I thank you.
Is there a step by step procedure to install RISE on the Kaggle notebook? I have not been able to get the command palette button or the cell toolbar. I used:
pip install RISE as explained in RISE ā RISE 5.7.1
Hi, I really hope this is the right place to ask my question.
Iām getting an error when I run the first example of the fastbook (the one that trains a model, shows an upload button, then determines whether my image is of a cat vs a dog)
- I was able to successfully train the model
- I was able to successfully load my image (tested that the problem was not with the upload button by showing a small thumbnail of the image I uploaded via
to_thumb()) - everytime a run
The predict function throws an erroris_cat,_,probs = learn.predict(img)
The error starts with learn.predict and ends with:
File ~/jupyter/lib/python3.10/site-packages/PIL/Image.py:529, in Image.__getattr__(self, name)
527 deprecate("Image categories", 10, "is_animated", plural=True)
528 return self._category
--> 529 raise AttributeError(name)
AttributeError: read
I can post the full backtrace but itās quite long. Any help would realy be appreciated.
Note: Iām running jupyter on my linux laptop inside a venv. Python version is 3.10.6.
Hi, Iām new to the course, and Iām trying just to train the cats&dogs model, using Google Colab it takes years (more than 1 hour and still running) to run the āfine_tuneā function.
Iām also trying it on Kaggle, it looks a bit faster, but still is taking lot of time. Is this normal?
Hi,
I donāt think that is normal. It looks like you are not using GPU.
On Google Colab, you can click on Runtime tab on the top left corner and click change runtime type. If itās CPU, you should change it to a GPU.
On Kaggle, you can take the similar approach.
1 Like
Ok! thank you for the tip, I thought the GPU choice in Kaggle was a payment option or something like that. I see theyāre limited to an amount of hours per week, which, of course, is enough for a student like me.
It took only 2 minutes now to train the cats & dogs model. What a difference 
Since it took me so long without the GPU, I setup a conda environment locally and it took 7 minutes in my non-Nvidia GPU desktop, that anyway, was much better than the 1h in Google Colab & Kaggle without GPU. But having the GPU option in Kaggle, Iāll do the things there.
3 Likes
Hello! I am new and am excited to check out this course. I noticed thereās an issue with the very first Kaggle demonstration notebook (Is it a bird?).
If this should be posted elsewhere, please let me know ā but PLEASE do not be mean like many on StackOverflow. I attempted to solve the error with GPT-4 as well, and it was having a tough time.
Without making any modifications, and just running the code as is, the last cell output produces an error. I find this unfortunate and it does not aid in my learning. All cells in the notebook were ran sequentially.
I have attempted to run the cells in both google colab and kaggle environments, and both produce the same error. I also could not similar questions posted.
Hereās the last cell ran:
is_bird,_,probs = learn.predict(PILImage.create('bird.jpg'))
print(f"This is a: {is_bird}.")
print(f"Probability it's a bird: {probs[0]:.4f}")
Error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/tmp/ipykernel_17/1213330699.py in <module>
----> 1 is_bird,_,probs = learn.predict(PILImage.create('bird.jpg'))
2 print(f"This is a: {is_bird}.")
3 print(f"Probability it's a bird: {probs[0]:.4f}")
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in predict(self, item, rm_type_tfms, with_input)
319 def predict(self, item, rm_type_tfms=None, with_input=False):
320 dl = self.dls.test_dl([item], rm_type_tfms=rm_type_tfms, num_workers=0)
--> 321 inp,preds,_,dec_preds = self.get_preds(dl=dl, with_input=True, with_decoded=True)
322 i = getattr(self.dls, 'n_inp', -1)
323 inp = (inp,) if i==1 else tuplify(inp)
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in get_preds(self, ds_idx, dl, with_input, with_decoded, with_loss, act, inner, reorder, cbs, **kwargs)
306 if with_loss: ctx_mgrs.append(self.loss_not_reduced())
307 with ContextManagers(ctx_mgrs):
--> 308 self._do_epoch_validate(dl=dl)
309 if act is None: act = getcallable(self.loss_func, 'activation')
310 res = cb.all_tensors()
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _do_epoch_validate(self, ds_idx, dl)
242 if dl is None: dl = self.dls[ds_idx]
243 self.dl = dl
--> 244 with torch.no_grad(): self._with_events(self.all_batches, 'validate', CancelValidException)
245
246 def _do_epoch(self):
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
197
198 def _with_events(self, f, event_type, ex, final=noop):
--> 199 try: self(f'before_{event_type}'); f()
200 except ex: self(f'after_cancel_{event_type}')
201 self(f'after_{event_type}'); final()
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in all_batches(self)
203 def all_batches(self):
204 self.n_iter = len(self.dl)
--> 205 for o in enumerate(self.dl): self.one_batch(*o)
206
207 def _backward(self): self.loss_grad.backward()
/opt/conda/lib/python3.7/site-packages/fastai/data/load.py in __iter__(self)
125 self.before_iter()
126 self.__idxs=self.get_idxs() # called in context of main process (not workers/subprocesses)
--> 127 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
128 # pin_memory causes tuples to be converted to lists, so convert them back to tuples
129 if self.pin_memory and type(b) == list: b = tuple(b)
/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
519 if self._sampler_iter is None:
520 self._reset()
--> 521 data = self._next_data()
522 self._num_yielded += 1
523 if self._dataset_kind == _DatasetKind.Iterable and \
/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)
559 def _next_data(self):
560 index = self._next_index() # may raise StopIteration
--> 561 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
562 if self._pin_memory:
563 data = _utils.pin_memory.pin_memory(data)
/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
32 raise StopIteration
33 else:
---> 34 data = next(self.dataset_iter)
35 return self.collate_fn(data)
36
/opt/conda/lib/python3.7/site-packages/fastai/data/load.py in create_batches(self, samps)
136 if self.dataset is not None: self.it = iter(self.dataset)
137 res = filter(lambda o:o is not None, map(self.do_item, samps))
--> 138 yield from map(self.do_batch, self.chunkify(res))
139
140 def new(self, dataset=None, cls=None, **kwargs):
/opt/conda/lib/python3.7/site-packages/fastcore/basics.py in chunked(it, chunk_sz, drop_last, n_chunks)
228 if not isinstance(it, Iterator): it = iter(it)
229 while True:
--> 230 res = list(itertools.islice(it, chunk_sz))
231 if res and (len(res)==chunk_sz or not drop_last): yield res
232 if len(res)<chunk_sz: return
/opt/conda/lib/python3.7/site-packages/fastai/data/load.py in do_item(self, s)
151 def prebatched(self): return self.bs is None
152 def do_item(self, s):
--> 153 try: return self.after_item(self.create_item(s))
154 except SkipItemException: return None
155 def chunkify(self, b): return b if self.prebatched else chunked(b, self.bs, self.drop_last)
/opt/conda/lib/python3.7/site-packages/fastai/data/load.py in create_item(self, s)
158 def retain(self, res, b): return retain_types(res, b[0] if is_listy(b) else b)
159 def create_item(self, s):
--> 160 if self.indexed: return self.dataset[s or 0]
161 elif s is None: return next(self.it)
162 else: raise IndexError("Cannot index an iterable dataset numerically - must use `None`.")
/opt/conda/lib/python3.7/site-packages/fastai/data/core.py in __getitem__(self, it)
456
457 def __getitem__(self, it):
--> 458 res = tuple([tl[it] for tl in self.tls])
459 return res if is_indexer(it) else list(zip(*res))
460
/opt/conda/lib/python3.7/site-packages/fastai/data/core.py in <listcomp>(.0)
456
457 def __getitem__(self, it):
--> 458 res = tuple([tl[it] for tl in self.tls])
459 return res if is_indexer(it) else list(zip(*res))
460
/opt/conda/lib/python3.7/site-packages/fastai/data/core.py in __getitem__(self, idx)
415 res = super().__getitem__(idx)
416 if self._after_item is None: return res
--> 417 return self._after_item(res) if is_indexer(idx) else res.map(self._after_item)
418
419 # %% ../../nbs/03_data.core.ipynb 53
/opt/conda/lib/python3.7/site-packages/fastai/data/core.py in _after_item(self, o)
375 raise
376 def subset(self, i): return self._new(self._get(self.splits[i]), split_idx=i)
--> 377 def _after_item(self, o): return self.tfms(o)
378 def __repr__(self): return f"{self.__class__.__name__}: {self.items}\ntfms - {self.tfms.fs}"
379 def __iter__(self): return (self[i] for i in range(len(self)))
/opt/conda/lib/python3.7/site-packages/fastcore/transform.py in __call__(self, o)
206 self.fs = self.fs.sorted(key='order')
207
--> 208 def __call__(self, o): return compose_tfms(o, tfms=self.fs, split_idx=self.split_idx)
209 def __repr__(self): return f"Pipeline: {' -> '.join([f.name for f in self.fs if f.name != 'noop'])}"
210 def __getitem__(self,i): return self.fs[i]
/opt/conda/lib/python3.7/site-packages/fastcore/transform.py in compose_tfms(x, tfms, is_enc, reverse, **kwargs)
156 for f in tfms:
157 if not is_enc: f = f.decode
--> 158 x = f(x, **kwargs)
159 return x
160
/opt/conda/lib/python3.7/site-packages/fastcore/transform.py in __call__(self, x, **kwargs)
79 @property
80 def name(self): return getattr(self, '_name', _get_name(self))
---> 81 def __call__(self, x, **kwargs): return self._call('encodes', x, **kwargs)
82 def decode (self, x, **kwargs): return self._call('decodes', x, **kwargs)
83 def __repr__(self): return f'{self.name}:\nencodes: {self.encodes}decodes: {self.decodes}'
/opt/conda/lib/python3.7/site-packages/fastcore/transform.py in _call(self, fn, x, split_idx, **kwargs)
89 def _call(self, fn, x, split_idx=None, **kwargs):
90 if split_idx!=self.split_idx and self.split_idx is not None: return x
---> 91 return self._do_call(getattr(self, fn), x, **kwargs)
92
93 def _do_call(self, f, x, **kwargs):
/opt/conda/lib/python3.7/site-packages/fastcore/transform.py in _do_call(self, f, x, **kwargs)
95 if f is None: return x
96 ret = f.returns(x) if hasattr(f,'returns') else None
---> 97 return retain_type(f(x, **kwargs), x, ret)
98 res = tuple(self._do_call(f, x_, **kwargs) for x_ in x)
99 return retain_type(res, x)
/opt/conda/lib/python3.7/site-packages/fastcore/dispatch.py in __call__(self, *args, **kwargs)
118 elif self.inst is not None: f = MethodType(f, self.inst)
119 elif self.owner is not None: f = MethodType(f, self.owner)
--> 120 return f(*args, **kwargs)
121
122 def __get__(self, inst, owner):
/opt/conda/lib/python3.7/site-packages/fastai/vision/core.py in create(cls, fn, **kwargs)
123 if isinstance(fn,bytes): fn = io.BytesIO(fn)
124 if isinstance(fn,Image.Image) and not isinstance(fn,cls): return cls(fn)
--> 125 return cls(load_image(fn, **merge(cls._open_args, kwargs)))
126
127 def show(self, ctx=None, **kwargs):
/opt/conda/lib/python3.7/site-packages/fastai/vision/core.py in load_image(fn, mode)
96 def load_image(fn, mode=None):
97 "Open and load a `PIL.Image` and convert to `mode`"
---> 98 im = Image.open(fn)
99 im.load()
100 im = im._new(im.im)
/opt/conda/lib/python3.7/site-packages/PIL/Image.py in open(fp, mode, formats)
2919 exclusive_fp = True
2920
-> 2921 prefix = fp.read(16)
2922
2923 preinit()
/opt/conda/lib/python3.7/site-packages/PIL/Image.py in __getattr__(self, name)
539 )
540 return self._category
--> 541 raise AttributeError(name)
542
543 @property
AttributeError: read
Looks like this may work ā but begs the question of why hasnāt anyone updated the default notebook? Perhaps because itās a free offering and no one is financially dependent on ensuring this course stays a good product for years to comeā¦
if iskaggle:
!pip install -Uqq fastai==2.7.10 duckduckgo_search
import fastai
fastai.__version__
Another note, it seems the -Uqq still updates to the newest version. When removing the āUā, it seemed to install the right one:
if iskaggle:
!pip install -qq fastai==2.7.10 duckduckgo_search
import fastai
fastai.__version__
1 Like
I 've started the course. I have nearly zero prior experience, other than trying different data analytics and python courses here and there. Feel quite excited! Hope I can come back to this my post in 3-4 months with a smile 
Here is my first result of identifying two different swimming strokes: fly or breast. Apparently, I need to learn how to train the model manually
Impressiveā¦you seemed to avoid all the issues myself and others have had in the past (regarding the out of date notebooks). You seem like youāre very knowledgeable in the field, already!
Thank you Appreciate your kind support
Hi, Iām just pretty new to the course. One thing Iād like sharing to other newcomers.
If youāre working on Kaggle and you change the accelerator (Non/GPU) youāll need to restart and run everything from the beginning.
Iāve been struggled a bit with the āis a bird practiceā because the āvision_learnerā function was not found. Then at some point I restarted an run everything without accelerator and everything worked fine.
Then I switched to GPU Tx2 and GPU P-100 and it didnāt find the function again, but I restarted the notebook and run all againā¦ and voilĆ”, the function was found.
So switching the accelerator implies resetting and run it all again.
Am I wrong?
I noticed that as well. Also, wasnāt there a breakthrough in Deep Learning in 2012 (2 years prior this comic) with AlexNet that could classify images?
I know that this is trivial stuff and doesnāt contribute in making models but I am a bit nerdy on these things 
.
Hello,
I am trying to follow the first lesson and code from scratch in my own kaggle notebook.
When running this code block I am getting an error that there is no module named fastbook:
!pip install -Uqq fastbook
import fastbook
from fastbook import *
from fastai.vision.all import *
What am I doing wrong?
1 Like
For reading the chapters I recommend you to follow the links to the chapters provided in (Practical Deep Learning for Coders - 1: Getting started)
In case you want to write the is_a_cat? model from scratch, you donāt need to install fastbook or import it, just:
from fastai.vision.all import *
import ipywidgets as widgets
Hi @eyp
thanks for answering. Could you explain why you dont need to import either fastbook or fastai? Without importing it my notebook would not have access to either of those libraries no?
your code worked, but when I try to run the next part of the lessons code:
from fastai.vision.all import *
path = untar_data(URLs.PETS)/'images'
def is_cat(x): return x[0].isupper()
dls = ImageDataLoaders.from_name_func(
path, get_image_files(path), valid_pct=0.2, seed=42,
label_func=is_cat, item_tfms=Resize(224))
learn = vision_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)
I get the following error:
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
URLError Traceback (most recent call last)
/tmp/ipykernel_27/1897567515.py in <module>
3 # CLICK ME
4 from fastai.vision.all import *
----> 5 path = untar_data(URLs.PETS)/'images'
6
7 def is_cat(x): return x[0].isupper()
Seems whatever is the URL at URLs.PETS is not valid.
However this code works when running it from here: Google Colab
1 Like
Hello guys,Iāve followed the instructions of chapter 1 and wanted to learn the model to check for busy vs empty supermarket parking photos (e.g. āLidl parking supermarket photosā). However, the accuracy is quite low at 0.4017. Any ideas how can I improve it?
searches = 'empty lidl parking photo', 'busy lidl parking photo'
path = Path('xoxo')
from time import sleep
for o in searches:
dest = (path/o)
dest.mkdir(exist_ok=True, parents=True)
download_images(dest, urls=search_images(f'{o} photo'))
sleep(10) # Pause between searches to avoid over-loading server
download_images(dest, urls=search_images(f'{o} sun photo'))
sleep(10)
download_images(dest, urls=search_images(f'{o} shade photo'))
sleep(10)
download_images(dest, urls=search_images(f'{o} dark photo'))
sleep(10)
resize_images(path/o, max_size=400, dest=path/o)
failed = verify_images(get_image_files(path))
failed.map(Path.unlink)
len(failed)
dls = DataBlock(
blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(valid_pct=0.3, seed=42),
get_y=parent_label,
item_tfms=[Resize(1024, method='squish')]
).dataloaders(path, bs=32)
dls.show_batch(max_n=6)
learn = vision_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(4)
is_parking,_,probs = learn.predict(PILImage.create('bird.jpg'))
print(f"This is a: {is_parking}.")
print(f"Probability it's a lidl parking: {probs[0]:.4f}")
Probability itās a lidl parking: 0.4017
