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A remote execution/clustering module for Python

Project description


EasyCluster is a remote execution / clustering module for Python.

Possible uses include:

  • computation (e.g. NumPy, PyOpenCL)
  • coordinated automation for testing networks / SANs
  • access to specific hardware in multiple systems (e.g. GPUs, video capture/encoding boards)



  • CPython 3.4+
  • SSH support requires an 'ssh' binary on the client, and 'sshd' on the server


  • Transparent calling of functions and methods
  • Transparent handling of exceptions
  • Convenience functions for calling one function in parallel on multiple remote systems
  • Automatic support for threading
  • Requests and responses protected with shared HMAC key
  • Connecting via SSH without installing anything on the server (Linux/Unix only)
  • Cross-platform compatible; Master scripts running on Linux/OSX can connect to servers running on Windows and vice/versa.


You can install EasyCluster with pip. Just run

python3 -m pip install EasyCluster

How it works

EasyCluster works by having a single master script connect to one or more servers running SSH or the cluster service. The master can then call Python functions on the remote service or send code to execute.

See for an example of how to use most of the features.

Since version 0.22.1, SSH is the preferred method of connecting to servers on all platforms except Windows. When using SSH, the server does not need to have easycluster installed - it only needs to have SSH and either Python 2.6, 2.7, or 3.2+. When using SSH, you should use SSH private keys and an SSH agent, otherwise SSH will prompt for a password whenever it connects.

If you don't want to use SSH, e.g. you need to run the server on Windows and don't want to run Cygwin, you will need to generate a secret key that is shared between the client and server. This key is used to authenticate requests, but does not encrypt data, therefore it should only be used on a trusted, firewalled network, not openly on the Internet. If you want to use EasyCluster to coordinate systems in remote geographic areas, consider using a VPN or SSH tunnel. The EasyCluster service operates over a single TCP port, so most tunneling solutions will work.

Connecting to a server

The easiest way to use EasyCluster is to use Client.from_spec:

>>> rmt = Client.from_spec('')
>>> rmt = ThreadedClient.from_spec('')

The connection spec looks like this::


The 'host' can be a hostname, IPv4 address, or bracketed IPv6 address.

For compatibility reasons, SSH is only used if the 'user' field is present. If you want to use SSH without specifying a user name, pass ':ssh=yes' as an option.

For standalone servers, the key is determined by specifying either the 'kf' or 'key' options.

If ':compress=1' is specified, then compression is enabled for the connection.

Example connection specifications::

''                   # Using SSH
''                # Using SSH without a user name (let SSH choose)
'' # Using a custom SSH path
'user@'                # IPv4 address on non-standard port
'user@[2001:db8::2]'                 # IPv6 addresses must be in brackets
''          # Connecting to a standalone server using a key from a file
''        # Using a key directly, with non-standard port

The recommended way of allowing the user of your script to specify remote options is to use optparse:

# File:

import sys
import optparse
import easycluster

options = optparse.OptionParser(description='Do some stuff')
opts, args = options.parse_args()
default_key = easycluster.key_from_options(opts)
remotes = []
for spec in args:
    params = easycluster.parse_connection_spec(spec, default_key=default_key)
    rmt = easycluster.Client(**params)

This example allows a user to specify a default key using -k (if multiple servers use the same key), but allows the user to specify individual keys if necessary:

python -k common.key host1 host2 oddhost:kf=key_for_oddhost.key

You can also specify a different TCP port to connect to. This is useful if you want to use SSH tunnels:

ssh host1 -N -f -L 11001:localhost:11999
ssh host2 -N -f -L 11002:localhost:11999
python -k common.key localhost:11001 localhost:11002

The master script can connect to the same server multiple times. Each connection creates a separate process with a clean environment. The master can also create a "local" instance using easycluster.server.spawn_local(), which starts a new server process without having to run a separate server.

Executing code remotely

The most straightforward way to execute code remotely is to define functions in a string, and call define_common():

>>> from easycluster import *
>>> define_common('''
... def addvals(a, b):
...     return a + b
... def subvals(a, b):
...     return a - b
... ''')
>>> key = read_key_file('secret.key')
>>> rmt = Client(key, 'localhost')
>>> rmt.addvals(3, 4)
>>> rmt.subvals(15, 4)

Any functions or classes you define in in the block of code passed to define_common can be called on the remote side. You can also call functions in classes defined in standard library modules:

>>>['/bin/echo', 'hello'])

This example won't actually echo anything to your terminal - echo is executed on the server, so if you have the server open in a terminal, you will see it echoed there.

The block of code you pass to define_common is also evaluated on the client, so functions, classes, and class instances can be pickled by reference and passed back and forth between client and server. By default, a virtual module called easycluster.remote_code is created to store the definitions. You can import this module on the client if you want to run a function on both client and server, or create a instance of a class that will be passed to the server by value:

>>> from easycluster.remote_code import addvals, subvals
>>> addvals(1, 2)

You can change the name of the module by specifying a different second parameter to define_common. Remember that since this code is executed in the context of a different module, you won't have access to global variables and imported modules from your master script:

>>> import os
>>> define_common('''
... def hello():
...     os.system('echo hello')
... ''')
>>> rmt.hello()
Traceback (most recent call last):
NameError: global name 'os' is not defined

You must remember to import whatever modules you need to use inside of your define_common block. Of course, the libraries you import must be available on the remote system too - EasyCluster will not copy them over.


If the remote code raises an exception, the exception will be pickled up and re-raised on the client, along with a stack trace. By default, the stack trace will be printed to STDERR, because otherwise it would be lost - the stack trace generated by raising the exception on the client only goes as far as the proxy wrapper. If you don't want exceptions to be printed, you can subclass Client and override report_exception. For a single request, you can also set origexc to False or 'quiet' (see the section on Parallel Execution below).

Manipulating objects on the server

By default, if you call a function on the server, and it returns a value, that value will be pickled, and a new copy of the object will be created on the client. This is fine for simple values such as strings, integers, tuples, dictionaries, etc., but a lot of objects can't or shouldn't be pickled; instead, EasyCluster allows you to mark classes as "server objects" that are not pickled, but remain on the server and are referenced by the client.

When the returned data structure is reconstructed on the client, any "server objects" are converted into "proxy" objects. Calling a method on this proxy calls the corresponding method on the server. These proxy objects can also be passed as arguments to other functions on the same connection, and will be unserialized as the original object on the server.

>>> define_common('''
... class TestObject1(ServerObject):
...    def __init__(self, val):
...        self.val = val
...    def getval(self):
...        return self.val
...    def newobj(self):
...        return TestObject1(self.val + 1)
... def get_object_vals(lst):
...     return [obj.val for obj in lst]
... ''')
>>> # Call this on every connection after calling define_common.
>>> rmt.update_definitions()
>>> obj1 = rmt.TestObject1(100)
>>> obj1
<RemoteProxy for oid 1 on localhost:11999>
>>> obj1.getval()
>>> obj2 = obj1.newobj()
>>> obj2
<RemoteProxy for oid 2 on localhost:11999>
>>> obj2.getval()
>>> rmt.get_object_vals[obj1, obj2]
[100, 101]

Classes can indicate that they should be proxied rather than copied by inheriting from ServerObject. Existing classes which are unaware of EasyCluster can be registered on the server by calling make_server_class.

There are two ways classes can specify which methods and attributes to export:

  • Specifying export_methods, export_attrs, or export_attrs_cache. Classes which inherit from ServerObject but do not specify a proxy class will have one dynamically created when they are first referenced. The server will examime the class to determine which methods and attributes should be exported.

    • If the class has a class attribute called export_methods, then the proxy class will only have wrappers for those methods.

    • If export_methods is not defined (default), or the special value '@auto' is in the list of exported method names, then the class will be examined, and all defined methods will be automatically added to the list.

    • The export_attrs class attribute works the same way: if it is defined, wrapper properties will be created on the proxy object for each attribute. If export_attrs is not defined, or '@auto' is included in export_attrs, then a special __getattr__ is defined on the proxy which will forward attribute accesses to the server.

    • If you know that an attribute contains data which will not change over the lifetime of the object, you can put it in export_attrs_cache. The client will cache the value of the attribute the first time it is accessed, and won't access it again.

  • Defining a proxy class directly. This is the most flexible way of exporting methods and attributes. This allows you to not only define proxy methods and attributes, but allows you to:

    • Implement simple methods on the client. For example, most iterators simply return self from __iter__. In fact, easycluster provides a proxy class you can inherit from called SelfIterProxy which does this.

    • Make your proxy object inherit from some other class so that isinstance(prox, clas) returns True.

Example of both methods:

>>> define_common('''
... class TestObject2(TestObject1):
...     export_methods = ('getval',)
...     export_attrs = ('val',)
... class TestObject3Proxy(RemoteProxy):
...     proxy_methods = ('getval',)
...     proxy_attrs = ('val',)
... class TestObject3(TestObject1):
...     proxy_class = TestObject3Proxy
... ''')
>>> rmt.update_definitions()
>>> obj2 = rmt.TestObject2(200)
>>> obj2.val
>>> obj2.getval()
>>> obj2.non_existant_method()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'dynamic_proxy_getval_val' object has no attribute 'non_existant_method'

>>> define_common('''
... ''')
>>> rmt.update_definitions()
>>> obj3 = rmt.TestObject3(300)
>>> type(obj3)
<class 'easycluster_code.TestObject3Proxy'>
>>> obj3.val

If you have a built-in class or a class from a library module that you want to treat as a "server object", you can call easycluster.make_server_class() in your define_common block:

>>> define_common('''
... import array
... make_server_class(array.array)
... ''')
>>> rmt.update_definitions()
>>> rmt_array = rmt.array.array('B', 1234)

You can pass export_methods, export_attrs, export_attrs_cache, and proxy_class to make_server_class; they have the same meaning as defined for ServerObject.

There is also a function called make_singleton, which behaves like make_server_class, except it operates on a single instance of a class; if that instance is returned, it will be proxied, but other instances of the same class will be pickled.

Parallel Execution

Usually, clustering implies you want to execute code in parallel on multiple systems. By default, calling remote code suspends execution of the master script while the remote code is executing. However, there are several ways to execute remote code in parallel.

The simplest way to do this is to use a non-blocking response:

>>> rmt2 = Client(key, 'otherhost')
>>> r1 = rmt.addvals(5, 8, nonblocking=True)
>>> r2 = rmt2.addvals(14, 18, nonblocking=True)
>>> r1.wait()
>>> r2.wait()

Passing nonblocking=True to any proxy method causes it to immediately return a special "non-blocking response" object which has a wait() method. The wait() method waits until the code has finished executing on the remote server and returns the response value. If the remote side raised an exception, wait() will raise the same exception (unless you pass origexc -- see below).

You can also use the convenience functions eval_multi, call_multi, and call_method_multi to call the same function in parallel on multiple systems:

>>> call_multi([rmt, rmt2], 'addvals', 2, 3)
[5, 5]

This function calls a specific function on multiple systems, waits for all of the responses, then returns a list of their responses.

Besides nonblocking, there are other common keyword arguments that can be passed to remote calls:

  • oncomplete - If this is specified, then the remote call will return immediately, and will call this function when the remote call completes. This can be either a function which will be called as func(response), or a tuple of (func, arg1, arg2, ...) which will be called as func(response, arg1, arg2, ...). If you're using the standard Client class, completion functions will not be called until something calls read_response() on the client object, or calls wait() on a non-blocking response associated with the client. If you're using ThreadedClient, completion functions are called from the thread which reads responses from the server.

  • onerror - Identical to oncomplete, but called with a RemoteException instance instead of a return value when the remote call raises an exception. If oncomplete is specified, but onerror is not, the oncomplete function is called in both cases.

  • threadid - An arbitrary integer specifying the thread on the server to run the request in. If not specified, the current default will be used. The default can be changed by calling set_default_thread() on the client object. If the specified thread does not exist on the server, it is created. If the threadid is the special constant easycluster.SINGLE, a new thread is created on the server for this request, then exits.

  • origexc - If True (default), and the request raises an exception, it will print the remote stack trace to the screen and raise the original exception. If it is False, a RemoteException is raised instead. If it is the value 'quiet', then the original exception is raised without a stack trace being printed. RemoteException instances have the two attributes: orig, the original exception; and text, the stack trace on the server.

You can start multiple threads on the same server by using non-blocking responses with threadid:

>>> r1 = rmt.addvals(101, 102, nonblocking=True, threadid=1)
>>> r2 = rmt.addvals(222, 333, nonblocking=True, threadid=2)
>>> r3 = rmt.addvals(555, 888, nonblocking=True, threadid=3)
>>> [r1.wait(), r2.wait(), r3.wait()]
[203, 555, 1443]

Using ThreadedClient

If your master script is already multi-threaded, you can use ThreadedClient to automatically manage server threads for you.

The ThreadedClient class starts a separate thread to read responses from the server. Because of this, completion functions are called as soon as the remote call returns, and the thread actively monitors the server to ensure that it hasn't gone down or locked up.

ThreadedClient will detect if you call remote functions from a separate thread in your master script, and will start a corresponding thread on the server to handle your request:

>>> import threading
>>> tc1 = ThreadedClient(key, 'host1')
>>> tc2 = ThreadedClient(key, 'host2')
>>> def client_thread(id, a, b):
...     print 'Thread %d: starting' % id
...     val1 = tc1.addvals(a, b)
...     print 'Thread %d: tc1 returned %r' % (id, val1)
...     val2 = tc2.addvals(a, b)
...     print 'Thread %d: tc2 returned %r' % (id, val2)
...     print 'Thread %d: finished' % id
>>> def run_threads():
...     t1 = threading.Thread(target=client_thread, args=(1, 200, 500))
...     t2 = threading.Thread(target=client_thread, args=(2, 300, 600))
...     t1.start(); t2.start()
...     t1.join(); t2.join()
>>> run_threads()
Thread 1: starting
Thread 2: starting
Thread 1: tc1 returned 700
Thread 2: tc1 returned 900
Thread 1: tc2 returned 700
Thread 2: tc2 returned 900
Thread 1: finished
Thread 2: finished

Once threads in your master script exit, ThreadedClient will detect it and stop the corresponding thread on the server.

Starting the standalone server

On POSIX systems (Linux, BSD, Solaris), a command called easycluster should be installed in /usr/local/bin. On Windows, the main entry point is installed under %PYTHON%\scripts\easycluster.exe. With Python 2.7 and 3.2, you can also run python -m easycluster.

Before you run the server, you should create a secret HMAC keyfile. Both the server and the client need this keyfile to be able to communicate:

easycluster -g secret.key

This will create a new file, called 'secret.key' which is readable only by the user that created it. You can then run the server with:

easycluster -S -k secret.key

If you don't want to see every remote call logged, run:

easycluster -S -k secret.key -c QuietServer

Running EasyCluster standalone server as a service on boot

You can have the easycluster service start automatically on boot on Windows, Solaris, and Linux (Redhat, Debian, Ubuntu, and SuSE have been tested):

easycluster --install

This will register a service with the system which will start on the next boot. You can unregister it with easycluster --uninstall. Once the service is registered, you can start and stop it with easycluster --start and easycluster --stop

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