. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.11 Internationalization and localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . signing • Security Middleware 1.7. Forms 3 Django Documentation, Release 1.11.30.dev20200305075726 1.11 Internationalization and localization Django offers a robust internationalization and localization Then at the Python prompt, try to import Django: >>> import django >>> print(django.get_version()) 1.11 You may have another version of Django installed. 2.2.6 That’s it! That’s it – you can now move

Framework Third-party distributions of Django For distributors Glossary Release notes Final releases 1.11 release 1.10 release 1.9 release 1.8 release 1.7 release 1.6 release 1.5 release 1.4 release 1.3 release Then at the Python prompt, try to import Django: >>> import django >>> print(django.get_version()) 1.11 You may have another version of Django installed. That’s it! That’s it – you can now move onto isn’t, you’ll get an error telling “No module named django”. This tutorial is written for Django 1.11 and Python 3.4 or later. If the Django version doesn’t match, you can refer to the tutorial for your

Instructions for Julia back end Also a lot of advanced tricks in the Julia back end *抽象指令 （没有实际指令） Julia“生成函数” Python 字节码 JuVM虚拟栈机 （具体指令集） 抽象机器 （只规定语义） JuVM栈机指令 Python JIT 函数包装 Julia AST/CodeInfo Instructions for Julia Back End Instructions for Julia Back End JuVM: An implementation of our AM Machine Runner: 从AM到VM Julia Code Generation Julia Type Level Encoding 和Python不一样， Julia在运行时不能随意地eval。 Julia在运行时不能随意地eval。 生成的Julia代码需要经过一些tricky的处理， 也就是将AST编码为Julia类型， 然后 使用Julia独特的基于类型的Staging代码生成… A long story here out of Python world, see GeneralizedGenerated.jl or the paper of Zygote.jl for more details. Why based

Linux users) Just In Time The Restrain Python JIT 为什么编译器从字节码开始着手? 栈机(stack machine)语义的优化问题？ Julia后端和Cython后端的差别？ 栈机到基于寄存器(register based)的 语义，控制流分析，SSA 和 Φ 节点和 栈机语义消除。 Cython JIT 基础架构。 为什么编译器从字节码开始着手 那可不可以不翻译？ 可以。 然后你要在编译时模拟栈的Push Pop。 Julia后端就是这样做的。 导致For循环编译后并不快。 而基本上编译器0优化的Cython端， 你模拟栈后， 还远不如Python解释器快， 所以必须写分析去掉栈机语义。 那用Julia后端就可以不写了? 来谈后端的问题。 Julia Cython 常见性能提升 100x 1-10x 小函数JIT 比Python慢1000倍 比Python快50% 启动时间 10s以上, “JAOT” 没有，调编译器快 循环折叠 有 有 Debug现场？ 每次打log等待30s 瞬秒(ms级) JIT 后端问题 同时， 由于Julia端目前固有的问题，不支持动态链接编译的 Python(例如Anaconda Python), hmmmmm 我投Cython亿票, debug福音。跨语言含JAOT的debug是屑。 Cython问题

completion. By default, the language associated to a code cell is Python, but other languages, such as Julia and R, can be handled using cell magic commands. When a code cell is executed, code that it contains language kernel to run in the Notebook server enables your server to run other languages, like R or Julia. 4.3 Notebook front-end client • How front-end configuration works – Example: Changing the notebook’s are kernels available in the following languages: • Python(https://github.com/ipython/ipython) • Julia (https://github.com/JuliaLang/IJulia.jl) • R (https://github.com/takluyver/IRkernel) • Ruby (https://github

completion. By default, the language associated to a code cell is Python, but other languages, such as Julia and R, can be handled using cell magic commands. When a code cell is executed, code that it contains language kernel to run in the Notebook server enables your server to run other languages, like R or Julia. 4.3 Notebook front-end client • How front-end configuration works – Example: Changing the notebook’s are kernels available in the following languages: • Python(https://github.com/ipython/ipython) • Julia (https://github.com/JuliaLang/IJulia.jl) • R (https://github.com/takluyver/IRkernel) • Ruby (https://github

completion. By default, the language associated to a code cell is Python, but other languages, such as Julia and R, can be handled using cell magic commands. When a code cell is executed, code that it contains language kernel to run in the Notebook server enables your server to run other languages, like R or Julia. 4.3 Notebook front-end client • How front-end configuration works – Example: Changing the notebook’s are kernels available in the following languages: • Python(https://github.com/ipython/ipython) • Julia (https://github.com/JuliaLang/IJulia.jl) • R (https://github.com/IRkernel/IRkernel) • Ruby (https://github

completion. By default, the language associated to a code cell is Python, but other languages, such as Julia and R, can be handled using cell magic commands [https://ipython.readthedocs.io/en/stable/interactive/tutorial html#installing-kernels] to run in the Notebook server enables your server to run other languages, like R or Julia. Notebook front-end client How front-end configuration works Example: Changing the notebook’s default there are kernels available in the following languages: Python(https://github.com/ipython/ipython) Julia (https://github.com/JuliaLang/IJulia.jl) R (https://github.com/takluyver/IRkernel) Ruby (https://github

completion. By default, the language associated to a code cell is Python, but other languages, such as Julia and R, can be handled using cell magic commands [https://ipython.readthedocs.io/en/stable/interactive/tutorial html#installing-kernels] to run in the Notebook server enables your server to run other languages, like R or Julia. Notebook front-end client How front-end configuration works Example: Changing the notebook’s default there are kernels available in the following languages: Python(https://github.com/ipython/ipython) Julia (https://github.com/JuliaLang/IJulia.jl) R (https://github.com/takluyver/IRkernel) Ruby (https://github