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CLAM 2.1.6

Turns command-line NLP tools into fully-fledged RESTful webservices with an auto-generated web-interface for human end-users.

Package Documentation

by Maarten van Gompel, Centre for Language and Speech Technology, Radboud University Nijmegen

Licensed under GPLv3

Website: https://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.

API Documentation is available on http://clam.readthedocs.io

Installation

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

Installation from the Python Package Index using the package manager pip it the recommended way to intall CLAM. This is the easiest method of installing CLAM, as it will automatically fetch and install any dependencies. 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:

CLAM can be installed from the Python Package Index using pip. Pip is usually part of the python3-pip package or similar. It downloads CLAM and all dependencies automatically::

$ pip3 install clam

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

$ python3 setup.py install
If pip3 is not yet installed on your system, install it using:

on debian-based linux systems (including Ubuntu):

$ apt-get install python3-pip

on RPM-based linux systems:

$ yum install python3-pip

Note that sudo/root access is needed to install globally. Ask your system administrator to install it if you do not own the system. Alternatively, you can install it locally in a Python virtual environment:

$ virtualenv –python=python3 clamenv

$ . clamenv/bin/activate

(clamenv)$ pip3 install clam

It is also possible to use Python 2.7 instead of Python 3, adapt the commands as necessary.

CLAM also has some optional dependencies. For MySQL support, install mysqlclient using pip. For FoLiA support, install FoLiA-Tools using pip.

Installation on Mac OS X

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

Installation on Windows

CLAM does not support Windows, i.e. you can’t run CLAM webservices on Windows. However, the CLAM Data API and client API will work, so clients connecting to CLAM webservices can run on Windows. Follow the same instructions as for Mac OS X.

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

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

$ pip install -U clam

or if you used easy_install:

$ easy_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 or nginx. This is the typically task of a system administrator, as certain skills are necessary and assumed. All this is explained in detail in the CLAM Manual, obtainable from https://proycon.github.io/clam/ .

 
File Type Py Version Uploaded on Size
CLAM-2.1.6.tar.gz (md5) Source 2016-11-13 604KB