'An attempt has been made to start' in lr_find with fast.ai
I am running this small piece of code to identify learning rate:

import cv2
from fastai.vision import *
from fastai.callbacks.hooks import *
path = untar_data(URLs.CAMVID)
path_lbl = path/'labels'
path_img = path/'images'
fnames = get_image_files(path_img)
lbl_names = get_image_files(path_lbl)
img_f = fnames[0]
img = open_image(img_f)
get_y_fn = lambda x: path_lbl/f'{x.stem}_P{x.suffix}'
mask = open_mask(get_y_fn(img_f))
src_size = np.array(mask.shape[1:])
src_size,mask.data
codes = np.loadtxt(path/'codes.txt', dtype=str); codes
size = src_size//2
bs=4
src = (SegmentationItemList.from_folder(path_img)
       .split_by_fname_file('../valid.txt')
       .label_from_func(get_y_fn, classes=codes))
data = (src.transform(get_transforms(), size=size, tfm_y=True)
        .databunch(bs=bs)
        .normalize(imagenet_stats))
name2id = {v:k for k,v in enumerate(codes)}
void_code = name2id['Void']
def acc_camvid(input, target):
    target = target.squeeze(1)
    mask = target != void_code
    return (input.argmax(dim=1)[mask]==target[mask]).float().mean()
wd=1e-2
learn = unet_learner(data, models.resnet34, metrics=acc_camvid, wd=wd)
lr_find(learn)
print("end")


And I get this error:

RuntimeError: 
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.


and also this one:

Traceback (most recent call last):
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2018.1.4\helpers\pydev\pydevd.py", line 1664, in &lt;module&gt;
    main()
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2018.1.4\helpers\pydev\pydevd.py", line 1658, in main
    globals = debugger.run(setup['file'], None, None, is_module)
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2018.1.4\helpers\pydev\pydevd.py", line 1068, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2018.1.4\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "C:/Users/steve/Project/fastai_unet/main.py", line 32, in &lt;module&gt;
    lr_find(learn)
  File "C:\Users\steve\Miniconda3\lib\site-packages\fastai\train.py", line 32, in lr_find
    learn.fit(epochs, start_lr, callbacks=[cb], wd=wd)
  File "C:\Users\steve\Miniconda3\lib\site-packages\fastai\basic_train.py", line 199, in fit
    fit(epochs, self, metrics=self.metrics, callbacks=self.callbacks+callbacks)
  File "C:\Users\steve\Miniconda3\lib\site-packages\fastai\basic_train.py", line 99, in fit
    for xb,yb in progress_bar(learn.data.train_dl, parent=pbar):
  File "C:\Users\steve\Miniconda3\lib\site-packages\fastprogress\fastprogress.py", line 72, in __iter__
    for i,o in enumerate(self._gen):
  File "C:\Users\steve\Miniconda3\lib\site-packages\fastai\basic_data.py", line 75, in __iter__
    for b in self.dl: yield self.proc_batch(b)
  File "C:\Users\steve\Miniconda3\lib\site-packages\torch\utils\data\dataloader.py", line 193, in __iter__
    return _DataLoaderIter(self)
  File "C:\Users\steve\Miniconda3\lib\site-packages\torch\utils\data\dataloader.py", line 469, in __init__
    w.start()
  File "C:\Users\steve\Miniconda3\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)
  File "C:\Users\steve\Miniconda3\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\Users\steve\Miniconda3\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "C:\Users\steve\Miniconda3\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
    reduction.dump(process_obj, to_child)
  File "C:\Users\steve\Miniconda3\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
BrokenPipeError: [Errno 32] Broken pipe


how can I fix this?

Oh, the solution is to wrap code in a method and invoke it:

import cv2
from fastai.vision import *
from fastai.callbacks.hooks import *

def main():
    path = untar_data(URLs.CAMVID)
    path_lbl = path/'labels'
    path_img = path/'images'
    fnames = get_image_files(path_img)
    lbl_names = get_image_files(path_lbl)
    get_y_fn = lambda x: path_lbl/f'{x.stem}_P{x.suffix}'
    mask = open_mask(get_y_fn(img_f))
    src_size = np.array(mask.shape[1:])
    src_size,mask.data
    codes = np.loadtxt(path/'codes.txt', dtype=str); codes
    size = src_size//2
    bs=4
    src = (SegmentationItemList.from_folder(path_img)
           .split_by_fname_file('../valid.txt')
           .label_from_func(get_y_fn, classes=codes))
    data = (src.transform(get_transforms(), size=size, tfm_y=True)
            .databunch(bs=bs)
            .normalize(imagenet_stats))
    name2id = {v:k for k,v in enumerate(codes)}
    void_code = name2id['Void']
    def acc_camvid(input, target):
        target = target.squeeze(1)
        mask = target != void_code
        return (input.argmax(dim=1)[mask]==target[mask]).float().mean()
    wd=1e-2
    learn = unet_learner(data, models.resnet34, metrics=acc_camvid, wd=wd)
    lr_find(learn)
    print("end")
if __name__ == '__main__':
    main()

