Exelixi is a distributed framework based on Apache Mesos,
mostly implemented in Python using gevent for high-performance concurrency
It is intended to run cluster computing jobs (partitioned batch jobs, which include some messaging) in pure Python.
By default, it runs genetic algorithms at scale.
However, it can handle a broad range of other problem domains by
using --uow command line option to override the UnitOfWorkFactory class definition.
Please see the project wiki for more details, including a tutorial on how to build Mesos-based frameworks.
To check out the GA on a laptop (with Python 2.7 installed), simply run:
./src/ga.py
Otherwise, to run at scale, the following steps will help you get Exelixi running on Apache Mesos. For help in general with command line options:
./src/exelixi.py -h
The following instructions are based on using the Elastic Mesos service, which uses Ubuntu Linux servers running on Amazon AWS. Even so, the basic outline of steps shown here apply in general.
First, launch an Apache Mesos cluster.
Once you have confirmation that your cluster is running
(e.g., Elastic Mesos sends you an email messages with a list of masters and slaves)
then use ssh to login on any of the masters:
ssh -A -l ubuntu <master-public-ip>
You must install the Python bindings for Apache Mesos.
The default version of Mesos changes in this code as there are updates to Elastic Mesos,
since the tutorials are based on that service.
You can check http://mesosphere.io/downloads/ for the latest.
If you run Mesos in different environment,
simply make a one-line change to the EGG environment variable in the bin/local_install.sh script.
Also, you need to install the Exelixi source.
On the Mesos master, download the master branch of the Exelixi code repo on GitHub and install the required libraries:
wget https://github.com/ceteri/exelixi/archive/master.zip ; \
unzip master.zip ; \
cd exelixi-master ; \
./bin/local_install.sh
If you've customized the code by forking your own GitHub code repo, then substitute that download URL instead.
Alternatively, if you've customized by subclassing the uow.UnitOfWorkFactory default GA,
then place that Python source file into the src/ subdirectory.
Next, run the installation command on the master, to set up each of the slaves:
./src/exelixi.py -n localhost:5050 | ./bin/install.sh
Now launch the Framework, which in turn launches the worker services remotely on slave nodes. In the following case, it runs workers on two slave nodes:
./src/exelixi.py -m localhost:5050 -w 2
Once everything has been set up successfully, the log file in exelixi.log will show a line:
all worker services launched and init tasks completed
From there, the GA runs. See a GitHub gist for an example of a successful run.