Hi itake, thank you for your questions! Hulse focuses on simplifying the deployment of pre-trained models at the moment, so you could expect to still run your

Hi itake, thank you for your questions! Hulse focuses on simplifying the deployment of pre-trained models at the moment, so you could expect to still run your training on a proper GPU cluster. On the ease of use side of things, I'd argue cloud VMs are not necessarily easier to work with: setting up your firewall & all the APIs to handle queries towards your pre-trained models is pretty cumbersome. On average a PC is used at around 30% of its computing power, thus leaving a good chunk of CPU/RAM available which would be more expensive to rent in the cloud than use locally. I completely agree with you about the data access with colleagues. Right now, the user right management is very simple: you share a cluster with people, who may come to analyze your data and vis-versa. This could be an interesting addition! Right now, the relay servers are hosted in the US by AWS. Running things in a p2p fashion could also be done (thus removing the GDPR problem - if combined with the above point, could help give the desired granularity). Considering the AWS S3 egress bills, I agree with you! I think vendor lock-in is a problem especially when it constrains you to rent more expensive cloud servers (here AWS) to avoid extra costs.