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.:

    @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.

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.:

    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.:

    from flytekit.extras.accelerators import A100

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

Directory

Classes

Variables

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.

class GPUAccelerator(
    device: str,
)

Methods

to_flyte_idl()

def to_flyte_idl()

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,
) -> ~MIG

to_flyte_idl()

def to_flyte_idl()

Properties