Energy consumption estimation utilities for Jetson-based platforms

Overview

jetson_power

Energy consumption estimation utilities for Jetson-based platforms

This repository contains a utility for measuring energy consumption when running various programs in NVIDIA Jetson-based platforms. Currently TX-2, NX, and AGX are supported.

Usage

If you want to measure the energy consumption of a program, you can directly run the utility providing the command that you want to measure:

./p_est program_to_run

You can test the utility using a stress test (make sure you have installed stress - apt install stress), e.g.,

./p_est stress --cpu 6 -t 5

You can also run a GPU-based test using CUDA examples:

sudo make  /usr/local/cuda-10.2/samples/0_Simple/matrixMul/matrixMul
./p_est.py /usr/local/cuda-10.2/samples/0_Simple/matrixMul/matrixMul -wA=9200 -hA=320 -wB=640 -hB=9200

You can play around jetson_clocks.sh and see the consumption indeed increasing.

Interfacing with Python for more precise measurements

Using the utility from the command line can include initialization cost in the power consumption. Even though this can be estimated and then subtracted, we also provide a simple Python API:

from p_est import PowerEstimator
from time import sleep

def my_fun():
    for i in range(5):
        sleep(1)
        print('sleeping')


p_est = PowerEstimator()
total_energy, total_energy_over_idle, total_time = p_est.estimate_fn_power(my_fun)

You can use the PowerEstimator class to directly measure the energy consumption of any function.

Things to consider

  • Currently, the tool has been tested only on AGX. Testing is pending on NX and TX2.
  • If sensors report overlapping power measurements, then the tools might overestimate power usage.
  • Power usage is estimated solely using the sensors provided by Jetsons. This usually underestimates the total power.
  • You can consider increasing sampling rate in order to have more precise measurements.
Owner
OpenDR
OpenDR H2020 Research Project
OpenDR
Fully Connected DenseNet for Image Segmentation

Fully Connected DenseNets for Semantic Segmentation Fully Connected DenseNet for Image Segmentation implementation of the paper The One Hundred Layers

Somshubra Majumdar 84 Oct 31, 2022
Constrained Logistic Regression - How to apply specific constraints to logistic regression's coefficients

Constrained Logistic Regression Sample implementation of constructing a logistic regression with given ranges on each of the feature's coefficients (v

1 Dec 29, 2021
PyTorch implementation of Spiking Neural Networks trained on surrogate gradient & BPTT using snntorch.

snn-localization repo PyTorch implementation of Spiking Neural Networks trained on surrogate gradient & BPTT using snntorch. Install Dependencies Orig

Sami BARCHID 1 Jan 06, 2022
PyTorch implementation for the paper Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime

Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime Created by Prarthana Bhattacharyya. Disclaimer: This is n

Prarthana Bhattacharyya 5 Nov 08, 2022
Spatial Transformer Nets in TensorFlow/ TensorLayer

MOVED TO HERE Spatial Transformer Networks Spatial Transformer Networks (STN) is a dynamic mechanism that produces transformations of input images (or

Hao 36 Nov 23, 2022
API for RL algorithm design & testing of BCA (Building Control Agent) HVAC on EnergyPlus building energy simulator by wrapping their EMS Python API

RL - EmsPy (work In Progress...) The EmsPy Python package was made to facilitate Reinforcement Learning (RL) algorithm research for developing and tes

20 Jan 05, 2023
A series of Jupyter notebooks with Chinese comment that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

Hands-on-Machine-Learning 目的 这份笔记旨在帮助中文学习者以一种较快较系统的方式入门机器学习, 是在学习Hands-on Machine Learning with Scikit-Learn and TensorFlow这本书的 时候做的个人笔记: 此项目的可取之处 原书的

Baymax 1.5k Dec 21, 2022
Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'

Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation (NeurIPS 2021) Code for our NeurIPS 2021 paper 'Exploiting the Intri

Shiqi Yang 53 Dec 25, 2022
SAAVN - Sound Adversarial Audio-Visual Navigation,ICLR2022 (In PyTorch)

SAAVN SAAVN Code release for paper "Sound Adversarial Audio-Visual Navigation,IC

YinfengYu 10 Aug 30, 2022
PyTorch implementation of our CVPR2021 (oral) paper "Prototype Augmentation and Self-Supervision for Incremental Learning"

PASS - Official PyTorch Implementation [CVPR2021 Oral] Prototype Augmentation and Self-Supervision for Incremental Learning Fei Zhu, Xu-Yao Zhang, Chu

67 Dec 27, 2022
Inkscape extensions for figure resizing and editing

Academic-Inkscape: Extensions for figure resizing and editing This repository contains several Inkscape extensions designed for editing plots. Scale P

192 Dec 26, 2022
The `rtdl` library + The official implementation of the paper

The `rtdl` library + The official implementation of the paper "Revisiting Deep Learning Models for Tabular Data"

Yandex Research 510 Dec 30, 2022
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral) This is the official implementat

Yifan Zhang 259 Dec 25, 2022
A Temporal Extension Library for PyTorch Geometric

Documentation | External Resources | Datasets PyTorch Geometric Temporal is a temporal (dynamic) extension library for PyTorch Geometric. The library

Benedek Rozemberczki 1.9k Jan 07, 2023
GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

GalaXC GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification @InProceedings{Saini21, author = {Saini, D. and Jain,

Extreme Classification 28 Dec 05, 2022
Keeper for Ricochet Protocol, implemented with Apache Airflow

Ricochet Keeper This repository contains Apache Airflow DAGs for executing keeper operations for Ricochet Exchange. Usage You will need to run this us

Ricochet Exchange 5 May 24, 2022
(AAAI 2021) Progressive One-shot Human Parsing

End-to-end One-shot Human Parsing This is the official repository for our two papers: Progressive One-shot Human Parsing (AAAI 2021) End-to-end One-sh

54 Dec 30, 2022
General neural ODE and DAE modules for power system dynamic modeling.

Py_PSNODE General neural ODE and DAE modules for power system dynamic modeling. The PyTorch-based ODE solver is developed based on torchdiffeq. Sample

14 Dec 31, 2022
Lab course materials for IEMBA 8/9 course "Coding and Artificial Intelligence"

IEMBA 8/9 - Coding and Artificial Intelligence Dear IEMBA 8/9 students, welcome to our IEMBA 8/9 elective course Coding and Artificial Intelligence, t

Artificial Intelligence & Machine Learning (AI:ML Lab) @ HSG 1 Jan 11, 2022
Data and code for the paper "Importance of Kernel Bandwidth in Quantum Machine Learning"

Reproducibility materials for "Importance of Kernel Bandwidth in Quantum Machine Learning" Repo structure: code contains Python scripts used to genera

Ruslan Shaydulin 3 Oct 23, 2022