4. Architecture¶
- compute
- computation
The definition & execution of networked operation is split in 1+2 phases:
… it is constrained by these IO data-structures:
… populates these low-level data-structures:
network graph (COMPOSE time)
execution dag (COMPILE time)
execution steps (COMPILE time)
solution (EXECUTE time)
… and utilizes these main classes:
graphtik.op.FunctionalOperation
(fn, name[, …])An operation performing a callable (ie a function, a method, a lambda).
graphtik.netop.NetworkOperation
(operations, …)An operation that can compute a network-graph of operations.
graphtik.network.Network
(*operations[, graph])A graph of operations that can compile an execution plan.
A pre-compiled list of operation steps that can execute for the given inputs/outputs.
graphtik.network.Solution
(plan, input_values)A chain-map collecting solution outputs and execution state (eg overwrites)
- compose
- composition
The phase where operations are constructed and grouped into netops and corresponding networks.
Tip
Use
operation
builder class to constructFunctionalOperation
instances.Use
compose()
factory to prepare the net internally, and buildNetworkOperation
instances.
- compile
- compilation
The phase where the
Network
creates a new execution plan by pruning all graph nodes into a subgraph dag, and deriving the execution steps.- execute
- execution
- sequential
The phase where the
ExecutionPlan
calls the underlying functions of all operations contained in execution steps, with inputs/outputs taken from the solution.Currently there are 2 ways to execute:
sequential
parallel, with a
multiprocessing.pool.ProcessPool
Plans may abort their execution by setting the abort run global flag.
- parallel
- parallel execution
- execution pool
- task
execute operations in parallel, with a thread pool or process pool (instead of sequential). Operations and netop are marked as such on construction, or enabled globally from configurations.
Note that sideffects are not expected to function with process pools, certainly not when marshalling is enabled.
- process pool
When the
multiprocessing.pool.Pool
class is used for parallel execution, the tasks must be communicated to/from the worker process, which requires pickling, and that may fail. With pickling failures you may try marshalling with dill library, and see if that helps.Note that sideffects are not expected to function at all. certainly not when marshalling is enabled.
- thread pool
When the
multiprocessing.dummy.Pool()
class is used for parallel execution, the tasks are run in process, so no marshalling is needed.- marshalling
Pickling parallel operations and their inputs/outputs using the
dill
module. It is configured either globally withset_marshal_tasks()
or set with a flag on each operation / netop.Note that sideffects do not work when this is enabled.
- configurations
- graphtik configuration
The functions controlling compile & execution globally are defined in
config
module and +1 ingraphtik.plot
module; the underlying global data are stored incontextvars.ContextVar
instances, to allow for nested control.All boolean configuration flags are tri-state (
None, False, True
), allowing to “force” all operations, when they are not set to theNone
value. All of them default toNone
(false).- graph
- network graph
The
Network.graph
(currently a DAG) contains allFunctionalOperation
and_DataNode
nodes of some netop.They are layed out and connected by repeated calls of
Network._append_operation()
by Network constructor.This graph is then pruned to extract the dag, and the execution steps are calculated, all ingredients for a new
ExecutionPlan
.- dag
- execution dag
- solution dag
There are 2 directed-acyclic-graphs instances used:
the
ExecutionPlan.dag
, in the execution plan, which contains the pruned nodes, used to decide the execution steps;the
Solution.dag
in the solution, which derives the canceled operations due to rescheduled/failed operations upstream.
- steps
- execution steps
The
ExecutionPlan.steps
contains a list of the operation-nodes only from the dag, topologically sorted, and interspersed with instruction steps needed to compute the asked outputs from the given inputs.It is built by
Network._build_execution_steps()
based on the subgraph dag.The only instruction step is for performing evictions.
- evictions
The
_EvictInstruction
steps erase items from solution as soon as they are not needed further down the dag, to reduce memory footprint while computing.- solution
A
Solution
instance created internally byNetworkOperation.compute()
to hold the values both inputs & outputs, and the status of executed operations. It is based on acollections.ChainMap
, to keep one dictionary for each operation executed +1 for inputs.The results of the last operation executed “wins” in the final outputs produced, BUT while executing, the needs of each operation receive the solution values in reversed order, that is, the 1st operation result (or given input) wins for some needs name.
Rational:
During execution we want stability (the same input value used by all operations), and that is most important when consuming input values - otherwise, we would use (possibly overwritten and thus changing)) intermediate ones.
But at the end we want to affect the calculation results by adding operations into some netop - furthermore, it wouldn’t be very useful to get back the given inputs in case of overwrites.
- overwrites
Values in the solution that have been written by more than one operations, accessed by
Solution.overwrites
:- net
- network
the
Network
contains a graph of operations and can compile an execution plan or prune a cloned network for given inputs/outputs/node predicate.- plan
- execution plan
Class
ExecutionPlan
perform the execution phase which contains the dag and the steps.compileed execution plans are cached in
Network._cached_plans
across runs with (inputs, outputs, predicate) as key.- inputs
The named input values that are fed into an operation (or netop) through
Operation.compute()
method according to its needs.These values are either:
given by the user to the outer netop, at the start of a computation, or
derived from solution using needs as keys, during intermediate execution.
- outputs
The dictionary of computed values returned by an operation (or a netop) matching its provides, when method
Operation.compute()
is called.Those values are either:
retained in the solution, internally during execution, keyed by the respective provide, or
returned to user after the outer netop has finished computation.
When no specific outputs requested from a netop,
NetworkOperation.compute()
returns all intermediate inputs along with the outputs, that is, no evictions happens.An operation may return partial outputs.
- returns dictionary
When an operation is marked with this flag, the underlying function is not expected to return a sequence but a dictionary; hence, no “zipping” of outputs/provides takes place.
- operation
Either the abstract notion of an action with specified needs and provides, or the concrete wrapper
FunctionalOperation
for arbitrary functions (anycallable()
), that feeds on inputs and update outputs, from/to solution, or given-by/returned-to the user by a netop.The distinction between needs/provides and inputs/outputs is akin to function parameters and arguments during define-time and run-time.
- netop
- network operation
The
NetworkOperation
class holding a network of operations.- needs
A list of (positionally ordered) names of the data needed by an operation to receive as inputs, roughly corresponding to the arguments of the underlying callable. The corresponding data-values will be extracted from solution (or given by the user) when
Operation.compute()
is called during execution.modifiers may annotate certain names as optionals, sideffects, or map them to differently named function arguments.
The graph is laid out by matching the needs & provides of all operations.
- provides
A list of names to be zipped with the data-values produced when the operation’s underlying callable executes. The resulting outputs dictionary will be stored into the solution or returned to the user after
Operation.compute()
is called during execution.modifiers may annotate certain names as sideffects.
The graph is laid out by matching the needs & provides of all operations.
- modifiers
Annotations on specific arguments of needs and/or provides such as optionals & sideffects (see
graphtik.modifiers
module).- optionals
needs corresponding either:
to function arguments-with-defaults (annotated with
optional
), or
that do not hinder execution of the operation if absent from inputs.
- sideffects
Fictive needs or provides not consumed/produced by the underlying function of an operation, annotated with
sideffect
. A sideffect participates in the compilation of the graph, and is updated into the solution, but is never given/asked to/from functions.- prune
- pruning
A subphase of compilation performed by method
Network._prune_graph()
, which extracts a subgraph dag that does not contain any unsatisfied operations.It topologically sorts the graph, and prunes based on given inputs, asked outputs, node predicate and operation needs & provides.
- unsatisfied operation
The core of pruning & rescheduling, performed by
network._unsatisfied_operations()
function, which collects all operations that fall into any of these 2 cases:- reschedule
- rescheduling
- partial outputs
- partial operation
- canceled operation
The partial pruning of the solution’s dag during execution. It happens when any of these 2 conditions apply:
an operation is marked with the
FunctionalOperation.rescheduled
attribute, which means that its underlying callable may produce only a subset of its provides (partial outputs);endurance is enabled, either globally (in the configurations), or for a specific operation.
the solution must then reschedule the remaining operations downstream, and possibly cancel some of those ( assigned in
Solution.canceled
).- endurance
Keep executing as many operations as possible, even if some of them fail. Endurance for an operation is enabled if
set_endure_operations()
is true globally in the configurations or ifFunctionalOperation.endured
is true.You may interrogate
Solution.executed
to discover the status of each executed operations or call one ofcheck_if_incomplete()
orscream_if_incomplete()
.- predicate
- node predicate
A callable(op, node-data) that should return true for nodes to be included in graph during compilation.
- abort run
A global configurations flag that when set with
abort_run()
function, it halts the execution of all currently or future plans.It is reset automatically on every call of
NetworkOperation.compute()
(after a successful intermediate compilation), or manually, by callingreset_abort()
.- plottable
Objects that can plot their graph network, such as those inheriting
Plottable
, (NetworkOperation
,Network
,ExecutionPlan
,Solution
) or apydot.Dot
instance (the result of thePlottable.plot()
method).Such objects may render as SVG in Jupter notebooks (through their
plot()
method) and can render in a Sphinx site with with thegraphtik
RsT directive. You may control the rendered image as explained in the tip of the Plotting section.SVGs are in rendered with the zoom-and-pan javascript library
Attention
Zoom-and-pan does not work in Sphinx sites for Chrome locally - serve the HTML files through some HTTP server, e.g. launch this command to view the site of this project:
python -m http.server 8080 --directory build/sphinx/html/
- plotter
A
Plotter
is responsible for rendering plottables as images. It is the active plotter that does that, unless overridden in aPlottable.plot()
call. Plotters can be customized by various means, such plot styles.- active plotter
- default active plotter
The plotter currently installed “in-context” of the respective graphtik configuration - this term implies also any Plot customizations done on the active plotter (such as plot styles).
Installation happens by calling one of
active_plotter_plugged()
orset_active_plotter()
functions.The default active plotter is the plotter instance that this project comes pre-configured with, ie, when no plot-customizations have yet happened.
- plot styles
The attributes of
plot.Style
class. The actual styles in-use are those in thePlotter.style
attribute of the active plotter.