A workflow is typically static when the directed acyclic graph’s (DAG) structure is known at compile-time.
However, in cases where a run-time parameter (for example, the output of an earlier task) determines the full DAG structure, you can use dynamic workflows by decorating a function with
A dynamic workflow is similar to the
workflow(), in that it represents a python-esque DSL to
declare task interactions or new workflows. One significant difference between a regular workflow and dynamic (workflow) is that
the latter is evaluated at runtime. This means the inputs are first materialized and sent to the actual function,
as if it were a task. However, the return value from a dynamic workflow is a Promise object instead of an actual value,
which is fulfilled by evaluating the various tasks invoked in the dynamic workflow.
Think of a dynamic workflow as a parent graph node that spins off new child nodes which would represent a new child graph. At runtime, dynamic workflows receive input and create new workflows. These new workflows have graph nodes.
@dynamic context (function), every invocation of a
task() or a derivative of
Task class will result in deferred evaluation using a promise, instead
of the actual value being materialized. You can also nest other
@workflow constructs within this
task, but it is not possible to interact with the outputs of a
task/workflow as they are lazily evaluated.
If you want to interact with the outputs, break up the logic in dynamic and create a new task to read and resolve the outputs.
dynamic() for documentation.
Here’s a code example that counts the common characters between any two strings.
Let’s first import all the required libraries.
Next, we write a task that returns the index of a character (A-Z/a-z is equivalent to 0 to 25).
@task def return_index(character: str) -> int: """ Computes the character index (which needs to fit into the 26 characters list)""" if character.islower(): return ord(character) - ord("a") else: return ord(character) - ord("A")
We now write a task that prepares the 26-character list by populating the frequency of every character.
Next we find the number of common characters between the two strings.
In this step, we perform the following:
Initialize the empty 26-character list to be sent to the
Loop through every character of the first string (s1) and populate the frequency list
Loop through every character of the second string (s2) and populate the frequency list
Derive the number of common characters by comparing the two frequency lists
The looping is dependent on the number of characters of both the strings which aren’t known until the run time. If the
@task decorator is used to encapsulate the calls mentioned above, the compilation will fail very early on due to the absence of the literal values.
@dynamic decorator has to be used.
Dynamic workflow is effectively both a task and a workflow. The key thing to note is that the _body of tasks is run at run time and the body of workflows is run at compile (aka registration) time. Essentially, this is what a dynamic workflow leverages – it’s a workflow that is compiled at run time (the best of both worlds)!
At execution (run) time, Flytekit runs the compilation step, and produces
WorkflowTemplate (from the dynamic workflow), which Flytekit then passes back to Flyte Propeller for further running, exactly how sub-workflows are handled.
The dynamic pattern isn’t the most efficient method to iterate over a list. Map tasks might be more efficient in certain cases. But they only work for Python tasks (tasks decorated with the @task decorator) not SQL, Spark, and so on.
We now define a dynamic workflow that encapsulates the above mentioned points.
@dynamic def count_characters(s1: str, s2: str) -> int: """ Calls the required tasks and returns the final result""" # s1 and s2 are accessible # initialize an empty list consisting of 26 empty slots corresponding to every alphabet (lower and upper case) freq1 =  * 26 freq2 =  * 26 # looping through the string s1 for i in range(len(s1)): # index and freq1 are not accessible as they are promises index = return_index(character=s1[i]) freq1 = update_list(freq_list=freq1, list_index=index) # looping through the string s2 for i in range(len(s2)): # index and freq2 are not accessible as they are promises index = return_index(character=s2[i]) freq2 = update_list(freq_list=freq2, list_index=index) # counting the common characters return derive_count(freq1=freq1, freq2=freq2)
When tasks are called within any workflow, they return Promise objects. Likewise, in a dynamic workflow, the tasks’ outputs are Promise objects that cannot be directly accessed (they shall be fulfilled by Flyte later).
Because of this fact, operations on the
index variable like
index + 1 are not valid.
To manage this problem, the values need to be passed to the other tasks to unwrap them.
The local execution will work when a
@dynamic decorator is used because Flytekit treats it like a
task that will run with the Python native inputs.
Therefore, there are no Promise objects locally within the function decorated with
@dynamic as it is treated as a
Finally, we define a workflow that calls the dynamic workflow.
@workflow def wf(s1: str, s2: str) -> int: """ Calls the dynamic workflow and returns the result""" # sending two strings to the workflow return count_characters(s1=s1, s2=s2) if __name__ == "__main__": print(wf(s1="Pear", s2="Earth"))
Dynamic Workflows Under the Hood#
What Is a Dynamic Workflow?#
A workflow whose directed acyclic graph (DAG) is computed at run-time is a dynamic workflow. The tasks in a dynamic workflow are executed at runtime using dynamic inputs.
Think of a dynamic workflow as a combination of a task and a workflow. It is used to dynamically decide the parameters of a workflow at runtime. It is both compiled and executed at run-time. You can define a dynamic workflow using the
Why Use Dynamic Workflows?#
Dynamic workflows simplify your pipelines, providing the flexibility to design workflows based on your project’s requirements, which can’t be achieved using static workflows.
Lower Pressure on etcd#
The workflow CRD and the states associated with static workflows are stored in etcd, which is the Kubernetes database. This database stores Flyte workflow CRD as key-value pairs and keeps track of the status of each node’s execution. A limitation of etcd is that there is a hard limit on the data size (data size refers to the aggregate of the size of the workflow and the status of the nodes). Due to this limitation, you need to ensure that your static workflows don’t consume too much memory.
Dynamic workflows offload the workflow spec (node/task definitions and connections, etc) to the blobstore but the node statuses are stored in the FlyteWorkflow CRD (in etcd). Dynamic workflows alleviate a portion of etcd storage space thereby reducing pressure on etcd.
How Is a Dynamic Workflow Executed?#
FlytePropeller executes the dynamic task in its k8s pod and results in a compiled Flyte DAG which is made available in the FlyteConsole. FlytePropeller uses the information obtained by executing the dynamic task to schedule and execute every node within the dynamic task. You can visualize the dynamic workflow’s graph in the UI only after the dynamic task has completed execution.
When a dynamic task is executed, it generates the entire workflow as its output. This output is known as the futures file. It is named so because the workflow is yet to be executed and all the subsequent outputs are futures.
How Does Flyte Handle Dynamic Workflows?#
A dynamic workflow is modeled as a task in the backend, but the body of the function is executed to produce a workflow at run-time. In both dynamic and static workflows, the output of tasks are Promise objects.
When a dynamic (or static) workflow calls a task, the workflow returns a Promise object. You can’t interact with this Promise object directly since it uses lazy evaluation (it defers the evaluation until absolutely needed). You can unwrap the Promise object by passing it to a task or a dynamic workflow.
Here is an example of house price prediction using dynamic workflows.
Where Are Dynamic Workflows Used?#
Dynamic workflow comes into the picture when you need to:
Modify the logic of the code at runtime
Change or decide on feature extraction parameters on-the-go
Build AutoML pipelines
Tune hyperparameters during execution
Dynamic versus Map Tasks#
Dynamic tasks have overhead for large fan-out tasks because they store metadata for the entire workflow. In contrast, map tasks are efficient for these large fan-out tasks since they don’t store the metadata, as a consequence of which overhead is less apparent.
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