PyTorch

“They have what I've, I would describe as a sandwich problem, which is that you end up sandwiching a lot of system code with user code, and then, like, adding more and more layers of that, and as you've sandwiched, like, you know, seven or eight layers of that,…”

“We're definitely going all in as PyTorch, you know, end to end. So I think initially when we launched the Onyx strategy, it was more like a multi-framework world. And we had actually two framework internally between PyTorch and Cafe Two, but we're still suppor…”

“We landed on using a framework that enables tree-based models. And we picked light GBM for that after trying a few different packages and also deep learning. And for that, we then used PyTorch. And so we started with those two core libraries.”

“I don't think they really, I think people tell this, the story of sort of the silver bullet. Of like you know, the eager execution model. But I think the reality is they just built a product with so much more empathy.”

“I think one of the biggest success we saw from the PyTorch experience is simplicity scales.”

“OpenAI switched to use PyTorch fully.”

“We have hundreds of engineers building PyTorch and infrastructure around PyTorch, but at the same time, I believe PyTorch within Meta probably has thousands of users.”

“It took us five years. Took us five years to get the stage supporting almost all internal needs using deep learning and mass and massive scale.”

“You build a LLM completely from scratch. You use PyTorch, but you build one from scratch that, you know, can be a chatbot. You train it on data. You RL it to solve math problems with tool calls. You do all of this. And this is a undergrad level course.”