How to run without progress bar?

What should I pass in for pbar in order to not have a progress bar?

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hey @source99, did you find the solution to your question?

Here you go. Hope it’s still helpful:

import fastai
from fastprogress import force_console_behavior
import fastprogress
fastprogress.fastprogress.NO_BAR = True
master_bar, progress_bar = force_console_behavior()
fastai.basic_train.master_bar, fastai.basic_train.progress_bar = master_bar, progress_bar

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thanks @Farah, apart from what you said I had to add this line to hide printing the metrics, not very nice but it works:

fastprogress.fastprogress.WRITER_FN = str
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Thanks for sharing. I found this when searching for how to use fastai with hyperopt. Here’s a codeblock that seems to work for me

from hyperopt import fmin, tpe, hp
import fastai
from fastprogress import force_console_behavior
import fastprogress


space = [
    {'layer1': hp.choice('layer1',[2,5,10,25,50,100,250,500])},
    {'layer2': hp.choice('layer2',[2,5,10,25,50,100,250,500])},
    {'ps': hp.uniform('ps',0,1)},
    {'epochs': hp.randint('epochs',10)},
    {'lr': hp.choice('lr',[1e-1,5e-2,1e-2])},
]

def objective(x):
    # suppress widgets
    fastprogress.fastprogress.NO_BAR = True
    master_bar, progress_bar = force_console_behavior()
    fastai.basic_train.master_bar, fastai.basic_train.progress_bar = master_bar, progress_bar
    fastprogress.fastprogress.WRITER_FN = str
    
    # learn using params from hyperopt
    learn = tabular_learner(data, layers=[x[0]['layer1'],x[1]['layer2']], metrics=accuracy, ps=x[2]['ps'])
    learn.fit(x[3]['epochs']+1, x[4]['lr'])
    return {'loss':learn.recorder.val_losses[-1:][0], 'status':'ok'}

best = fmin(objective,
    space=space,
    algo=tpe.suggest,
    max_evals=100)
print(best)```
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Incase anyone finds themselves here, to save you some digging around, there is a pattern that I think was just implemented that worked very well for me!

from fastai.utils.mod_display import *
with progress_disabled_ctx(learn) as learn:
        learn.fit_one_cycle(1)
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None of the above examples worked for. I am trying to run a learner in a Linux terminal. No mater what I do, the progress bar throws and exception (division by zero). Is there no way to complete get rid of any UI elements when running the code in production?

Hi @MichaelHeliso, can you clarify your division by zero error? What kind of model are you training? And what are you passing in for a learning rate? I believe this may have to do with the training itself rather than the progress bar.

I am just making use of load_learner in order to load a learner previously saved. I have checked the data frame that is passed as test data and it is not empty. Same code works just fine if I run it via a notebook.

Thank you very much!

If you came here and is using fastai (not fastai1 or fastai2) let me save you some time.

It is now much simpler to remove it now, just use:

with learner.no_bar():
    # do whatever you wanna do, example:
    learner.fine_tune(5)
    # or
    learner.predict(foo)