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.
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.
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
File details
Details for the file deepgnn_ge-0.1.60.dev1-py3-none-win_amd64.whl.
File metadata
- Download URL: deepgnn_ge-0.1.60.dev1-py3-none-win_amd64.whl
- Upload date:
- Size: 3.1 MB
- Tags: Python 3, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 67905ac9bfffcaca06d6b3c976a175e11b0c3ceefc534f21d7744ac861245bfb |
|
| MD5 | 83ef73201c0fab6a9c2e030f5147c0d7 |
|
| BLAKE2b-256 | f114c6f77c90d9ee664015a4baf015938fa8c41dc23755b02ee5cd027cdcacd9 |
File details
Details for the file deepgnn_ge-0.1.60.dev1-py3-none-manylinux1_x86_64.whl.
File metadata
- Download URL: deepgnn_ge-0.1.60.dev1-py3-none-manylinux1_x86_64.whl
- Upload date:
- Size: 11.8 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 4b1b428962dc441b5341eb4ddcd7d071060ce256188e766d44ed681930a7dbbe |
|
| MD5 | e2e89f08f9b56faef394214613259018 |
|
| BLAKE2b-256 | b5ca6fdaaa7b845f3b0b756adb34953e6f3b731990418e7130f0944c4ea9b598 |
File details
Details for the file deepgnn_ge-0.1.60.dev1-py3-none-macosx_10_9_x86_64.whl.
File metadata
- Download URL: deepgnn_ge-0.1.60.dev1-py3-none-macosx_10_9_x86_64.whl
- Upload date:
- Size: 110.7 kB
- Tags: Python 3, macOS 10.9+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 863e6b97e34d6166d886b520089c501f94dc98b96e897bc3031af9a1ff7e1d9e |
|
| MD5 | d39ff2d5af716030cfc5561bbca2c1ea |
|
| BLAKE2b-256 | 5e3ff4fa796848ec92d238bd1221403f5bb3d811411c4cb3e8e81b868c6fc07c |
