Produce a plan that dispatches calls based on a graph of functions, satisfying data dependencies.
Project description
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 --process-dependency-links
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.
- Note: A successful application is CO_2MPAS, 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 >>> import os.path as osp >>> dsp = schedula.Dispatcher() >>> dsp.add_function(function=osp.split, inputs=['path'], ... outputs=['dirname', 'basename']) 'split' >>> dsp.add_function(function=osp.splitext, inputs=['basename'], ... outputs=['fname', 'suffix']) 'splitext'[graph]
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')])[graph]
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')])[graph]
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[graph]
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)])[graph]
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'))[graph]
- 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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file schedula-0.1.1.tar.gz.
File metadata
- Download URL: schedula-0.1.1.tar.gz
- Upload date:
- Size: 110.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 6ae9bcef2f9e807b4fccc544cec4b69fa4b0c685b495d5511921a4516f59c605 |
|
| MD5 | 334de12bc8b1a24e99dbece473759237 |
|
| BLAKE2b-256 | bf42b574893e909966766e4d4d561fd378e7c81be63d096acb92951439c59ece |
