/analytics-zoo) 是Intel开源的统一的大数据分析和AI平台，能让用户在现有的大数据集群上直接运行分布式TensorFlow, Keras和PyTorch的应用。Project Orca是Analytics Zoo中专门为了帮助用户很容易地扩展Python AI应用开发的一个子项目，做了更high-level的封装，提供了数据并行的接口来支持常用Python数据处理库以及scikit-learn风格的API来支持各种深度学习框架在大数据平台上无缝的分布式训练和推理。Burger King作为Orca的初期客户，已经成功地用Orca构建了一个完整的端到端的推荐系统，将基于Spark的数据处理和基于MXNet和Ray的分布式训练集成到一个统一的流水线中，直接运行在存储数据的同一个大数据集群上。本次分享主要给大家详细介绍Analytics Zoo和Project Orca，以及Burger King利用Orca实现落地的具体案例。

Python框架的安全性已经日益强大,例如SQL注入,XSS等常规漏洞已经在框架底层实现过程得到了很好的防御,往往我们只需要按照正常的开发以及安全设备就完全可以避免这些常规漏洞,但是业务逻辑漏洞是框架无法进行修复,安全设备无法防御的东西,因为业务逻辑漏洞取决于开发者对于业务的理解,那么哪些业务容易遭到黑客攻击,如何防御已经是是一个开发人员必须去理解的东西。

So what on earth is a reptile? How to write a crawler from scratch? What tools can be used to help us quickly analyze network requests? Which Python libraries can improve the efficiency of our development?

Python 作为一门功能强大的编程语言, 被广泛的应用于计算机行业之中; 在微服务系统架构盛行的今天, Python 以其丰富的软件生态和灵活的语言特性在服务端编程领域也占有重要的一席之地;
本次分享将阐述 Apache SkyWalking 在微服务架构中要解决的问题, 展示如何使用 Apache SkyWalking 来近乎自动化地监控 Python 后端应用服务, 并对 Apache SkyWalking 的 Python 语言探针的实现技术进行解读.

As a powerful programming language, Python is widely used in the computer industry; today when the microservice system architecture is prevalent, Python is also important in the field of server-side programming with its rich software ecology and flexible language features. A place;

This sharing will explain the problems that Apache SkyWalking needs to solve in the microservice architecture, show how to use Apache SkyWalking to monitor Python back-end application services almost automatically, and interpret the implementation technology of Apache SkyWalking's Python language probes.

用Python打造全自动漏洞挖掘工具，在实战中让漏洞挖掘效率飞起。
一、漏洞挖掘
1.1 手工漏洞挖掘
1.2 手动漏洞挖掘的局限性
1.3 自动漏洞挖掘的应用场景
二、打造自动漏洞挖掘机
2.1 指纹识别
2.2 POC 编写
2.3 WAF 识别
2.4 自动 Bypass
三、实战与总结
3.1 实战应用
3.2 总结展望

Building a fully automatic vulnerability mining tool in Python, make you much more efficient in practice.

1. Vulnerability Mining
1.1 Manual Vulnerability Mining
1.2 Limitations of Manual Vulnerability Mining
1.3 Application Scenarios for Automated Vulnerability Mining
2. Build an automatic vulnerability miner
2.1 Fingerprint Identification
2.2 POC Writing
2.3 WAF Identification
2.4 Automatic Bypass
3. Practice and Summary
3.1 Practice Applications
3.2 Summary and Outlook

Databricks是微软为Python开发者提供的基于Spark的云上大数据及人工智能处理平台，该平台为开发者提供了托管的Spark和Machine Learning一站式体验，使用基于Web页面的Databricks workspace，开发者和数据科学家可以即刻开始进行大数据分析与处理，减少花费在底层平台搭建所需要的学习成本和时间精力，本场讲座带有Demo演示，将带领广大开发者深入体验Databricks平台。

Databricks is a Spark based big data and artificial intelligence processing platform on the cloud provided by Microsoft for Python developers. The platform provides developers with a one-stop experience of hosted Spark and machine learning, and uses Web page based databricks Workspace, developers and data scientists can immediately start to analyze and process big data, and reduce the learning cost and time and energy spent on the construction of the underlying platform. This lecture will be presented by Demo, and he will lead the developers to deeply experience the Databricks platform.

随着 Docker/Kubernetes 等容器技术的盛行，越来越多的 Python 应用已经运行在容器中了。
在带来便利性的同时，也让生产环境中的 debug 变的复杂。
eBPF 是一项革命性的技术，可以在 Linux 内核中运行沙盒程序，而无需更改内核源码。
本次分享将为你介绍如何使用 eBPF 技术对 Python 应用的容器进行 debug，更高效的定位和解决问题。

With the prevalence of container technologies such as Docker/Kubernetes, more and more Python applications are already running in containers.
While bringing convenience, it also makes debugging in the production environment more complicated.
eBPF is a revolutionary technology that can run sandboxed programs in the Linux kernel without changing the kernel source code.
This sharing will introduce you how to use eBPF technology to debug the Python application container, locate and solve problems more efficiently.

python不光用于web渗透的工具链打造中，在移动安全的领域，我们也会时常通过Python来做一些工具来优化我们日常的审计效率，那么如何从一个工具延展到一个整体性的APP审计平台呢？
我们会从APP审计的视角出发，介绍如何打造轻量级的APP自动化审计平台。
目录：
一、自动化审计场景介绍
1.1 宏观视角下的APP审计
1.2 自动化审计诉求
1.3 自动化审计应用场景
二、如何在实战中提高审计效率
2.1 运行效率提高
2.2 数据处理效率提高
三、总结与展望

Python is not only used for web penetration toolchain building, but also in the field of mobile security, we often use Python to make tools to optimize the efficiency of our daily audit, so how to extend a tool to a holistic app auditing platform?

We will present how to build a lightweight app automation audit platform from the perspective of app auditing.

Table of Contents.
I. Automated Audit Scenarios
1.1 APP Auditing from a Macro Perspective
1.2 Automated Auditing Requirements
1.3 Automated Auditing Scenarios
II. How to improve audit efficiency in the field
2.1 Operational Efficiency Improvement
2.2 Data processing efficiency improvement
III. Summary and Outlook

由于Python自身的魅力，已经被越来越多的业内、业外人士所熟知。同时，作为一门表达能力非常强的语言，在特定的领域内，将其作为一种领域特定语言（DSL）应用在我们的业务框架中，可以大幅度提升业务框架的灵活性。然而，由于CPython解释器的特点，使其在嵌入其他程序框架时，往往会"喧宾夺主"，且考虑到上述应用场景极有可能会执行用户提交的不安全代码，解释器自身的安全性也需要考虑。本次演讲将简要介绍除CPython以外的其他解释器实现，并重点介绍RustPython的发展现状。希望通过本次演讲，能鼓励大家在更多的场景下引入Python这门优秀的语言。

Because of its own appeal, Python has become more and more well known by people both inside and outside the industry.At the same time, as a very expressive language, using it as a Domain Specific Language (DSL) in our business frameworks can greatly increase the flexibility of our business frameworks in certain domains.  However, due to the characteristics of the CPython interpreter, it tends to be "clumsy" when embedded in other application frameworks, and the security of the interpreter itself needs to be considered, since the above scenarios are likely to execute unsafe code submitted by users. This talk will briefly introduce other interpreter implementations besides CPython, and focus on the current development of RustPython. Hope that this presentation may encourage people to introduce the excellent Python language in more scenarios.

Python是一种极其灵活的、支持热重载的编程语言。但想要写出高性能的，安全的Python程序并不轻松。
Rust是一种安全的、高性能和低碳的编程语言。但在需要快速迭代的场景中，它的开发效率并不理想。
因此，两者可以很好的互相补足和协调。
我会向大家介绍它俩是如何协同的。
以及安利Rust——这个现代编程语言工业界的结晶

对于使用 Python 开发的商用软件来说，如果不想公开源码，那么如何进行发布是一项挑战。PyArmor 是发布加密 Python 脚本的一个成熟工具，目前有 1000+ 注册用户，包括腾讯，华为，富士通，IBM（日本），通用（印度）等，已经被证明是一种能够成功保护 Python 脚本的解决方案。
本次议题会介绍 PyArmor 的优势和特点，PyArmor 的加密安全性，以及如何使用 PyArmor 进行加密脚本，如何打包加密脚本为可执行文件，如何设置加密脚本的有效期或者绑定加密脚本到固定的机器。

If you use Python to develop commercial software and do not want to make the source code public, how to release it is a challenge. PyArmor is a mature tool for publishing encrypted Python s. There are currently 1000+ registered users, including Tencent, Huawei, Fujitsu, IBM (Japan), GM (India), etc. It has been proven to be a solution that can successfully protect Python s.
In this talk, I would like to show the highlights of PyArmor, the security of PyArmor, and how to obfuscate the s, pack the obfuscated s into one bundle, expired the obfuscated s or bind them to fixed machine.

不讲云计算，也不讲人工智能，没有流行，也没有风口，回归本源，回归10年前的老派技术分享，没有框架和套路，代码说话，拳拳到肉，就讲讲我在带人的过程中发现的新人甚至熟手也经常犯的一些错，还有就是Python新版本的一些“更好的编码”的特性，看了就能用得上，在这个主题里，我只讲如何写好每一行代码。

No cloud computing, no artificial intelligence, no popularity or hot new business. I'm going back to the origin, to the old school way of technology sharing 10 years ago. There's no framework or routine other than code.

I'll talk about some mistakes I have found that newbie and even experienced developers often make. There are also some "better way of coding" features of the new version of Python which you can use immediately after this presentation. Yes, in this topic, I will only talk about how to write each line of code in an elegant way.

一个可复现的部署工具和虚拟环境工具对 Python 这种语言来说尤其重要，特别是和依赖 C 库进行混合编程时。
Nix 是一个可复现的包管理器，能够提供终极的虚拟环境隔离机制, 以及 Devops 工具。对 Pythonistas 来是一个非常重要的补充。
　　　　　　　　

A reproducible deployment tool and virtual environment tool is especially important for language like Python, especially when hybrid programming with the C dependency library.
Nix is a reproducible package manager which provides the ultimate virtual environment isolation mechanism, as well as devops tools. It is very benifit for Pythonistas.

近年Visual Studio Code 成为开发者的开发利器，Visual Studio Code 在Python后端应用，到机器学习都有很好的支持，本课程介绍如何利用Visual Studio Code 的Python 插件快速搭建一个基于人工智能的云端解决方案， 从需求，开发，运维，测试的现代化应用场景。

Azure machine learning service is launched in China, which expands the application scenarios of Microsoft azure cloud in machine learning field. This course uses Python to complete a machine learning case from data collection, model training, testing and publishing through the machine learning service provided by azure.

Lightning是一款基于Django的应用开发框架，由”闪电数据管理”团队开源的核心框架。
Lightning允许开发者仅编写Django Model即可拥有一个现代的、功能丰富、适合国人使用习惯的SPA管理后台或企业应用；它拥有强大的管理页面与Dashboard图表可视化配置功能，极大缩减管理后台的研发成本。
本主題主要介绍该框架的基本使用方式以及背后的设计理念。本次演讲也是本项目的开源仪式，主題演讲完毕当即开源。

Lightning is an application development framework based on Django, which is open-source by the lightning data management team. Lightning allows developers to write only Django model, which can have a modern, rich function, suitable for Chinese people to use SPA management background or enterprise application. It has powerful management page and Dashboard chart visual configuration function, greatly reducing the R&D cost of management background.
This topic mainly introuduce the basic use of the framework and the design concept behind it. This topic is also the open source ceremony of the project. After the keynote speech, the project will open source immediately.

一 从“漏洞”看django
   1.1 ssti  (从ssti角度分析)
   1.2 密码重置  （从密码重置角度分析）
   1.3 sql注入  (从sql注入角度分析)
   1.4 CVE-2017-7233&7234

二 django与安全开发
   2.1 django安全开发 (django安全开发技巧)
   2.2 总结与展望 (总结与展望)

A look at django from a "vulnerability" perspective
1.1 ssti (analysis from the ssti perspective)
1.2 Password Reset (analysis from the perspective of password reset)
1.3 SQL injection (analysis from the SQL injection perspective)
1.4 CVE-2017-7233&7234

II. Django and Security Development
2.1 Django security development (django security development tips)
2.2 Summary and Outlook (Summary and Outlook)

邻域特定语言（DSL）在部分工作场景中非常有用，比如游戏活动的配置，常规业务的生成等。本次分享我将展示我是如何使用Python从0开始构建ToyPL的，ToyPL是一个支持基本运算、变量、列表、函数、注释的一个简单的脚本语言，构建的过程不会使用第三方语法生成工具，主要介绍如何通过简单的递归算法来构建这门语言，分享的过程中会介绍编译原因的一些背景知识，然后便将时间花在如何构建词法分析器、语法分析器以及解释器上，这些看上去复杂的东西其核心都是递归算法，当你掌握这些基本知识后，你便可以尝试编写自己的脚本语言了，本次分享不会涉及特别深奥的编译原理知识，只是希望给未接触编译原理的同学一个初步的印象，然后我们一同学习研究这一领域。

Domain-specific language is very useful in some work scenarios, such as configuration of game activities, generation of routine business, etc. In this sharing, I will show how I built ToyPL from scratch using Python. ToyPL is a simple ing language that supports basic operations, variables, lists, functions, and annotations. The building process does not use third-party syntax generation tools. I will mainly introduce how to build this language through a simple recursive algorithm. During the sharing process, I will introduce some background knowledge of compilation principles, and then I will spend time on how to build a lexical analyzer, a syntax analyzer, and an interpreter. The core of these seemingly complicated things are recursive algorithms. Once you have mastered these basic knowledge, you can try to write your own ing language. This sharing will not involve particularly profound knowledge of compilation principles, but just want to give a preliminary impression to students who have not yet come across compilation principles, and then we will learn and research this field together.

随着人工智能与自然语言处理领域的飞速发展，目前国内大部分公司都已经拥有了自己的客服系统，但是目前很多客服系统都是基于最简单的规则，或者通过关键字匹配进行查找的，一旦关键字匹配不到，则会出现无法查询出正确结果的现象。
本次分享将介绍西山居使用Rasa框架在智能客服领域的实际落地情况，以及如何将基于QA问答对、KBQA和闲聊结合在一起做整体问答系统解决方案，并介绍在落地过程中的常见trick，以及在上线工程化方面的一些经验。

With the rapid development of artificial intelligence and natural language processing, most domestic companies already have their own customer service systems, but many customer service systems are currently based on the simplest rules or search by keyword matching. Once the keywords are not matched, the correct results will not be found. This sharing will introduce the actual implementation of Seasun's use of the Rasa framework in the field of intelligent customer service, and how to combine QA-based question and answer pairs, KBQA and chatting to make an overall question and answer system solution. Will also introduce common tricks in the implementation process and some experience in engineering.

STEM 教育从上世纪持续至今，造福了一代又一代的理工科学生，STEAM 教育和 AI 教育也在继续兴起，我和我的团队正在通过 Python 来打造适合 K–12 学生的编程学习平台，本次主题想给观众分享，我们是如何使用 Python 和 MicroPython 来打造一系列的软件编程环境和硬件平台，内容包括积木式编程、边缘计算、计算机视觉、模型训练。

STEM education has benefited generations of science and engineering students since last century.  STEAM education and AI education are also continuing to rise. My team and I are using Python to build a programming learning platform suitable for K–12 students . This time The topic wants to share with the audience how we use Python and MicroPython to create a series of software programming environments and hardware platforms, including building block programming, edge computing, computer vision, and model training.

少儿编程没有统一的教材，每个培训机构都要自己设计和研发课件，不同机构的课件各有特色，但不管怎样，课件研发绕不开的两个点就是知识点大纲的安排和和围绕知识点的项目设计。图形化编程的主流是 Scratch，纯代码编程的主流是 Python。Python 的知识点是确定的，不同机构的知识点大纲安排大同小异，项目设计是一个体现差异化很重要的点，好的项目设计要简单有趣，还要合理地融入知识点，所以做项目设计需要借助一些库和工具，主流的 python 库有3个:turtle（图形与动画）, tkinter（桌面软件）和 pygame（游戏）。本议题是想介绍一下这3个库的优缺点，谈一下我们在使用这3个库做课件研发的过程中遇到问题和解决思路，以及其它有用的 Python 库和工具，如 pgzero, python-cocos2d, freegames, mcpi和piskel, page等。同时希望能抛砖引玉，吸引更多的人才加入这一领域的研究工作。

There is no standard teaching material for kids programming.  Each training institution must design and develop its own course materials, and different institutes have their own characteristics. But in any case, the two points that cannot be avoided in the development are the design of the knowledge points outline and the project design around the knowledge points. The mainstream of graphical programming is Scratch, and the mainstream of pure code programming is Python. The knowledge points of Python are determined, and the outline design of the knowledge points of different institutions are similar. Therefore, project design is very important to reflect differentiation.  Good project design should be simple and interesting, and knowledge points should be reasonably integrated. In order to make such a good project design, we need to use some libraries and tools. There are 3 mainstream python libraries: turtle (graphics and animation), tkinter (desktop software) and pygame (game). This topic is to a) introduce the advantages and disadvantages of these three libraries; b) talk about the problems and solutions we encountered in the process of using these three libraries for course development; 3) introduce other useful Python libraries and tools, such as pgzero, python- cocos2d, freegames, mcpi and piskel, page etc. At the same time, we hope to attract more talented people to join the research work in this field.

简要的介绍自刚刚毕业到从业三年的这段时间里，
从5个人的小公司，到现在某大厂旗下的中厂。
期间从事的各种python相关的岗位，与呆过的各种小公司的经验，先后从事过web后端，爬虫，数据分析，少儿编程教学等，可以说踩尽了python相关不需要研究生以上的坑。
以及职业中的仲裁故事，被学校老师欺骗等。
供后人参考。

This is a brief introduction of the three years since graduation, from a small company with only 5 people, to a medium-sized company under a large enterprise.
During this three years, has engaged in various Python related jobs and has experience in small companies. Has successively engaged in Web back-end, crawler, data analysis, children's programming teaching and so on. It can be said that I have stepped into the all of the holes Python related which graduate students are not required.
This topic will also talk about stories of arbitration and being cheated by school teachers and etc.
For reference.