🎨 Using Composer

Composer provides both a Functional API (similar to torch.nn.functional) and a Trainer (that abstracts away the training loop) to provide flexibility to users.

Functional API

For users who choose to use their own training loop, we provide state-less functional implementations of our algorithms for a end-user to integrate.

The following example highlights using [BlurPool](https://arxiv.org/abs/1904.11486), which applies an anti-aliasing filter before every downsampling operation.

from composer import functional as cf
import torchvision

model = torchvision.models.resnet50()

# Apply model surgery before training by replacing eligible layers
# with a BlurPool-enabled layer (Zhang, 2019)
model = cf.apply_blurpool(model)

# Start your training loop here
train_loop(model)

As another example, to apply Progressive Resizing, which increases the image size over the course of training:

from composer import functional as CF

scale = 0.5
for (image, label) in your_dataloader:
    CF.resize_inputs(image, label, scale_factor=scale)
    scale += 0.01

    # your train step here
    train_step()

For more details, please see composer.functional.

Composer Trainer

For maximal speedups, we recommend using our Trainer, which manages handling user state, performant algorithm implementations, and provides useful engineering abstractions to permit rapid experimentation.

Our trainer features:

• interface to flexibly add algorithms to the training loop

• engine that manages the ordering of algorithms for composition

• trainer to handle boilerplate around numerics, distributed training, and others

• integration with popular model libraries such as TIMM or HuggingFace Transformers.

For more details, see Using Trainer