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pathos 0.3.2

About the Pathos Framework
pathos is a framework for heterogeneous computing. It provides a consistent
high-level interface for configuring and launching parallel computations
across heterogeneous resources. pathos provides configurable launchers for
parallel and distributed computing, where each launcher contains the
syntactic logic to configure and launch jobs in an execution environment.
Examples of launchers that plug into pathos are: a queue-less MPI-based
launcher (in pyina), a ssh-based launcher (in pathos), and a multi-process
launcher (in multiprocess).
pathos provides a consistent interface for parallel and/or distributed
versions of map and apply for each launcher, thus lowering the barrier
for users to extend their code to parallel and/or distributed resources.
The guiding design principle behind pathos is that map and apply
should be drop-in replacements in otherwise serial code, and thus switching
to one or more of the pathos launchers is all that is needed to enable
code to leverage the selected parallel or distributed computing resource.
This not only greatly reduces the time to convert a code to parallel, but it
also enables a single code-base to be maintained instead of requiring
parallel, serial, and distributed versions of a code. pathos maps can be
nested, thus hierarchical heterogeneous computing is possible by merely
selecting the desired hierarchy of map and pipe (apply) objects.
The pathos framework is composed of several interoperating packages:

dill: serialize all of Python
pox: utilities for filesystem exploration and automated builds
klepto: persistent caching to memory, disk, or database
multiprocess: better multiprocessing and multithreading in Python
ppft: distributed and parallel Python
pyina: MPI parallel map and cluster scheduling
pathos: graph management and execution in heterogeneous computing

About Pathos
The pathos package provides a few basic tools to make parallel and
distributed computing more accessible to the end user. The goal of pathos
is to enable the user to extend their own code to parallel and distributed
computing with minimal refactoring.
pathos provides methods for configuring, launching, monitoring, and
controlling a service on a remote host. One of the most basic features
of pathos is the ability to configure and launch a RPC-based service
on a remote host. pathos seeds the remote host with the portpicker
script, which allows the remote host to inform the localhost of a port
that is available for communication.
Beyond the ability to establish a RPC service, and then post requests,
is the ability to launch code in parallel. Unlike parallel computing
performed at the node level (typically with MPI), pathos enables the
user to launch jobs in parallel across heterogeneous distributed resources.
pathos provides distributed map and pipe algorithms, where a mix of
local processors and distributed workers can be selected. pathos
also provides a very basic automated load balancing service, as well as
the ability for the user to directly select the resources.
The high-level pool.map interface, yields a map implementation that
hides the RPC internals from the user. With pool.map, the user can launch
their code in parallel, and as a distributed service, using standard Python
and without writing a line of server or parallel batch code.
RPC servers and communication in general is known to be insecure. However,
instead of attempting to make the RPC communication itself secure, pathos
provides the ability to automatically wrap any distributes service or
communication in a ssh-tunnel. Ssh is a universally trusted method.
Using ssh-tunnels, pathos has launched several distributed calculations
on national lab clusters, and to date has performed test calculations
that utilize node-to-node communication between several national lab clusters
and a user’s laptop. pathos allows the user to configure and launch
at a very atomistic level, through raw access to ssh and scp.
pathos is the core of a Python framework for heterogeneous computing.
pathos is in active development, so any user feedback, bug reports, comments,
or suggestions are highly appreciated. A list of issues is located at https://github.com/uqfoundation/pathos/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.

Major Features
pathos provides a configurable distributed parallel map interface
to launching RPC service calls, with:

a map interface that meets and extends the Python map standard
the ability to submit service requests to a selection of servers
the ability to tunnel server communications with ssh

The pathos core is built on low-level communication to remote hosts using
ssh. The interface to ssh, scp, and ssh-tunneled connections can:

configure and launch remote processes with ssh
configure and copy file objects with scp
establish an tear-down a ssh-tunnel

To get up and running quickly, pathos also provides infrastructure to:

easily establish a ssh-tunneled connection to a RPC server

Current Release
The latest released version of pathos is available from:

https://pypi.org/project/pathos

pathos is distributed under a 3-clause BSD license.

Development Version
You can get the latest development version with all the shiny new features at:

https://github.com/uqfoundation

If you have a new contribution, please submit a pull request.

Installation
pathos can be installed with pip:
$ pip install pathos

Requirements
pathos requires:

python (or pypy), >=3.8
setuptools, >=42
pox, >=0.3.4
dill, >=0.3.8
ppft, >=1.7.6.8
multiprocess, >=0.70.16

More Information
Probably the best way to get started is to look at the documentation at
http://pathos.rtfd.io. Also see pathos.tests and https://github.com/uqfoundation/pathos/tree/master/examples for a set of scripts that demonstrate the
configuration and launching of communications with ssh and scp, and demonstrate
the configuration and execution of jobs in a hierarchical parallel workflow.
You can run the test suite with python -m pathos.tests. Tunnels and other
connections to remote servers can be established with the pathos_connect
script (or with python -m pathos). See pathos_connect --help for more
information. pathos also provides a portpicker script to select an
open port (also available with python -m pathos.portpicker). The source
code is generally well documented, so further questions may be resolved by
inspecting the code itself. Please feel free to submit a ticket on github,
or ask a question on stackoverflow (@Mike McKerns). If you would like to
share how you use pathos in your work, please send an email (to mmckerns
at uqfoundation dot org).
Important classes and functions are found here:

pathos.abstract_launcher [the worker pool API definition]
pathos.pools [all of the pathos worker pools]
pathos.core [the high-level command interface]
pathos.hosts [the hostname registry interface]
pathos.serial.SerialPool [the serial Python worker pool]
pathos.parallel.ParallelPool [the parallelpython worker pool]
pathos.multiprocessing.ProcessPool [the multiprocessing worker pool]
pathos.threading.ThreadPool [the multithreading worker pool]
pathos.connection.Pipe [the launcher base class]
pathos.secure.Pipe [the secure launcher base class]
pathos.secure.Copier [the secure copier base class]
pathos.secure.Tunnel [the secure tunnel base class]
pathos.selector.Selector [the selector base class]
pathos.server.Server [the server base class]
pathos.profile [profiling in threads and processes]
pathos.maps [standalone map instances]

pathos also provides two convenience scripts that are used to establish
secure distributed connections. These scripts are installed to a directory
on the user’s $PATH, and thus can be run from anywhere:

portpicker [get the portnumber of an open port]
pathos_connect [establish tunnel and/or RPC server]

Typing --help as an argument to any of the above scripts will print out an
instructive help message.

Citation
If you use pathos to do research that leads to publication, we ask that you
acknowledge use of pathos by citing the following in your publication:
M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
"Building a framework for predictive science", Proceedings of
the 10th Python in Science Conference, 2011;
http://arxiv.org/pdf/1202.1056

Michael McKerns and Michael Aivazis,
"pathos: a framework for heterogeneous computing", 2010- ;
https://uqfoundation.github.io/project/pathos
Please see https://uqfoundation.github.io/project/pathos or
http://arxiv.org/pdf/1202.1056 for further information.