modinfo nvidia
cat /usr/local/cuda/version.txt
When working with imbalanced data for machine learning tasks in PyTorch, and simple random split might not be able to partly divide classes that are not well represented. Resulting sample splits might not portray the real-world population, leading to poor predictive peformance in the resulting model.
Therefore, I have created a simple function for conducting a stratified split with random shuffling, similar to that of StratifiedShuffleSplit from scikit-learn (https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html)
import random
import math
import torch.utils.data
Git for Windows comes bundled with the "Git Bash" terminal which is incredibly handy for unix-like commands on a windows machine. It is missing a few standard linux utilities, but it is easy to add ones that have a windows binary available.
The basic idea is that C:\Program Files\Git\mingw64\
is your /
directory according to Git Bash (note: depending on how you installed it, the directory might be different. from the start menu, right click on the Git Bash icon and open file location. It might be something like C:\Users\name\AppData\Local\Programs\Git
, the mingw64
in this directory is your root. Find it by using pwd -W
).
If you go to that directory, you will find the typical linux root folder structure (bin
, etc
, lib
and so on).
If you are missing a utility, such as wget, track down a binary for windows and copy the files to the corresponding directories. Sometimes the windows binary have funny prefixes, so