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retrain same model via http-endpoint #529
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This was also requested in the comments on #433. #438 isn't entirely dissimilar either. if I get around to it I'll consolidate all of these into a single issue for http API updates. @mnannt for the time being I would recommend a version number or training datetime or something similar in the model name such that you aren't loading exactly the same model. |
@wrathagom thanks. But because I also need to provide the model-name in the parse-queries, I would also need to change the model-name in all systems that make requests to the nlu with every deploy and unfortunately that's a bit unpractical :-( |
sorry, we've put a small wrapper around Rasa to implement features like this. Maybe if #449 gets pulled it will solve your problem. |
@mnannt my 2 cents :) Retraining on the same model can be a problem for production systems. I used to overwrite my models and then at some point, one of the training didn't work perfectly and I started to see a critical drop in my responses confidence. I had to find where the problem was coming from and retrain the model. Training new model all the time (with a timestamp) is good because it makes rollbacks easier (and they will happen in production systems). I then fetch the up-to-date model names from DB. |
I'd still like to see the functionality of deleting/unloading a model. But that is fully covered by #438 |
Sure it absolutely makes sense ! |
Ha, fixed it. Subsequent people coming here will be like, "expensive, what the heck is that crazy person talking about"! :) That being said I'm also not the original poster. |
gonna close this because you can delete models now |
…asaHQ#529) fixes RasaHQ#528 Co-authored-by: Hugh Lunt <hugh.lunt@itv.com> Co-authored-by: Hugh Lunt <hugh.lunt@itv.com>
rasa NLU version (e.g.
0.7.3
): unreleased-master (0.9.1?)Used backend / pipeline (
mitie
,spacy_sklearn
, ...): spacy_sklearnOperating system (windows, osx, ...): osx
Issue:
When I train a model named
my_model
via the /train http-endpoint it trains and serves the model in the right way. When I retrain the same model again, the training works well but queries to this model still runs against the old version.first training:
second training:
after this, queries against the parse endpoint still returns the intent/entities of expressions.json
Our goal is to easily retrain our server without deploying new code/a new model in the code.
Content of configuration file (if used & relevant):
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