> ## Documentation Index
> Fetch the complete documentation index at: https://docs.prefect.io/llms.txt
> Use this file to discover all available pages before exploring further.
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# How to write and run a workflow
### Define a workflow
Make a Python function a workflow by adding the `@flow` decorator to it:
```python theme={null}
from prefect import flow
@flow
def my_workflow() -> str:
return "Hello, world!"
```
Run the workflow by calling it like a normal Python function:
```python theme={null}
my_workflow()
```
You can create a workflow from a method on a class:
```python theme={null}
from prefect import flow
class MyClass:
@flow
def my_instance_method(self):
return "Hello, from an instance method!"
@flow
@classmethod
def my_class_method(cls):
return "Hello, from a class method!"
@flow
@staticmethod
def my_static_method():
return "Hello, from a static method!"
MyClass().my_instance_method()
MyClass.my_class_method()
MyClass.my_static_method()
```
or from a generator function:
```python theme={null}
from prefect import flow
@flow
def generator():
for i in range(10):
yield i
for val in generator():
print(val)
```
You can view all workflow runs in the Prefect UI.
### Create tasks and child workflows
You can create tasks and child flows to organize your workflow logic.
```python theme={null}
from prefect import flow, task
@task
def generate_a_number():
return random.randint(0, 100)
@flow
def is_number_even(number: int):
return number % 2 == 0
@flow
def even_or_odd():
number = generate_a_number()
if is_number_even(number):
print("The number is even")
else:
print("The number is odd")
```
Each task and child flow is a separate unit of work and is displayed in the Prefect UI.
### Cancel a workflow if it runs for too long
To apply a timeout to a flow or task to prevent it from running for too long, use the `timeout_seconds` keyword argument.
```python theme={null}
from prefect import flow
import time
@flow(timeout_seconds=1, log_prints=True)
def show_timeouts():
print("I will execute")
time.sleep(5)
print("I will not execute")
```
### Task timeout behavior
Task timeouts work differently depending on how the task is executed:
**Async tasks**: Timeouts use cooperative cancellation at `await` points. When the timeout is reached, the task is cancelled at the next `await` statement. This provides reliable timeout behavior for async code.
**Sync tasks called directly**: When a sync task is called directly (not via `.submit()`), it typically runs on the main thread where timeouts can use OS signals to interrupt execution, including blocking operations.
**Sync tasks via `.submit()` with ThreadPoolTaskRunner**: When a sync task is submitted using to a `ThreadPoolTaskRunner` (the default), it runs in a worker thread. In this context, timeouts **cannot interrupt blocking operations** like `time.sleep()`, network requests, or file I/O. The timeout will only take effect after the blocking operation completes naturally.
If you need reliable timeout behavior for sync tasks that perform blocking operations, consider:
1. Using `ProcessPoolTaskRunner` instead of the default `ThreadPoolTaskRunner`
2. Converting the task to async and using `await`-based I/O
3. Breaking long blocking operations into smaller chunks to allow for interruption for cancellation
4. Using libraries that support timeout parameters natively (e.g., `requests.get(url, timeout=5)`)
```python theme={null}
from prefect import task, flow
from prefect.task_runners import ThreadPoolTaskRunner, ProcessPoolTaskRunner
import time
# This timeout may not interrupt the sleep when submitted with ThreadPoolTaskRunner
@task(timeout_seconds=1)
def sync_task_with_blocking():
time.sleep(10) # Will complete before timeout triggers
# This timeout works reliably with async
@task(timeout_seconds=1)
async def async_task_with_sleep():
import anyio
await anyio.sleep(10) # Will be interrupted at timeout
@flow(task_runner=ThreadPoolTaskRunner())
def flow_with_threads():
sync_task_with_blocking.submit() # Timeout may not interrupt blocking operations
@flow(task_runner=ProcessPoolTaskRunner())
def flow_with_processes():
sync_task_with_blocking.submit() # Timeout can interrupt because the task runs on the main thread of the new process
```
## Configure flows and tasks
### Flow configuration
All flows can be configured by passing arguments to the decorator. Flows accept the following optional settings:
| Argument | Description |
| -------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `description` | An optional string description for the flow. If not provided, the description is pulled from the docstring for the decorated function. |
| `name` | An optional name for the flow. If not provided, the name is inferred from the function. |
| `retries` | An optional number of times to retry on flow run failure. |
| `retry_delay_seconds` | An optional number of seconds to wait before retrying the flow after failure. This is only applicable if `retries` is nonzero. |
| `flow_run_name` | An optional name to distinguish runs of this flow; this name can be provided as a string template with the flow's parameters as variables; you can also provide this name as a function that returns a string. |
| `task_runner` | An optional [task runner](/v3/concepts/task-runners/) to use for task execution within the flow when you `.submit()` tasks. If not provided and you `.submit()` tasks, the `ThreadPoolTaskRunner` is used. |
| `timeout_seconds` | An optional number of seconds indicating a maximum runtime for the flow. If the flow exceeds this runtime, it is marked as failed. Flow execution may continue until the next task is called. |
| `validate_parameters` | Boolean indicating whether parameters passed to flows are validated by Pydantic. Default is `True`. |
| `version` | An optional version string for the flow. If not provided, we will attempt to create a version string as a hash of the file containing the wrapped function. If the file cannot be located, the version will be null. |
### Task configuration
Tasks allow for customization through optional arguments that can be provided to the [task decorator](https://reference.prefect.io/prefect/tasks/#prefect.tasks.task).
| Argument | Description |
| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | An optional name for the task. If not provided, the name is inferred from the function name. |
| `description` | An optional string description for the task. If not provided, the description is pulled from the docstring for the decorated function. |
| `tags` | An optional set of tags associated with runs of this task. These tags are combined with any tags defined by a `prefect.tags` context at task runtime. |
| `timeout_seconds` | An optional number of seconds indicating a maximum runtime for the task. If the task exceeds this runtime, it will be marked as failed. Note: For sync tasks using `ThreadPoolTaskRunner` (the default), the timeout cannot interrupt blocking operations. See [Task timeout behavior](#task-timeout-behavior) for details. |
| `cache_key_fn` | An optional callable that, given the task run context and call parameters, generates a string key. If the key matches a previous completed state, that state result is restored instead of running the task again. |
| `cache_policy` | An optional policy that determines what information is used to generate cache keys. Available policies include `INPUTS`, `TASK_SOURCE`, `RUN_ID`, `FLOW_PARAMETERS`, and `NO_CACHE`. Can be combined using the + operator. |
| `cache_expiration` | An optional amount of time indicating how long cached states for this task are restorable; if not provided, cached states will never expire. |
| `retries` | An optional number of times to retry on task run failure. |
| `retry_delay_seconds` | An optional number of seconds to wait before retrying the task after failure. This is only applicable if `retries` is nonzero. |
| `log_prints` | An optional boolean indicating whether to log print statements. |
See all possible options in the [Python SDK docs](https://reference.prefect.io/prefect/tasks/#prefect.tasks.task).
For example, provide optional `name` and `description` arguments to a task:
```python theme={null}
from prefect import task
@task(name="hello-task", description="This task says hello.")
def my_task():
print("Hello, I'm a task")
```
Distinguish runs of this task by providing a `task_run_name`.\
Python's standard string formatting syntax applies:
```python theme={null}
import datetime
from prefect import flow, task
@task(name="My Example Task",
description="An example task for a tutorial.",
task_run_name="hello-{name}-on-{date:%A}")
def my_task(name, date):
pass
@flow
def my_flow():
# creates a run with a name like "hello-marvin-on-Thursday"
my_task(name="marvin", date=datetime.datetime.now(datetime.timezone.utc))
if __name__ == "__main__":
my_flow()
```
Additionally, this setting accepts a function that returns a string for the task run name:
```python theme={null}
import datetime
from prefect import flow, task
def generate_task_name():
date = datetime.datetime.now(datetime.timezone.utc)
return f"{date:%A}-is-a-lovely-day"
@task(name="My Example Task",
description="An example task for the docs.",
task_run_name=generate_task_name)
def my_task(name):
pass
@flow
def my_flow():
# creates a run with a name like "Thursday-is-a-lovely-day"
my_task(name="marvin")
if __name__ == "__main__":
my_flow()
```
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