ElegantKangaroo44 I think TrainsCheckpoint would probably be the easiest solution. I mean it will not be a must, but another option to deepen the integration, and allow us more flexibility.

So, using ignite I do the following:
task.phases['valid'].add_event_handler( Events.EPOCH_COMPLETED(every=1), Checkpoint(to_save, TrainsSaver(output_uri=self.checkpoint_path), 'best', n_saved=1, score_function=lambda x: task.phases['valid'].state.metrics[self.monitor] if self.monitor_mode == 'max' else -task.phases['valid'].state.metrics[self.monitor], score_name=self.monitor))This means as you said: The last model saved with this checkpointing is the best model.
But since I passed a score_function, ignite will automatically append the value of it on the suffix, and I end up with: checkpoint_best_acc= http://0.9.pt . I guess it's also implied that when you use the score_function parameter, you're saving your best model (why would you use a score otherwise?).
However, as we discussed in the issue in the ignite repo, there is no way to have a checkpoint that is simply named checkpoint_best at this moment. So, if I am to use the ignite Checkpoint to save the models, I have no power to change the suffix as an end user. But when I use it together with trains, I end up uploading to output_uri all the best models ever saved, regardless of me defining n_saved=1 (because of the issue we're discussing in ignite)
Now on top of that I also have
task.phases['train'].add_event_handler( Events.EPOCH_COMPLETED(every=self.save_freq), Checkpoint(to_save, TrainsSaver(output_uri=self.checkpoint_path), 'epoch', n_saved=5, global_step_transform=global_step_from_engine(task.phases['train'])))That saves checkpoints every 20 epochs as a backup (regardless of their accuracy).
That means, that I have no knowledge as to what type of checkpoint was the last torch.save that was called. It might be a backup, it might be a best model it might be a backup every 20 epochs.

Based on all that discussion I'm actually thinking maybe for Trains and ignite integration, there should be a TrainsCheckpoint instead of a TrainsSaver , that takes care of everything properly with respect to trains. But not sure if that's something the ignite ppl would want.

WDYT?

task.models["outputs"][-1].tags (plural, a list of strings) and yes I mean the UI 🙂

I get the n_saved what's missing for me is how would you tell the TrainsLogger/Trains the current one is the best? Or are we assuming the last saved model is always the best ? (in that case there is no need for tag, you just take the last in the list)

If we are going with: "I'm only saving the model if it is better than the previous checkpoint" then just always use the same name i.e. " http://model.pt " Trains will override the entry and you will have a single output model (this is the same idea that was suggest with n_saved, basically model_{:d}.pt.format(counter % n_saved)

You see what I mean by "telling" ? the automagic will log the "torch.save" call, but we need a way to signal to it (before or after the call) that this is the best model.

Good news:
 new 

best_model

 is saved, add a tag 

best

,
Already supported, (you just can't see the tag, but it is there :))

Interesting! Could you point me where the tagging happens? Also, by "see" do you mean UI? I tried doing task.models["outputs"][-1].tag but there's no property tag ( AttributeError: 'Model' object has no attribute 'tag' , seems that only OutputModel s have the tag property?)

I'll answer specifically for ignite on your question regarding the interface:
Doing something like passing the tag to TrainSaver seems convenient. Same for the config / metadata etc
TrainsSaver(output_uri, tag="best", metadata={})

(btw, obviously if we know it's not good, why do we bother to store it in the first place...)

During training you find a new best model every some epochs, so you save that. I agree no need to save all the best models, only one is enough (that's why I have n_saved=1 in my ignite checkpoints for best models). So you save a best_model , tag it, add metadata to it. Then you find a new best_model , and overwrite the previous.

But in ignite they give the option to keep n_saved so you would need to remove the "best" tag from the previously saved best models and only add it on the last one.

I hope this makes sense 😄

I'd prefer to use config_dict, I think it's cleaner

I'm definitely with you

Good news:

new 

best_model

 is saved, add a tag 

best

,

Already supported, (you just can't see the tag, but it is there :))

My question is, what do you think would be the easiest interface to tell (post/pre) store, tag/mark this model as best so far (btw, obviously if we know it's not good, why do we bother to store it in the first place...)

I'd prefer to use config_dict, I think it's cleaner (as a workaround for metadata). However, since I'm using ignite, I think I have no way to actually do that, or at least i'm not aware of it.

But I think that whatever way one chooses, you will have to go through N best models right after training and find the best one (because of the issue we're discussing on ignite).

I think ideal would be one of the two:
Only store a single model_best . After training you just find the model with that name in task.models["output"] Store as many model_best as you want (with n_saved from ignite). Everytime a new best_model is saved, add a tag best , and remove the tag from the previous best models (not sure how straightforward that is though)So one can just do at the end best = task.get_model_best After that you could also programmatically check against the accuracy of your production model (using metadata / json config) and decide what youwant to do with the model .

what do you think?

ElegantKangaroo44 what do you think?

It all depends how we store the meta-data on the performance. You could actually retrieve it from the say val metric and deduce the epoch based on that

ElegantKangaroo44 good question, that depends on where we store the score of the model itself. you can obviously parse the file name task.models['output'][-1].url and retrieve the score from it. you can also store it on the model name task.models['output'][-1].name and you can put it as general purpose blob o text on what is currently model.config_text (for convenience you can have model parse a json like text and use model.config_dict