about reproduction

[konioy]

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Question

Same data, same code, I trained it twice. The loss curve is very close, but the effect on the test data is very different. Why is this？

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[glenn-jocher]

@konioy current master with torch>=1.12.0 is fully reproducible:

[konioy]

Is it a problem with the pytorch version? Is the 1.9 version of pytorch unreproducible?

[konioy]

I looked at (#8213) and (https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html?highlight=torch%20use_deterministic_algorithms#torch.use_deterministic_algorithms), but don't understand how torch.nn.Upsample is reproducible?

[glenn-jocher]

@konioy torch>=1.12 should be fully reproducible using single GPU. Multi-GPU is not yet reproducible and we don't have a clear reason why.

[konioy]

I am using pytorch1.9, which I know cannot be reproduced. It may be that the choice of convolution operator is different (because torch.cudnn, benchmark=true).
But from the loss point of view of two repeated training (same code, same data), the difference in loss is very small.
train1 on the left, train2 on the right

But there is a big difference in the validation set. Do you have any ideas or suggestions?
train1:

train2

[glenn-jocher]

@konioy zero val loss typically indicates your validation set has no labels

EDIT: if you used --no-val then the above is normal

[konioy]

yes, i used --no-val.
I think two repetitions of training, the effect on the validation set should be similar. But my experiments show that repeating the training twice, there is indeed a big gap, as shown below, do you have any suggestions?
map 0.5:
train1:0.8162
train2:0.9167
precision:
train1:0.8159
train2:0.8985
recall:
train1:0.7381
train2:0.8517

[glenn-jocher]

@konioy use torch>=12.0 for reproducible Single-GPU CUDA trainings runs

[konioy]

Thanks, I know.
Do you have any suggestions for my situation?

[konioy]

have you encountered a similar situation？

[glenn-jocher]

@konioy your results are expected. torch<1.12 will not produce reproducible results.

[konioy]

However, the results fluctuated greatly. this is not expexted.

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