Installation

MosaicML Composer can be installed via pip:

pip install mosaicml

Additional dependencies can be installed by specifying mosaicml[tag]. The following tags are available:

nlp: installs huggingface transformers and datasets
dev: packages needed for testing, linting, and docs.
wandb: enables the weights & biases logger
unet: enables the U-Net model and BRATS dataset
all: installs all of the above.

For a developer install, clone directly:

git clone git@github.com/mosaicml/composer.git
cd composer && pip install -e .

Note

For performance in image-based operations, we highly recommend installing Pillow-SIMD. To install, vanilla pillow must first be uninstalled.

pip uninstall pillow && pip install pillow-simd

Pillow-SIMD is not supported for Apple M1 Macs.

Composer has been tested with Ubuntu 20.04, PyTorch 1.8.1, and Python 3.8.

Docker

To access our docker, either pull the latest image from our Docker repository with:

docker pull mosaicml/composer:latest

or build our Dockerfile:

git clone git@github.com:mosaicml/composer.git
cd composer/docker && make build

Our dockerfile has Ubuntu 18.04, Python 3.8.0, PyTorch 1.9.0, and CUDA 11.1.1, and has been tested to work with GPU-based instances on AWS, GCP, and Azure. Pillow-SIMD is installed by default in our docker image.

Please see the README in the docker area for additional details.

Verification

Test Composer was installed properly by opening a python prompt, and running:

import logging
from composer import functional as CF
import torchvision.models as models

logging.basicConfig(level=logging.INFO)
model = models.resnet50()

CF.apply_blurpool(model)

This creates a ResNet50 model and replaces several pooling and convolution layers with BlurPool variants (Zhang et al, 2019. The method should log:

Applied BlurPool to model ResNet Model now has 1 BlurMaxPool2d and 6 BlurConv2D layers.

Next, train a small classifier on MNIST with the label smoothing algorithm:

git clone git@github.com:mosaicml/composer.git
cd composer
python examples/run_mosaic_trainer.py -f composer/models/classify_mnist/hparams_cpu.yaml --datadir /datasets/ --algorithms label_smoothing --alpha 0.1