Have you followed the hints/instructions it gives there to install git-lfs?
The other option is to upload the binary files through the web interface on your app’s huggingface spaces page.
Go to the ‘files and versions tab’ and click the ‘add file’ button and then select ‘upload files’ option.
Thanks AllenK,
yes I did install git-lfs and attempted to activate it, after which I receive this confirmation
Updated Git hooks.
Git LFS initialized.
but I still get the same error when pushing my commits
However, your suggestion about uploading directly to the web interface worked
Hi,
(Creating a dataset)
I am new to the forums and also to fastai. I was going through the text classifier tutorial and found it to be very interesting, considering this I would like to build a classifier on my own data. Can someone please guide me to the documentation or tutorial where I can understand the format of the dataset that is required to train the text classifier so that I can convert my own data in a required format.
It would be very helpful, looking forward to learn a lot from this community.
Regards,
Chef
The dataset format in the Text transfer learning – fastai is described in the text.
The data follows an ImageNet-style organization, in the train folder, we have two subfolders,
posandneg(for positive reviews and negative reviews)
You can create a new dataset by following the same format, to get a better look at the structure for the dataset, take a look in the directory path where the dataset is downloaded.
path = untar_data(URLs.IMDB)
path.ls()
At its simplest, the structure for this dataset is just a dataset name folder which contains a train and test directory, which contain a ‘neg’ and ‘pos’ directory (the categories), and inside those go plain text files that contain the example text (e.g ‘Great movie. I was laughing all time through’)
and saved with a unique filename. It looks like this.
datasetname_folder/
train/
neg/
00001.txt
00002.txt
pos/
10001.txt
10002.txt
test/
neg/
20001.txt
20002.txt
pos/
30001.txt
30002.txt
Read more about the various datasets in fast.ai here External data – fastai.
That page also links to this original source project for the IMDB dataset, where you can read the related papers.
Hi, I am currently following lesson 2 (Part 1 2022) and trying to deploy the app on HuggingFace spaces (the “testing” example). When I use git push I get the following error
fatal: the remote end hung up unexpectedly
@Kamui I found in my case the remote end hung up error was followed by:
git-lfs filter-process: git-lfs: command not found
I installed git-lfs using homebrew (brew install git-lfs) and that solved the issue for me.
I just tried that and I still get the error:
Enumerating objects: 11, done.
Counting objects: 100% (11/11), done.
Delta compression using up to 8 threads
Compressing objects: 100% (8/8), done.
error: RPC failed; HTTP 408 curl 22 The requested URL returned error: 408
fatal: the remote end hung up unexpectedly
Writing objects: 100% (10/10), 41.72 MiB | 135.00 KiB/s, done.
Total 10 (delta 0), reused 0 (delta 0)
fatal: the remote end hung up unexpectedly
Everything up-to-date
Here are a couple similar cases that others have encountered:
https://confluence.atlassian.com/stashkb/git-push-fails-fatal-the-remote-end-hung-up-unexpectedly-282988530.html
Maybe one of those answers would help?
Thank you so much for your help, I used a work around (I uploaded the file via the website). I will try that for the next project.
Hi. I’m stuck importing gradio with this error:
import gradio as gr
ImportError: cannot import name ‘Doc’ from ‘typing_extensions’ (/Users/m/anaconda3/lib/python3.11/site-packages/typing_extensions.py)
I’m running this Jupyter notebook locally with Python 3.8. Gradio is v3.50.2 and FastAPI is v 0.104.0.
I’ve tried uninstalling gradio, FastAPI and typing-extensions and downgrading the versions but to no avail.
How can I proceed with Lesson 2? Any help is much appreciated.
Here’s the error in full:
File ~/anaconda3/lib/python3.11/site-packages/fastapi/exceptions.py:6
4 from starlette.exceptions import HTTPException as StarletteHTTPException
5 from starlette.exceptions import WebSocketException as StarletteWebSocketException
----> 6 from typing_extensions import Annotated, Doc # type: ignore [attr-defined]
9 class HTTPException(StarletteHTTPException):
10 “”"
11 An HTTP exception you can raise in your own code to show errors to the client.
12
(…)
34 ```
35 “”"
ImportError: cannot import name ‘Doc’ from ‘typing_extensions’ (/Users/m/anaconda3/lib/python3.11/site-packages/typing_extensions.py)
I get the same error in Google Colab. I copy-pasted the code from Tanishq’s tutorial on Gradio+Huggingface Spaces into a copy of 02_production.ipynb (2nd notebook from the book).
labels = learn_inf.dls.vocab
def predict(img):
img = PILImage.create(img)
pred,pred_idx,probs = learn_inf.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}```
import gradio as gr
gr.Interface(fn=predict, inputs=gr.Image(shape=(224, 224)), outputs=gr.Label(num_top_classes=3)).launch(share=True)
<ipython-input-28-859b48895f67> in <cell line: 1>()
----> 1 import gradio as gr
2 gr.Interface(fn=predict, inputs=gr.Image(shape=(224, 224)), outputs=gr.Label(num_top_classes=3)).launch(share=True)
13 frames
/usr/local/lib/python3.10/dist-packages/fastapi/exceptions.py in <module>
4 from starlette.exceptions import HTTPException as StarletteHTTPException
5 from starlette.exceptions import WebSocketException as StarletteWebSocketException
----> 6 from typing_extensions import Annotated, Doc # type: ignore [attr-defined]
7
8
ImportError: cannot import name 'Doc' from 'typing_extensions' (/usr/local/lib/python3.10/dist-packages/typing_extensions.py)
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.`Omg, this error drove me completely bonkers. I can’t remember exactly how to fixed it. I think it was by downgrading gradio to 3.0
pip install gradio==3.0
no module named ‘gradio’
I’ve been banging my head against the wall on this for several hours now. import gradio is giving me this error message: no module named ‘gradio’. I’ve installed gradio, I’m able to locate it in Finder. It is located in:
/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/
sys.path gives this return:
[‘/kaggle/working’,
‘/kaggle/lib/kagglegym’,
‘/kaggle/lib’,
‘/opt/conda/lib/python310.zip’,
‘/opt/conda/lib/python3.10’,
‘/opt/conda/lib/python3.10/lib-dynload’,
‘’,
‘/root/.local/lib/python3.10/site-packages’,
‘/opt/conda/lib/python3.10/site-packages’,
‘/root/src/BigQuery_Helper’]
I’m using Kaggle, in Firefox, on an iMac.
Any assistance or suggestions is greatly appreciated.
Hello, I am having a challenge downloading the dataset from lecture two. It says no datasets available. Am I supposed to upload any data?
In the lesson 2 Saving a basic fastai model the dataset is downloaded with the following line of code:
path = untar_data(URLs.PETS)/'images'
untar_data is a fastai function that takes a URL and extracts the paths from it. URLs is a fastai object that contains different URL values corresponding to freely available datasets.
If you are looking for the saved model output from the following line:
Either download it while your Kaggle notebook is running from the '/kaggle/workig folder in the sidebar, or after you save your notebook (by clicking “Save Version” > “Save & Run All (Commit)”), you should be able to access the exported model in the “Output” tab on the page in your screenshot.
What challenges are you experiencing with datasets?
Thank you. I think I still have to play with different datasets. I am starting to understand. Would you like to create a study group?
I am experimenting with a tabular data where the Unique ids and zip codes are in number format , but they are categorical features so I converted them to string
but the model is not predicting on them saying its object
how do I proceed further
If you haven’t already, check out the fastai Discord server where there are a number of active study group channels!
Hi, I want to create a classifier for one of the Tensorflow datasets called bee_dataset with multiple class labels. Is there any tutorial on how to do it, meaning how to get from the Tensorflow’s dataset to fastai’s DataLoaders? I am trying to first convert the tf dataset into Pytorch’s DataLoader and then pass it to DataLoaders but I came across multiple problems with this approach. Is there some notebook that is doing the similar thing that I can examine? Thanks!
Hi, I tried accessing the Discord server, but the link seems to be invalid. Could you please check the invite link? Thanks!
