Using Tutorial Data from Google Drive in Colab¶
We’ve added a new feature to tutorials that allows users to open the notebook associated with a tutorial in Google Colab. You may need to copy data to your Google drive account to get the more complex tutorials to work.
In this example, we’ll demonstrate how to change the notebook in Colab to work with the Chatbot Tutorial. To do this, you’ll first need to be logged into Google Drive. (For a full description of how to access data in Colab, you can view their example notebook here.)
To get started open the Chatbot Tutorial in your browser.
At the top of the page click Run in Google Colab.
The file will open in Colab.
If you select Runtime, and then Run All, you’ll get an error as the file can’t be found.
To fix this, we’ll copy the required file into our Google Drive account.
- Log into Google Drive.
- In Google Drive, make a folder named data, with a subfolder named cornell.
- Visit the Cornell Movie Dialogs Corpus and download the ZIP file.
- Unzip the file on your local machine.
- Copy the files movie_lines.txt and movie_conversations.txt to the data/cornell folder that you created in Google Drive.
Now we’ll need to edit the file in_ _Colab to point to the file on Google Drive.
In Colab, add the following to top of the code section over the line that begins corpus_name:
from google.colab import drive drive.mount('/content/gdrive')
Change the two lines that follow:
- Change the corpus_name value to “cornell”.
- Change the line that begins with corpus to this:
corpus = os.path.join("/content/gdrive/My Drive/data", corpus_name)
We’re now pointing to the file we uploaded to Drive.
Now when you click the Run cell button for the code section, you’ll be prompted to authorize Google Drive and you’ll get an authorization code. Paste the code into the prompt in Colab and you should be set.
Rerun the notebook from the Runtime / Run All menu command and you’ll see it process. (Note that this tutorial takes a long time to run.)
Hopefully this example will give you a good starting point for running some of the more complex tutorials in Colab. As we evolve our use of Colab on the PyTorch tutorials site, we’ll look at ways to make this easier for users.