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NLP with Nick Grattan
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User Instructions

Instructions for running the tutorials at Cork AI Meetup3

NOTE: If you attended Meetup1 or Meetup2 you will have already completed some of these steps. Ensure you get to see a command prompt $ for your AWS instance.

Slides and other links for this meetup are here:


Sign in to your AWS account

Launching AWS virtual machine:

  • Go to "Services" and under the "compute" heading, choose "EC2"
  • Set "region" in top-right corner to be Ireland
  • Click on "Launch Instance"
  • Scroll down and click "Select" for "Deep Learning AMI (Ubuntu) Version 5.0 - ami-0ebac377"

NOTE: This AMI image is frequently updated, and so you may see a later version than "5.0"

  • Scroll down and select "GPU compute ... p2.xlarge"
  • Click "Review and Launch"
  • Click "Launch"
  • If you do not have an existing key pair, then select "Create a new key pair". This will direct you to create and download a .pem file to your disk. Otherwise select an existing key pair. Note that you must have access to the key pair PEM file locally.
  • Click "Launch Instances"

Connecting to the launched instance:

Now you should be logged into the machine and see a command-line prompt $.

2: Clone the GitHub repository for Meetup3

Folder setup Type the following commands to get setup for running the code:

  • mkdir cork_ai (make a new folder to work in)
  • cd cork_ai (switch to the newly created folder)
  • git clone (this will make a Meetup3 folder with all the code/data we need)
  • cd Meetup3 (switch to the Meetup3 folder)

Launch conda environment Our AWS machine has multiple deep-learning environments installed (conda environments). We need to launch one with a suitable Python version and TensorFlow already installed:

  • Type source activate tensorflow_p36

Note: In previous meetups "tensorflow_p27" was used to activate Python version 2.7. Here, we're using Python version 3.6.

Install NLTK The "doccluster" example requires the NLTK (Natural Language Tool Kit) module to be installed. To do this:

  • Type pip install nltk

Now the "Punkt" English parser will be installed.

  • Type python (Runs the Python 3.6.4 interpreter)
  • Type import nltk (Imports the NLTK module into Python)
  • Type'punkt') (Downloads and installs the Punkt parser)
  • Type exit() (Exits the Python interpreter)

Install BeautifulSoup In addition, "BeautifulSoup" (bs4) is required to extract text from the HTML documents together with the 'lxml' parser. To do this:

  • Type pip install bs4
  • Type pip install lxml

Install SkLearn The "doccluster" and "word2vec" examples require the SkLearn module be installed. To do this:

  • Type pip install sklearn

3: Execute

Executing will cluster the documents in the "Data" directory and create the "docclust.png" image with the dendrogram showing the similarity between docments.

  • Type python

The following blog post provides a short description of this program:

The output file 'docclust.png' is written in folder 'output_images'.

  • Use scp to copy the output images to your local machine for inspection:
    • (linux, mac, cygwin): open a new shell on your local machine and create a fresh empty directory. Then copy the output images to your local system:
      • mkdir output_images
      • cd output_images
      • scp -i /path/my-key-pair.pem ubuntu@[copied-DNS]:/home/ubuntu/cork_ai/Meetup3/output_images/* .
      • View the image using Finder / Explorer or your preferred image viewer.
    • (putty on Windows): Open a command line prompt (cmd)
      • pscp -i C:\path\my-key-pair.ppk ubuntu@[copied-DNS]:/home/ubuntu/cork_ai/Meetup3/output_images/* c:\[my_local_directory]
      • View the image using your preferred image viewer

4: Execute

You can now execute to calculate the word embeddings.

  • Type python

As well as reporting semantically similar words, the program creates a plot 'tsne.png' in the "output_images" folder. This visually shows semantic similarities between terms. Use the instuctions from above to download and view the image.

The following blog post provides a short description of this program:

5: Ending your AWS session

When you are finished working on AWS you need to stop (or terminate) your AWS instance to discontinue usage charges. This is not achieved by just logging out in the terminal!!

Stopping/Terminating your instance.

  • From EC2 dashboard->instances
  • You should see your launched instance listed (and selected with blue checkbox)
  • In the "Actions" drop-down menu choose "Instance State" and either "stop" or "terminate"
    • "stop" will end your session, but keep your instance and data safe for next time you want to use it. The fee for maintaining the data volume only will be around $5.50 per month.
    • "terminate" will end your session and will not retain your data or your instance state. There will be no further charge on your account if you choose terminate
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