Jupyterlab vs vscode
#Jupyterlab vs vscode install#
Next, let’s install the QuantEcon lecture notes to our machine and run them (for more details on the tools we’ll use, see our lecture on version control). Then search for Julia in the Marketplace. Otherwise: run VS Code and open the extensions with or selecting extensions in the left-hand side of the VS Code window. (Optional) Install the VS Code Julia extensionĪfter installation of VS Code, you should be able to choose Install on the webpage of any extensions and it will open on your desktop. While optional, we find the experience with VS Code will be much easier and the transition to more advanced tools will be more seamless. This lets you open VS Code from inside File Explorer folders directly. On Windows, during install under Select Additional Tasks, choose all options that begin with Add "Open with Code" action. (Optional) Install VS Code for your platform and open it If you allow Git to add to your path, then you can run it with the git command, but we will frequently use the built-in VS Code features. Install Git and accept the default arguments.
![jupyterlab vs vscode jupyterlab vs vscode](https://i.stack.imgur.com/mzfDD.png)
This lets you download both the files and the entire version history from a server (e.g. We will explore these topics in detail in the lectures on source code control and continuous integration and test-driven development, but it is worth installing and beginning to use these tools immediately.įirst, we will install Git, which has become the industry standard open-source version-control tool.
![jupyterlab vs vscode jupyterlab vs vscode](https://user-images.githubusercontent.com/1738353/81239498-f26d4780-8ffc-11ea-9e15-fdd6fb0272c1.png)
Reproducibility will ensure that you, your future self, your collaborators, and eventually the public will be able to run the exact code with the identical environment with which you provided the results - or even roll back to a snapshot in the past where the results may have been different in order to compare. A primary benefit of using open-source languages such as Julia, Python, and R is that they can enable far better workflows for both collaboration and reproducible research.