Remote VSCode and Nuclide are the two options I’ll discuss here. Your editor needs to that machine and coordinates changes via a custom protocol.
#ATOM FOR MAC REMOTE SERVER TUNNEL CODE#
In this case, there is one copy of the code and it lives on the remote server. The disadvantage is you have 2 copies to keep in sync which can occasionally load to overwriting your work and the workflow can be clunky.Įdit remote files. The advantage is that you can use any editor you want, can edit code offline, and the entire codebase easily accessible. Rsync and P圜harm are the two options I’ll discuss here. You edit the files locally and sync changes to the remote server. In this case, there are two copies of the code, on your laptop and on the remote server. I consider two basic approaches to editing code to be run on a remote server:Įdit local files and sync. I highly suggest you read those regardless of what approach 2 major approaches However, the 2 sections below on repository organization and SSH settings are applicable to any approach and will make your remote development workflow easier. If you’re still unhappy with those approaches, come back here. If you’re starting out, I’d suggest you start there and invest some time learning those tools. This is guide also assumes you’re comfortable working on the command line, working with configuration files, and installing software. The vast majority of people are happy with Jupyter and Vim for their needs. If you’re asking “Do I need this?” the answer is probably "No". The focus is on deep learning and Python, but the majority of this advice is general enough for any other applications. I cover the other approaches to editing code on a remote server, explain how to set them up, and weigh their comparative strengths and weaknesses. I assume you the reader aren’t happy with the standard recommendations. This guide is the guide I wish had found 4 months ago. Also, I like having the features of a modern IDE available to me when developing, and while both have decent interfaces, I feel they still fall short.
#ATOM FOR MAC REMOTE SERVER TUNNEL SOFTWARE#
Juypter notebooks rapidly grow to unwieldy sizes, are difficult to use with version control, and otherwise retard normal software practices. However, I’ve never been happy with that workflow. To get started I recommend Learn Vim Progressively, Vimcasts, and DataCamp’s Jupyter notebook and Notebooks on EC2 tutorials.
The most common remote development approach for deep learning is a combination of Vim and Jupyter notebooks, which is covered in many tutorials So, while I’m still sitting at my laptop writing code, I need to run the code on a machine that’s often somewhere else, such as AWS. All these projects call for a big GPU, of the variety that definitely doesn’t fit into my laptop. Over the last year, I’ve been spending more and more time working on deep learning projects.
Developing on a remote server Developing on a remote server
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