This repo contains reference examples for training ML models quickly and to high accuracy. It's designed to be easily forked and modified.

It currently features the following examples:

• ResNet-50 + ImageNet
• DeeplabV3 + ADE20k
• GPT / Large Language Models
• BERT

To get started, either clone or fork this repo and install whichever example[s] you're interested in. E.g., to get started training GPT-style Large Language Models, just:

git clone https://github.com/mosaicml/examples.git
cd examples # cd into the repo
pip install -e ".[llm]"  # or pip install -e ".[llm-cpu]" if no NVIDIA GPU
cd examples/llm # cd into the specific example's folder

Available examples include llm, stable-diffusion, resnet-imagenet, resnet-cifar, bert, deeplab, nemo, gpt-neox, and trlx.

Each example provides a short main.py that constructs a Trainer object and all of the arguments to it. There are three easy ways to extend an example:

1. Change the configuration. Each example has a yamls subdirectory, and each yaml file therein contains settings like the learning rate, where to load data from, and more. These settings are read within main.py and used to configure the Trainer.
2. Modify the arguments to Trainer directly. For example, if you want to use a different optimizer, the simplest way is to construct this optimizer in main.py and pass it as the optimizer argument to the Trainer.
3. Write an Algorithm and add it to the Trainer's algorithms argument. This lets you inject arbitrary code almost anywhere in your training loop and modify training as it happens. Composer includes a big list of Algorithms whose source code you can use as examples.

We also provide some convenient string-to-object mappings in common/builders.py. These let you avoid cluttering main.py with code like:

if cfg.optimizer == 'adam':
   ...
elif cfg.optimizer == 'sgd':
  ...

and instead write:

opt = builders.build_optimizer(cfg.optimizer, model)

with all the if-elif logic wrapped in this reusable function. You can easily extend these functions to include new options; e.g., to add an optimizer, you could extend build_optimizer to import and construct your optimizer.

If you run into any issues extending the code, or just want to discuss an idea you have, please open an issue or join our community Slack!

If you already have the dependencies for a given example installed, you can just run:

pre-commit run --all-files  # autoformatting for whole repo
cd examples/llm  # or bert, resnet_imagenet, etc
pyright .  # type checking
pytest tests/  # run tests

from the example's directory.

To run the full suite of tests for all examples, invoke make test in the project's root directory. Similarly, invoke make lint to autoformat your code and detect type issues throughout the whole codebase. This is much slower than linting or testing just one example because it installs all the dependencies for each example from scratch in a fresh virtual environment.

These examples use OmegaConf to manage configuration. OmegaConf allows us to make configuration explicit via separate YAML files while also allowing rapid experimentation and easy command line override. There's no special language or format for these configurations; they're just a convenient way of writing out a dictionary that gets used in each main.py file.

Here's a simple example. Let's say you have this YAML config file:

a: 1
nested:
    foo: bar

and we run:

python script.py b=baz nested.foo=different

The main.py file will end up with:

{'a': 1, 'b': 'baz', 'nested': {'foo': 'different'}}

This repo features the following examples, each as their own subdirectory:

Figure 1: Comparison of MosaicML recipes against other results, all measured on 8x A100s on the MosaicML platform.

Train the MosaicML ResNet, which is currently the fastest ResNet50 implementation there is and yields a ✨ 7x ✨ faster time-to-train than a strong baseline.

🚀 Get started with the code here.

Train the MosaicML DeepLabV3, which yields a ✨5x✨ faster time-to-train than a strong baseline.

🚀 Get started with the code here.

A simple yet feature-complete implementation of GPT, which scales to 70B parameters while maintaining high performance on GPU clusters. Flexible code, written with vanilla PyTorch, that uses PyTorch FSDP and some recent efficiency improvements.

🚀 Get started with the code here.

This benchmark covers both pre-training and fine-tuning a BERT model. With this starter code, you'll be able to do Masked Language Modeling (MLM) pre-training on the C4 dataset and classification fine-tuning on GLUE benchmark tasks.

We also provide the source code and recipe behind our Mosaic BERT model, which you can train yourself using this repo.

🚀 Get started with the code here.