# flytekit.core.base_task

# flytekit.core.base_task

This module provides the core task-related functionality in Flytekit.

## Core Components

### kwtypes
Utility for creating keyword type annotations for tasks.

### PythonTask
Base class for Python-based task implementations.

### Task
The base class for all Flyte tasks.

### TaskResolverMixin
Mixin class that helps resolve a task implementation.

### IgnoreOutputs
Exception that can be raised to ignore task outputs.
## Directory

### Classes

| Class | Description |
|-|-|
| [`PythonTask`](https://www.union.ai/docs/v1/union/api-reference/flytekit-sdk/packages/flytekit.core.base_task/page.md#flytekitcorebase_taskpythontask) | Base Class for all Tasks with a Python native ``Interface``. |
| [`Task`](https://www.union.ai/docs/v1/union/api-reference/flytekit-sdk/packages/flytekit.core.base_task/page.md#flytekitcorebase_tasktask) | The base of all Tasks in flytekit. |
| [`TaskMetadata`](https://www.union.ai/docs/v1/union/api-reference/flytekit-sdk/packages/flytekit.core.base_task/page.md#flytekitcorebase_tasktaskmetadata) | Metadata for a Task. |
| [`TaskResolverMixin`](https://www.union.ai/docs/v1/union/api-reference/flytekit-sdk/packages/flytekit.core.base_task/page.md#flytekitcorebase_tasktaskresolvermixin) | Flytekit tasks interact with the Flyte platform very, very broadly in two steps. |

### Errors

| Exception | Description |
|-|-|
| [`IgnoreOutputs`](https://www.union.ai/docs/v1/union/api-reference/flytekit-sdk/packages/flytekit.core.base_task/page.md#flytekitcorebase_taskignoreoutputs) | This exception should be used to indicate that the outputs generated by this can be safely ignored. |

### Methods

| Method | Description |
|-|-|
| [`kwtypes()`](#kwtypes) | This is a small helper function to convert the keyword arguments to an OrderedDict of types. |

### Variables

| Property | Type | Description |
|-|-|-|
| `DATA_CARD` | `str` |  |
| `DYNAMIC_PARTITIONS` | `str` |  |
| `MODEL_CARD` | `str` |  |
| `T` | `TypeVar` |  |
| `UNSET_CARD` | `str` |  |

## Methods

#### kwtypes()

```python
def kwtypes(
    kwargs,
) -> typing.OrderedDict[str, typing.Type]
```
This is a small helper function to convert the keyword arguments to an OrderedDict of types.

```python
kwtypes(a=int, b=str)
```

| Parameter | Type | Description |
|-|-|-|
| `kwargs` | `**kwargs` | |

## flytekit.core.base_task.IgnoreOutputs

This exception should be used to indicate that the outputs generated by this can be safely ignored.
This is useful in case of distributed training or peer-to-peer parallel algorithms.

## flytekit.core.base_task.PythonTask

Base Class for all Tasks with a Python native ``Interface``. This should be directly used for task types, that do
not have a python function to be executed. Otherwise refer to {{< py_class_ref flytekit.PythonFunctionTask >}}.

### Parameters

```python
class PythonTask(
    task_type: str,
    name: str,
    task_config: typing.Optional[~T],
    interface: typing.Optional[flytekit.core.interface.Interface],
    environment: typing.Optional[typing.Dict[str, str]],
    disable_deck: typing.Optional[bool],
    enable_deck: typing.Optional[bool],
    deck_fields: typing.Optional[typing.Tuple[flytekit.deck.deck.DeckField, ...]],
    kwargs,
)
```
task_type (str): defines a unique task-type for every new extension. If a backend plugin is required then
    this has to be done in-concert with the backend plugin identifier
name (str): A unique name for the task instantiation. This is unique for every instance of task.
task_config (T): Configuration for the task. This is used to configure the specific plugin that handles this
    task
interface (Optional[Interface]): A python native typed interface ``(inputs) -> outputs`` that declares the
    signature of the task
environment (Optional[Dict[str, str]]): Any environment variables that should be supplied during the
    execution of the task. Supplied as a dictionary of key/value pairs
disable_deck (bool): (deprecated) If true, this task will not output deck html file
enable_deck (bool): If true, this task will output deck html file
deck_fields (Tuple[DeckField]): Tuple of decks to be
    generated for this task. Valid values can be selected from fields of ``flytekit.deck.DeckField`` enum

| Parameter | Type | Description |
|-|-|-|
| `task_type` | `str` | |
| `name` | `str` | |
| `task_config` | `typing.Optional[~T]` | |
| `interface` | `typing.Optional[flytekit.core.interface.Interface]` | |
| `environment` | `typing.Optional[typing.Dict[str, str]]` | |
| `disable_deck` | `typing.Optional[bool]` | |
| `enable_deck` | `typing.Optional[bool]` | |
| `deck_fields` | `typing.Optional[typing.Tuple[flytekit.deck.deck.DeckField, ...]]` | |
| `kwargs` | `**kwargs` | |

### Properties

| Property | Type | Description |
|-|-|-|
| `deck_fields` | `None` | If not empty, this task will output deck html file for the specified decks |
| `disable_deck` | `None` | If true, this task will not output deck html file |
| `docs` | `None` |  |
| `enable_deck` | `None` | If true, this task will output deck html file |
| `environment` | `None` | Any environment variables that supplied during the execution of the task. |
| `instantiated_in` | `None` |  |
| `interface` | `None` |  |
| `lhs` | `None` |  |
| `location` | `None` |  |
| `metadata` | `None` |  |
| `name` | `None` |  |
| `python_interface` | `None` | Returns this task's python interface. |
| `security_context` | `None` |  |
| `task_config` | `None` | Returns the user-specified task config which is used for plugin-specific handling of the task. |
| `task_type` | `None` |  |
| `task_type_version` | `None` |  |

### Methods

| Method | Description |
|-|-|
| [`compile()`](#compile) | Generates a node that encapsulates this task in a workflow definition. |
| [`construct_node_metadata()`](#construct_node_metadata) | Used when constructing the node that encapsulates this task as part of a broader workflow definition. |
| [`dispatch_execute()`](#dispatch_execute) | This method translates Flyte's Type system based input values and invokes the actual call to the executor. |
| [`execute()`](#execute) | This method will be invoked to execute the task. |
| [`find_lhs()`](#find_lhs) |  |
| [`get_config()`](#get_config) | Returns the task config as a serializable dictionary. |
| [`get_container()`](#get_container) | Returns the container definition (if any) that is used to run the task on hosted Flyte. |
| [`get_custom()`](#get_custom) | Return additional plugin-specific custom data (if any) as a serializable dictionary. |
| [`get_extended_resources()`](#get_extended_resources) | Returns the extended resources to allocate to the task on hosted Flyte. |
| [`get_input_types()`](#get_input_types) | Returns the names and python types as a dictionary for the inputs of this task. |
| [`get_k8s_pod()`](#get_k8s_pod) | Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte. |
| [`get_sql()`](#get_sql) | Returns the Sql definition (if any) that is used to run the task on hosted Flyte. |
| [`get_type_for_input_var()`](#get_type_for_input_var) | Returns the python type for an input variable by name. |
| [`get_type_for_output_var()`](#get_type_for_output_var) | Returns the python type for the specified output variable by name. |
| [`local_execute()`](#local_execute) | This function is used only in the local execution path and is responsible for calling dispatch execute. |
| [`local_execution_mode()`](#local_execution_mode) |  |
| [`post_execute()`](#post_execute) | Post execute is called after the execution has completed, with the user_params and can be used to clean-up,. |
| [`pre_execute()`](#pre_execute) | This is the method that will be invoked directly before executing the task method and before all the inputs. |
| [`sandbox_execute()`](#sandbox_execute) | Call dispatch_execute, in the context of a local sandbox execution. |

#### compile()

```python
def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, NoneType]
```
Generates a node that encapsulates this task in a workflow definition.

| Parameter | Type | Description |
|-|-|-|
| `ctx` | `flytekit.core.context_manager.FlyteContext` | |
| `args` | `*args` | |
| `kwargs` | `**kwargs` | |

#### construct_node_metadata()

```python
def construct_node_metadata()
```
Used when constructing the node that encapsulates this task as part of a broader workflow definition.

#### dispatch_execute()

```python
def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> typing.Union[flytekit.models.literals.LiteralMap, flytekit.models.dynamic_job.DynamicJobSpec, typing.Coroutine]
```
This method translates Flyte's Type system based input values and invokes the actual call to the executor
This method is also invoked during runtime.

* ``VoidPromise`` is returned in the case when the task itself declares no outputs.
* ``Literal Map`` is returned when the task returns either one more outputs in the declaration. Individual outputs
  may be none
* ``DynamicJobSpec`` is returned when a dynamic workflow is executed

| Parameter | Type | Description |
|-|-|-|
| `ctx` | `flytekit.core.context_manager.FlyteContext` | |
| `input_literal_map` | `flytekit.models.literals.LiteralMap` | |

#### execute()

```python
def execute(
    kwargs,
) -> typing.Any
```
This method will be invoked to execute the task.

| Parameter | Type | Description |
|-|-|-|
| `kwargs` | `**kwargs` | |

#### find_lhs()

```python
def find_lhs()
```
#### get_config()

```python
def get_config(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[typing.Dict[str, str]]
```
Returns the task config as a serializable dictionary. This task config consists of metadata about the custom
defined for this task.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_container()

```python
def get_container(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Container]
```
Returns the container definition (if any) that is used to run the task on hosted Flyte.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_custom()

```python
def get_custom(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[typing.Dict[str, typing.Any]]
```
Return additional plugin-specific custom data (if any) as a serializable dictionary.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_extended_resources()

```python
def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flyteidl.core.tasks_pb2.ExtendedResources]
```
Returns the extended resources to allocate to the task on hosted Flyte.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_input_types()

```python
def get_input_types()
```
Returns the names and python types as a dictionary for the inputs of this task.

#### get_k8s_pod()

```python
def get_k8s_pod(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.K8sPod]
```
Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_sql()

```python
def get_sql(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Sql]
```
Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_type_for_input_var()

```python
def get_type_for_input_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]
```
Returns the python type for an input variable by name.

| Parameter | Type | Description |
|-|-|-|
| `k` | `str` | |
| `v` | `typing.Any` | |

#### get_type_for_output_var()

```python
def get_type_for_output_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]
```
Returns the python type for the specified output variable by name.

| Parameter | Type | Description |
|-|-|-|
| `k` | `str` | |
| `v` | `typing.Any` | |

#### local_execute()

```python
def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, typing.Coroutine, NoneType]
```
This function is used only in the local execution path and is responsible for calling dispatch execute.
Use this function when calling a task with native values (or Promises containing Flyte literals derived from
Python native values).

| Parameter | Type | Description |
|-|-|-|
| `ctx` | `flytekit.core.context_manager.FlyteContext` | |
| `kwargs` | `**kwargs` | |

#### local_execution_mode()

```python
def local_execution_mode()
```
#### post_execute()

```python
def post_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
    rval: typing.Any,
) -> typing.Any
```
Post execute is called after the execution has completed, with the user_params and can be used to clean-up,
or alter the outputs to match the intended tasks outputs. If not overridden, then this function is a No-op

| Parameter | Type | Description |
|-|-|-|
| `user_params` | `typing.Optional[flytekit.core.context_manager.ExecutionParameters]` | are the modified user params as created during the pre_execute step |
| `rval` | `typing.Any` | |

#### pre_execute()

```python
def pre_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
) -> typing.Optional[flytekit.core.context_manager.ExecutionParameters]
```
This is the method that will be invoked directly before executing the task method and before all the inputs
are converted. One particular case where this is useful is if the context is to be modified for the user process
to get some user space parameters. This also ensures that things like SparkSession are already correctly
setup before the type transformers are called

This should return either the same context of the mutated context

| Parameter | Type | Description |
|-|-|-|
| `user_params` | `typing.Optional[flytekit.core.context_manager.ExecutionParameters]` | |

#### sandbox_execute()

```python
def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> flytekit.models.literals.LiteralMap
```
Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

| Parameter | Type | Description |
|-|-|-|
| `ctx` | `flytekit.core.context_manager.FlyteContext` | |
| `input_literal_map` | `flytekit.models.literals.LiteralMap` | |

## flytekit.core.base_task.Task

The base of all Tasks in flytekit. This task is closest to the FlyteIDL TaskTemplate and captures information in
FlyteIDL specification and does not have python native interfaces associated. Refer to the derived classes for
examples of how to extend this class.

### Parameters

```python
class Task(
    task_type: str,
    name: str,
    interface: flytekit.models.interface.TypedInterface,
    metadata: typing.Optional[flytekit.core.base_task.TaskMetadata],
    task_type_version,
    security_ctx: typing.Optional[flytekit.models.security.SecurityContext],
    docs: typing.Optional[flytekit.models.documentation.Documentation],
    kwargs,
)
```
| Parameter | Type | Description |
|-|-|-|
| `task_type` | `str` | |
| `name` | `str` | |
| `interface` | `flytekit.models.interface.TypedInterface` | |
| `metadata` | `typing.Optional[flytekit.core.base_task.TaskMetadata]` | |
| `task_type_version` |  | |
| `security_ctx` | `typing.Optional[flytekit.models.security.SecurityContext]` | |
| `docs` | `typing.Optional[flytekit.models.documentation.Documentation]` | |
| `kwargs` | `**kwargs` | |

### Properties

| Property | Type | Description |
|-|-|-|
| `docs` | `None` |  |
| `interface` | `None` |  |
| `metadata` | `None` |  |
| `name` | `None` |  |
| `python_interface` | `None` |  |
| `security_context` | `None` |  |
| `task_type` | `None` |  |
| `task_type_version` | `None` |  |

### Methods

| Method | Description |
|-|-|
| [`compile()`](#compile) |  |
| [`dispatch_execute()`](#dispatch_execute) | This method translates Flyte's Type system based input values and invokes the actual call to the executor. |
| [`execute()`](#execute) | This method will be invoked to execute the task. |
| [`get_config()`](#get_config) | Returns the task config as a serializable dictionary. |
| [`get_container()`](#get_container) | Returns the container definition (if any) that is used to run the task on hosted Flyte. |
| [`get_custom()`](#get_custom) | Return additional plugin-specific custom data (if any) as a serializable dictionary. |
| [`get_extended_resources()`](#get_extended_resources) | Returns the extended resources to allocate to the task on hosted Flyte. |
| [`get_input_types()`](#get_input_types) | Returns python native types for inputs. |
| [`get_k8s_pod()`](#get_k8s_pod) | Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte. |
| [`get_sql()`](#get_sql) | Returns the Sql definition (if any) that is used to run the task on hosted Flyte. |
| [`get_type_for_input_var()`](#get_type_for_input_var) | Returns the python native type for the given input variable. |
| [`get_type_for_output_var()`](#get_type_for_output_var) | Returns the python native type for the given output variable. |
| [`local_execute()`](#local_execute) | This function is used only in the local execution path and is responsible for calling dispatch execute. |
| [`local_execution_mode()`](#local_execution_mode) |  |
| [`pre_execute()`](#pre_execute) | This is the method that will be invoked directly before executing the task method and before all the inputs. |
| [`sandbox_execute()`](#sandbox_execute) | Call dispatch_execute, in the context of a local sandbox execution. |

#### compile()

```python
def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
)
```
| Parameter | Type | Description |
|-|-|-|
| `ctx` | `flytekit.core.context_manager.FlyteContext` | |
| `args` | `*args` | |
| `kwargs` | `**kwargs` | |

#### dispatch_execute()

```python
def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> flytekit.models.literals.LiteralMap
```
This method translates Flyte's Type system based input values and invokes the actual call to the executor
This method is also invoked during runtime.

| Parameter | Type | Description |
|-|-|-|
| `ctx` | `flytekit.core.context_manager.FlyteContext` | |
| `input_literal_map` | `flytekit.models.literals.LiteralMap` | |

#### execute()

```python
def execute(
    kwargs,
) -> typing.Any
```
This method will be invoked to execute the task.

| Parameter | Type | Description |
|-|-|-|
| `kwargs` | `**kwargs` | |

#### get_config()

```python
def get_config(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[typing.Dict[str, str]]
```
Returns the task config as a serializable dictionary. This task config consists of metadata about the custom
defined for this task.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_container()

```python
def get_container(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Container]
```
Returns the container definition (if any) that is used to run the task on hosted Flyte.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_custom()

```python
def get_custom(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[typing.Dict[str, typing.Any]]
```
Return additional plugin-specific custom data (if any) as a serializable dictionary.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_extended_resources()

```python
def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flyteidl.core.tasks_pb2.ExtendedResources]
```
Returns the extended resources to allocate to the task on hosted Flyte.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_input_types()

```python
def get_input_types()
```
Returns python native types for inputs. In case this is not a python native task (base class) and hence
returns a None. we could deduce the type from literal types, but that is not a required exercise
# TODO we could use literal type to determine this

#### get_k8s_pod()

```python
def get_k8s_pod(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.K8sPod]
```
Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_sql()

```python
def get_sql(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Sql]
```
Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |

#### get_type_for_input_var()

```python
def get_type_for_input_var(
    k: str,
    v: typing.Any,
) -> type
```
Returns the python native type for the given input variable
# TODO we could use literal type to determine this

| Parameter | Type | Description |
|-|-|-|
| `k` | `str` | |
| `v` | `typing.Any` | |

#### get_type_for_output_var()

```python
def get_type_for_output_var(
    k: str,
    v: typing.Any,
) -> type
```
Returns the python native type for the given output variable
# TODO we could use literal type to determine this

| Parameter | Type | Description |
|-|-|-|
| `k` | `str` | |
| `v` | `typing.Any` | |

#### local_execute()

```python
def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, typing.Coroutine, NoneType]
```
This function is used only in the local execution path and is responsible for calling dispatch execute.
Use this function when calling a task with native values (or Promises containing Flyte literals derived from
Python native values).

| Parameter | Type | Description |
|-|-|-|
| `ctx` | `flytekit.core.context_manager.FlyteContext` | |
| `kwargs` | `**kwargs` | |

#### local_execution_mode()

```python
def local_execution_mode()
```
#### pre_execute()

```python
def pre_execute(
    user_params: flytekit.core.context_manager.ExecutionParameters,
) -> flytekit.core.context_manager.ExecutionParameters
```
This is the method that will be invoked directly before executing the task method and before all the inputs
are converted. One particular case where this is useful is if the context is to be modified for the user process
to get some user space parameters. This also ensures that things like SparkSession are already correctly
setup before the type transformers are called

This should return either the same context of the mutated context

| Parameter | Type | Description |
|-|-|-|
| `user_params` | `flytekit.core.context_manager.ExecutionParameters` | |

#### sandbox_execute()

```python
def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> flytekit.models.literals.LiteralMap
```
Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

| Parameter | Type | Description |
|-|-|-|
| `ctx` | `flytekit.core.context_manager.FlyteContext` | |
| `input_literal_map` | `flytekit.models.literals.LiteralMap` | |

## flytekit.core.base_task.TaskMetadata

Metadata for a Task. Things like retries and whether or not caching is turned on, and cache version are specified
here.

See the :std:ref:`IDL <idl:protos/docs/core/core:taskmetadata>` for the protobuf definition.

### Parameters

```python
class TaskMetadata(
    cache: bool,
    cache_serialize: bool,
    cache_version: str,
    cache_ignore_input_vars: typing.Tuple[str, ...],
    interruptible: typing.Optional[bool],
    deprecated: str,
    retries: int,
    timeout: typing.Union[datetime.timedelta, int, NoneType],
    pod_template_name: typing.Optional[str],
    generates_deck: bool,
    is_eager: bool,
    labels: typing.Optional[dict[str, str]],
    annotations: typing.Optional[dict[str, str]],
)
```
| Parameter | Type | Description |
|-|-|-|
| `cache` | `bool` | Indicates if caching should be enabled. See :std:ref:`Caching <cookbook:caching>`. |
| `cache_serialize` | `bool` | Indicates if identical (i.e. same inputs) instances of this task should be executed in serial when caching is enabled. See :std:ref:`Caching <cookbook:caching>`. |
| `cache_version` | `str` | Version to be used for the cached value. |
| `cache_ignore_input_vars` | `typing.Tuple[str, ...]` | Input variables that should not be included when calculating hash for cache. |
| `interruptible` | `typing.Optional[bool]` | Indicates that this task can be interrupted and/or scheduled on nodes with lower QoS guarantees that can include pre-emption. |
| `deprecated` | `str` | Can be used to provide a warning message for a deprecated task. An absence or empty string indicates that the task is active and not deprecated. |
| `retries` | `int` | for retries=n; n > 0, on failures of this task, the task will be retried at-least n number of times. |
| `timeout` | `typing.Union[datetime.timedelta, int, NoneType]` | The maximum duration for which one execution of this task should run. The execution will be terminated if the runtime exceeds this timeout. |
| `pod_template_name` | `typing.Optional[str]` | The name of an existing PodTemplate resource in the cluster which will be used for this task. |
| `generates_deck` | `bool` | Indicates whether the task will generate a Deck URI. |
| `is_eager` | `bool` | Indicates whether the task should be treated as eager. |
| `labels` | `typing.Optional[dict[str, str]]` | Labels to be applied to the task resource. |
| `annotations` | `typing.Optional[dict[str, str]]` | Annotations to be applied to the task resource. |

### Properties

| Property | Type | Description |
|-|-|-|
| `retry_strategy` | `None` |  |

### Methods

| Method | Description |
|-|-|
| [`to_taskmetadata_model()`](#to_taskmetadata_model) | Converts to _task_model. |

#### to_taskmetadata_model()

```python
def to_taskmetadata_model()
```
Converts to _task_model.TaskMetadata

## flytekit.core.base_task.TaskResolverMixin

Flytekit tasks interact with the Flyte platform very, very broadly in two steps. They need to be uploaded to Admin,
and then they are run by the user upon request (either as a single task execution or as part of a workflow). In any
case, at execution time, for most tasks (that is those that generate a container target) the container image
containing the task needs to be spun up again at which point the container needs to know which task it's supposed
to run and how to rehydrate the task object.

For example, the serialization of a simple task ::

    # in repo_root/workflows/example.py
    @task
    def t1(...) -> ...: ...

might result in a container with arguments like ::

    pyflyte-execute --inputs s3://path/inputs.pb --output-prefix s3://outputs/location         --raw-output-data-prefix /tmp/data         --resolver flytekit.core.python_auto_container.default_task_resolver         --         task-module repo_root.workflows.example task-name t1

At serialization time, the container created for the task will start out automatically with the ``pyflyte-execute``
bit, along with the requisite input/output args and the offloaded data prefix. Appended to that will be two things,

#. the ``location`` of the task's task resolver, followed by two dashes, followed by
#. the arguments provided by calling the ``loader_args`` function below.

The ``default_task_resolver`` declared below knows that

* When ``loader_args`` is called on a task, to look up the module the task is in, and the name of the task (the
  key of the task in the module, either the function name, or the variable it was assigned to).
* When ``load_task`` is called, it interprets the first part of the command as the module to call
  ``importlib.import_module`` on, and then looks for a key ``t1``.

This is just the default behavior. Users should feel free to implement their own resolvers.

### Properties

| Property | Type | Description |
|-|-|-|
| `location` | `None` |  |

### Methods

| Method | Description |
|-|-|
| [`get_all_tasks()`](#get_all_tasks) | Future proof method. |
| [`load_task()`](#load_task) | Given the set of identifier keys, should return one Python Task or raise an error if not found. |
| [`loader_args()`](#loader_args) | Return a list of strings that can help identify the parameter Task. |
| [`name()`](#name) |  |
| [`task_name()`](#task_name) | Overridable function that can optionally return a custom name for a given task. |

#### get_all_tasks()

```python
def get_all_tasks()
```
Future proof method. Just making it easy to access all tasks (Not required today as we auto register them)

#### load_task()

```python
def load_task(
    loader_args: typing.List[str],
) -> flytekit.core.base_task.Task
```
Given the set of identifier keys, should return one Python Task or raise an error if not found

| Parameter | Type | Description |
|-|-|-|
| `loader_args` | `typing.List[str]` | |

#### loader_args()

```python
def loader_args(
    settings: flytekit.configuration.SerializationSettings,
    t: flytekit.core.base_task.Task,
) -> typing.List[str]
```
Return a list of strings that can help identify the parameter Task

| Parameter | Type | Description |
|-|-|-|
| `settings` | `flytekit.configuration.SerializationSettings` | |
| `t` | `flytekit.core.base_task.Task` | |

#### name()

```python
def name()
```
#### task_name()

```python
def task_name(
    t: flytekit.core.base_task.Task,
) -> typing.Optional[str]
```
Overridable function that can optionally return a custom name for a given task

| Parameter | Type | Description |
|-|-|-|
| `t` | `flytekit.core.base_task.Task` | |

---
**Source**: https://github.com/unionai/unionai-docs/blob/main/content/api-reference/flytekit-sdk/packages/flytekit.core.base_task.md
**HTML**: https://www.union.ai/docs/v1/union/api-reference/flytekit-sdk/packages/flytekit.core.base_task/