Predict tags on StackOverflow with linear models¶

jupynet notebook + python codes can be found under "codes" folder. Submitted repository contains all dependensies. All you need to install and run the code. You may read information regarding related libraries whic is given below.

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()

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