TC-GNN with Pytorch integration

Related tags

Deep Learningdeep-learningpytorchgraph-neural-networks
Overview

TC-GNN (Running Sparse GNN on Dense Tensor Core on Ampere GPU)

  • Cite this project and paper.
@inproceedings{TC-GNN,
  title={TC-GNN: Accelerating Sparse Graph Neural Network Computation Via Dense Tensor Core on GPUs},
  author={Yuke Wang and Boyuan Feng and Yufei Ding},
  booktitle={Arxiv},
  year={2022}
}
  • Clone this project.
git clone [email protected]:YukeWang96/TCGNN-Pytorch.git
  • OS & Compiler:
  • Ubuntu 16.04+
  • gcc >= 7.5
  • cmake >= 3.14
  • CUDA >= 11.0 and nvcc >= 11.0

Files and Directories.

  • config.py: the configuration file for the shape of a TC block.
  • bench.py: the benchmark file for invoking main_tcgnn.py for various datasets and models.
  • main_tcgnn.py: the main entry for running TC-GNN.
  • count_TC_blocks.py: counting the total number of TC blocks without sparse-graph translation.
  • proc_prof.py: get the detailed GPU kernel metrics from the ncu csv output.
  • TCGNN_conv/: the directory for core TC-GNN implementations, including TCGNN_kernel.cu and TCGNN.cpp.

Environment Setup.

[Method-1] Install via Docker (Recommended).

  • Go to Docker/
  • Run ./build.sh
  • Run ./launch.sh

[Method-2] Install via Conda.

  • Install conda on system Toturial.
  • Create a conda environment:
conda create -n env_name python=3.6
  • Install Pytorch:
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge

or using pip [Note that make sure the pip you use is the pip from current conda environment. You can check this by which pip]

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html

conda install -c dglteam dgl-cuda11.0
pip install torch requests tqdm

pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu111.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+cu111.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+cu111.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.0+cu111.html
pip install torch-geometric

Install TC-GNN.

Go to TCGNN_conv/, then run

./build.sh

to install the TCGNN_conv modules with Pytorch binding. Note that this step is required for both Docker and Conda setup.

Download graph datasets.

Get the preprocessed datasets in .npy at here, then run

tar -zxvf tcgnn-ae-graphs.tar.gz

Running PyG baseline.

  • Go to pyg_baseline/ directory;
  • Pass the --model parameter in pyg_main.py with gcn and gin to profile the example GCN and GIN model, respectively;
  • ./0_bench.py| tee run_pyg.log to run the script and the report 10 epoch runtime for all evaluated datasets.
  • ./1_log2csv.py to convert the run_pyg.log to run_pyg.csv for ease of analysis.

Running DGL baseline.

  • Go to dgl_baseline/ directory
  • Pass the --model parameter in dgl_main.py with gcn and gin to profile the example GCN and GIN model, respectively;
  • ./0_bench.py| tee run_dgl.log to run the script and the report 10 epoch runtime for all evaluated datasets.
  • ./1_log2csv.py to convert the run_dgl.log to run_dgl.csv for ease of visualization.

Running TC-GNN.

  • Under the current project directory
  • ./0_bench.py| tee run_TCGNN.log to run the script and the report 10 epoch runtime for all evaluated datasets.
  • ./1_log2csv.py to convert the run_TCGNN.log to run_TCGNN.csv for ease of analysis.
You might also like...
🐸STT integration examples

🐸 STT 0.9.x Examples These are various examples on how to use or integrate 🐸 STT using our packages. It is a good way to just try out 🐸 STT before

coqui 92 Dec 19, 2022
Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021
Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021

AutoInt: Automatic Integration for Fast Neural Volume Rendering CVPR 2021 Project Page | Video | Paper PyTorch implementation of automatic integration

Stanford Computational Imaging Lab 149 Dec 22, 2022
Hand gesture recognition based whiteboard that allows you to write on live webcam. This is the first version and has features like 4 different colors, eraser and a recording option that records your session and saves it in a "recordings" folder. Use index finger to draw and two or more fingers to move around and select items. Future version will contain more functionalities like changeable thickness, color palette, integration with zoom and google meet etc.

hand-write Hand gesture recognition based whiteboard that allows you to write on live webcam. This is the first version and has features like 4 differ

null 27 Dec 16, 2022
An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

null 45 Dec 8, 2022
Dahua Camera and Doorbell Home Assistant Integration
Dahua Camera and Doorbell Home Assistant Integration

Home Assistant Dahua Integration The Dahua Home Assistant integration allows you to integrate your Dahua cameras and doorbells in Home Assistant. It's

Ronnie 216 Dec 26, 2022
MaRS - a recursive filtering framework that allows for truly modular multi-sensor integration
MaRS - a recursive filtering framework that allows for truly modular multi-sensor integration

The Modular and Robust State-Estimation Framework, or short, MaRS, is a recursive filtering framework that allows for truly modular multi-sensor integration

Control of Networked Systems - University of Klagenfurt 143 Dec 29, 2022
ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration
ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration

ROSITA News & Updates (24/08/2021) Release the demo to perform fine-grained semantic alignments using the pretrained ROSITA model. (15/08/2021) Releas

Vision and Language Group@ MIL 48 Dec 23, 2022
Wafer Fault Detection using MlOps Integration
Wafer Fault Detection using MlOps Integration

Wafer Fault Detection using MlOps Integration This is an end to end machine learning project with MlOps integration for predicting the quality of wafe

Sethu Sai Medamallela 0 Mar 11, 2022
Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)
Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling This repo contains the official implementation for the paper On Path Int

Ruiqi Gao 39 Nov 10, 2022
Comments
  • Any docs about this project?

    Any docs about this project?

    Hi I came across this project and found the implementation is quite interesting. Is there any docs/paper that detail this project? Or you have any plan to release these kinds of information in the future?

    Thanks

    opened by mmmeee1111 1
Releases(v0.2)
Owner
YUKE WANG
https://wang-yuke.com
YUKE WANG
GitHub Repository
Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid

SPN: Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyrami

12 Jun 27, 2022
QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

Introduction QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and

Yu 1.4k Jan 01, 2023
This is the first released system towards complex meters` detection and recognition, which is implemented by computer vision techniques.

A three-stage detection and recognition pipeline of complex meters in wild This is the first released system towards detection and recognition of comp

Yan Shu 19 Nov 28, 2022
Jax/Flax implementation of Variational-DiffWave.

jax-variational-diffwave Jax/Flax implementation of Variational-DiffWave. (Zhifeng Kong et al., 2020, Diederik P. Kingma et al., 2021.) DiffWave with

YoungJoong Kim 37 Dec 16, 2022
A clean and extensible PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

A clean and extensible PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners A PyTorch re-implementation of Mask Autoencoder trai

Tianyu Hua 23 Dec 13, 2022
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

Realtime Unsupervised Depth Estimation from an Image This is the caffe implementation of our paper "Unsupervised CNN for single view depth estimation:

Ravi Garg 227 Nov 28, 2022
This repo provides the source code & data of our paper "GreaseLM: Graph REASoning Enhanced Language Models"

GreaseLM: Graph REASoning Enhanced Language Models This repo provides the source code & data of our paper "GreaseLM: Graph REASoning Enhanced Language

137 Jan 02, 2023
PyTorch implementations of the NeRF model described in "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"

PyTorch NeRF and pixelNeRF NeRF: Tiny NeRF: pixelNeRF: This repository contains minimal PyTorch implementations of the NeRF model described in "NeRF:

Michael A. Alcorn 178 Dec 20, 2022
Contrastive Language-Image Pretraining

CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pair

OpenAI 11.5k Jan 08, 2023
MDETR: Modulated Detection for End-to-End Multi-Modal Understanding

MDETR: Modulated Detection for End-to-End Multi-Modal Understanding Website • Colab • Paper This repository contains code and links to pre-trained mod

Aishwarya Kamath 770 Dec 28, 2022
AgML is a comprehensive library for agricultural machine learning

AgML is a comprehensive library for agricultural machine learning. Currently, AgML provides access to a wealth of public agricultural datasets for common agricultural deep learning tasks.

Plant AI and Biophysics Lab 1 Jul 07, 2022
BERT model training impelmentation using 1024 A100 GPUs for MLPerf Training v1.1

Pre-trained checkpoint and bert config json file Location of checkpoint and bert config json file This MLCommons members Google Drive location contain

SAIT (Samsung Advanced Institute of Technology) 12 Apr 27, 2022
Code for Subgraph Federated Learning with Missing Neighbor Generation (NeurIPS 2021)

To run the code Unzip the package to your local directory; Run 'pip install -r requirements.txt' to download required packages; Open file ~/nips_code/

32 Dec 26, 2022
Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021)

Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021) Overview of paths used in DIG and IG. w is the word being attributed. The

INK Lab @ USC 17 Oct 27, 2022
PyTorch implementation of DirectCLR from paper Understanding Dimensional Collapse in Contrastive Self-supervised Learning

DirectCLR DirectCLR is a simple contrastive learning model for visual representation learning. It does not require a trainable projector as SimCLR. It

Meta Research 49 Dec 21, 2022
Generate images from texts. In Russian

ruDALL-E Generate images from texts pip install rudalle==1.1.0rc0 🤗 HF Models: ruDALL-E Malevich (XL) ruDALL-E Emojich (XL) (readme here) ruDALL-E S

AI Forever 1.6k Dec 31, 2022
PyTorch implementation of "Debiased Visual Question Answering from Feature and Sample Perspectives" (NeurIPS 2021)

D-VQA We provide the PyTorch implementation for Debiased Visual Question Answering from Feature and Sample Perspectives (NeurIPS 2021). Dependencies P

Zhiquan Wen 19 Dec 22, 2022
A GUI to automatically create a TOPAS-readable MLC simulation file

Python script to create a TOPAS-readable simulation file descriring a Multi-Leaf-Collimator. Builds the MLC using the data from a 3D .stl file.

Sebastian Schäfer 0 Jun 19, 2022
Spectral Tensor Train Parameterization of Deep Learning Layers

Spectral Tensor Train Parameterization of Deep Learning Layers This repository is the official implementation of our AISTATS 2021 paper titled "Spectr

Anton Obukhov 12 Oct 23, 2022
Code for CVPR2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》

Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data. This is the code of CVPR 2019 paper《Unequal Training for Deep Face Recognition

Zhong Yaoyao 68 Jan 07, 2023