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Computational Linguistics Application Mediator -- Quickly turn NLP applications into RESTful webservices with a web-application front-end. You provide a specification of your command line application, its input, output and parameters, and CLAM wraps around your application to form a fully fledged RESTful webservice.
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README.rst

CLAM: Computational Linguistics Application Mediator

https://travis-ci.org/proycon/clam.svg?branch=master

by Maarten van Gompel, Centre for Language Studies, Radboud University Nijmegen

Licensed under GPLv3

Website: http://proycon.github.io/clam Source repository: https://github.com/proycon/clam/

CLAM allows you to quickly and transparently transform your Natural Language Processing application into a RESTful webservice, with which both human end-users as well as automated clients can interact. CLAM takes a description of your system and wraps itself around the system, allowing end-users or automated clients to upload input files to your application, start your application with specific parameters of their choice, and download and view the output of the application once it is completed.

CLAM is set up in a universal fashion, requiring minimal effort on the part of the service developer. Your actual NLP application is treated as a black box, of which only the parameters, input formats and output formats need to be described. Your application itself needs not be network aware in any way, nor aware of CLAM, and the handling and validation of input can be taken care of by CLAM.

CLAM is entirely written in Python, runs on UNIX-derived systems, and is available as open source under the GNU Public License (v3). It is set up in a modular fashion, and offers an API, and as such is easily extendable. CLAM communicates in a transparent XML format, and using XSL transformation offers a full web 2.0 web-interface for human end users.

Installation instruction can be found below. For full documentation see the manual in docs/clam_manual.pdf , also accessible through the CLAM website at http://proycon.github.io/clam . It is recommended to read this prior to starting with CLAM.

Installation

IMPORTANT NOTICE: It's discouraged to download the zip packages or tarballs from github, install CLAM from the Python Package Index or use git properly.

Installation On Linux

Easy install is part of the Python setup tools and can install CLAM globally on your system for you from the Python Package Index. This is the easiest method of installing CLAM, as it will automatically fetch and install any dependencies. This procedure downloads CLAM for you automatically. Alternatively, you can use pip (usually part of the python-pip package). We recommend to use a virtual environment (virtualenv) if you want to install CLAM locally as a user, if you want to install globally, prepend the following commands with sudo:

$ easy_install clam

If you already downloaded CLAM manually (from github), you can do:

$ ./setup.py install
If easy_install is not yet installed on your system, install it using:

on debian-based linux systems (including Ubuntu):

$ apt-get install python-setuptools

on RPM-based linux systems:

$ yum install python-setuptools

on MacOS X: (follow the manual steps further down this document)

Note that sudo/root access is needed to install globally. Ask your system administrator to install it. Alternatively, you can install in a local custom path using the -d flag, this however complicates matters as you need to take care to add these local directories to your python library path:

$ easy_install -d /path/to/dir clam

Installation on Mac OS X

Install a Python distribution such as Anaconda and follow the Linux instructions above.

Installation on Windows

Not supported, delete Windows and install a decent OS ;)

Running a test webservice

If you installed CLAM using the above method, then you can launch a clam test webservice using the development server as follows:

$ clamservice -H localhost -p 8080 clam.config.textstats

Navigate your browser to http://localhost:8080 and verify everything works

If any problems occur during installation regarding pycurl, then install the pycurl package supplied by your distribution (python-pycurl on Debian/ubuntu)

Note: It is important to regularly keep CLAM up to date as fixes and improvements are implemented on a regular basis. Update CLAM using:

$ easy_install -U clam

or if you used pip:

$ pip install -U clam

Installing a particular clam webservice for production use

When installating a particular CLAM webservice on a new server, it is first necessary to edit the service configuration file of the webservice and make sure all the paths in there are set correctly for the new server. Of interest is in particular the ROOT path, which is where user data will be stored, this directory must exist and be writable by the webserver.

For testing, the built-in development server can be used. Suppose the webservice configuration is in /path/to/mywebservice/ and is called mywebservice.py, then the development server can be started as follows:

$ clamservice -P /path/to/mywebservice mywebservice

For production, however, it is strongly recommended to embed CLAM in Apache. This is the typically task of a system administrator, as certain skills are necessary and assumed.

Embedding CLAM in Apache is accomplished through WSGI, for which you need to have the package libapache2-mod-wsgi installed (Debian/Ubuntu). Next it involves the writing of a small WSGI script and adaptation of the Apache configuration to load this script. All this is explained in detail in the CLAM Manual, obtainable from http://proycon.github.io/clam/ .

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