Conda

A quick guide to using VS Code with SSH for remote development, plus a plug for notebooks. Once you know the flow, spinning up a remote Python/Notebook workflow is incredibly convenient.

I recently needed to move a Conda environment. Deep learning stacks are usually tightly coupled to driver versions and package versions, so being able to package a working environment and drop it onto another box saves a lot of time.

No root access on the remote server, but still want a newer PyTorch and transformers setup? These are my notes on using Conda to install CUDA and the tools I need without changing the system driver. I cover version choices, checking that PyTorch can use the GPU, pointing programs to the Conda CUDA installation, and watching out for xformers dependency changes.

Due to the author's frequent use of conda to configure the R kernel jupyter notebook on Linux remote servers, some steps are forgotten over time (the main problem is that the same pit will be stepped on differently every time). After summarizing the relevant tutorials and personal experience on the internet, a guide is provided for everyone and future me to refer to.