#Docker for mac general setting how to#
#Docker for mac general setting code#
#Docker for mac general setting windows#

#Docker for mac general setting how to#

If you are using Linux, you can find here a guide on how to install Docker.

#Docker for mac general setting windows#

For Windows users, if you can please install windows subsystem for Linux 2 (WSL2) and the WSL2 kernel as it makes Docker much faster. If you are using Windows or Mac, I suggest that you install Docker for Windows/Mac. I will not delve into the details as the procedure is well explained here on the Docker website. Install DockerДЇirst we need to install Docker. If you want to read more about docker, I encourage you to look at the projectвЂ™s webiste.

#Docker for mac general setting code#

This feature is extraordinary important, if you need to develop a program to analyze some data and you need to run it on a cluster: once you have submitted your code it will be run, and if there are some bugs it means that you have wasted computational power and possibly your research funds! What I think is extraordinary about Docker, is that once you have built an image, you can install it on any machine running docker, and you can be sure that if you code works on your docker container, it will work anywhere else if you run it inside the same image. Think of it as a Virtual Machine on steroids: it enables you to craft a developing environment in minutes with exactly the specs you need. What is Docker?Дocker is a container technology for Linux that allows a developer to package up an application with all of the parts it needs. If you want to learn more about Julia, you can look at its website and its wonderful documentation. You write the code, you can interact with the code in the REPL (like an IPython notebook) but functions are compiled as soon as you run them, making your code orders of magnitude faster, in some circumstances.ДЇor some benchmarks you can look at this github repository. Julia aims to solve this problem thanks to its JIT (Just In Time) compiler: coding in Julia is as easy as coding in Python, and thanks to the possibility to write type annotations and generate type optimized code, it can be almost as fast as compiled languages.

That is the so called two language problem, i.e the need to write the code two times, to cut it short. ThatвЂ™s the reason why ideas are often quickly prototyped using Python and then they are re-implemented using C++ for deployment. Languages like C++ and Fortran can be blazing fast, as the binaries are optimized for the machine they run on, but on the other hand it is difficult to code fluently in those languages. Thus, core libraries for computation are implemented using вЂњlow level languagesвЂќ such as C++ or Fortran, and they are then called by Python functions.

Python is considered one of the best languages (in the scientific world) for its simplicity and the vast community, but it tends to be slow and it doesnвЂ™t always use all the computational resource we have at hand. In physics, we want our code to run fast, but as we are not programmers we want also to quickly build a program to test new ideas.

When programming, especially for scientific computations, one has to come into terms with both the complexity of the problem and the task of writing a good code. Julia is a high-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science, without the typical need of separate compilation to be fast, while also being effective for general-purpose programming, web use or as a specification language. If you canвЂ™t wait getting to work with Julia, you can directly skip to Install Docker! About the programs Julia We will show you how to connect a docker container to both VSCode and Atom, though it is suggested to use VSCode as the procedure is simpler and the Julia extension for Atom is no longer under active development.Д«ut first, what are Julia and Docker? The following two paragraphs are meant to give you some context but they are not needed in order to follow through the tutorial. In this guide we will learn how to setup a fully containerized development environment for the Julia language.

Connect to the Docker container from Atom.

Connect to the Docker container from VSCode.