Programming with Neural Surrogates of Programs

Overview

Programming with Neural Surrogates of Programs

Citation

@inproceedings{renda2021programming, 
  title={Programming with Neural Surrogates of Programs}, 
  author={Renda, Alex and Ding, Yi and Carbin, Michael}, 
  booktitle={ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!)}, 
  doi = {10.1145/3486607.3486748}, 
  year={2021}, 
}

Dependencies

The codebase has been validated on Debian 10 (Buster) with the following dependencies/versions:

  • python=3.7
    • datasets=1.16.1
    • matplotlib=3.5.0
    • numpy=1.21.2
    • onnxruntime=1.10.0
    • pandas=1.3.4
    • pytorch=1.10.0
    • tokenizers=0.10.3
    • tqdm=4.62.3
    • transformers=4.13.0
  • numactl
  • cmake
  • git
  • ninja-build
  • build-essential

You should also have cuda installed and pytorch configured to use cuda to train efficiently (though it is not required).

How to use

Hyperparameter Search / Surrogate Compilation

To run the initial hyperparameter search, using [JOBS] parallel jobs: python train.py --jobs [JOBS] hyperparameter-search

To print the results of the hyperparameter search (corresponding to Table 4 in the paper), run:

python results.py table-4

To print the results of surrogate compilation (corresponding to Section 3in the paper), first build llvm-mca:

./build_llvm.sh

Them print the results:

python results.py section-3

To generate the telemetry figures in Appendix B, run:

python results.py surrogate-compilation-telemetry

which will create plots in the figures directory.

Surrogate Adaptation

To run the surrpgate adaptation experiments, using [JOBS] parallel jobs: python train.py --jobs [JOBS] adaptation

To generate Figure 2 in the paper:

python results.py figure-2

To generatee the telemetry figures in Appendix B, run:

python results.py surrogate-adaptation-telemetry

POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propagation including diffraction

POPPY: Physical Optics Propagation in Python POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propaga

Space Telescope Science Institute 132 Dec 15, 2022
Learning High-Speed Flight in the Wild

Learning High-Speed Flight in the Wild This repo contains the code associated to the paper Learning Agile Flight in the Wild. For more information, pl

Robotics and Perception Group 391 Dec 29, 2022
A data annotation pipeline to generate high-quality, large-scale speech datasets with machine pre-labeling and fully manual auditing.

About This repository provides data and code for the paper: Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development (subm

Appen Repos 86 Dec 07, 2022
Convolutional Neural Network for 3D meshes in PyTorch

MeshCNN in PyTorch SIGGRAPH 2019 [Paper] [Project Page] MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used f

Rana Hanocka 1.4k Jan 04, 2023
UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation

UNION Automatic Evaluation Metric described in the paper UNION: An UNreferenced MetrIc for Evaluating Open-eNded Story Generation (EMNLP 2020). Please

50 Dec 30, 2022
A FAIR dataset of TCV experimental results for validating edge/divertor turbulence models.

TCV-X21 validation for divertor turbulence simulations Quick links Intro Welcome to TCV-X21. We're glad you've found us! This repository is designed t

0 Dec 18, 2021
YOLOV4运行在嵌入式设备上

在嵌入式设备上实现YOLO V4 tiny 在嵌入式设备上实现YOLO V4 tiny 目录结构 目录结构 |-- YOLO V4 tiny |-- .gitignore |-- LICENSE |-- README.md |-- test.txt |-- t

Liu-Wei 6 Sep 09, 2021
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation

Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation [Arxiv] [Video] Evaluation code for Unrestricted Facial Geometry Reconstr

Matan Sela 242 Dec 30, 2022
A simple library that implements CLIP guided loss in PyTorch.

pytorch_clip_guided_loss: Pytorch implementation of the CLIP guided loss for Text-To-Image, Image-To-Image, or Image-To-Text generation. A simple libr

Sergei Belousov 74 Dec 26, 2022
An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and Machine Learning.

ALgorithmic_Trading_with_ML An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and

1 Mar 14, 2022
Improving 3D Object Detection with Channel-wise Transformer

"Improving 3D Object Detection with Channel-wise Transformer" Thanks for the OpenPCDet, this implementation of the CT3D is mainly based on the pcdet v

Hualian Sheng 107 Dec 20, 2022
Implementing DropPath/StochasticDepth in PyTorch

%load_ext memory_profiler Implementing Stochastic Depth/Drop Path In PyTorch DropPath is available on glasses my computer vision library! Introduction

Francesco Saverio Zuppichini 13 Jan 05, 2023
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations

Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations This repo contains official code for the NeurIPS 2021 paper Imi

Jiayao Zhang 2 Oct 18, 2021
An open-source, low-cost, image-based weed detection device for fallow scenarios.

Welcome to the OpenWeedLocator (OWL) project, an opensource hardware and software green-on-brown weed detector that uses entirely off-the-shelf compon

Guy Coleman 145 Jan 05, 2023
Pytorch implementation for the paper: Contrastive Learning for Cold-start Recommendation

Contrastive Learning for Cold-start Recommendation This is our Pytorch implementation for the paper: Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan L

45 Dec 13, 2022
Official Codes for Graph Modularity:Towards Understanding the Cross-Layer Transition of Feature Representations in Deep Neural Networks.

Dynamic-Graphs-Construction Official Codes for Graph Modularity:Towards Understanding the Cross-Layer Transition of Feature Representations in Deep Ne

11 Dec 14, 2022
The authors' official PyTorch SigWGAN implementation

The authors' official PyTorch SigWGAN implementation This repository is the official implementation of [Sig-Wasserstein GANs for Time Series Generatio

9 Jun 16, 2022
my graduation project is about live human face augmentation by projection mapping by using CNN

Live-human-face-expression-augmentation-by-projection my graduation project is about live human face augmentation by projection mapping by using CNN o

1 Mar 08, 2022
MultiTaskLearning - Multi Task Learning for 3D segmentation

Multi Task Learning for 3D segmentation Perception stack of an Autonomous Drivin

2 Sep 22, 2022
Official implementation for ICDAR 2021 paper "Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer"

Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer Description Convert offline handwritten mathematical expressi

Wenqi Zhao 87 Dec 27, 2022