While using Active Learning(using Custom Model) with prodigy, I was wondering if there is a way by which we can keep on yielding out data to the UI, without waiting for model training to complete.
I am using the two base methods : “predict” to give out the data to the UI, and “Update” to train the model in batches after every ‘n’ sentences and make predictions every time the model is updated.
So every time the update function is training the model, we get ‘Loading…’ on the screen, and my model training time might be huge which leads to huge waiting time.
So, is there a workaround to yield the data without waiting for the model training to complete?
Hi! Are you sure that what takes so long is the updating and not the predicting? The
update callback shouldn’t necessarily be blocking and it’s just something that’ll run in the background, usually while you’re annotating the examples in the current queue.
However, there are several things that could make fetching a new batch of tasks take long: long texts in NER mode (because generating all possible analyses for the text will take a long time), large model with word vectors, rare labels that are almost never predicted, or lots of duplicates in the data (because most of the time will be spent skipping examples).
Which recipe are you using and which model are you starting with? And what’s the configured batch size?
No, I am not sure, that was just a guess because I am predicting right after training the model (both training and predictions will be done in one process sequentially, and this process is spawned inside the Update function), the “predict” function will just yield out the sentences from the updated dictionary.
No, skipping lots of sentences shouldn’t be the reason, as the dictionary yielding out sentences always contains non-confident data only.
I am using a custom recipe, which yields out sentences in the batches of size 10, and I am starting with a blank model.
So, you mean to say that predict and update are 2 parallel processes? As far as I observed, both the update and predict function have same pid.
Ah, sorry, I just realised that the way I described this was bad – just edited my original post. What I meant to say was: What happens asynchronously is the action on the front-end (sending answers, requesting a new batch) and the action on the back-end. The updating usually happens while you’re annotating the current queue in the app, so it’d really have to take very long for it to have an impact like that.
This is definitely pretty strange if you’re working with batches of only 10. How are you updating the model in your
update callback? Are you updating it with all of the examples or only the current batch?