Easily benchmark PyTorch model FLOPs, latency, throughput, max allocated memory and energy consumption

Overview

pytorch-benchmark

Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption

Install

pip install pytorch-benchmark

Usage

import torch
from torchvision.models import efficientnet_b0
from pytorch_benchmark import benchmark


model = efficientnet_b0()
sample = torch.randn(8, 3, 224, 224)  # (B, C, H, W)
results = benchmark(model, sample, num_runs=100)

Sample results 💻

Macbook Pro (16-inch, 2019), 2.6 GHz 6-Core Intel Core i7
device: cpu
flops: 401669732
machine_info:
  cpu:
    architecture: x86_64
    cores:
      physical: 6
      total: 12
    frequency: 2.60 GHz
    model: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
  gpus: null
  memory:
    available: 5.86 GB
    total: 16.00 GB
    used: 7.29 GB
  system:
    node: d40049
    release: 21.2.0
    system: Darwin
params: 5288548
timing:
  batch_size_1:
    on_device_inference:
      human_readable:
        batch_latency: 74.439 ms +/- 6.459 ms [64.604 ms, 96.681 ms]
        batches_per_second: 13.53 +/- 1.09 [10.34, 15.48]
      metrics:
        batches_per_second_max: 15.478907181264278
        batches_per_second_mean: 13.528026359855625
        batches_per_second_min: 10.343281300091244
        batches_per_second_std: 1.0922382209314958
        seconds_per_batch_max: 0.09668111801147461
        seconds_per_batch_mean: 0.07443853378295899
        seconds_per_batch_min: 0.06460404396057129
        seconds_per_batch_std: 0.006458734193132054
  batch_size_8:
    on_device_inference:
      human_readable:
        batch_latency: 509.410 ms +/- 30.031 ms [405.296 ms, 621.773 ms]
        batches_per_second: 1.97 +/- 0.11 [1.61, 2.47]
      metrics:
        batches_per_second_max: 2.4673319862230025
        batches_per_second_mean: 1.9696935126370148
        batches_per_second_min: 1.6083039834656554
        batches_per_second_std: 0.11341204895590185
        seconds_per_batch_max: 0.6217730045318604
        seconds_per_batch_mean: 0.509410228729248
        seconds_per_batch_min: 0.40529608726501465
        seconds_per_batch_std: 0.030031445467788704
Server with NVIDIA GeForce RTX 2080 and Intel Xeon 2.10GHz CPU
device: cuda
flops: 401669732
machine_info:
  cpu:
    architecture: x86_64
    cores:
      physical: 16
      total: 32
    frequency: 3.00 GHz
    model: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
  gpus:
  - memory: 8192.0 MB
    name: NVIDIA GeForce RTX 2080
  - memory: 8192.0 MB
    name: NVIDIA GeForce RTX 2080
  - memory: 8192.0 MB
    name: NVIDIA GeForce RTX 2080
  - memory: 8192.0 MB
    name: NVIDIA GeForce RTX 2080
  memory:
    available: 119.98 GB
    total: 125.78 GB
    used: 4.78 GB
  system:
    node: monster
    release: 4.15.0-167-generic
    system: Linux
max_inference_memory: 736250368
params: 5288548
post_inference_memory: 21402112
pre_inference_memory: 21402112
timing:
  batch_size_1:
    cpu_to_gpu:
      human_readable:
        batch_latency: "144.815 \xB5s +/- 16.103 \xB5s [136.614 \xB5s, 272.751 \xB5\
          s]"
        batches_per_second: 6.96 K +/- 535.06 [3.67 K, 7.32 K]
      metrics:
        batches_per_second_max: 7319.902268760908
        batches_per_second_mean: 6962.865857677197
        batches_per_second_min: 3666.3496503496503
        batches_per_second_std: 535.0581873859935
        seconds_per_batch_max: 0.0002727508544921875
        seconds_per_batch_mean: 0.00014481544494628906
        seconds_per_batch_min: 0.0001366138458251953
        seconds_per_batch_std: 1.6102982159292097e-05
    gpu_to_cpu:
      human_readable:
        batch_latency: "106.168 \xB5s +/- 17.829 \xB5s [53.167 \xB5s, 248.909 \xB5\
          s]"
        batches_per_second: 9.64 K +/- 1.60 K [4.02 K, 18.81 K]
      metrics:
        batches_per_second_max: 18808.538116591928
        batches_per_second_mean: 9639.942102368092
        batches_per_second_min: 4017.532567049808
        batches_per_second_std: 1595.7983033708472
        seconds_per_batch_max: 0.00024890899658203125
        seconds_per_batch_mean: 0.00010616779327392578
        seconds_per_batch_min: 5.316734313964844e-05
        seconds_per_batch_std: 1.7829135190772566e-05
    on_device_inference:
      human_readable:
        batch_latency: "15.567 ms +/- 546.154 \xB5s [15.311 ms, 19.261 ms]"
        batches_per_second: 64.31 +/- 1.96 [51.92, 65.31]
      metrics:
        batches_per_second_max: 65.31149174711928
        batches_per_second_mean: 64.30692850265713
        batches_per_second_min: 51.918698784442846
        batches_per_second_std: 1.9599322351815833
        seconds_per_batch_max: 0.019260883331298828
        seconds_per_batch_mean: 0.015567030906677246
        seconds_per_batch_min: 0.015311241149902344
        seconds_per_batch_std: 0.0005461537255227954
    total:
      human_readable:
        batch_latency: "15.818 ms +/- 549.873 \xB5s [15.561 ms, 19.461 ms]"
        batches_per_second: 63.29 +/- 1.92 [51.38, 64.26]
      metrics:
        batches_per_second_max: 64.26476266356143
        batches_per_second_mean: 63.28565696640637
        batches_per_second_min: 51.38378232692614
        batches_per_second_std: 1.9198343850767468
        seconds_per_batch_max: 0.019461393356323242
        seconds_per_batch_mean: 0.01581801414489746
        seconds_per_batch_min: 0.015560626983642578
        seconds_per_batch_std: 0.0005498731526138171
  batch_size_8:
    cpu_to_gpu:
      human_readable:
        batch_latency: "805.674 \xB5s +/- 157.254 \xB5s [773.191 \xB5s, 2.303 ms]"
        batches_per_second: 1.26 K +/- 97.51 [434.24, 1.29 K]
      metrics:
        batches_per_second_max: 1293.3407338883749
        batches_per_second_mean: 1259.5653105357776
        batches_per_second_min: 434.23791282741485
        batches_per_second_std: 97.51424036939879
        seconds_per_batch_max: 0.002302885055541992
        seconds_per_batch_mean: 0.000805673599243164
        seconds_per_batch_min: 0.0007731914520263672
        seconds_per_batch_std: 0.0001572538140613121
    gpu_to_cpu:
      human_readable:
        batch_latency: "104.215 \xB5s +/- 12.658 \xB5s [59.605 \xB5s, 128.031 \xB5\
          s]"
        batches_per_second: 9.81 K +/- 1.76 K [7.81 K, 16.78 K]
      metrics:
        batches_per_second_max: 16777.216
        batches_per_second_mean: 9806.840626578907
        batches_per_second_min: 7810.621973929236
        batches_per_second_std: 1761.6008872740726
        seconds_per_batch_max: 0.00012803077697753906
        seconds_per_batch_mean: 0.00010421514511108399
        seconds_per_batch_min: 5.9604644775390625e-05
        seconds_per_batch_std: 1.2658293070174213e-05
    on_device_inference:
      human_readable:
        batch_latency: "16.623 ms +/- 759.017 \xB5s [16.301 ms, 22.584 ms]"
        batches_per_second: 60.26 +/- 2.22 [44.28, 61.35]
      metrics:
        batches_per_second_max: 61.346243290283894
        batches_per_second_mean: 60.25881046175457
        batches_per_second_min: 44.27827629162004
        batches_per_second_std: 2.2193085956672296
        seconds_per_batch_max: 0.02258443832397461
        seconds_per_batch_mean: 0.01662288188934326
        seconds_per_batch_min: 0.01630091667175293
        seconds_per_batch_std: 0.0007590167680596548
    total:
      human_readable:
        batch_latency: "17.533 ms +/- 836.015 \xB5s [17.193 ms, 23.896 ms]"
        batches_per_second: 57.14 +/- 2.20 [41.85, 58.16]
      metrics:
        batches_per_second_max: 58.16374528511205
        batches_per_second_mean: 57.140338855126565
        batches_per_second_min: 41.84762740950632
        batches_per_second_std: 2.1985066663972677
        seconds_per_batch_max: 0.023896217346191406
        seconds_per_batch_mean: 0.01753277063369751
        seconds_per_batch_min: 0.017192840576171875
        seconds_per_batch_std: 0.0008360147274630088

Limitations

Usage assumptions:

  • The model has as a __call__ method that takes the sample, i.e. model(sample).
  • The Model also works if the sample had a batch size of 1 (first dimension).

Feature limitations:

  • Allocated memory uses torch.cuda.max_memory_allocated, which is only available if the model resides on a CUDA device.
  • Energy consumption can only be measured on NVIDIA Jetson platforms at the moment.

Citation

If you like the tool and use it in you research, please consider citing it:

@article{hedegaard2022torchbenchmark,
  title={PyTorch Benchmark},
  author={Lukas Hedegaard},
  journal={GitHub. Note: https://github.com/LukasHedegaard/pytorch-benchmark},
  year={2022}
}
You might also like...
SpeechNAS Better Trade off between Latency and Accuracy for Large Scale Speaker Verification
SpeechNAS Better Trade off between Latency and Accuracy for Large Scale Speaker Verification

SpeechNAS Better Trade off between Latency and Accuracy for Large Scale Speaker Verification

Segcache: a memory-efficient and scalable in-memory key-value cache for small objects

Segcache: a memory-efficient and scalable in-memory key-value cache for small objects This repo contains the code of Segcache described in the followi

Demo for the paper
Demo for the paper "Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation"

Streaming speaker diarization Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation by Juan Manuel Coria, Hervé

Predict the latency time of the deep learning models

Deep Neural Network Prediction Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num

Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(

This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.
PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"

Code for On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models This repository will reproduce the main results from our pape

PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models

Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.
In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Contrastive Learning of Object Representations Supervisor: Prof. Dr. Gemma Roig Institutions: Goethe University CVAI - Computational Vision & Artifici

Comments
  • torch cuda synchronize on GPUs?

    torch cuda synchronize on GPUs?

    Hello,

    Very happy to see your repo.

    I have tested the code and found that for the GPU tests, there may lack of torch synchronize when computing the device time. I am not sure how this may impact the results but I think it would make difference.

    What do you think?

    Best,

    opened by jizongFox 1
Releases(0.3.5)
Owner
Lukas Hedegaard
PhD Student | AI Researcher | Open Source Contributor
Lukas Hedegaard
🏃‍♀️ A curated list about human motion capture, analysis and synthesis.

Awesome Human Motion 🏃‍♀️ A curated list about human motion capture, analysis and synthesis. Contents Introduction Human Models Datasets Data Process

Dennis Wittchen 274 Dec 14, 2022
Tensorflow/Keras Plug-N-Play Deep Learning Models Compilation

DeepBay This project was created with the objective of compile Machine Learning Architectures created using Tensorflow or Keras. The architectures mus

Whitman Bohorquez 4 Sep 26, 2022
codes for "Scheduled Sampling Based on Decoding Steps for Neural Machine Translation" (long paper of EMNLP-2022)

Scheduled Sampling Based on Decoding Steps for Neural Machine Translation (EMNLP-2021 main conference) Contents Overview Background Quick to Use Furth

Adaxry 13 Jul 25, 2022
Hypercomplex Neural Networks with PyTorch

HyperNets Hypercomplex Neural Networks with PyTorch: this repository would be a container for hypercomplex neural network modules to facilitate resear

Eleonora Grassucci 21 Dec 27, 2022
A JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short.

BraVe This is a JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short. The model provided in this package wa

DeepMind 44 Nov 20, 2022
(ICCV'21) Official PyTorch implementation of Relational Embedding for Few-Shot Classification

Relational Embedding for Few-Shot Classification (ICCV 2021) Dahyun Kang, Heeseung Kwon, Juhong Min, Minsu Cho [paper], [project hompage] We propose t

Dahyun Kang 82 Dec 24, 2022
YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4. YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitraril

Adam Van Etten 161 Jan 06, 2023
Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

Knowledge Informed Machine Learning using a Weibull-based Loss Function Exploring the concept of knowledge-informed machine learning with the use of a

Tim 43 Dec 14, 2022
Code and data form the paper BERT Got a Date: Introducing Transformers to Temporal Tagging

BERT Got a Date: Introducing Transformers to Temporal Tagging Satya Almasian*, Dennis Aumiller*, and Michael Gertz Heidelberg University Contact us vi

54 Dec 04, 2022
Bravia core script for python

Bravia-Core-Script You need to have a mandatory account If this L3 does not work, try another L3. enjoy

5 Dec 26, 2021
Official implementation of the Neurips 2021 paper Searching Parameterized AP Loss for Object Detection.

Parameterized AP Loss By Chenxin Tao, Zizhang Li, Xizhou Zhu, Gao Huang, Yong Liu, Jifeng Dai This is the official implementation of the Neurips 2021

46 Jul 06, 2022
Out of Distribution Detection on Natural Adversarial Examples

OOD-on-NAE Research project on out of distribution detection for the Computer Vision course by Prof. Rob Fergus (CSCI-GA 2271) Paper out on arXiv - ht

Anugya 1 Jun 08, 2022
Exporter for Storage Area Network (SAN)

SAN Exporter Prometheus exporter for Storage Area Network (SAN). We all know that each SAN Storage vendor has their own glossary of terms, health/perf

vCloud 32 Dec 16, 2022
Main Results on ImageNet with Pretrained Models

This repository contains Pytorch evaluation code, training code and pretrained models for the following projects: SPACH (A Battle of Network Structure

Microsoft 151 Dec 14, 2022
Convert ONNX model graph to Keras model format.

Convert ONNX model graph to Keras model format.

Grigory Malivenko 175 Dec 28, 2022
Enabling dynamic analysis of Legacy Embedded Systems in full emulated environment

PENecro This project is based on "Enabling dynamic analysis of Legacy Embedded Systems in full emulated environment", published on hardwear.io USA 202

Ta-Lun Yen 10 May 17, 2022
A Protein-RNA Interface Predictor Based on Semantics of Sequences

PRIP PRIP:A Protein-RNA Interface Predictor Based on Semantics of Sequences installation gensim==3.8.3 matplotlib==3.1.3 xgboost==1.3.3 prettytable==2

李优 0 Mar 25, 2022
XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Microsoft 125 Jan 04, 2023
TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers.

TransMVSNet This repository contains the official implementation of the paper: "TransMVSNet: Global Context-aware Multi-view Stereo Network with Trans

旷视研究院 3D 组 155 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