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Composer

Composer is an open-source Python deep-learning training framework built on PyTorch. Its Trainer abstraction organizes training workflows and supports distributed execution, customizable callbacks, sharded checkpointing and integration with MosaicML StreamingDataset.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Composer — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Composer when implementing PyTorch model training and you need a Trainer abstraction for scaling, training-loop customization, distributed execution or checkpointing.

Who it is for

  • Python developers with basic deep-learning and PyTorch familiarity.
  • Machine-learning teams training neural networks on one or more GPUs or across clusters.
  • Researchers and engineers who need configurable distributed training workflows.

Documented uses

  • Structure a PyTorch model-training workflow with Composer’s Trainer.
  • Configure data loaders, metrics, loggers and parallelization schemes.
  • Train with FSDP or standard PyTorch DDP.
  • Save and resume elastic sharded checkpoints across different GPU counts.
  • Integrate StreamingDataset to read datasets from cloud blob storage during training.
Before you choose

Requirements & limits

The project describes Composer as open source and lists an Apache-2.0 license. The README documents installation with pip and recommends pre-built Docker images for environment setup.

  • Requires Python familiarity and basic knowledge of deep learning and PyTorch.
  • The getting-started material calls for a recent PyTorch version.
  • Composer runs on CPUs, but accelerator hardware is recommended for full benefits; the stated hardware requirement is CUDA-compatible GPUs.
  • FSDP and DDP are documented distributed-training options; FSDP is presented for models too large to fit on GPUs.
  • StreamingDataset integration is the documented route for downloading datasets from cloud blob storage during training.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train an MNIST model with Composer

Set up Composer and use its Trainer to train a small PyTorch model on MNIST.

Before you start

  • Python familiarity and basic familiarity with deep learning and PyTorch.
  • A recent version of PyTorch.
  • Composer can run on CPUs; CUDA-compatible GPUs are recommended to get its full benefits.

Steps

  1. Install Composer with the command below.

    Documented command
    pip install mosaicml

  2. Create an MNIST training dataset with torchvision.datasets.MNIST, convert images to tensors, and wrap the dataset in a DataLoader with batch size 128.

  3. Define a PyTorch model and wrap it with ComposerClassifier, then create a Composer Trainer using the model, training data loader, a maximum duration of 2ep, and the listed LabelSmoothing, CutMix, and ChannelsLast algorithms.

  4. Call trainer.fit() to start training.

Expected outcome

The example configures Composer to train a PyTorch classifier on the MNIST training dataset for two epochs.

Guide limitations

  • The recipe documents a small MNIST example, not a multi-node training configuration.
  • The source does not specify version pins or detail installation of the separate PyTorch and torchvision dependencies.
  • CPU execution is supported, but hardware accelerators are recommended for full benefits.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
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Sources checked
10 October 2026
Original website title
mosaicml/composer · Official homepage title · retained in its source language
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