Now multiple checkpoints will be saved after using -stopk option in train_model.py #4978
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| if ( | ||
| opt['validation_metric_mode'] == 'max' | ||
| and self.best_k_models[-1][1] >= opt['validation_cutoff'] | ||
| ) or ( | ||
| opt['validation_metric_mode'] == 'min' | ||
| and self.best_k_models[-1][1] <= opt['validation_cutoff'] | ||
| ): | ||
| logging.info('task solved! stopping.') | ||
| return True |
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i think this check requires that we look at self.best_k_models[0], right? since we're looking at the best metric
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My idea here was that we want the last saved model's metric to be better than validation_cutoff so I checked the last one with self.best_k_models[-1]
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This PR has not had activity in 30 days. Closing due to staleness. |
klshuster
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donotreap
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@jxmsML any interest in taking over this PR? |
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Hi Kurt,
Sorry for the late reply.
I was very busy lately and I forgot to check my GitHub
notifications recently. I can design tests as you mentioned in 2 weeks if
there is no one interested.
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Patch description
In this PR, the train_model.py script has the option to keep and store multiple checkpoints based on validation metrics. Some checkpoints that have best validation metrics during training will be kept in the directory specified by
--model-file. Resolves #4970Testing steps
To test this PR, you can run something like
parlai train_model --task babi:task10k:1 --model seq2seq --model-file seq2seq/babi_task10k --batchsize 32 --validation-every-n-secs 30 -stopk 5 -vstep 50Note that
-vstepoption must be exist to save and store 5 top checkpoints in this example. For me, the list of files in the-model-filedirectory after 1350 steps of training is like this: