[kevinskii]

We're currently running a single NVIDIA RTX2080 with Tensorflow 2.0 on a Windows 10 station. We'll soon be switching to a standard multi-GPU rig running an air gapped Linux distro. Linux seems overall much better for ML because of better Docker integration and tensor core support on the newer GPUs. Also, we'll probably be switching from Tensorflow to Pytorch for model development. Pytorch requires a little bit more code, but debugging is 10X easier.

[jedieaston]

Why airgapped? Is it a business/security requirement? If you have to share the machine, does everybody have to thumb drive over their files to run with the big GPU?

[kevinskii]

Yes. The air-gapping isn’t ideal, but trying to get our IT org to accommodate a Linux workstation on the network just isn’t worth the hassle.

[imagiko]

And how do you get data on the fly [prediction phase]. Do you have an API call you make to get data that your ML algorithm can munch on?