Package: schedula
About schedulaschedulais a dynamic flow-based programming environment for
python, that handles automatically the control flow of the program.
The control flow generally is represented by a Directed Acyclic Graph
(DAG), where nodes are the operations/functions to be executed and
edges are the dependencies between them.The algorithm ofscheduladates back to 2014, when a colleague
asked for a method to automatically populate the missing data of a
database. The imputation method chosen to complete the database was a
system of interdependent physical formulas - i.e., the inputs of a
formula are the outputs of other formulas. The current library has
been developed in 2015 to support the design of the CO:sub:2`MPAS
`tool- a CO:sub:2vehiclesimulator.
During the developing phase, the physical formulas (more than 700)
were known on the contrary of the software inputs and outputs.Why schedula?The design of flow-based programs begins with the definition of the
control flow graph, and implicitly of its inputs and outputs. If the
program accepts multiple combinations of inputs and outputs, you have
to design and code all control flow graphs. With normal schedulers, it
can be very demanding.While withschedula, giving whatever set of inputs, it
automatically calculates any of the desired computable outputs,
choosing the most appropriate DAG from the dataflow execution model.Note: The DAG is determined at runtime and it is extracted using theshortest path from the provided inputs. The path is calculated
based on a weighted directed graph (dataflow execution model) with
a modified Dijkstra algorithm.schedulamakes the code easy to debug, to optimize, and to present
it to a non-IT audience through its interactive graphs and charts. It
provides the option to run a model asynchronously or in parallel
managing automatically the Global Interpreter Lock (GIL), and to
convert a model into a web API service.InstallationTo install it use (with root privileges):$pipinstallschedulaor download the last git version and use (with root privileges):$pythonsetup.pyinstallInstall extrasSome additional functionality is enabled installing the following
extras:io: enables to read/write functions.plot: enables the plot of the Dispatcher model and workflow
(seeplot()).web: enables to build a dispatcher Flask app (seeweb()).sphinx: enables the sphinx extension directives (i.e.,
autosummary and dispatcher).parallel: enables the parallel execution of Dispatcher model.To installschedulaand all extras, do:$pipinstall'schedula[all]'Note:plotextra requiresGraphviz. Make sure that the directorycontaining thedotexecutable is on your systems’ path. If you
have not you can install it from itsdownload page.TutorialLet’s assume that we want develop a tool to automatically manage the
symmetric cryptography. The base idea is to open a file, read its
content, encrypt or decrypt the data and then write them out to a new
file. This tutorial shows how to:defineandexecutea
dataflow execution model,extracta sub-model, anddeploya web API service.Note: You can find more examples, on how to use theschedulalibrary,into the folderexamples.Model definitionFirst of all we start defining an emptyDispatchernamedsymmetric_cryptographythat defines the dataflow execution model:>>> import schedula as sh
>>> dsp = sh.Dispatcher(name='symmetric_cryptography')There are two main ways to get a key, we can either generate a new one
or use one that has previously been generated. Hence, we can define
three functions to simply generate, save, and load the key. To
automatically populate the model inheriting the arguments names, we
can use the decoratoradd_function()as follow:>>> import os.path as osp
>>> from cryptography.fernet import Fernet
>>> @sh.add_function(dsp, outputs=['key'], weight=2)
... def generate_key():
...     return Fernet.generate_key().decode()
>>> @sh.add_function(dsp)
... def write_key(key_fpath, key):
...     with open(key_fpath, 'w') as f:
...         f.write(key)
>>> @sh.add_function(dsp, outputs=['key'], input_domain=osp.isfile)
... def read_key(key_fpath):
...     with open(key_fpath) as f:
...         return f.read()Note: Since Python does not come with anything that can encrypt/decryptfiles, in this tutorial, we use a third party module namedcryptography. To install it executepip install cryptography.To encrypt/decrypt a message, you will need a key as previously
defined and your dataencryptedordecrypted. Therefore, we can
define two functions and add them, as before, to the model:>>> @sh.add_function(dsp, outputs=['encrypted'])
... def encrypt_message(key, decrypted):
...     return Fernet(key.encode()).encrypt(decrypted.encode()).decode()
>>> @sh.add_function(dsp, outputs=['decrypted'])
... def decrypt_message(key, encrypted):
...     return Fernet(key.encode()).decrypt(encrypted.encode()).decode()Finally, to read and write the encrypted or decrypted message,
according to the functional programming philosophy, we can reuse the
previously defined functionsread_keyandwrite_keychanging
the model mapping (i.e.,function_id,inputs, andoutputs). To
add to the model, we can simply use theadd_functionmethod as
follow:>>> dsp.add_function(
...     function_id='read_decrypted',
...     function=read_key,
...     inputs=['decrypted_fpath'],
...     outputs=['decrypted']
... )
'read_decrypted'
>>> dsp.add_function(
...     'read_encrypted', read_key, ['encrypted_fpath'], ['encrypted'],
...     input_domain=osp.isfile
... )
'read_encrypted'
>>> dsp.add_function(
...     'write_decrypted', write_key, ['decrypted_fpath', 'decrypted'],
...     input_domain=osp.isfile
... )
'write_decrypted'
>>> dsp.add_function(
...     'write_encrypted', write_key, ['encrypted_fpath', 'encrypted']
... )
'write_encrypted'Note: For more details on how to create aDispatchersee:add_data(),add_func(),add_function(),add_dispatcher(),SubDispatch,MapDispatch,SubDispatchFunction,SubDispatchPipe, andDispatchPipe.To inspect and visualize the dataflow execution model, you can simply
plot the graph as follow:>>> dsp.plot()[graph]Tip: You can explore the diagram by clicking on it.DispatchingTo see the dataflow execution model in action and its workflow to
generate a key, to encrypt a message, and to write the encrypt data,
you can simply invokedispatch()or__call__()methods of thedsp:>>> import tempfile
>>> tempdir = tempfile.mkdtemp()
>>> message = "secret message"
>>> sol = dsp(inputs=dict(
...     decrypted=message,
...     encrypted_fpath=osp.join(tempdir, 'data.secret'),
...     key_fpath=osp.join(tempdir,'key.key')
... ))
>>> sol.plot(index=True)[graph]Note: As you can see from the workflow graph (orange nodes), when somefunction’s inputs does not respect its domain, the Dispatcher
automatically finds an alternative path to estimate all computable
outputs. The same logic applies when there is a function failure.Now to decrypt the data and verify the message without saving the
decrypted message, you just need to execute again thedspchanging
theinputsand setting the desiredoutputs. In this way, the
dispatcher automatically selects and executes only a sub-part of the
dataflow execution model.>>> dsp(
...     inputs=sh.selector(('encrypted_fpath', 'key_fpath'), sol),
...     outputs=['decrypted']
... )['decrypted'] == message
TrueIf you want to visualize the latest workflow of the dispatcher, you
can use theplot()method with the keywordworkflow=True:>>> dsp.plot(workflow=True, index=True)[graph]