Is there a way to get notebooks with pinned versions of dependencies? The notebooks from pages like Practical Deep Learning for Coders - 4: Natural Language (NLP) generally don’t work for me, and the problem generally looks like version conflicts between the various libraries.
For example, when I clone the latest notebook from How does a neural net really work? | Kaggle
I get an error about timm not being installed. So I add a new code block with
!pip install timm
Then, I run it again and I get a little further, but it still errors out with
NameError Traceback (most recent call last)
/tmp/ipykernel_17/2451914947.py in
----> 1 learn = vision_learner(dls, resnet34, metrics=error_rate)
2 learn.fine_tune(3)NameError: name ‘vision_learner’ is not defined
which is a bit harder to diagnose.
I seem to hit these kinds of problems on every notebook.
Did you link to the right notebook? (How does a neural net really work? | Kaggle) does not appear to use vision_learner or fine_tune.
Generally if you see “NameError: name ‘vision_learner’ is not defined”. It indicates that the library has not been imported. e.g
from fastai.vision.all import *
“Is it a bird? Creating a model from your own data notebook” has an issue with the vision learner predict method with Pillow images.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/tmp/ipykernel_18/3073739047.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}")
4 im.to_thumb(256,256)
/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)
2960 exclusive_fp = True
2961
-> 2962 prefix = fp.read(16)
2963
2964 preinit()
/opt/conda/lib/python3.7/site-packages/PIL/Image.py in __getattr__(self, name)
517 )
518 return self._category
--> 519 raise AttributeError(name)
520
521 @property
AttributeError: read
Since version 2.7.11, you can no longer pass PIL images. You can just add the filename as a string and it will work again.
I commented on the exact same issue here:
1 Like
Hmm. You make a good point. I must have accidentally renamed it. And now I’m not sure where I got the original from. But here’s the workbook I’m having trouble with. How does a neural net really work? | Kaggle
On kaggle, you need to run the install for the libraries you need, not all libraries are included in it by default, and in some cases those libraries are old versions. the -U in !pip install fastai -U will upgrade this kaggle session to use the latest fastai library version.
So make sure you run those initial installs each session…
!pip install timm
!pip install fastai -U
The reason why it seem ok for some fastai imports but not for vision_leaner() in this case was because that function did not exist in the older version that Kaggle loaded (it used to be called cnn_leaner() but the name was changed to better reflect the variety of vision models now that aren’t just CNN’s)
You can check the versions with
from fastai.test_utils import show_install
show_install()
1 Like
Hi team,
I’m trying to setup the system, and I have created an account on Kaggle and done a clone of the github repo, and then created a notebook ok Kaggle and made File → Link to Github. Then tried the first instruction “from fastbook import *”, and it returns me an error:
`---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
/tmp/ipykernel_27/1480085386.py in
----> 1 from fastbook import *
ModuleNotFoundError: No module named ‘fastbook’`
How can I solve it? I attatch you a screenshot.
Thank you very much.
Josep
You need to install it first. Just use the command below:
!pip install -Uqq fastbook
Hope this helps 
1 Like
Hello! I am trying to get the Kaggle notebook to run for lesson 1: “Is it a bird?” However, I’m getting stuck on the very first cell. It seems like it’s picking up that my internet is not enabled. However, I don’t see an option to enable it!
My account is phone-verified:
Has anyone experienced this before?
Notebook settings are kinda hidden away. You can access it by clicking on the left arrow in right bottom of the screen:
Relevant setting is in Notebook options section:
1 Like
Wonderful, bugo, that solved my problem. Thank you very much.
1 Like
After scratching my head for a while over the various cloud notebook options, I wrote this blog post to hopefully help others:
3 Likes
Getting the following error when running the following commands from docs.fast.ai:
“conda install -c fastchan fastai”
or
“conda install -c fastchan fastai anaconda”
It happily chugs along downloading packages until it hits this:
------------- 219.9MB / 552.4MB @ 6.4MB/s cython-blis 29.5ssympy 17.0 B @ ??.?MB/s 0.3s
Multi-download failed. Reason: Transfer finalized, status: 404 [https://conda.anaconda.org/fastchan/win-64/sympy-1.11.1-py310h5588dad_2.tar.bz2] 17 bytes
So I assume that the link for sympy 1.11.1 in fastai ( I’m not very familiar with conda package dependency management structures ) is not an active link. Copy and pasting that link into a browser shows “release not found”.
I next wiped the environment, installed sympy seperately with "mamba install sympy " and then ran “mamba install -c fastchan fastai”
Now conda is throwing an “unexpected error” but doesn’t list the actual error. It only shows the environment variables.
I’ll try the “pytorch > pip install fastai” method next.
I’m posting this just in case anyone else sees or has seen these issues.
EDIT:
To be clear I’m trying to install this on a local environment, not one of the notebook hosting providers.
EDIT 2: edited for clarity.
SOLUTION:
PyTorch does not support python 3.11 so I re-created the environment with 3.9. I still got the sympy error, but this time after installing it separately I was able to install the rest of fastai using:
mamba install -c fastchan fastai -y
1 Like
Hi everyone,
I made my copy of the first lesson (Is it a bird?) on Kaggle, but am stuck on step 1:
from duckduckgo_search import ddg_images
from fastcore.all import *
def search_images(term, max_images=30):
print(f"Searching for '{term}'")
return L(ddg_images(term, max_results=max_images)).itemgot('image')
returns
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/tmp/ipykernel_18/1717929076.py in <module>
----> 1 from duckduckgo_search import ddg_images
2 from fastcore.all import *
3
4 def search_images(term, max_images=30):
5 print(f"Searching for '{term}'")
/opt/conda/lib/python3.7/site-packages/duckduckgo_search/__init__.py in <module>
8
9 # isort: off
---> 10 from .compat import (
11 ddg,
12 ddg_answers,
/opt/conda/lib/python3.7/site-packages/duckduckgo_search/compat.py in <module>
2 import warnings
3
----> 4 from .duckduckgo_search import DDGS
5
6 logger = logging.getLogger(__name__)
/opt/conda/lib/python3.7/site-packages/duckduckgo_search/duckduckgo_search.py in <module>
11
12 import requests
---> 13 from requests.exceptions import HTTPError, JSONDecodeError, Timeout
14 from requests.models import Response
15
ImportError: cannot import name 'JSONDecodeError' from 'requests.exceptions' (/opt/conda/lib/python3.7/site-packages/requests/exceptions.py)
In the next cell it says that I should try again, if I get a JSON error, but even after many tries it seems not to work. I am new to Python, so any hint is appreciated. Thanks!
1 Like
Yeah I am getting same error too
Please help
Me too - the exact same error.
@hewag1975, @Devesh2000, @oliverbogler,
As a diagnostic, select menu Run > Factory Reset, then add the following cell to the top of the notebook and execute it…
!pip install -U requests
import requests
print("IMPORTED VERSION IS ", requests.__version__)
from requests.exceptions import JSONDecodeError
print("NOW OKAY")
and you might see…
Although you see it reports that version 2.31.0 was successfuly installed in the operating system, it shows an older version 2.26.0 was imported from a cache.
Now go Run > Restart and clear outputs, and run that first cell again. This time you should see…
and your original problem should also have been resolved.
The root cause was that history shows JSONDecodeError was not added until version 2.27.0
5 Likes
Thank you - that has indeed removed this error - much appreciated.