prefect.tasks
¶
Module containing the base workflow task class and decorator - for most use cases, using the @task
decorator is preferred.
Task
¶
Bases: Generic[P, R]
A Prefect task definition.
Note
We recommend using the @task
decorator for most use-cases.
Wraps a function with an entrypoint to the Prefect engine. Calling this class within a flow function creates a new task run.
To preserve the input and output types, we use the generic type variables P and R for "Parameters" and "Returns" respectively.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fn |
Callable[P, R]
|
The function defining the task. |
required |
name |
str
|
An optional name for the task; if not provided, the name will be inferred from the given function. |
None
|
description |
str
|
An optional string description for the task. |
None
|
tags |
Iterable[str]
|
An optional set of tags to be associated with runs of this task. These
tags are combined with any tags defined by a |
None
|
version |
str
|
An optional string specifying the version of this task definition |
None
|
cache_key_fn |
Callable[[TaskRunContext, Dict[str, Any]], Optional[str]]
|
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 will be restored instead of running the task again. |
None
|
cache_expiration |
timedelta
|
An optional amount of time indicating how long cached states for this task should be restorable; if not provided, cached states will never expire. |
None
|
task_run_name |
Optional[Union[Callable[[], str], str]]
|
An optional name to distinguish runs of this task; this name can be provided as a string template with the task's keyword arguments as variables, or a function that returns a string. |
None
|
retries |
Optional[int]
|
An optional number of times to retry on task run failure. |
None
|
retry_delay_seconds |
Optional[Union[float, int, List[float], Callable[[int], List[float]]]]
|
Optionally configures how long to wait before retrying the
task after failure. This is only applicable if |
None
|
retry_jitter_factor |
Optional[float]
|
An optional factor that defines the factor to which a retry can be jittered in order to avoid a "thundering herd". |
None
|
persist_result |
Optional[bool]
|
An optional toggle indicating whether the result of this task
should be persisted to result storage. Defaults to |
None
|
result_storage |
Optional[ResultStorage]
|
An optional block to use to persist the result of this task. Defaults to the value set in the flow the task is called in. |
None
|
result_storage_key |
Optional[str]
|
An optional key to store the result in storage at when persisted. Defaults to a unique identifier. |
None
|
result_serializer |
Optional[ResultSerializer]
|
An optional serializer to use to serialize the result of this task for persistence. Defaults to the value set in the flow the task is called in. |
None
|
timeout_seconds |
Union[int, float]
|
An optional number of seconds indicating a maximum runtime for the task. If the task exceeds this runtime, it will be marked as failed. |
None
|
log_prints |
Optional[bool]
|
If set, |
False
|
refresh_cache |
Optional[bool]
|
If set, cached results for the cache key are not used.
Defaults to |
None
|
on_failure |
Optional[List[Callable[[Task, TaskRun, State], None]]]
|
An optional list of callables to run when the task enters a failed state. |
None
|
on_completion |
Optional[List[Callable[[Task, TaskRun, State], None]]]
|
An optional list of callables to run when the task enters a completed state. |
None
|
viz_return_value |
Optional[Any]
|
An optional value to return when the task dependency tree is visualized. |
None
|
Source code in /home/runner/work/docs/docs/prefect_source/src/prefect/tasks.py
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|
map
¶
Submit a mapped run of the task to a worker.
Must be called within a flow function. If writing an async task, this call must be awaited.
Must be called with at least one iterable and all iterables must be the same length. Any arguments that are not iterable will be treated as a static value and each task run will receive the same value.
Will create as many task runs as the length of the iterable(s) in the
backing API and submit the task runs to the flow's task runner. This
call blocks if given a future as input while the future is resolved. It
also blocks while the tasks are being submitted, once they are
submitted, the flow function will continue executing. However, note
that the SequentialTaskRunner
does not implement parallel execution
for sync tasks and they are fully resolved on submission.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
*args |
Any
|
Iterable and static arguments to run the tasks with |
()
|
return_state |
bool
|
Return a list of Prefect States that wrap the results of each task run. |
False
|
wait_for |
Optional[Iterable[PrefectFuture]]
|
Upstream task futures to wait for before starting the task |
None
|
**kwargs |
Any
|
Keyword iterable arguments to run the task with |
{}
|
Returns:
Type | Description |
---|---|
Any
|
A list of futures allowing asynchronous access to the state of the |
Any
|
tasks |
Define a task
>>> from prefect import task
>>> @task
>>> def my_task(x):
>>> return x + 1
Create mapped tasks
>>> from prefect import flow
>>> @flow
>>> def my_flow():
>>> my_task.map([1, 2, 3])
Wait for all mapped tasks to finish
>>> @flow
>>> def my_flow():
>>> futures = my_task.map([1, 2, 3])
>>> for future in futures:
>>> future.wait()
>>> # Now all of the mapped tasks have finished
>>> my_task(10)
Use the result from mapped tasks in a flow
>>> @flow
>>> def my_flow():
>>> futures = my_task.map([1, 2, 3])
>>> for future in futures:
>>> print(future.result())
>>> my_flow()
2
3
4
Enforce ordering between tasks that do not exchange data
>>> @task
>>> def task_1(x):
>>> pass
>>>
>>> @task
>>> def task_2(y):
>>> pass
>>>
>>> @flow
>>> def my_flow():
>>> x = task_1.submit()
>>>
>>> # task 2 will wait for task_1 to complete
>>> y = task_2.map([1, 2, 3], wait_for=[x])
Use a non-iterable input as a constant across mapped tasks
>>> @task
>>> def display(prefix, item):
>>> print(prefix, item)
>>>
>>> @flow
>>> def my_flow():
>>> display.map("Check it out: ", [1, 2, 3])
>>>
>>> my_flow()
Check it out: 1
Check it out: 2
Check it out: 3
Use `unmapped` to treat an iterable argument as a constant
>>> from prefect import unmapped
>>>
>>> @task
>>> def add_n_to_items(items, n):
>>> return [item + n for item in items]
>>>
>>> @flow
>>> def my_flow():
>>> return add_n_to_items.map(unmapped([10, 20]), n=[1, 2, 3])
>>>
>>> my_flow()
[[11, 21], [12, 22], [13, 23]]
Source code in /home/runner/work/docs/docs/prefect_source/src/prefect/tasks.py
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|
submit
¶
Submit a run of the task to a worker.
Must be called within a flow function. If writing an async task, this call must be awaited.
Will create a new task run in the backing API and submit the task to the flow's
task runner. This call only blocks execution while the task is being submitted,
once it is submitted, the flow function will continue executing. However, note
that the SequentialTaskRunner
does not implement parallel execution for sync tasks
and they are fully resolved on submission.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
*args |
Any
|
Arguments to run the task with |
()
|
return_state |
bool
|
Return the result of the flow run wrapped in a Prefect State. |
False
|
wait_for |
Optional[Iterable[PrefectFuture]]
|
Upstream task futures to wait for before starting the task |
None
|
**kwargs |
Any
|
Keyword arguments to run the task with |
{}
|
Returns:
Type | Description |
---|---|
Union[PrefectFuture, Awaitable[PrefectFuture]]
|
If |
Union[PrefectFuture, Awaitable[PrefectFuture]]
|
If |
Define a task
>>> from prefect import task
>>> @task
>>> def my_task():
>>> return "hello"
Run a task in a flow
>>> from prefect import flow
>>> @flow
>>> def my_flow():
>>> my_task.submit()
Wait for a task to finish
>>> @flow
>>> def my_flow():
>>> my_task.submit().wait()
Use the result from a task in a flow
>>> @flow
>>> def my_flow():
>>> print(my_task.submit().result())
>>>
>>> my_flow()
hello
Run an async task in an async flow
>>> @task
>>> async def my_async_task():
>>> pass
>>>
>>> @flow
>>> async def my_flow():
>>> await my_async_task.submit()
Run a sync task in an async flow
>>> @flow
>>> async def my_flow():
>>> my_task.submit()
Enforce ordering between tasks that do not exchange data
>>> @task
>>> def task_1():
>>> pass
>>>
>>> @task
>>> def task_2():
>>> pass
>>>
>>> @flow
>>> def my_flow():
>>> x = task_1.submit()
>>>
>>> # task 2 will wait for task_1 to complete
>>> y = task_2.submit(wait_for=[x])
Source code in /home/runner/work/docs/docs/prefect_source/src/prefect/tasks.py
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|
with_options
¶
Create a new task from the current object, updating provided options.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name |
str
|
A new name for the task. |
None
|
description |
str
|
A new description for the task. |
None
|
tags |
Iterable[str]
|
A new set of tags for the task. If given, existing tags are ignored, not merged. |
None
|
cache_key_fn |
Callable[[TaskRunContext, Dict[str, Any]], Optional[str]]
|
A new cache key function for the task. |
None
|
cache_expiration |
timedelta
|
A new cache expiration time for the task. |
None
|
task_run_name |
Optional[Union[Callable[[], str], str]]
|
An optional name to distinguish runs of this task; this name can be provided as a string template with the task's keyword arguments as variables, or a function that returns a string. |
None
|
retries |
Optional[int]
|
A new number of times to retry on task run failure. |
NotSet
|
retry_delay_seconds |
Union[float, int, List[float], Callable[[int], List[float]]]
|
Optionally configures how long to wait before retrying
the task after failure. This is only applicable if |
NotSet
|
retry_jitter_factor |
Optional[float]
|
An optional factor that defines the factor to which a retry can be jittered in order to avoid a "thundering herd". |
NotSet
|
persist_result |
Optional[bool]
|
A new option for enabling or disabling result persistence. |
NotSet
|
result_storage |
Optional[ResultStorage]
|
A new storage type to use for results. |
NotSet
|
result_serializer |
Optional[ResultSerializer]
|
A new serializer to use for results. |
NotSet
|
result_storage_key |
Optional[str]
|
A new key for the persisted result to be stored at. |
NotSet
|
timeout_seconds |
Union[int, float]
|
A new maximum time for the task to complete in seconds. |
None
|
log_prints |
Optional[bool]
|
A new option for enabling or disabling redirection of |
NotSet
|
refresh_cache |
Optional[bool]
|
A new option for enabling or disabling cache refresh. |
NotSet
|
on_completion |
Optional[List[Callable[[Task, TaskRun, State], None]]]
|
A new list of callables to run when the task enters a completed state. |
None
|
on_failure |
Optional[List[Callable[[Task, TaskRun, State], None]]]
|
A new list of callables to run when the task enters a failed state. |
None
|
viz_return_value |
Optional[Any]
|
An optional value to return when the task dependency tree is visualized. |
None
|
Returns:
Type | Description |
---|---|
A new |
Create a new task from an existing task and update the name
>>> @task(name="My task")
>>> def my_task():
>>> return 1
>>>
>>> new_task = my_task.with_options(name="My new task")
Create a new task from an existing task and update the retry settings
>>> from random import randint
>>>
>>> @task(retries=1, retry_delay_seconds=5)
>>> def my_task():
>>> x = randint(0, 5)
>>> if x >= 3: # Make a task that fails sometimes
>>> raise ValueError("Retry me please!")
>>> return x
>>>
>>> new_task = my_task.with_options(retries=5, retry_delay_seconds=2)
Use a task with updated options within a flow
>>> @task(name="My task")
>>> def my_task():
>>> return 1
>>>
>>> @flow
>>> my_flow():
>>> new_task = my_task.with_options(name="My new task")
>>> new_task()
Source code in /home/runner/work/docs/docs/prefect_source/src/prefect/tasks.py
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|
exponential_backoff
¶
A task retry backoff utility that configures exponential backoff for task retries. The exponential backoff design matches the urllib3 implementation.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
backoff_factor |
float
|
the base delay for the first retry, subsequent retries will increase the delay time by powers of 2. |
required |
Returns:
Type | Description |
---|---|
Callable[[int], List[float]]
|
a callable that can be passed to the task constructor |
Source code in /home/runner/work/docs/docs/prefect_source/src/prefect/tasks.py
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|
task
¶
Decorator to designate a function as a task in a Prefect workflow.
This decorator may be used for asynchronous or synchronous functions.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
name |
str
|
An optional name for the task; if not provided, the name will be inferred from the given function. |
None
|
description |
str
|
An optional string description for the task. |
None
|
tags |
Iterable[str]
|
An optional set of tags to be associated with runs of this task. These
tags are combined with any tags defined by a |
None
|
version |
str
|
An optional string specifying the version of this task definition |
None
|
cache_key_fn |
Callable[[TaskRunContext, Dict[str, Any]], Optional[str]]
|
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 will be restored instead of running the task again. |
None
|
cache_expiration |
timedelta
|
An optional amount of time indicating how long cached states for this task should be restorable; if not provided, cached states will never expire. |
None
|
task_run_name |
Optional[Union[Callable[[], str], str]]
|
An optional name to distinguish runs of this task; this name can be provided as a string template with the task's keyword arguments as variables, or a function that returns a string. |
None
|
retries |
int
|
An optional number of times to retry on task run failure |
None
|
retry_delay_seconds |
Union[float, int, List[float], Callable[[int], List[float]]]
|
Optionally configures how long to wait before retrying the
task after failure. This is only applicable if |
None
|
retry_jitter_factor |
Optional[float]
|
An optional factor that defines the factor to which a retry can be jittered in order to avoid a "thundering herd". |
None
|
persist_result |
Optional[bool]
|
An optional toggle indicating whether the result of this task
should be persisted to result storage. Defaults to |
None
|
result_storage |
Optional[ResultStorage]
|
An optional block to use to persist the result of this task. Defaults to the value set in the flow the task is called in. |
None
|
result_storage_key |
Optional[str]
|
An optional key to store the result in storage at when persisted. Defaults to a unique identifier. |
None
|
result_serializer |
Optional[ResultSerializer]
|
An optional serializer to use to serialize the result of this task for persistence. Defaults to the value set in the flow the task is called in. |
None
|
timeout_seconds |
Union[int, float]
|
An optional number of seconds indicating a maximum runtime for the task. If the task exceeds this runtime, it will be marked as failed. |
None
|
log_prints |
Optional[bool]
|
If set, |
None
|
refresh_cache |
Optional[bool]
|
If set, cached results for the cache key are not used.
Defaults to |
None
|
on_failure |
Optional[List[Callable[[Task, TaskRun, State], None]]]
|
An optional list of callables to run when the task enters a failed state. |
None
|
on_completion |
Optional[List[Callable[[Task, TaskRun, State], None]]]
|
An optional list of callables to run when the task enters a completed state. |
None
|
viz_return_value |
Any
|
An optional value to return when the task dependency tree is visualized. |
None
|
Returns:
Type | Description |
---|---|
A callable |
Examples:
Define a simple task
>>> @task
>>> def add(x, y):
>>> return x + y
Define an async task
>>> @task
>>> async def add(x, y):
>>> return x + y
Define a task with tags and a description
>>> @task(tags={"a", "b"}, description="This task is empty but its my first!")
>>> def my_task():
>>> pass
Define a task with a custom name
>>> @task(name="The Ultimate Task")
>>> def my_task():
>>> pass
Define a task that retries 3 times with a 5 second delay between attempts
>>> from random import randint
>>>
>>> @task(retries=3, retry_delay_seconds=5)
>>> def my_task():
>>> x = randint(0, 5)
>>> if x >= 3: # Make a task that fails sometimes
>>> raise ValueError("Retry me please!")
>>> return x
Define a task that is cached for a day based on its inputs
>>> from prefect.tasks import task_input_hash
>>> from datetime import timedelta
>>>
>>> @task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
>>> def my_task():
>>> return "hello"
Source code in /home/runner/work/docs/docs/prefect_source/src/prefect/tasks.py
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|
task_input_hash
¶
A task cache key implementation which hashes all inputs to the task using a JSON or cloudpickle serializer. If any arguments are not JSON serializable, the pickle serializer is used as a fallback. If cloudpickle fails, this will return a null key indicating that a cache key could not be generated for the given inputs.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
context |
TaskRunContext
|
the active |
required |
arguments |
Dict[str, Any]
|
a dictionary of arguments to be passed to the underlying task |
required |
Returns:
Type | Description |
---|---|
Optional[str]
|
a string hash if hashing succeeded, else |
Source code in /home/runner/work/docs/docs/prefect_source/src/prefect/tasks.py
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|