The Brown Dog DTS is a highly extensible/distributed service providing a uniform means of managing and accessing transformation capabilities within the web. Utilized tools can come in the form of command line applications, GUI driven applications, libraries, and/or other services. Here we go over the process of preparing a new transformation tool for usage with the DTS.
Here is described the entire process for taking a working piece of code and deploying it as a Brown Dog Extractor. It is assumed that the method can be invoked from a single call. In this example, we are using the python extractor wrapper and will invoke a python function. In a very similar fashion, a method developed in a language other than python can be invoked using subprocess.
The main steps:
A few assumptions are that you have a tool that extracts some kind of metadata from a file or dataset and that you have installed Python, Git, and Docker as well as any other specific software needed by your extractor (if any) on your computer.
Install pyClowder2, which is a Python library that helps to easily communicate with Clowder - the backend services of Brown Dog which handles extractions. The advantage of using this library is that it manages all communications with Clowder and RabbitMQ (the distributed messaging bus) and the developer doesn't have to take care of such tasks. Needless to say, an extractor can also be written in native Python without the use of pyClowder2, but it would be more time consuming.
pip install git+https://opensource.ncsa.illinois.edu/stash/scm/cats/pyclowder.git
We have developed a template extractor written in Python. It is a simple wordcount extractor that counts lines, words, and characters in a text file. Clone the template extractor and rename the directory to an appropriate name that reflects the purpose of your extractor.
git clone https://opensource.ncsa.illinois.edu/bitbucket/scm/bd/extractors-template.git mv extractors-template/ <your_extractor_name> cd <your_extractor_name>
Make changes to extractors.py (main program). Consider the process_file method as the main method of an extractor and accordingly it needs to contain the main logic. You can call other methods in your python code from this method after importing necessary modules into this file.
Edit extractor configuration file config.py:
Change the rabbitmq queue name - in this case replace "wordCount" with an appropriate name for your extractor
This file contains metadata about the extractor in JSON-LD format. Update all relevant fields as needed.
Update the Dockerfile to install your software dependencies, provide necessary instructions in Dockerfile using the RUN command. You will need to add a line in Dockerfile to switch to the root user (
USER root) for getting proper permissions. For e.g., to install ImageMagick package using apt-get, add the following commands to Dockerfile:
USER root RUN apt-get update && apt-get install -y imagemagick
You can test your extractor as follows:
docker-compose up -d docker build -t <your_extractor_name> . docker run --rm -i -t --link <your_extractor_name_with_only_alphabets>_rabbitmq_1:rabbitmq <your_extractor_name>
You should see the following in the terminal. This means that the extractor is running and waiting for messages:
INFO : pyclowder.extractors - Waiting for messages. To exit press CTRL+C
In this section, we describe the creation of a converter using the image converter written using ImageMagick.
Get the template converter code.
We have developed a template or example converter. It is a simple image converter that images between different formats using ImageMagick tool. Clone the template converter and rename the directory to an appropriate name that reflects the purpose of your converter
git clone https://opensource.ncsa.illinois.edu/bitbucket/scm/bd/convertors-template.git mv convertors-template/ <your_converter_name> cd <your_converter_name>
Full local path to available scratch space (optional)
Modify Dockerfile in the converter directory to replace ImageMagick with MyTool. Specifically change line numbers 11, 15, 16 and 17. You need to also change other fields like maintainer and may need to add instructions to install any specific software required by your converter. For example, you can see instruction to install ImageMagick software in the example Dockerfile:
# Create softwareserver for polyglot. FROM ncsapolyglot/polyglot:develop MAINTAINER Rob Kooper <firstname.lastname@example.org> USER root # - install requirements # - enable shellscripts to be scanned # - enable imagemagick conversion by adding to .aliases.txt RUN apt-get update && apt-get -y install vim nano imagemagick && \ /bin/sed -i -e 's/^\([^#]*Scripts=\)/#\1/' -e 's/^#\(ShellScripts=\)/\1/' /home/polyglot/polyglot/SoftwareServer.conf && \ echo "ImageMagick" > /home/polyglot/polyglot/scripts/sh/.aliases.txt # copy convert file to scripts/sh folder in container # this is done to keep cache so you can debug script easily COPY ImageMagick_convert.sh /home/polyglot/polyglot/scripts/sh/ RUN chown polyglot /home/polyglot/polyglot/scripts/sh/ImageMagick_convert.sh && \ chmod +x /home/polyglot/polyglot/scripts/sh/ImageMagick_convert.sh # back to polyglot CMD ["softwareserver"]
echo "ImageMagick" > /home/polyglot/polyglot/scripts/sh/.aliases.txt
echo "MyTool" > /home/polyglot/polyglot/scripts/sh/.aliases.txt
COPY ImageMagick_convert.sh /home/polyglot/polyglot/scripts/sh/
COPY MyTool_convert.sh /home/polyglot/polyglot/scripts/sh/
RUN chown polyglot /home/polyglot/polyglot/scripts/sh/ImageMagick_convert.sh && \ chmod +x /home/polyglot/polyglot/scripts/sh/ImageMagick_convert.sh
RUN chown polyglot /home/polyglot/polyglot/scripts/sh/MyTool_convert.sh && \ chmod +x /home/polyglot/polyglot/scripts/sh/MyTool_convert.sh
Build the Dockerfile and start the converter
docker-compose stop docker build –t mytool . docker-compose up
BD-tmux runs the necessary dockerized Brown Dog Data Transformation Services (Polyglot, Clowder, Fence, ImageMagick converter and OCR extractor) and combines them into one integrated program.
The BD-tmux script will split your terminal into panes and start each of the services needed for the Brown Dog Data Transformation Services. It provides a useful and convenient way to view the logs of running services in panes.
There is an example to perform a conversion from jpg to bmp.