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How To Upload And Save Large Data To Google Colaboratory From Local Drive?

I have downloaded large image training data as zip from this Kaggle link https://www.kaggle.com/c/yelp-restaurant-photo-classification/data How do I efficiently achieve the follow

Solution 1:

!pip install kaggle
api_token = {"username":"USERNAME","key":"API_KEY"}
import json
import zipfile
import os
with open('/content/.kaggle/kaggle.json', 'w') as file:
    json.dump(api_token, file)
!chmod 600 /content/.kaggle/kaggle.json
!kaggle config set -n path -v /content
!kaggle competitions download -c jigsaw-toxic-comment-classification-challenge
os.chdir('/content/competitions/jigsaw-toxic-comment-classification-challenge')
for file inos.listdir():
    zip_ref = zipfile.ZipFile(file, 'r')
    zip_ref.extractall()
    zip_ref.close()

There is minor change on line 9, without which was encountering error. source: https://gist.github.com/jayspeidell/d10b84b8d3da52df723beacc5b15cb27 couldn't add as comment cause rep.

Solution 2:

You may refer with these threads:

Also check out the I/O example notebook. Example, for access to xls files, you'll want to upload the file to Google Sheets. Then, you can use the gspread recipes in the same I/O example notebook.

Solution 3:

You may need to use kaggle-cli module to help with the download.

It’s discussed in this fast.ai thread.

Solution 4:

I just wrote this script that downloads and extracts data from the Kaggle API to a Colab notebook. You just need to paste in your username, API key, and competition name.

https://gist.github.com/jayspeidell/d10b84b8d3da52df723beacc5b15cb27

The manual upload function in Colab is kind of buggy now, and it's better to download files via wget or an API service anyway because you start with a fresh VM each time you open the notebook. This way the data will download automatically.

Solution 5:

Another option is to upload the data to dropbox (if it can fit), get a download link. Then in the notebook do

!wget link -0 new-name && ls

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