Accelerate your code with Fabric

################################ Accelerate your code with Fabric ################################

.. video:: https://pl-public-data.s3.amazonaws.com/assets_lightning/fabric/animations/accelerators.mp4 :width: 800 :autoplay: :loop: :muted: :nocontrols:

Set accelerator and devices

Fabric enables you to take full advantage of the hardware on your system. It supports

• CPU
• GPU (NVIDIA, AMD, Apple Silicon)
• TPU

By default, Fabric tries to maximize the hardware utilization of your system

.. code-block:: python

# Default settings
fabric = Fabric(accelerator="auto", devices="auto", strategy="auto")

# Same as
fabric = Fabric()

This is the most flexible option and makes your code run on most systems. You can also explicitly set which accelerator to use:

.. code-block:: python

# CPU (slow)
fabric = Fabric(accelerator="cpu")

# GPU
fabric = Fabric(accelerator="gpu", devices=1)

# GPU (multiple)
fabric = Fabric(accelerator="gpu", devices=8)

# GPU: Apple M1/M2 only
fabric = Fabric(accelerator="mps")

# GPU: NVIDIA CUDA only
fabric = Fabric(accelerator="cuda", devices=8)

# TPU
fabric = Fabric(accelerator="tpu", devices=8)

For running on multiple devices in parallel, also known as "distributed", read our guide for :doc:Launching Multiple Processes <./launch>.

Access the Device

You can access the device anytime through fabric.device. This lets you replace boilerplate code like this:

.. code-block:: diff

- if torch.cuda.is_available():
-     device = torch.device("cuda")
- else:
-     device = torch.device("cpu")

+ device = fabric.device