- A simple note for how to start multi-node-training on slurm scheduler with PyTorch.
- Useful especially when scheduler is too busy that you cannot get multiple GPUs allocated, or you need more than 4 GPUs for a single job.
- Requirement: Have to use PyTorch DistributedDataParallel(DDP) for this purpose.
- Warning: might need to re-factor your own code.
- Warning: might be secretly condemned by your colleagues because using too many GPUs.
HaydenFaulkner
/ video_to_frames.py
Last active
March 19, 2024 16:37
Fast frame extraction from videos using Python and OpenCV
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| from concurrent.futures import ProcessPoolExecutor, as_completed | |
| import cv2 | |
| import multiprocessing | |
| import os | |
| import sys | |
| def print_progress(iteration, total, prefix='', suffix='', decimals=3, bar_length=100): | |
| """ | |
| Call in a loop to create standard out progress bar |
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| mkdir coco | |
| cd coco | |
| mkdir images | |
| cd images | |
| wget http://images.cocodataset.org/zips/train2017.zip | |
| wget http://images.cocodataset.org/zips/val2017.zip | |
| wget http://images.cocodataset.org/zips/test2017.zip | |
| wget http://images.cocodataset.org/zips/unlabeled2017.zip |