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.
Project 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
Hashes for deepgnn_ge-0.1.60.dev1-py3-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 67905ac9bfffcaca06d6b3c976a175e11b0c3ceefc534f21d7744ac861245bfb |
|
MD5 | 83ef73201c0fab6a9c2e030f5147c0d7 |
|
BLAKE2b-256 | f114c6f77c90d9ee664015a4baf015938fa8c41dc23755b02ee5cd027cdcacd9 |
Hashes for deepgnn_ge-0.1.60.dev1-py3-none-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4b1b428962dc441b5341eb4ddcd7d071060ce256188e766d44ed681930a7dbbe |
|
MD5 | e2e89f08f9b56faef394214613259018 |
|
BLAKE2b-256 | b5ca6fdaaa7b845f3b0b756adb34953e6f3b731990418e7130f0944c4ea9b598 |
Hashes for deepgnn_ge-0.1.60.dev1-py3-none-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 863e6b97e34d6166d886b520089c501f94dc98b96e897bc3031af9a1ff7e1d9e |
|
MD5 | d39ff2d5af716030cfc5561bbca2c1ea |
|
BLAKE2b-256 | 5e3ff4fa796848ec92d238bd1221403f5bb3d811411c4cb3e8e81b868c6fc07c |