Eager Workflows#

Tags: Intermediate


This feature is experimental and the API is subject to breaking changes. If you encounter any issues please consider submitting a bug report.

So far, the two types of workflows you’ve seen are static workflows, which are defined with @workflow-decorated functions or imperative Workflow class, and dynamic workflows, which are defined with the @dynamic decorator.

Static workflows are created at compile time when you call pyflyte run, pyflyte register, or pyflyte serialize. This means that the workflow is static and cannot change its shape at any point: all of the variables defined as an input to the workflow or as an output of a task or subworkflow are promises. Dynamic workflows, on the other hand, are compiled at runtime so that they can materialize the inputs of the workflow as Python values and use them to determine the shape of the execution graph.

In this guide you’ll learn how to use eager workflows, which allow you to create extremely flexible workflows that give you run-time access to intermediary task/subworkflow outputs.

Why Eager Workflows?#

Both static and dynamic workflows have a key limitation: while they provide compile-time and run-time 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

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

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

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’re defining 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 it Works#

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.


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 flyte 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 Flyte. Flyte has concurrency built into it, so all tasks/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. We’ll see how to configure @eager functions to run on a remote Flyte cluster later in this guide.

What can you do with eager workflows?#

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 now 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:

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:

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

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
        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 Flyte task.


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

import asyncio

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

    for i in range(x):

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

Static subworkflows#

You can also invoke static workflows from within an eager workflow:

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

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 nest eager subworkflows inside a parent eager workflow:

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

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

Catching exceptions#

You can also catch exceptions in eager workflows through EagerException:

from flytekit.experimental import EagerException

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

async def eager_workflow_with_exception(x: int) -> int:
        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.


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

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 configration does not require key-based authentication.

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

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


When executing eager workflows on a remote Flyte cluster, it 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 Flyte 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 pyflyte register:

pyflyte --config <path/to/config.yaml> register \
 --project <project> \
 --domain <domain> \
 --image <image> \

And then run it with pyflyte run:

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


You need to register the tasks/workflows associated with your eager workflow because eager workflows are actually flyte tasks under the hood, which means that pyflyte 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 Flyte Console, the UI for Flyte. When you register an eager workflow, you’ll be able to see it in the task view:

Eager Workflow UI 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 Flyte has no way of knowing the shape of the execution graph before actually executing them.

Eager Workflow Execution

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:

Eager Workflow Deck


As this feature is still experimental, there are a few limitations that you need 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.

  • Flyte 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.

Summary of Workflows#

Eager workflows are a powerful new construct that trades-off compile-time type safety for flexibility in the shape of the execution graph. The table below will help you to reason about the different workflow constructs in Flyte in terms of promises and materialized values:



Flyte Promises




Compiled at compile-time

All inputs and intermediary outputs are promises

Type errors caught at compile-time

Constrained by Flyte DSL


Compiled at run-time

Inputs are materialized, but outputs of all Flyte entities are Promises

More flexible than @workflow, e.g. can do Python operations on inputs

Can’t use a lot of Python constructs (e.g. try/except)


Never compiled

Everything is materialized!

Can effectively use all Python constructs via asyncio syntax

No compile-time benefits, this is the wild west 🏜