I have a dataset stored locally on my laptop, unfortunately i can't upload it to drive even in zip format, how can i train my model on this dataset(stored locally) using google colab
One option is to use Google Drive File Stream to mount your Google Drive on your local machine.
Then, you can put files there from your local machine and access them easily in Colab by mounting your Google Drive in the filesystem after running the following snippet:
from google.colab import drive
drive.mount('/content/drive')
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I have mounted the google drive to google colab but don't know how to import and use other colab notebooks now.
What I want to do is to replace 'model.pkl' to the address of my notebook stored in google drive.
model = pickle.load(open('model.pkl', 'rb'))
I use Google Colab extensively. In order to get an easy access to files in my Google drive, I mount the drive to the file system of the virtual machine that runs Colab. Like that:
from google.colab import drive as cdrive
cdrive.mount('/content/gdrive')
% cd /content/gdrive/'My Drive'/'Colab Notebooks'/my_directory
In the beginning of each session, I need to give a permission to access my drive. In order to do that, I need to press 'Allow', copy a one-time-password and paste it to a dedicated text area. It's a bit tedious.
Is there a better way? can I give a permanent permission based on my machine? any other ideas?
At this moment, we can access google drive with the Mount Drive button on the left menu bar.
Confirm the access Google Drive action.
Then it will be mounted to your Colab notebook.
I need to understand whether there is a way to incremental upload to Google Colaboratory.
I was trying to upload a huge number of image files to Google Colaboratory when my Internet connection failed and I had to start again. I observed that the images, which were already uploaded, where now getting duplicated.
Is there any way that only missed files get uploaded? This will save time and space.
I suggest you not to upload them just in Colab, because there is no solution to this problem, you just need to re-select manually the files not uploaded yet. I suggest you to use the google.colab package to manage these problems in Colab. Just upload everything you need to your google drive, then import:
from google.colab import drive
drive.mount('/content/gdrive')
In this way, you just need to login to your google account through google authentication API, and you can use files/folders as if they were uploaded on Colab. In this way, you can manage connection errors, since you're uploading them to google drive, and you can choose between overwriting existing files or just skip them.
We are sharing a google drive folder where we put the colab notebooks. Now we need to upload some text files permanently for notebook usage. I do not want to upload files every time I open colab. From what I searched, I had to upload files to google drive and mount it to colab in some way.
So, when I mount google drive to colab, can my teammates access all my files in it, or simply the shared folder.If not, is there a way to share only a folder or a file of google drive in colab.
If you share a folder with your teammates in Google Drive then that folder will appear in each of their drive mounts in colab. Each person running code in a notebook (even if they share a notebook) gets their own VM. One person should never see another person's Drive mount.
An alternative to sharing a data-file folder in Drive is to upload your data to GCS and have your notebook fetch it from there (example).
Colaboratory allows to mount Google Drive and use data from Drive but I have massive datasets (including images) on my local system that would take a long time and huge space on drive.
So, I am looking for something similar but here I want to mount my local system's Drive.
One option is to run Jupyter locally and connect to it using Colab, thereby providing the benefits of Drive storage and sharing for your notebooks, but allowing easy access to local files.
Instructions are here: https://research.google.com/colaboratory/local-runtimes.html