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.
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.
Following the basic Qinglong setup and JD coins automation, here’s a walkthrough for the BiliTools project. I added notes based on the original docs—use as a reference.