Hey, have you ever felt like you’re stuck in a never-ending loop of trial and error when trying to write PyTorch code? You’re not alone. I’ve been there too, and it’s frustrating. You have an idea, you start coding, but then you hit a roadblock. The documentation is unclear, the models don’t work as expected, and those pesky NaN values just won’t go away.
I’ve been in the same situation, and it feels like I’m just throwing code at the wall, hoping something will stick. The workflow is chaotic: have an idea, check if there’s a simple Hugging Face workflow, try to alter it to my needs, write a simple PyTorch model, get some data, and then… tokenization fails. Or maybe there’s a size mismatch somewhere. And don’t even get me started on those NaN values that seem to appear out of nowhere.
It’s like I’m stuck in a cycle of trial and error, with no clear way out. I’ve tried using print statements to debug, but let’s be real, that’s not a scalable solution. And don’t even get me started on ChatGPT or Claude – they’re not always helpful, and sometimes they just rewrite your code to do something entirely different.
So, what’s the solution? I wish I knew. I’m still searching for good resources on how to write good PyTorch code and debug it efficiently. I want to know how researchers actually write code and get somewhere with it. Are there any good debugging tools out there that can help me tame the chaos?
If you’re in the same boat, I’d love to hear from you. Let’s commiserate and maybe, just maybe, we can find a way to make our lives easier.