[ilmoi]

Both are valid points. And no, there's currently no programmatic access to machines - but that's high on the roadmap.

Regarding environments, it's funny, when I was starting out I would kill for a pre-configured instance because I wanted to focus on modeling and didn't care if package X was version x.x.y or x.x.z. But as you grow as an ML engineer and develop your own toolkit these things start to matter.

So when creating a machine on gpu.land you have the choice of going pre-configured or just having a clean Ubuntu image. The former is meant for newcommers while the latter for pros. That was my thingking!

[iujjkfjdkkdkf]

Thanks a lot for answering my questions. I am definitely going to give this a try. Right now I see this as a great option for an always on dev instance that I can work in all day with GPU access and not worry about breaking the bank.

I saw someone ask on reddit about separating storage from the GPU instance, so that one could do data transfer and other setup without reserving a gpu. I want to echo how important this is. Another case is where I might want to use only one GPU and then scale up to 8 for training, or i might want to have N GPU instances attached to the same storage to run jobs in parallel. There are lots of other examples, but overall it would add much flexibility.

On the other hand, it might encourage people to be more "peaky" in their use, which could be a challenge for you. From what I understand, you are much better off if I want 1 gpu for 70 hrs vs, 70 gpus for an hour, in which case I understand how you might want to encourage steady use.