Though the initial goal is to only upgrade PyTorch and torchvision, it's inevitable to upgrade lightning and torchmetrics, because the old lightning is not aware of the different API of LR scheduler in PyTorch 2.0, and the new lightning requires newer torchmetrics.
Upgrade dependency.
Adapt our code to the new dependency.
Type of change
Please check all relevant options.
[x] Improvement (non-breaking)
[ ] Bug fix (non-breaking)
[ ] New feature (non-breaking)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update
Testing
Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.
What does this PR do?
Though the initial goal is to only upgrade PyTorch and
torchvision
, it's inevitable to upgradelightning
andtorchmetrics
, because the oldlightning
is not aware of the different API of LR scheduler in PyTorch 2.0, and the newlightning
requires newertorchmetrics
.Type of change
Please check all relevant options.
Testing
Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.
pytest
CUDA_VISIBLE_DEVICES=0 python -m mart experiment=CIFAR10_CNN_Adv trainer=gpu trainer.precision=16
reports 70% (21 sec/epoch).CUDA_VISIBLE_DEVICES=0,1 python -m mart experiment=CIFAR10_CNN_Adv trainer=ddp trainer.precision=16 trainer.devices=2 model.optimizer.lr=0.2 trainer.max_steps=2925 datamodule.ims_per_batch=256 datamodule.world_size=2
reports 70% (14 sec/epoch).Before submitting
pre-commit run -a
command without errorsDid you have fun?
Make sure you had fun coding 🙃