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-torch
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/graphsage
./run.sh
Training other models
Examples folder contains various models one can experiment with DeepGNN. To train models with Tensorflow you need to install deepgnn-tf package, deepgnn-torch package contains packages to train pytorch examples. Each model folder contains a shell script run.sh that will train a corresponding model on a toy graph, a README.md file with a short description of a model, reference to original paper, and explanation of command line arguments.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for deepgnn_ge-0.1.53-py3-none-win_amd64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 838f28aa53e3db2b34e7e376867f75164e7cbf26cb5b0b2cb82c6c6ddcb2aa02 |
|
| MD5 | 963729d0e0632e7776728fd3efd876e5 |
|
| BLAKE2b-256 | aad82643d694ed7007bcb29603cae3e5068a012075d4b99285f171ac9c69a73a |
Hashes for deepgnn_ge-0.1.53-py3-none-manylinux1_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 7decae15c107980e99283fd06a44f685d2d1ce0bfd8e9843845718d9ed0b2e52 |
|
| MD5 | 2849d719b13e127d819b423066d60a95 |
|
| BLAKE2b-256 | 03cd556b3e7db19f07bcf9a090fc7e853b266ffa4b4275a71709f05fe8bb7028 |
Hashes for deepgnn_ge-0.1.53-py3-none-macosx_10_9_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 65ad99aab4f918a99c97f11aae9cb088233cfdcb30d1fdf9c0951cec505561dc |
|
| MD5 | 620835517311c8fd91059dc0f31308b7 |
|
| BLAKE2b-256 | 3718d18438001640b75f58142277fa3a62a89002e0acd0e713bd15b6c55e82cf |
