Create a new instance from a non-FastAI template, which don’t call pre-run.sh.
Then you can login in and edit the file.
btw, in case its useful… GitHub - fastai/paperspace-setup: Setup a paperspace instance for fastai
2 Likes
Wow! Worked like a charm! Thank you!
1 Like
I am encountering an error while attempting to install the fastai package using the command “mamba install -c fastchan fastai” and also receiving a similar error when using conda. The error states that there is a “Download error (28) Timeout was reached [https://conda.anaconda.org/fastchan/noarch/joblib-1.2.0-pyhd8ed1ab_0.tar.bz2]
Operation too slow. Less than 30 bytes/sec transferred the last 60 seconds”.
I’m not a conda adept, but with a poor internet connection, I guess you have in general two options:
-
Extend the timeout, something like… anaconda - Changing timeout limit when using conda install - Stack Overflow
-
Make an offline cache and isntall from there… Cache conda packages for offline installation - HackMD
Disclaimer, I’ve not done either of those myself. Just found them in a google search.
Thanks for your input.
The problem I faced was not related to my internet connection. I tried the same commands today and it worked. It was likely a temporary problem with the server from wherever it was downloading.
The solutions you suggested are useful to keep in mind for future reference.
1 Like
Hi, I am setting up Jarvis.ai RTX5000 and running the first notebook I get the error:
executing — from fastai.vision.all import *
i get error:
NameError: name ‘_C’ is not defined
Which I think means Pytorch is not found?
I am using Fastai 2.74 configuration from Jarvis menu.
Any help on what I may have missed? Thanks
David
Traceback:
File /opt/conda/lib/python3.8/site-packages/torch/init.py:231, in
216 raise ImportError(textwrap.dedent(‘’’
217 Failed to load PyTorch C extensions:
218 It appears that PyTorch has loaded the torch/_C folder
(…)
226 or by running Python from a different directory.
227 ‘’‘).strip()) from None
228 raise # If file is not None the cause is unknown, so just re-raise.
→ 231 all += [name for name in dir(C)
232 if name[0] != '’ and
233 not name.endswith(‘Base’)]
235 if not TYPE_CHECKING:
236 # issue 38137 and python issue 43367. Submodules of a C extension are
237 # non-standard, and attributes of those submodules cannot be pickled since
238 # pickle expect to be able to import them as “from _C.sub import attr”
239 # which fails with "_C is not a package
240 for attr in dir(_C):
NameError: name ‘_C’ is not defined
Hi David,
I was going to try to help by creating a Javis account, but couldn’t see a free option, so thats out.
You’ve not shown the research you’ve already tried, so please read the section “Before You Ask” here…
How To Ask Questions The Smart Way
Browsing the first couple of results here looked promising…
NameError:+name+‘_C’+is+not+defined
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.
See how you go with… how to pin versions with pip install - Google Search
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