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Question - ML models are hard to debug because it’s all happening on the GPU. If you have shared memory with the CPU could that make it easier to see what’s goi
by jonplackett 6y ago
Question - ML models are hard to debug because it’s all happening on the GPU. If you have shared memory with the CPU could that make it easier to see what’s going on inside your model?
- BobbyJo 6y agoIs that why they are hard to debug? I thought they were hard to debug because deep learning models consist of lots of black-box-ish components. I've worked with GPU software before, and it is a pain to debug, but you generally don't have to care about that level when using things like tensorflow or pytorch.
- skwb 6y agoNot unless you're writing the backend CUDA code for TF honestly. Most of the time you're accessing your data/models/structure through the TF API so you really aren't doing too much that directly interfaces with the GPU. To put it another way, when you're developing ML models (99.9% of the time), it's not like people are writing if statements in CUDA.