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Nonsense, Pytorch is great in production. I use it for Mathpix (mathpix.com) which processes 20 million images per month.
by nicodjimenez 9y ago
Nonsense, Pytorch is great in production. I use it for Mathpix (mathpix.com) which processes 20 million images per month.
- boulos 9y agoDon't take this the wrong way, but 20M images per month is only about 20 qps or so (20M / 3600 / 730 => 7.6 qps, but that's unrealistically even). That's pretty manageable even on a single box, depending on the network and the size of the box. When people say they're concerned about a system in production, they often mean when needing to roll it out to many machines due to higher scalability requirements. Disclosure: I work at Google, but not TensorFlow.
- edanm 9y agoThat's a fair point, but just as a counterpoint, for plenty of people, 20M per month is a lot of traffic, and it's good and valid to hear about experiences of people putting PyTorch into production with this scale.
- agibsonccc 9y ago"Production" tends to be an overloaded term. Arguments like this tend to lead to "My production is bigger than your production!" which is frankly silly. You could mean large scale, real time, "small batch job with online lookups from a CRUD database",.. Let's just admit it's use case specific and move on.
- 0xbear 9y agoJust one instance of our product has to process 39 million inferences per month (number is dictated by frame rate), with predictable latency, 24x7, on both Windows and Linux.
- foo101 9y agoAbout 8 images per second? How does that prove that Pytorch is great in production? I am not saying that Pytorch is bad in production but I fail to see how your metric of 8 images per second proves anything.
- nicodjimenez 9y agoWhile you're absolutely right that vanity metrics prove nothing, it does prove that you can choose Pytorch for a production environment with a decent amount of traffic and be happy with your choice.