•1 min read•from Machine Learning
How do you experiment with a (very) large model architecture? [D]
Our take
When experimenting with large model architectures, particularly in the context of compute-intensive training regimes, it's essential to adopt strategies that maximize efficiency. This involves techniques such as using only a fraction of the dataset—typically 5-10%—to quickly validate hypotheses. Additionally, reducing batch sizes while adjusting the learning rate accordingly can help manage resource constraints. Furthermore, consider decreasing the number of epochs or iterations to expedite experimentation. Exploring these approaches can streamline your efforts and provide insights into the model's performance without overwhelming computational resources.
Im trying to reproduce a paper (a very particular kind of diffusion model), and their training regime is incredibly compute heavy.
In general, how are quick experiments performed to validate hypotheses when the models are large and compute is expensive?
Some cursory browsing yields the following: 1) Using only 5-10% of the entire dataset. 2) Drastically reducing the batch size and compensating for it in the learning rate 3) Reducing the number of epochs/iterations.
But I've had to infer these from resources online and what LLMs tell me. Is there anything in addition to/beyond/contradicting these?
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