Before starting a new machine learning side project, it would be very useful to estimate how long it will take to run 1, 10, 100, 1k epochs. A crude estimate is more than sufficient (i.e. 1 epoch would take 1 second, 10 seconds, 1 minute, 1 hour, etc..).
Given the variables below, can you recommend any heuristics that could provide an estimate?
- Problem type (e.g. Image Segmentation)
- Model type (e.g. PyTorch Unet)
- Dataset (e.g. 10k images, 512x512)
- Compute (e.g AWS p2.xlarge)
- Library (e.g. PyTorch)
Is an empirical method (e.g train on smaller subsets of the data and scale accordingly) a better approach to solving this problem?