#############
Fabric (Beta)
#############
Fabric is the fast and lightweight way to scale PyTorch models without boilerplate code.
- Easily switch from running on CPU to GPU (Apple Silicon, CUDA, ...), TPU, multi-GPU or even multi-node training
- State-of-the-art distributed training strategies (DDP, FSDP, DeepSpeed) and mixed precision out of the box
- Handles all the boilerplate device logic for you
- Brings useful tools to help you build a trainer (callbacks, logging, checkpoints, ...)
- Designed with multi-billion parameter models in mind
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.. code-block:: diff
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
+ from lightning.fabric import Fabric
class PyTorchModel(nn.Module):
...
class PyTorchDataset(Dataset):
...
+ fabric = Fabric(accelerator="cuda", devices=8, strategy="ddp")
+ fabric.launch()
- device = "cuda" if torch.cuda.is_available() else "cpu
model = PyTorchModel(...)
optimizer = torch.optim.SGD(model.parameters())
+ model, optimizer = fabric.setup(model, optimizer)
dataloader = DataLoader(PyTorchDataset(...), ...)
+ dataloader = fabric.setup_dataloaders(dataloader)
model.train()
for epoch in range(num_epochs):
for batch in dataloader:
input, target = batch
- input, target = input.to(device), target.to(device)
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
- loss.backward()
+ fabric.backward(loss)
optimizer.step()
lr_scheduler.step()
.. note:: Fabric is currently in Beta. Its API is subject to change based on feedback.
----
***********
Why Fabric?
***********
Fabric differentiates itself from a fully-fledged trainer like :doc:`Lightning Trainer <../common/trainer>` in these key aspects:
**Fast to implement**
There is no need to restructure your code: Just change a few lines in the PyTorch script and you'll be able to leverage Fabric features.
**Maximum Flexibility**
Write your own training and/or inference logic down to the individual optimizer calls.
You aren't forced to conform to a standardized epoch-based training loop like the one in :doc:`Lightning Trainer <../common/trainer>`.
You can do flexible iteration based training, meta-learning, cross-validation and other types of optimization algorithms without digging into framework internals.
This also makes it super easy to adopt Fabric in existing PyTorch projects to speed-up and scale your models without the compromise on large refactors.
Just remember: With great power comes a great responsibility.
**Maximum Control**
The :doc:`Lightning Trainer <../common/trainer>` has many built in features to make research simpler with less boilerplate, but debugging it requires some familiarity with the framework internals.
In Fabric, everything is opt-in. Think of it as a toolbox: You take out the tools (Fabric functions) you need and leave the other ones behind.
This makes it easier to develop and debug your PyTorch code as you gradually add more features to it.
Fabric provides important tools to remove undesired boilerplate code (distributed, hardware, checkpoints, logging, ...), but leaves the design and orchestration fully up to you.
----
************
Fundamentals
************
.. raw:: html
.. displayitem::
:header: Getting Started
:description: Learn how to add Fabric to your PyTorch code
:button_link: fundamentals/convert.html
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: Accelerators
:description: Take advantage of your hardware with a switch of a flag
:button_link: fundamentals/accelerators.html
:col_css: col-md-4
:height: 150
:tag: intermediate
.. displayitem::
:header: Code Structure
:description: Best practices for setting up your training script with Fabric
:button_link: fundamentals/code_structure.html
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: Launch Distributed Training
:description: Launch a Python script on multiple devices and machines
:button_link: fundamentals/launch.html
:col_css: col-md-4
:height: 150
:tag: intermediate
.. displayitem::
:header: Fabric in Notebooks
:description: Launch on multiple devices from within a Jupyter notebook
:button_link: fundamentals/notebooks.html
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: Mixed Precision Training
:description: Save memory and speed up training using mixed precision
:button_link: fundamentals/precision.html
:col_css: col-md-4
:height: 150
:tag: intermediate
.. raw:: html
----
**********************
Build Your Own Trainer
**********************
.. raw:: html
.. displayitem::
:header: The LightningModule
:description: Organize your code in a LightningModule and use it with Fabric
:button_link: guide/lightning_module.html
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: Callbacks
:description: Make use of the Callback system in Fabric
:button_link: guide/callbacks.html
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: Logging
:description: Learn how Fabric helps you remove boilerplate code for tracking metrics with a logger
:button_link: guide/logging.html
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: Trainer Template
:description: Take our Fabric Trainer template and customize it for your needs
:button_link: guide/trainer_template.html
:col_css: col-md-4
:height: 150
:tag: intermediate
.. raw:: html
----
***************
Advanced Topics
***************
.. raw:: html
.. displayitem::
:header: Efficient Gradient Accumulation
:description: Learn how to perform efficient gradient accumulation in distributed settings
:button_link: advanced/gradient_accumulation.html
:col_css: col-md-4
:height: 160
:tag: advanced
.. displayitem::
:header: Distributed Communication
:description: Learn all about communication primitives for distributed operation. Gather, reduce, broadcast, etc.
:button_link: advanced/distributed_communication.html
:col_css: col-md-4
:height: 160
:tag: advanced
.. raw:: html
----
.. _Fabric Examples:
********
Examples
********
.. raw:: html
.. displayitem::
:header: Image Classification
:description: Train an image classifier on the MNIST dataset
:button_link: https://github.com/Lightning-AI/lightning/blob/master/examples/fabric/image_classifier
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: GAN
:description: Train a GAN that generates realistic human faces
:button_link: https://github.com/Lightning-AI/lightning/blob/master/examples/fabric/dcgan
:col_css: col-md-4
:height: 150
:tag: intermediate
.. displayitem::
:header: Meta-Learning
:description: Distributed training with the MAML algorithm on the Omniglot and MiniImagenet datasets
:button_link: https://github.com/Lightning-AI/lightning/blob/master/examples/fabric/meta_learning
:col_css: col-md-4
:height: 150
:tag: intermediate
.. displayitem::
:header: Large Language Models
:description: Pre-train a GPT-2 language model on OpenWebText data
:button_link: https://github.com/Lightning-AI/nanoGPT/blob/master/train_fabric.py
:col_css: col-md-4
:height: 150
:tag: advanced
.. displayitem::
:header: Reinforcement Learning
:description: Coming soon
:col_css: col-md-4
:height: 150
.. displayitem::
:header: Active Learning
:description: Coming soon
:col_css: col-md-4
:height: 150
.. raw:: html
----
***
API
***
.. raw:: html
.. displayitem::
:header: Fabric Arguments
:description: All configuration options for the Fabric object
:button_link: api/fabric_args.html
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: Fabric Methods
:description: Explore all methods that Fabric offers
:button_link: api/fabric_methods.html
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: Utilities
:description: Explore utility functions that make your life easier
:button_link: api/utilities.html
:col_css: col-md-4
:height: 150
:tag: basic
.. displayitem::
:header: Full API Reference
:description: Reference of all public classes, methods and functions. Useful for developers.
:button_link: api/api_reference.html
:col_css: col-md-4
:height: 150
:tag: intermediate
.. raw:: html