schedula: An intelligent function scheduler¶
release: | 0.1.19 |
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date: | 2018-06-05 13:00:00 |
repository: | |
pypi-repo: | |
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keywords: | scheduling, dispatch, dataflow, processing, calculation, dependencies, scientific, engineering, simulink, graph theory |
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license: |
What is schedula?¶
Schedula implements a intelligent function scheduler, which selects and executes functions. The order (workflow) is calculated from the provided inputs and the requested outputs. A function is executed when all its dependencies (i.e., inputs, input domain) are satisfied and when at least one of its outputs has to be calculated.
Note
Schedula is performing the runtime selection of the minimum-workflow to be invoked. A workflow describes the overall process - i.e., the order of function execution - and it is defined by a directed acyclic graph (DAG). The minimum-workflow is the DAG where each output is calculated using the shortest path from the provided inputs. The path is calculated on the basis of a weighed directed graph (data-flow diagram) with a modified Dijkstra algorithm.
Installation¶
To install it use (with root privileges):
$ pip install schedula
Or download the last git version and use (with root privileges):
$ python setup.py install
What is schedula?¶
Schedula implements a intelligent function scheduler, which selects and executes functions. The order (workflow) is calculated from the provided inputs and the requested outputs. A function is executed when all its dependencies (i.e., inputs, input domain) are satisfied and when at least one of its outputs has to be calculated.
Note
Schedula is performing the runtime selection of the minimum-workflow to be invoked. A workflow describes the overall process - i.e., the order of function execution - and it is defined by a directed acyclic graph (DAG). The minimum-workflow is the DAG where each output is calculated using the shortest path from the provided inputs. The path is calculated on the basis of a weighed directed graph (data-flow diagram) with a modified Dijkstra algorithm.
Installation¶
To install it use (with root privileges):
$ pip install schedula
Or download the last git version and use (with root privileges):
$ python setup.py install
Why may I use schedula?¶
Imagine we have a system of interdependent functions - i.e. the inputs of a function are the output for one or more function(s), and we do not know which input the user will provide and which output will request. With a normal scheduler you would have to code all possible implementations. I’m bored to think and code all possible combinations of inputs and outputs from a model.
Solution¶
Schedula allows to write a simple model (Dispatcher()
) with
just the basic functions, then the Dispatcher()
will select and
execute the proper functions for the given inputs and the requested outputs.
Moreover, schedula provides a flexible framework for structuring code. It
allows to extract sub-models from a bigger one.
Very simple example¶
Let’s assume that we have to extract some filesystem attributes and we do not
know which inputs the user will provide. The code below shows how to create a
Dispatcher()
adding the functions that define your system.
Note that with this simple system the maximum number of inputs combinations is
31 (\((2^n - 1)\), where n is the number of data).
>>> import schedula >>> import os.path as osp >>> dsp = schedula.Dispatcher() >>> dsp.add_data(data_id='dirname', default_value='.', initial_dist=2) 'dirname' >>> dsp.add_function(function=osp.split, inputs=['path'], ... outputs=['dirname', 'basename']) 'split' >>> dsp.add_function(function=osp.splitext, inputs=['basename'], ... outputs=['fname', 'suffix']) 'splitext' >>> dsp.add_function(function=osp.join, inputs=['dirname', 'basename'], ... outputs=['path']) 'join' >>> dsp.add_function(function_id='union', function=lambda *a: ''.join(a), ... inputs=['fname', 'suffix'], outputs=['basename']) 'union'
Tip
You can explore the diagram by clicking on it.
Note
For more details how to created a Dispatcher()
see:
add_data()
,
add_function()
,
add_dispatcher()
,
SubDispatch()
,
SubDispatchFunction()
,
SubDispatchPipe()
, and
DFun()
.
The next step to calculate the outputs would be just to run the
dispatch()
method. You can invoke it with just the
inputs, so it will calculate all reachable outputs:
>>> inputs = {'path': 'schedula/_version.py'} >>> o = dsp.dispatch(inputs=inputs) >>> o Solution([('path', 'schedula/_version.py'), ('basename', '_version.py'), ('dirname', 'schedula'), ('fname', '_version'), ('suffix', '.py')])
or you can set also the outputs, so the dispatch will stop when it will find all outputs:
>>> o = dsp.dispatch(inputs=inputs, outputs=['basename']) >>> o Solution([('path', 'schedula/_version.py'), ('basename', '_version.py')])
Advanced example (circular system)¶
Systems of interdependent functions can be described by “graphs” and they might contains circles. This kind of system can not be resolved by a normal scheduler.
Suppose to have a system of sequential functions in circle - i.e., the input of a function is the output of the previous function. The maximum number of input and output permutations is \((2^n - 1)^2\), where n is the number of functions. Thus, with a normal scheduler you have to code all possible implementations, so \((2^n - 1)^2\) functions (IMPOSSIBLE!!!).
Schedula will simplify your life. You just create a
Dispatcher()
, that contains all functions that link your data:
>>> import schedula >>> dsp = schedula.Dispatcher() >>> plus, minus = lambda x: x + 1, lambda x: x - 1 >>> n = j = 6 >>> for i in range(1, n + 1): ... func = plus if i < (n / 2 + 1) else minus ... f = dsp.add_function('f%d' % i, func, ['v%d' % j], ['v%d' % i]) ... j = i
Then it will handle all possible combination of inputs and outputs
(\((2^n - 1)^2\)) just invoking the dispatch()
method, as follows:
>>> out = dsp.dispatch(inputs={'v1': 0, 'v4': 1}, outputs=['v2', 'v6']) >>> out Solution([('v1', 0), ('v4', 1), ('v2', 1), ('v5', 0), ('v6', -1)])
Sub-system extraction¶
Schedula allows to extract sub-models from a model. This could be done with the
shrink_dsp()
method, as follows:
>>> sub_dsp = dsp.shrink_dsp(('v1', 'v3', 'v5'), ('v2', 'v4', 'v6'))
Note
For more details how to extract a sub-model see:
get_sub_dsp()
,
get_sub_dsp_from_workflow()
,
SubDispatch()
,
SubDispatchFunction()
, and
SubDispatchPipe()
.
Next moves¶
Things yet to do include a mechanism to allow the execution of functions in parallel.
API Reference¶
The core of the library is composed from the following modules:
It contains a comprehensive list of all modules and classes within schedula.
Docstrings should provide sufficient understanding for any individual function.
Modules:
dispatcher |
It provides Dispatcher class. |
utils |
It contains utility classes and functions. |
ext |
It provides sphinx extensions. |