1. If your data can be fit into the CPU memory, it is a good practice to save your data into one pickle file (or other format that you know how to deserialize). This comes with several advantages. First, it is easier and faster to read from a single big file rather than many small files. Second, it avoids the possible system error of openning too many files (even though avoiding lazying data loading is another way to fix the issue). Some example datasets (e.g., MNIST) have separate training and testing files (i.e., 2 pickle files), so that research work based on it can be easily reproduced. I personally suggest that you keep only 1 file containing all data when implementing your own Dataset class. You can always use the function torch.utils.data.random_split

checkmate¶

checkmate breaks the GPU memory wall by enabling researchers to train large state-of-the-art models that do not fit in GPU memory. Checkmate applies optimal tensor rematerialization (as detailed in the paper) to trade off space and time.

Tips and Traps¶

1. Adpative pooling an generate fixed-sized output, which enables a model to handle varying sized input. The models in torchvision are such examples.

AdaptiveMaxPool2d¶

References¶

Torchvision ResNet Input Size

set_grad_enable¶