The problem became a complex one to solve as even running a mere ls command was not an option on the network drive.

Firing up a jupyter notebook server everytime to view an image was not a feasible option. And there had to be a better way.

Upon some searching I found some popular options

1. feh
2. TerminalImageViewer (tiv)
3. Viu

In my use case, the best one which worked the best in my use case was viu because of its support for jpgs.

bash foo.sh &

would solve the problem for me, however I was working on a remote machine, which meant that closing the ssh connection would terminate the execution of the command.

Enter nohup. This is a wonderful bash utility which allows you to run your script in the background on a remote machine even when you close the connection.

nohup is just short for no hang up. You can use this command in multiple ways. There’s a wonderful article on DigitalOcean¹ explaining this command. I’ll just list a few of them here.

By default, nohup will save the output of the execution in nohup.out. You can change that with the redirect output command.

nohup ./foo.sh
nohup ./foo.sh > output.txt     # write (redirect) output to output.txt
nohup ./foo.sh & > output.txt   # write (redirect) output to output.txt and run the script in the background

Here’s another good reference from StackOverFlow²

This TIL has come into existence as I struggled with a issue that I was trying to fix at work.

s = 'foo'
if type(s) == str:
    print('this variable is of string type')

if isinstance(s, str):
    print('this variable is of string type')

class Person: pass

class Student(Person): pass


isinstance(Person(), Person)        # returns True
type(Student()) == Student          # returns True
isinstance(Student(), Person)       # returns True
type(Student()) == Person           # returns False, and this probably won't be what you want.

n = 5
if n == 5: print('Yep!')    # prints Yep!
if n is 5: print('Yay!')    # prints Yep!

L = []
L.append(1)

if L == [1]: print('Yay!')  # prints Yep!
if L is [1]: print('Yay!')  # prints nothing

Let me show you what I mean by that,

from fastai.vision.all import *
set_seed(99, True)
path = untar_data(URLs.PETS)/'images'

def label_func(x): return x[0].isupper()

dls = ImageDataLoaders.from_name_func(
    path, get_image_files(path), valid_pct=0.2,
    label_func=label_func, item_tfms=Resize(224))

learn = vision_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)
learn.export('saved-model.pkl')

Here, we are training a simple model for image classification and then exporting the saved model.

Now, if you were to use this model in a different jupyter notebook or a py file using the load_learner function.

from fastai.vision.all import *
learn = load_learner('saved-model.pkl')

You would get an error something like this…

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-c010bc50794d> in <module>
----> 1 learn = load_learner('saved-model.pkl')

3 frames
/usr/local/lib/python3.7/dist-packages/torch/serialization.py in find_class(self, mod_name, name)
   1040                     pass
   1041             mod_name = load_module_mapping.get(mod_name, mod_name)
-> 1042             return super().find_class(mod_name, name)
   1043
   1044     # Load the data (which may in turn use `persistent_load` to load tensors)

AttributeError: Custom classes or functions exported with your `Learner` not available in namespace.\Re-declare/import before loading:
    Can't get attribute 'label_func' on <module '__main__'>

This error is essentially saying that, before you load your model, the script (py file) or the notebook is looking for the function label_func.

Previously, I would just copy that whole function again from the previous file and paste it in my new file, something like this.

from fastai.vision.all import *
def label_func(x): return x[0].isupper()

learn = load_learner('saved-model.pkl')

But, as it turns out, the load_learner is just looking for a reference to the label_func and not it’s entire definition. So, in theory, you could have your code look something like…

from fastai.vision.all import *

## notice the empty function declaration
def label_func(x): pass

learn = load_learner('saved-model.pkl')

With this declaration, your inference would work just fine and you won’t get any problems.

I hope this was helpful. Happy Learning. Cheers!