Therefore, I recommend that you archive your dataset first. Uploading a large number of images (or files) individually will take a very long time, since Dropbox (or Google Drive) has to individually assign IDs and attributes to every image. You can also follow the same steps for other notebook services, such as Paperspace Gradient. Transferring via Dropbox is relatively easier. This sets an upper limit on the amount of data that you can transfer at any moment. Dropboxĭropbox offers upto 2GB free storage space per account. The most efficient method to transfer large files is to use a cloud storage system such as Dropbox or Google Drive. Some of the methods can be extended to other remote Jupyter notebook services, like Paperspace Gradient. I’ve also included additional methods that can useful for transferring smaller files with less effort. This blog compiles some of the methods that I’ve found useful for uploading and downloading large files from your local system to Google Colab.

But you might have become exasperated because of the complexity involved in transferring large datasets. If you have heard about it, chances are that you gave it shot. If you haven’t heard about it, Google Colab is a platform that is widely used for testing out ML prototypes on its free K80 GPU. By Bharath Raj How to Upload large files to Google Colab and remote Jupyter notebooks Photo by Thomas Kelley on Unsplash

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