Finite-temperature variational Monte Carlo calculation of uniform electron gas using neural canonical transformation.

Overview

CoulombGas

Build Status

This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX, it utilizes (both forward- and backwark-mode) automatic differentiation and the pmap mechanism to achieve a large-scale single-program multiple-data (SPMD) training on multiple GPUs.

Requirements

  • JAX with Nvidia GPU support
  • A handful of GPUs. The more the better :P
  • haiku
  • optax
  • To analytically computing the thermal entropy of a non-interacting Fermi gas in the canonical ensemble based on arbitrary-precision arithmetic, we have used the python library mpmath.

Demo run

To start, try running the following commands to launch a training of 13 spin-polarized electrons in 2D with the dimensionless density parameter 10.0 and (reduced) temperature 0.15 on 8 GPUs:

export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python main.py --n 13 --dim 2 --rs 10.0 --Theta 0.15 --Emax 25 --sr --batch 4096 --num_devices 8 --acc_steps 2

Note that we effectively sample a batch of totally 8192 samples in each training step. However, such a batch size will result in too large a memory consumption to be accommodated by 8 GPUs. To overcome this problem, we choose to split the batch into two equal pieces, and accumulate the gradient and various observables for each piece in two sequential substeps. In other words, the argument batch in the command above actually stands for the batch per accumulation step.

If you have only, say, 4 GPUs, you can set batch, num_devices, acc_steps to be 2048, 4 and 4 respectively to launch the same training process, at the expense of doubling the running time. The GPU hours are nevertheless the same.

For the detail meaning of other command line arguments, run

python main.py --help

or directly refer to the source code.

Trained model and data

A training process from complete scratch actually contains two stages. In the first stage, a variational autoregressive network is pretrained to approximate the Boltzmann distribution of the corresponding non-interacting electron gas. The resulting model can be saved and then loaded later. In fact, we have provided such a model file for the parameter settings of the last section for your convenience, so you can quickly get a feeling of the second stage of training the truly interacting system of our interest. We encourage you to remove the file to pretrain the model by yourself; it is actually much faster than the training in the second stage.

To facilitate further developments, we also provide the training models and logged data for various calculations in the paper, which are located in the data directory.

To cite

arxiv

Owner
FermiFlow
ab-initio study of fermions at finite temperature
FermiFlow
Implementation of the paper "Fine-Tuning Transformers: Vocabulary Transfer"

Transformer-vocabulary-transfer Implementation of the paper "Fine-Tuning Transfo

LEYA 13 Nov 30, 2022
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

PyStan NOTE: This documentation describes a BETA release of PyStan 3. PyStan is a Python interface to Stan, a package for Bayesian inference. Stan® is

Stan 229 Dec 29, 2022
Code for Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

EMS-COLS-recourse Initial Code for Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions Folder structure: data folder contains raw an

Prateek Yadav 1 Nov 25, 2022
Speech Enhancement Generative Adversarial Network Based on Asymmetric AutoEncoder

ASEGAN: Speech Enhancement Generative Adversarial Network Based on Asymmetric AutoEncoder 中文版简介 Readme with English Version 介绍 基于SEGAN模型的改进版本,使用自主设计的非

Nitin 53 Nov 17, 2022
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging This repository contains an implementation

Computational Photography Lab @ SFU 1.1k Jan 02, 2023
This project deals with the detection of skin lesions within the ISICs dataset using YOLOv3 Object Detection with Darknet.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Skin Lesion detection using YOLO This project deal

Lalith Veerabhadrappa Badiger 1 Nov 22, 2021
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, an

VITA 112 Nov 07, 2022
harmonic-percussive-residual separation algorithm wrapped as a VST3 plugin (iPlug2)

Harmonic-percussive-residual separation plug-in This work is a study on the plausibility of a sines-transients-noise decomposition inspired algorithm

Derp Learning 9 Sep 01, 2022
Knowledge Distillation Toolbox for Semantic Segmentation

SegDistill: Toolbox for Knowledge Distillation on Semantic Segmentation Networks This repo contains the supported code and configuration files for Seg

9 Dec 12, 2022
PyTorch implementation of our ICCV paper DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection.

Introduction This repo contains the official PyTorch implementation of our ICCV paper DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection. Up

133 Dec 29, 2022
Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

S2VC Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In thi

81 Dec 15, 2022
This is a TensorFlow implementation for C2-Rec

This is a TensorFlow implementation for C2-Rec We refer to the repo SASRec. Requirements requirement.txt Datasets This repo includes Amazon Beauty dat

7 Nov 14, 2022
Database Reasoning Over Text project for ACL paper

Database Reasoning over Text This repository contains the code for the Database Reasoning Over Text paper, to appear at ACL2021. Work is performed in

Facebook Research 320 Dec 12, 2022
InsTrim: Lightweight Instrumentation for Coverage-guided Fuzzing

InsTrim The paper: InsTrim: Lightweight Instrumentation for Coverage-guided Fuzzing Build Prerequisite llvm-8.0-dev clang-8.0 cmake = 3.2 Make git cl

75 Dec 23, 2022
Automatic caption evaluation metric based on typicality analysis.

SeMantic and linguistic UndeRstanding Fusion (SMURF) Automatic caption evaluation metric described in the paper "SMURF: SeMantic and linguistic UndeRs

Joshua Feinglass 6 Jan 09, 2022
ICNet and PSPNet-50 in Tensorflow for real-time semantic segmentation

Real-Time Semantic Segmentation in TensorFlow Perform pixel-wise semantic segmentation on high-resolution images in real-time with Image Cascade Netwo

Oles Andrienko 219 Nov 21, 2022
Highway networks implemented in PyTorch.

PyTorch Highway Networks Highway networks implemented in PyTorch. Just the MNIST example from PyTorch hacked to work with Highway layers. Todo Make th

Conner Vercellino 56 Dec 14, 2022
A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.

collie Collie is a library for preparing, training, and evaluating implicit deep learning hybrid recommender systems, named after the Border Collie do

ShopRunner 96 Dec 29, 2022
Fuse radar and camera for detection

SAF-FCOS: Spatial Attention Fusion for Obstacle Detection using MmWave Radar and Vision Sensor This project hosts the code for implementing the SAF-FC

ChangShuo 18 Jan 01, 2023
A new video text spotting framework with Transformer

TransVTSpotter: End-to-end Video Text Spotter with Transformer Introduction A Multilingual, Open World Video Text Dataset and End-to-end Video Text Sp

weijiawu 67 Jan 03, 2023