flytekit.extras.accelerators

Specifying Accelerators

.. tags:: MachineLearning, Advanced, Hardware

Flyte allows you to specify gpu resources for a given task. However, in some cases, you may want to use a different accelerator type, such as TPU, specific variations of GPUs, or fractional GPUs. You can configure the Flyte backend to use your preferred accelerators, and those who write workflow code can import the flytekit.extras.accelerators module to specify an accelerator in the task decorator.

If you want to use a specific GPU device, you can pass the device name directly to the task decorator, e.g.:

.. code-block::

@task(
    limits=Resources(gpu="1"),
    accelerator=GPUAccelerator("nvidia-tesla-v100"),
)
def my_task() -> None:
    ...

Base Classes

These classes can be used to create custom accelerator type constants. For example, you can create a TPU accelerator.

.. currentmodule:: flytekit.extras.accelerators

.. autosummary:: :template: custom.rst :toctree: generated/ :nosignatures:

BaseAccelerator GPUAccelerator MultiInstanceGPUAccelerator

But, often, you may want to use a well known accelerator type, and to simplify this, flytekit provides a set of predefined accelerator constants, as described in the next section.

Predefined Accelerator Constants

The flytekit.extras.accelerators module provides some constants for known accelerators, listed below, but this is not a complete list. If you know the name of the accelerator, you can pass the string name to the task decorator directly.

If using the constants, you can import them directly from the module, e.g.:

.. code-block::

from flytekit.extras.accelerators import T4

@task(
    limits=Resources(gpu="1"),
    accelerator=T4,
)
def my_task() -> None:
    ...

if you want to use a fractional GPU, you can use the partitioned method on the accelerator constant, e.g.:

.. code-block::

from flytekit.extras.accelerators import A100

@task(
    limits=Resources(gpu="1"),
    accelerator=A100.partition_2g_10gb,
)
def my_task() -> None:
    ...

.. currentmodule:: flytekit.extras.accelerators

.. autosummary:: :toctree: generated/ :nosignatures:

A10G L4 K80 M60 P4 P100 T4 V100 A100 A100_80GB

Directory

Classes

flytekit.extras.accelerators.BaseAccelerator

Base class for all accelerator types. This class is not meant to be instantiated directly.

Methods

to_flyte_idl()

def to_flyte_idl()

flytekit.extras.accelerators.GPUAccelerator

Class that represents a GPU accelerator. The class can be instantiated with any valid GPU device name, but it is recommended to use one of the pre-defined constants below, as name has to match the name of the device configured on the cluster.

def GPUAccelerator(
    device: str,
):

Methods

to_flyte_idl()

def to_flyte_idl()

flytekit.extras.accelerators.Generic

Abstract base class for generic types.

On Python 3.12 and newer, generic classes implicitly inherit from Generic when they declare a parameter list after the class’s name::

class Mapping[KT, VT]: def getitem(self, key: KT) -> VT: …

Etc.

On older versions of Python, however, generic classes have to explicitly inherit from Generic.

After a class has been declared to be generic, it can then be used as follows::

def lookup_name[KT, VT](mapping: Mapping[KT, VT], key: KT, default: VT) -> VT: try: return mapping[key] except KeyError: return default

flytekit.extras.accelerators.MultiInstanceGPUAccelerator

Base class for all multi-instance GPU accelerator types. It is recommended to use one of the pre-defined constants below, as name has to match the name of the device configured on the cluster. For example, to specify a 10GB partition of an A100 GPU, use A100.partition_2g_10gb.

Methods

partitioned()

def partitioned(
    partition_size: str,
):

to_flyte_idl()

def to_flyte_idl()

Properties

flytekit.extras.accelerators.TypeVar

Type variable.

The preferred way to construct a type variable is via the dedicated syntax for generic functions, classes, and type aliases::

class Sequence[T]: # T is a TypeVar …

This syntax can also be used to create bound and constrained type variables::

S is a TypeVar bound to str

class StrSequence[S: str]: …

A is a TypeVar constrained to str or bytes

class StrOrBytesSequence[A: (str, bytes)]: …

However, if desired, reusable type variables can also be constructed manually, like so::

T = TypeVar(‘T’) # Can be anything S = TypeVar(‘S’, bound=str) # Can be any subtype of str A = TypeVar(‘A’, str, bytes) # Must be exactly str or bytes

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function and type alias definitions.

The variance of type variables is inferred by type checkers when they are created through the type parameter syntax and when infer_variance=True is passed. Manually created type variables may be explicitly marked covariant or contravariant by passing covariant=True or contravariant=True. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.