3 ms·
Because TF lets you run _just_ the model, with no Python stuff at all. For us there is no “server side”, our stuff is deployed on prem. The fewer variables ther
by 0xbear 9y ago
Because TF lets you run _just_ the model, with no Python stuff at all. For us there is no “server side”, our stuff is deployed on prem. The fewer variables there are the easier our lives are going to be.
Additionally, PyTorch download page warns you point blank that it’s an early version of the software and that you should “expect some adventures”. Adventures are fine for research, but inadvisable in production IMO.
- cjalmeida 9y agoI understand the appeal, but IMO wrapping the Python stuff in a docker container is fairly simple. Making it run at scale using something like kubernetes is more advanced stuff but still within good devops practices. Unless, of course if you want to run on exotic HW, like TPUs. But that's an issue for Googlish scales. Edit: BTW, if on premise you mean Windows client software your point is totally valid, Python would suck for this.
- puzzle 9y agoDepending on how much extra logic you add around the model serving library, then you are exposing your source code, too. Maybe you care, maybe not. Re: Windows, there's no TF serving for that, so TF is no better than other Python frameworks.