schedula: An intelligent function scheduler¶
| release: | 0.3.7 |
|---|---|
| date: | 2019-12-06 15:50:00 |
| repository: | |
| pypi-repo: | |
| docs: | |
| wiki: | |
| download: | |
| keywords: | scheduling, dispatch, dataflow, processing, calculation, dependencies, scientific, engineering, simulink, graph theory |
| developers: |
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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
Install extras¶
Some additional functionality is enabled installing the following extras:
- plot: enables the plot of the Dispatcher model and workflow
(see
plot()). - web: enables to build a dispatcher Flask app (see
web()). - sphinx: enables the sphinx extension directives (i.e., autosummary and dispatcher).
- parallel: enables the parallel execution of Dispatcher model.
To install schedula and all extras, do:
$ pip install schedula[all]
Note
plot extra requires Graphviz. Make sure that the directory
containing the dot executable is on your systems’ path. If you have not
you can install it from its download page.
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
Install extras¶
Some additional functionality is enabled installing the following extras:
- plot: enables the plot of the Dispatcher model and workflow
(see
plot()). - web: enables to build a dispatcher Flask app (see
web()). - sphinx: enables the sphinx extension directives (i.e., autosummary and dispatcher).
- parallel: enables the parallel execution of Dispatcher model.
To install schedula and all extras, do:
$ pip install schedula[all]
Note
plot extra requires Graphviz. Make sure that the directory
containing the dot executable is on your systems’ path. If you have not
you can install it from its download page.
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 and to run your model
asynchronously or in parallel without extra coding.
Note
A successful application is CO2MPAS, where schedula has been used to model an entire vehicle.
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 as sh >>> import os.path as osp >>> dsp = sh.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_func(),
add_function(),
add_dispatcher(),
SubDispatch,
SubDispatchFunction,
SubDispatchPipe,
DispatchPipe, 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 as sh >>> dsp = sh.Dispatcher() >>> increment = lambda x: x + 1 >>> for k, (i, j) in enumerate(sh.pairwise([1, 2, 3, 4, 5, 6, 1])): ... dsp.add_function('f%d' % k, increment, ['v%d' % i], ['v%d' % j]) '...'
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', 2), ('v6', 3)])
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,
DispatchPipe, and
SubDispatchPipe.
Iterated function¶
Schedula allows to build an iterated function, i.e. the input is recalculated.
This could be done easily with the DispatchPipe,
as follows:
>>> func = sh.DispatchPipe(dsp, 'func', ('v1', 'v4'), ('v1', 'v4'))
>>> x = [[1, 4]]
>>> for i in range(6):
... x.append(func(*x[-1]))
>>> x
[[1, 4], [7, 4], [7, 10], [13, 10], [13, 16], [19, 16], [19, 22]]
Asynchronous and Parallel dispatching¶
When there are heavy calculations which takes a significant amount of time, you want to run your model asynchronously or in parallel. Generally, this is difficult to achieve, because it requires an higher level of abstraction and a deeper knowledge of python programming and the Global Interpreter Lock (GIL). Schedula will simplify again your life. It has four default executors to dispatch asynchronously or in parallel:
- async: execute all functions asynchronously in the same process,
- parallel: execute all functions in parallel excluding
SubDispatchfunctions,- parallel-pool: execute all functions in parallel using a process pool excluding
SubDispatchfunctions,- parallel-dispatch: execute all functions in parallel including
SubDispatch.
Note
Running functions asynchronously or in parallel has a cost. Schedula will spend time creating / deleting new threads / processes.
The code below shows an example of a time consuming code, that with the concurrent execution it requires at least 6 seconds to run. Note that the slow function return the process id.
>>> import schedula as sh >>> dsp = sh.Dispatcher() >>> def slow(): ... import os, time ... time.sleep(1) ... return os.getpid() >>> for o in 'abcdef': ... dsp.add_function(function=slow, outputs=[o]) '...'
while using the async executor, it lasts a bit more then 1 second:
>>> import time
>>> start = time.time()
>>> sol = dsp(executor='async').result() # Asynchronous execution.
>>> (time.time() - start) < 2 # Faster then concurrent execution.
True
all functions have been executed asynchronously, but in the same process:
>>> import os
>>> pid = os.getpid() # Current process id.
>>> {sol[k] for k in 'abcdef'} == {pid} # Single process id.
True
if we use the parallel executor all functions are executed in different processes:
>>> sol = dsp(executor='parallel').result() # Parallel execution.
>>> pids = {sol[k] for k in 'abcdef'} # Process ids returned by `slow`.
>>> len(pids) == 6 # Each function returns a different process id.
True
>>> pid not in pids # The current process id is not in the returned pids.
True
>>> sorted(sh.shutdown_executors())
['async', 'parallel']
Next moves¶
Things yet to do: utility to transform a dispatcher in a command line tool.
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. |
Changelog¶
v0.3.7 (2019-12-06)¶
Feat¶
- (drw): Update the index GUI of the plot.
- (appveyor): Drop appveyor in favor of travis.
- (travis): Update travis configuration file.
- (plot): Add node link and id in graph plot.
v0.3.6 (2019-10-18)¶
v0.3.4 (2019-07-15)¶
Feat¶
- (binder): Add @jupyterlab/plotly-extension.
- (binder): Customize Site._repr_html_ with env SCHEDULA_SITE_REPR_HTML.
- (binder): Add jupyter-server-proxy.
- (doc): Add binder examples.
- (gen): Create super-class of Token.
- (dsp): Improve error message.
Fix¶
- (binder): Simplify processing_chain example.
- (setup): Exclude binder and examples folders as packages.
- (doc): Correct binder data.
- (doc): Update examples for binder.
- (doc): Add missing requirements binder.
- (test): Add state to fake directive.
- (import): Remove stub file to enable autocomplete.
- Update to canonical pypi name of beautifulsoup4.
v0.3.3 (2019-04-02)¶
Feat¶
- (dispatcher): Improve error message.
Fix¶
- (doc): Correct bug for sphinx AutoDirective.
- (dsp): Add dsp as kwargs for a new Blueprint.
- (doc): Update PEP and copyright.
v0.3.2 (2019-02-23)¶
Feat¶
- (core): Add stub file.
- (sphinx): Add Blueprint in Dispatcher documenter.
- (sphinx): Add BlueDispatcher in documenter.
- (doc): Add examples.
- (blue): Customizable memo registration of blueprints.
Fix¶
- (sphinx): Correct bug when “ is in csv-table directive.
- (core): Set module attribute when __getattr__ is invoked.
- (doc): Correct utils description.
- (setup): Improve keywords.
- (drw): Correct tooltip string format.
- (version): Correct import.
v0.3.1 (2018-12-10)¶
Fix¶
- (setup): Correct long description for pypi.
- (dsp): Correct bug DispatchPipe when dill.
v0.3.0 (2018-12-08)¶
Feat¶
- (blue, dispatcher): Add method extend to extend Dispatcher or Blueprint with Dispatchers or Blueprints.
- (blue, dsp): Add BlueDispatcher class + remove DFun util.
- (core): Remove weight attribute from Dispatcher struc.
- (dispatcher): Add method add_func to Dispatcher.
- (core): Remove remote_links attribute from dispatcher data nodes.
- (core): Implement callable raise option in Dispatcher.
- (core): Add feature to dispatch asynchronously and in parallel.
- (setup): Add python 3.7.
- (dsp): Use the same dsp.solution class in SubDispatch functions.
Fix¶
- (dsp): Do not copy solution when call DispatchPipe, but reset solution when copying the obj.
- (alg): Correct and clean get_sub_dsp_from_workflow algorithm.
- (sol): Ensure bool output from input_domain call.
- (dsp): Parse arg and kw using SubDispatchFunction.__signature__.
- (core): Do not support python 3.4.
- (asy): Do not dill the Dispatcher solution.
- (dispatcher): Correct bug in removing remote links.
- (core): Simplify and correct Exception handling.
- (dsp): Postpone __signature__ evaluation in add_args.
- (gen): Make Token constant when pickled.
- (sol): Move callback invocation in _evaluate_node.
- (core) #11: Lazy import of modules.
- (sphinx): Remove warnings.
- (dsp): Add missing code option in add_function decorator.
Other¶
- Refact: Update documentation.
v0.2.6 (2018-09-13)¶
Feat¶
- (setup): Patch to use sphinxcontrib.restbuilder in setup long_description.
v0.2.5 (2018-09-13)¶
Fix¶
- (doc): Correct link docs_status.
- (setup): Use text instead rst to compile long_description + add logging.
v0.2.4 (2018-09-13)¶
Fix¶
- (sphinx): Correct bug sphinx==1.8.0.
- (sphinx): Remove all sphinx warnings.
v0.2.2 (2018-08-02)¶
Fix¶
- (des): Correct bug of get_id when tuple ids nodes are given as input or outputs of a sub_dsp.
- (des): Correct bug when tuple ids are given as inputs or outputs of add_dispatcher method.
v0.2.1 (2018-07-24)¶
Feat¶
- (setup): Update Development Status to 5 - Production/Stable.
- (setup): Add additional project_urls.
- (doc): Add changelog to rtd.
Fix¶
- (doc): Correct link docs_status.
- (des): Correct bugs get_des.
v0.2.0 (2018-07-19)¶
Feat¶
- (doc): Add changelog.
- (travis): Test extras.
- (des): Avoid using sphinx for getargspec.
- (setup): Add extras_require to setup file.
Fix¶
- (setup): Correct bug in get_long_description.
v0.1.19 (2018-06-05)¶
Fix¶
- (dsp): Add missing content block in note directive.
- (drw): Make sure to plot same sol as function and as node.
- (drw): Correct format of started attribute.
v0.1.18 (2018-05-28)¶
Feat¶
- (dsp): Add DispatchPipe class (faster pipe execution, it overwrite the existing solution).
- (core): Improve performances replacing datetime.today() with time.time().
v0.1.17 (2018-05-18)¶
Feat¶
- (travis): Run coveralls in python 3.6.
Fix¶
- (web): Skip Flask logging for the doctest.
- (ext.dispatcher): Update to the latest Sphinx 1.7.4.
- (des): Use the proper dependency (i.e., sphinx.util.inspect) for getargspec.
- (drw): Set socket option to reuse the address (host:port).
- (setup): Correct dill requirements dill>=0.2.7.1 –> dill!=0.2.7.
v0.1.14 (2017-07-11)¶
Fix¶
- (io): pin dill version <=0.2.6.
- (abort): abort was setting Exception.args instead of sol attribute.
v0.1.11 (2017-05-04)¶
Feat¶
Fix¶
- (doc): Replace type function with callable.
- (drw): Folder name without ext.
- (test): Avoid Documentation of DspPlot.
- (doc): fix docstrings types.
v0.1.10 (2017-04-03)¶
Feat¶
- (sol): Close sub-dispatcher solution when all outputs are satisfied.
Fix¶
- (drw): Log error when dot is not able to render a graph.
v0.1.8 (2017-02-09)¶
Feat¶
- (drw): Update plot index + function code highlight + correct plot outputs.
v0.1.6 (2017-02-08)¶
Fix¶
- (setup): Avoid setup failure due to get_long_description.
- (drw): Avoid to plot unneeded weight edges.
- (dispatcher): get_sub_dsp_from_workflow set correctly the remote links.
v0.1.5 (2017-02-06)¶
Feat¶
- (exl): Drop exl module because of formulas.
- (sol): Add input value of filters in solution.
Fix¶
- (drw): Plot just one time the filer attribute in workflow +filers|solution_filters .
v0.1.4 (2017-01-31)¶
Feat¶
- (drw): Save autoplot output.
- (sol): Add filters and function solutions to the workflow nodes.
- (drw): Add filters to the plot node.
Fix¶
- (dispatcher): Add missing function data inputs edge representation.
- (sol): Correct value when apply filters on setting the node output.
- (core): get_sub_dsp_from_workflow blockers can be applied to the sources.
v0.1.3 (2017-01-29)¶
Fix¶
- (dsp): Raise a DispatcherError when the pipe workflow is not respected instead KeyError.
- (dsp): Unresolved references.
v0.1.2 (2017-01-28)¶
Feat¶
- (dsp): add_args _set_doc.
- (dsp): Remove parse_args class.
- (readme): Appveyor badge status == master.
- (dsp): Add _format option to get_unused_node_id.
- (dsp): Add wildcard option to SubDispatchFunction and SubDispatchPipe.
- (drw): Create sub-package drw.
Fix¶
- (dsp): combine nested dicts with different length.
- (dsp): are_in_nested_dicts return false if nested_dict is not a dict.
- (sol): Remove defaults when setting wildcards.
- (drw): Misspelling outpus –> outputs.
- (directive): Add exception on graphviz patch for sphinx 1.3.5.
v0.1.1 (2017-01-21)¶
Fix¶
- (site): Fix ResourceWarning: unclosed socket.
- (setup): Not log sphinx warnings for long_description.
- (travis): Wait util the server is up.
- (rtd): Missing requirement dill.
- (travis): Install first - pip install -r dev-requirements.txt.
- (directive): Tagname from _img to img.
- (directive): Update minimum sphinx version.
- (readme): Badge svg links.
Other¶
- Add project descriptions.
- (directive): Rename schedula.ext.dsp_directive –> schedula.ext.dispatcher.
- Update minimum sphinx version and requests.