I am using Google Cloud (or other cloud services) to develop my programs and run experiments. I encounter a problem when using 1) multiple VM instances, 2) multiple local machines, 3) multiple Anaconda environments, 4) multiple projects. Let's consider the following situations:
1) Create a new project: On each local machine, I need to A) create a project, B) set the remote interpreter for each VM instances/Anaconda environments, C) set the deployment settings, D) sync the local folder.
2) Create a new VM instance: On each local machine and each project, I need to A) set the remote interpreter, B) set the deployment setting, C) sync the remote folder.
3) Use a new local machine: On this new local machine, I need to A) create each project, B) set all the remote interpreters, C) set all the deployment settings, D) sync all the project folders.
4) Add a new Anaconda environment: One each local machine, I need to A) set the new remote interpreter, B) set the new deployment settings.
You can imagine how complicated it is to do all of these and remember whether all the local machines are all up-to-date. Are there any better practices to do all of these?