/ Natural Language Processing

Resources for Natural Language Processing Coursera course

Resources for Natural Language Processing Coursera course

Natural Language Processing course resources

Resources for "Natural Language Processing" Coursera course.

Running on Google Colab

Google has released its own flavour of Jupyter called Colab, which has free GPUs!

Here's how you can use it:

  1. Open https://colab.research.google.com, click Sign in in the upper right corner, use your Google credentials to sign in.
  2. Click GITHUB tab, paste https://github.com/hse-aml/natural-language-processing and press Enter
  3. Choose the notebook you want to open, e.g. week1/week1-MultilabelClassification.ipynb
  4. Click File -> Save a copy in Drive... to save your progress in Google Drive
  5. If you need a GPU, click Runtime -> Change runtime type and select GPU in Hardware accelerator box
  6. Execute the following code in the first cell that downloads dependencies (change for your week number):
! wget https://raw.githubusercontent.com/hse-aml/natural-language-processing/master/setup_google_colab.py -O setup_google_colab.py
import setup_google_colab
setup_google_colab.setup_week1()  # change to the week you're working on
  1. If you run many notebooks on Colab, they can continue to eat up memory,
    you can kill them with ! pkill -9 python3 and check with ! nvidia-smi that GPU memory is freed.

Known issues:

  • No support for ipywidgets, so we cannot use fancy tqdm progress bars.
    For now, we use a simplified version of a progress bar suitable for Colab.
  • Blinking animation with IPython.display.clear_output().
    It's usable, but still looking for a workaround.