Hey all!
I am getting errors with the lesson 3 planets kernel, could someone help me with the updated commands? I think ImageItemList got removed.
Hi. When I was doing the second lesson I wanted to try to run the bear recognition model on my machine. As they say in the video itās better to learn the neural network with GPU but after model is ready I can use a CPU for image recognition. I saved a model to a file in a Kaggle kernel but I canāt find a way of downloading it. Is there a good way to get files from kernel or itād be easier to do it in different environment?
I am sorry I did not get back to you. I did not see this response until now. Did you get this to work?
if you export the file to the main directory, it should come up in the output tab of the kernel where you can download it
The only directory I see in the tab (Workspace section) is the input directory that located at /kaggle/input. The directory is in RO so I canāt move a file there. I tried to place a file in /, /kaggle etc. but canāt make this file to appear in the output tab.
try placing the file in ../
Hey @yappo , first of all welcome to the community. I usually save my models using learn.save("/kaggle/working/NameOfModel")
If you want to download them without committing you can refer to this
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Hi Dipam
I used these codes -
learn.save("/kaggle/working/devanagari")
OR
learn.save("/kaggle/working/devanagari.pkl")
And when I am using below code to see and download the files, its throwing me 404 error-
from IPython.display import FileLinks
FileLinks(ā/kaggle/working/ā) # input argument is specified folder
Can you please help?
Hey, sorry for the late response. Did you get your answer? Can you try FileLinks(ā.ā) i.e the root directory? I think that works. Otherwise Iāll check it once I get time. The input argument for learn.save() is for when you want to download models after committing. They appear in the output tab.
Hi again, thanks everyone for the quick answers. Still trying to figure out how to load a pretrained model Platform: Kaggle Kernels . What I did is I trained a model on Kaggle, saved it and then downloaded a .pth file. Then I run fastai on my commodity machine and wanted to load the model. But then I found out that to load a saved model I have to create a Learner object first. And to do that I have to specify a data bunch object. There is no problem to do this but the question is why do I need the data I trained the model on to build a learner from a saved model? Does a model uses this data in some way to evaluate samples? Should the data for loading and for training be exactly the same?
Something is off with lesson fast-ai-v3-lesson-3-planet.
There is a snippet
np.random.seed(42)
src = (ImageItemList.from_csv(path, 'train_v2.csv', folder='train-jpg', suffix='.jpg')
.random_split_by_pct(0.2)
.label_from_df(sep=' '))
I found out ImageItemList was changed to ImageList so it became
np.random.seed(42)
src = (ImageList.from_csv(path=path, csv_name='/kaggle/input/train_v2.csv', folder='train-jpg', suffix='.jpg')
.random_split_by_pct(0.2))
But I ecountered a proble on the next line. When I try to run
data = (src.transform(tfms, size=128)
.databunch(num_workers=0).normalize(imagenet_stats))
I get an error
/opt/conda/lib/python3.6/site-packages/fastai/data_block.py in transform(self, tfms, **kwargs)
491 if not tfms: tfms=(None,None)
492 assert is_listy(tfms) and len(tfms) == 2, "Please pass a list of two lists of transforms (train and valid)."
--> 493 self.train.transform(tfms[0], **kwargs)
494 self.valid.transform(tfms[1], **kwargs)
495 if self.test: self.test.transform(tfms[1], **kwargs)
AttributeError: 'ImageList' object has no attribute 'transform'
So my internal attributes of src object are not exactly what is expected. Donāt have a clue how it should be done then. Has anybody figured it out?
Itās not the first time I have problems with outdated notebooks. I wonder whatās the situation on other platforms. AFAIK Gradient is an official platform for fast.ai. Has anyone tried it and what can you say about it? Does it worth switching to Gradient?
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Hey, Iām not sure why you are getting this error but it might be due to the fact that you are not following all the steps of creating a databunch and in the order they have to be in. More specifically, after your random split method, you should be telling it how to label your data.
The order goes as follows:
- Where to find the data?
- How to split it?
- How to label it?
- Optionally add a test folder
- Data augmentation and create a data bunch.
Let me know if you are able to solve the problem with this or in any other way.
Cheers
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I have problem trying to save a file. When I run:
data.save("/kaggle/working/jigsaw.pkl")
The Kernel dies every time.
try doing it without the extension. Just data.save("/kaggle/working.jigsaw").
Then I get this error:
FileNotFoundError: [Errno 2] No such file or directory: '../input/../kaggle/working/jigsaw'
Another question, is it possible to do transfer learning with Kaggle and fastai? When I try to run the learner:
learn = text_classifier_learner(data_cl, arch=AWD_LSTM)
I get an error:
OSError: [Errno 30] Read-only file system: '../input/models'
Iām connected to the internet, but Iām assuming it wonāt download the pre-trained model since it canāt save to the input folder.
You need to pass another parameter to your learner. After, arch=AWD_LSTM, just add model_dir="/tmp/model/" and things will work fine.
can you share the full code for this? You can also check my Kaggle. Iāve been running all codes on Kaggle without any errors. Some kernels wonāt be up to date though.
Sorry folks my thread was at āTrackingā instead of āwatchingā so I missed out on the issues.
Iāve fixed that, for further issues, please tag me directly: @init_27 and share any issues that you might be facing. (Iām maintaining the kernels)
Iāll update all of the kernels that are having issues this weekend, apologies for not keeping an eye out.
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