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My $0.02 is Cloud machines are easier to work with and give you control over your hardware. If my local cluster isn't powerful enough to train the data (e.g. hi

by itake 4y ago

My $0.02 is Cloud machines are easier to work with and give you control over your hardware. If my local cluster isn't powerful enough to train the data (e.g. high RAM requirements), then I would have to fallback to cloud computing anyways. Plus, there might be data access risks. There might be data that I should only have access to that my colleagues machines shouldn't see. In the context of GDPR, how do I know geographically where the server are running? Consider where the data is stored. Most company data lives in cloud s3 buckets and AWS charges hefty fees to access that data outside of their network. Downloading terrabytes of training data locally could leave a large AWS bill.

sachalevy92 4y ago
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.

by itake 4y ago

sachalevy92 4y ago

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.