Nested graphs using Eval

To create this graph we need only to install the tierkreis package:

pip install tierkreis

Graph

We can run graphs from within other graphs by using GraphBuilder.eval. Recall the fib_step graph that we wrote last time:

from typing import NamedTuple

from tierkreis.builder import GraphBuilder
from tierkreis.builtins.stubs import iadd
from tierkreis.models import TKR


class FibData(NamedTuple):
    a: TKR[int]
    b: TKR[int]


fib_step = GraphBuilder(FibData, FibData)
sum = fib_step.task(iadd(fib_step.inputs.a, fib_step.inputs.b))
fib_step.outputs(FibData(fib_step.inputs.b, sum))

We create a graph fib4 that calls fib_step three times. The graph will have no inputs and gives a single integer as output:

from tierkreis.models import EmptyModel

fib4 = GraphBuilder(EmptyModel, TKR[int])

The GraphBuilder.eval method takes a GraphBuilder object as its first argument and the appropriately typed input data as the second object.

second = fib4.eval(fib_step, FibData(a=fib4.const(0), b=fib4.const(1)))

We can iterate manually as follows:

third = fib4.eval(fib_step, second)
fourth = fib4.eval(fib_step, third)
fib4.outputs(fourth.b)

In the next tutorial we will see how to iterate programmatically.

Execution

Since we still only use built-in functions, we execute the graph in the same way as before.

from uuid import UUID
from pathlib import Path

from tierkreis import run_graph
from tierkreis.storage import FileStorage, read_outputs
from tierkreis.executor import ShellExecutor

storage = FileStorage(UUID(int=99), name="Nested graphs using Eval")
executor = ShellExecutor(Path("."), logs_path=storage.logs_path)

storage.clean_graph_files()
run_graph(storage, executor, fib4.get_data(), {})
print(read_outputs(storage))

3