Eager workflows#

Important

This feature is experimental and the API is subject to breaking changes. If you encounter any issues please reach out to the Union team.

Eager workflows allow you to create workflows that give you runtime access to intermediary task/subworkflow outputs.

Both static and dynamic workflows have a key limitation: while they provide compile-time and runtime type safety, respectively, they both suffer from inflexibility in expressing asynchronous execution graphs that many Python programmers may be accustomed to by using, for example, the asyncio library.

Unlike static and dynamic workflows, eager workflows allow you to use all of the Python constructs that you’re familiar with via the asyncio API. To understand what this looks like, let’s define a very basic eager workflow using the @eager decorator.

from flytekit import task, workflow
from flytekit.experimental import eager


@task
def add_one(x: int) -> int:
    return x + 1


@task
def double(x: int) -> int:
    return x * 2


@eager
async def simple_eager_workflow(x: int) -> int:
    out = await add_one(x=x)
    if out < 0:
        return -1
    return await double(x=out)

As we can see in the code above, we define an async function called simple_eager_workflow that takes an integer as input and returns an integer. By decorating this function with @eager, we now have the ability to invoke tasks, static subworkflows, and even other eager subworkflows in an eager fashion such that we can materialize their outputs and use them inside the parent eager workflow itself.

In the simple_eager_workflow function, we can see that we’re awaiting the output of the add_one task and assigning it to the out variable. If out is a negative integer, the workflow will return -1. Otherwise, it will double the output of add_one and return it.

Unlike in static and dynamic workflows, this variable is actually the Python integer that is the result of x + 1 and not a Promise.

How eager workflows work#

When you decorate a function with @eager, any function invoked within it that’s decorated with @task, @workflow, or @eager becomes an awaitable object within the lifetime of the parent eager workflow execution. Note that this happens automatically and you don’t need to use the async keyword when defining a task or workflow that you want to invoke within an eager workflow.

Note

With eager workflows, you basically have access to the Python asyncio interface to define extremely flexible execution graphs. The trade-off is that you lose the compile-time type safety that you get with regular static workflows and to a lesser extent, dynamic workflows.

We’re leveraging Python’s native async capabilities in order to:

  1. Materialize the output of tasks and subworkflows so you can operate on them without spinning up another pod and also determine the shape of the workflow graph in an extremely flexible manner.

  2. Provide an alternative way of achieving concurrency in Union. Union has concurrency built into it, so all tasks and subworkflows will execute concurrently assuming that they don’t have any dependencies on each other. However, eager workflows provide a Python-native way of doing this, with the main downside being that you lose the benefits of statically compiled workflows, such as compile-time analysis and first-class data lineage tracking.

Similar to dynamic workflows, eager workflows are actually tasks. The main difference is that, while dynamic workflows compile a static workflow at runtime using materialized inputs, eager workflows do not compile any workflow at all. Instead, they use the FlyteRemote object together with Python’s asyncio API to kick off tasks and subworkflow executions eagerly whenever you await on a coroutine. This means that eager workflows can materialize an output of a task or subworkflow and use it as a Python object in the underlying runtime environment.

Eager workflow use cases#

In this section, we’ll cover a few of the use cases that you can accomplish with eager workflows, some of which you can’t accomplish with static or dynamic workflows.

Operating on task and subworkflow outputs#

One of the biggest benefits of eager workflows is that you can materialize task and subworkflow outputs as Python values and do operations on them just like you would in any other Python function. Let’s look at another example:

@eager
async def another_eager_workflow(x: int) -> int:
    out = await add_one(x=x)

    # out is a Python integer
    out = out - 1

    return await double(x=out)

Since out is an actual Python integer and not a Promise, we can do operations on it at runtime, inside the eager workflow function body. This is not possible with static or dynamic workflows.

Pythonic conditionals#

As you saw in the simple_eager_workflow workflow above, you can use regular Python conditionals in your eager workflows. Let’s look at a more complicated example:

@task
def gt_100(x: int) -> bool:
    return x > 100


@eager
async def eager_workflow_with_conditionals(x: int) -> int:
    out = await add_one(x=x)

    if out < 0:
        return -1
    elif await gt_100(x=out):
        return 100
    else:
        out = await double(x=out)

    assert out >= -1
    return out

In the above example, we’re using the eager workflow’s Python runtime to check if out is negative, but we’re also using the gt_100 task in the elif statement, which will be executed in a separate task.

Loops#

You can gather the outputs of multiple tasks or subworkflows into a list:

import asyncio


@eager
async def eager_workflow_with_for_loop(x: int) -> int:
    outputs = []

    for i in range(x):
        outputs.append(add_one(x=i))

    outputs = await asyncio.gather(*outputs)
    return await double(x=sum(outputs))

Static subworkflows#

You can invoke static workflows from within an eager workflow:

@workflow
def subworkflow(x: int) -> int:
    out = add_one(x=x)
    return double(x=out)


@eager
async def eager_workflow_with_static_subworkflow(x: int) -> int:
    out = await subworkflow(x=x)
    assert out == (x + 1) * 2
    return out

Eager subworkflows#

You can have nested eager subworkflows inside a parent eager workflow:

@eager
async def eager_subworkflow(x: int) -> int:
    return await add_one(x=x)


@eager
async def nested_eager_workflow(x: int) -> int:
    out = await eager_subworkflow(x=x)
    return await double(x=out)

Catching exceptions#

You can catch exceptions in eager workflows through EagerException:

from flytekit.experimental import EagerException


@task
def raises_exc(x: int) -> int:
    if x <= 0:
        raise TypeError
    return x


@eager
async def eager_workflow_with_exception(x: int) -> int:
    try:
        return await raises_exc(x=x)
    except EagerException:
        return -1

Even though the raises_exc exception task raises a TypeError, the eager_workflow_with_exception runtime will raise an EagerException and you’ll need to specify EagerException as the exception type in your try... except block.

Note

This is a current limitation in the @eager workflow implementation.

Executing eager workflows#

As with most Flyte constructs, you can execute eager workflows both locally and remotely.

Local execution#

You can execute eager workflows locally by simply calling them like a regular async function:

if __name__ == "__main__":
    result = asyncio.run(simple_eager_workflow(x=5))
    print(f"Result: {result}")  # "Result: 12"

This just uses the asyncio.run function to execute the eager workflow just like any other Python async code. This is useful for local debugging as you’re developing your workflows and tasks.

Remote Union cluster execution#

Under the hood, @eager workflows use the FlyteRemote object to kick off task, static workflow, and eager workflow executions.

In order to actually execute them on a Union cluster, you’ll need to configure eager workflows with a FlyteRemote object and secrets configuration that allows you to authenticate into the cluster via a client secret key.

from unionai.remote import FlyteRemote
from flytekit.configuration import Config

@eager(
    remote=FlyteRemote(
        config=Config.auto(config_file="config.yaml"),
        default_project="flytesnacks",
        default_domain="development",
    ),
    client_secret_group="<my_client_secret_group>",
    client_secret_key="<my_client_secret_key>",
)
async def eager_workflow_remote(x: int) -> int:
    ...

Where config.yaml contains a flytectl-compatible config file and my_client_secret_group and my_client_secret_key are the secret group and key that you’ve configured for your Flyte cluster to authenticate via a client key.

Sandbox Flyte cluster execution#

When using a sandbox cluster started with flytectl demo start, however, the client_secret_group and client_secret_key are not required, since the default sandbox configuration does not require key-based authentication.

from flytekit.configuration import Config
from unionai.remote import FlyteRemote


@eager(
    remote=FlyteRemote(
        config=Config.for_sandbox(),
        default_project="flytesnacks",
        default_domain="development",
    )
)
async def eager_workflow_sandbox(x: int) -> int:
    out = await add_one(x=x)
    if out < 0:
        return -1
    return await double(x=out)

Note

When executing eager workflows on a remote Union cluster, Union will execute the latest version of tasks, static workflows, and eager workflows that are on the default_project and default_domain as specified in the FlyteRemote object. This means that you need to pre-register all entities that are invoked inside of the eager workflow.

Registering and running#

Assuming that your flytekit code is configured correctly, you will need to register all of the task and subworkflows that are used with your eager workflow with unionai register:

unionai --config <path/to/config.yaml> register \
 --project <project> \
 --domain <domain> \
 --image <image> \
 path/to/eager_workflows.py

And then run it with unionai run:

unionai --config <path/to/config.yaml> run \
 --project <project> \
 --domain <domain> \
 --image <image> \
 path/to/eager_workflows.py simple_eager_workflow --x 10

Note

You need to register the tasks/workflows associated with your eager workflow because eager workflows are actually tasks under the hood, which means that unionai run has no way of knowing what tasks and subworkflows are invoked inside of it.

Eager workflows on Flyte console#

Since eager workflows are an experimental feature, there is currently no first-class representation of them on the console. When you register an eager workflow, you’ll be able to see it in the task view.

When you execute an eager workflow, the tasks and subworkflows invoked within it won’t show up on the node, graph, or timeline view. As mentioned above, this is because eager workflows are actually Flyte tasks under the hood and Union has no way of knowing the shape of the execution graph before actually executing them.

However, at the end of execution, you’ll be able to use Flyte Decks to see a list of all the tasks and subworkflows that were executed within the eager workflow.

Limitations#

As eager workflows are still experimental, there are a few limitations to keep in mind:

  • You cannot invoke dynamic workflows, map tasks, or launch plans inside an eager workflow.

  • Context managers will only work on locally executed functions within the eager workflow, i.e. using a context manager to modify the behavior of a task or subworkflow will not work because they are executed on a completely different pod.

  • All exceptions raised by Flyte tasks or workflows will be caught and raised as an EagerException at runtime.

  • All task/subworkflow outputs are materialized as Python values, which includes offloaded types like FlyteFile, FlyteDirectory, StructuredDataset, and pandas.DataFrame will be fully downloaded into the pod running the eager workflow. This prevents you from incrementally downloading or streaming very large datasets in eager workflows.

  • Flyte entities that are invoked inside of an eager workflow must be registered under the same project and domain as the eager workflow itself. The eager workflow will execute the latest version of these entities.

  • Union console currently does not have a first-class way of viewing eager workflows, but it can be accessed via the task list view and the execution graph is viewable via Flyte Decks.