jdc08161063/natural-language-processing

Resources for "Natural Language Processing" Coursera course.

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
setup_google_colab.setup_week1()  # change to the week you're working on

7. 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