About

A very naive attempt at running a redun workflow using Modal as a backend.

Redun

redun is a fantastic workflow engine with very a minimal design.

It let's you write code which looks something like this:

@redun.task
def load() -> pd.DataFrame:
    return pd.DataFrame()


@redun.task
def aggregate(df: pd.DataFrame, method: str) -> pd.DataFrame:
    return df.groupby(...).agg(method)


@redun.task
def plot(data: pd.DataFrame) -> Figure:
    return data.plot.scatter("x", "y")


@redun.task
def analysis():
    df = load_data()
    fig_mean = plot_data(aggregate(df, "mean"))
    fig_median = plot_data(aggregate(df, "median"))

It then gives you a few benefits compared to raw python code:

• Smart caching: Each intermediate result is cached, and is only recomputed if either the source code changes, or input arguments change
• Parallelism: fig_mean and fig_median can be generated in parallel
• Provenance: redun takes care of logging the data lineage if you ever need to understand how a given result was produced.

Modal

Modal "is a new tool that lets you run code in the cloud without having to think about infrastructure."

It essentially allows you to run your code in the cloud with a ridiculously low overhead.

import modal

stub = modal.Stub(
    image=modal.DebianSlim().pip_install(["numpy", "pandas", "matplotlib"])
)

@stub.function
def load() -> pd.DataFrame:
    return pd.DataFrame()


@stub.function
def aggregate(df: pd.DataFrame, method: str) -> pd.DataFrame:
    return df.groupby(...).agg(method)


@stub.function
def plot(data: pd.DataFrame) -> Figure:
    return data.plot.scatter("x", "y")


@stub.function
def analysis():
    df = load_data()
    fig_mean = plot_data(aggregate(df, "mean"))
    fig_median = plot_data(aggregate(df, "median"))

When executed, these functions will almost instantly run in the cloud using the specified environment.

Modal x Redun

It's tempting to ask the obvious question: can we use redun to provide parallelism and caching, but use Modal as the execution engine?

Unfortunately it's not as straightforward,

Modal expects that the functions inside the module are truly functions, so something like this will not work (because foo is now a task, not a function):

# my_module.py

@redun.task
@stub.function
def foo():
    ...

modal also cannot (currently?) generate functions dynamically, so we cannot write something like

def submit_task(task, stub, *args, **kwargs):
    raw_fn = task.func
    modal_fn = stub.function(raw_fn)
    result = modal_fn(*args, **kwargs)

So for this dumb PoC when calling a task one needs to write foo.lazy(*args) instead of foo(*args), which is ugly as hell but at least it works.

@modal_task(stub=stub)
def add(x, y):
    return x + y


@modal_task(stub=stub)
def analysis():
    res_1 = add.lazy(1, 1)
    res_2 = add.lazy(2, 2)

For this PoC we also decided it would be better to run the redun scheduler in the cloud, storing the database in a persistent SharedVolume

Setup

Install the Modal client package, and then add redun:

python -m pip install redun

Demo

Running

python demo.py

Will execute the code for the first time, printing out something like:

2022-08-12T14:25:19+0000 Execution duration: 7.63 seconds
2022-08-12 14:25:17 :: 2.0 + 3.0 = 5.0
2022-08-12 14:25:17 :: 2.0 + 3.0 = 6.0

Re-running the same code again, we will see exactly the same message, since the results are cached:

2022-08-12 14:25:17 :: 2.0 + 3.0 = 5.0
2022-08-12 14:25:17 :: 2.0 + 3.0 = 6.0

However if we modify the code, e.g. by setting mul_result = mul.lazy(a=3.0, b=3.0) one of the results will be updated:

2022-08-12 14:25:17 :: 2.0 + 3.0 = 5.0
2022-08-12 14:26:07 :: 3.0 + 3.0 = 9.0

Which is exactly what we wanted.