## 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.:
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.:
```python 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.:
```python from flytekit.extras.accelerators import A100
- @task(
limits=Resources(gpu=”1”), accelerator=A100.partition_2g_10gb,
) def my_task() -> None:
…