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Pipegoose

Pipegoose is a Python framework for distributed pre-training of Hugging Face Transformers models using parallel training methods, with a stated research direction in multimodal mixture-of-experts models.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pipegoose — 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 Pipegoose when investigating distributed Transformer training and its documented BLOOM examples, particularly if you can provide the multi-GPU environment required by the hybrid example.

Who it is for

  • Researchers exploring distributed Transformer pre-training.
  • Engineers evaluating parallel training workflows for BLOOM models.

Documented uses

  • Install Pipegoose from its source repository.
  • Explore a hybrid tensor- and data-parallel training example.
  • Structure a model-training loop with distributed sampling and a distributed optimizer.
Before you choose

Requirements & limits

The source is publicly available on GitHub under the MIT license. The documented hybrid example requires at least four GPUs.

  • The README says only Transformers' BLOOM is currently supported for parallelization.
  • The hybrid tensor-and-data-parallel example requires at least four GPUs.
  • The project is described as work in progress, and the README reports a convergence bug in tensor-parallel testing.

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

Install Pipegoose and run the hybrid parallel example

Install the repository package and start its documented hybrid tensor-and-data-parallel training example.

Before you start

  • Git and pip
  • At least four GPUs for the hybrid 2D parallelism example

Steps

  1. Clone the Pipegoose repository.

  2. Change into the repository directory and install it with pip in editable mode.

  3. Change into the examples directory.

  4. Launch hybrid_parallelism.py with torchrun using one node and four processes per node.

Expected outcome

Starts the repository's hybrid tensor-and-data-parallel example.

Guide limitations

  • The README states that at least four GPUs are required for this example.
  • Pipegoose is marked as work in progress; the README says only Transformers' BLOOM is currently supported for parallelization.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
xrsrke/pipegoose · Official homepage title · retained in its source language
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