Skip to main content

GPipe for PyTorch

Project description

A GPipe implementation in PyTorch.

from torchgpipe import GPipe

model = nn.Sequential(a, b, c, d)
model = GPipe(model, balance=[1, 1, 1, 1], chunks=8)

for input in data_loader:
    output = model(input)

What is GPipe?

GPipe is a scalable pipeline parallelism library published by Google Brain, which allows efficient training of large, memory-consuming models. According to the paper, GPipe can train a 25x larger model by using 8x devices (TPU), and train a model 3.5x faster by using 4x devices.

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Google trained AmoebaNet-B with 557M parameters over GPipe. This model has achieved 84.3% top-1 and 97.0% top-5 accuracy on ImageNet classification benchmark (the state-of-the-art performance as of May 2019).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

torchgpipe-0.0.7-py3-none-any.whl (39.1 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page