Ilyushin / natural-language-processing

Resources for "Natural Language Processing" Coursera course.

Home Page:https://www.coursera.org/learn/language-processing

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Natural Language Processing course resources

https://www.coursera.org/learn/language-processing

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
# please, uncomment the week you're working on
# setup_google_colab.setup_week1()  
# setup_google_colab.setup_week2()
# setup_google_colab.setup_week3()
# setup_google_colab.setup_week4()
# setup_google_colab.setup_project()
# setup_google_colab.setup_honor()
  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.

Running elsewhere

AWS

Docker

About

Resources for "Natural Language Processing" Coursera course.

https://www.coursera.org/learn/language-processing


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