Skip to main content

Graph engine - distributed graph engine to host graphs.

Project description

DeepGNN Overview

DeepGNN is a framework for training machine learning models on large scale graph data. DeepGNN contains all the necessary features including:

  • Distributed GNN training and inferencing on both CPU and GPU.
  • Custom graph neural network design.
  • Online Sampling: Graph Engine (GE) will load all graph data, each training worker will call GE to get node/edge/neighbor features and labels.
  • Automatic graph partitioning.
  • Highly performant and scalable.

Project is in alpha version, there might be breaking changes in the future and they will be documented in the changelog.

Usage

Install pip package:

python -m pip install deepgnn

If you want to build package from source, see instructions in CONTRIBUTING.md.

Train and evaluate a graphsage model with pytorch on cora dataset:

cd examples/pytorch
python sage.py

Migrating Scripts

We provide a python module to help you upgrade your scripts to new deepgnn versions.

pip install google-pasta
python -m deepgnn.migrate.0_1_56 --script_dir directory_to_migrate

See CHANGELOG.md for full change details.

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 Distributions

deepgnn_ge-0.1.61-py3-none-win_amd64.whl (3.1 MB view details)

Uploaded Python 3 Windows x86-64

deepgnn_ge-0.1.61-py3-none-manylinux1_x86_64.whl (11.9 MB view details)

Uploaded Python 3

deepgnn_ge-0.1.61-py3-none-macosx_10_9_x86_64.whl (4.6 MB view details)

Uploaded Python 3 macOS 10.9+ x86-64

File details

Details for the file deepgnn_ge-0.1.61-py3-none-win_amd64.whl.

File metadata

File hashes

Hashes for deepgnn_ge-0.1.61-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 b7b562dcd2f660187505b6824e6a091e8c9c8f46d95ad42e3f92bf5404776adf
MD5 e99598156cfd3d3025782d788f5a8438
BLAKE2b-256 55237a8d03e09f6f6e356ef2d92fcfcfe1c71ec54b9487c09900e731f9ab7a13

See more details on using hashes here.

File details

Details for the file deepgnn_ge-0.1.61-py3-none-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for deepgnn_ge-0.1.61-py3-none-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 78bd43850bf03d454446d1dfbf7d12ce9642298221f2d3e2002e7f82cf563462
MD5 b8bc36e3f4fe034137dd3a3a05ffcad6
BLAKE2b-256 7a759c227d4de42d86f390ca80f0f63620812963f4dfaf018a4f3bdca4e7d9a6

See more details on using hashes here.

File details

Details for the file deepgnn_ge-0.1.61-py3-none-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for deepgnn_ge-0.1.61-py3-none-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 f056036175522d644535f04bd99cb3e7eb2d8e09facebd43fa4726812d29caa2
MD5 2db3f569dc71de09abc7d3290b95e478
BLAKE2b-256 123c313982890c6e7ce18a07b0acda675c3e67664d7520b982104ae94d93a146

See more details on using hashes here.

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