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

Related tags

Deep Learningpytorch-benchmark
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 đź’»

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

Wentao Zhu 24 May 20, 2022
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

TheSys Group @ CMU CS 78 Jan 7, 2023
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

Juanma Coria 187 Jan 6, 2023
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

QAQ 1 Nov 12, 2021
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(

Phil Wang 180 Jan 5, 2023
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.
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.

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

Soubhik Sanyal 71 Dec 30, 2022
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

Mitch Hill 32 Nov 25, 2022
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

Rithesh Kumar 135 Oct 27, 2022
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

Dirk Neuhäuser 6 Dec 8, 2022
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
GitHub Repository
Code and Resources for the Transformer Encoder Reasoning Network (TERN)

Transformer Encoder Reasoning Network Code for the cross-modal visual-linguistic retrieval method from "Transformer Reasoning Network for Image-Text M

Nicola Messina 53 Dec 30, 2022
NLP made easy

GluonNLP: Your Choice of Deep Learning for NLP GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you l

Distributed (Deep) Machine Learning Community 2.5k Jan 04, 2023
《Towards High Fidelity Face Relighting with Realistic Shadows》(CVPR 2021)

Towards High Fidelity Face-Relighting with Realistic Shadows Andrew Hou, Ze Zhang, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu. In CVPR, 2021. T

114 Dec 10, 2022
code for "Feature Importance-aware Transferable Adversarial Attacks"

Feature Importance-aware Attack(FIA) This repository contains the code for the paper: Feature Importance-aware Transferable Adversarial Attacks (ICCV

Hengchang Guo 44 Nov 24, 2022
Keras implementation of Deeplab v3+ with pretrained weights

Keras implementation of Deeplabv3+ This repo is not longer maintained. I won't respond to issues but will merge PR DeepLab is a state-of-art deep lear

1.3k Dec 07, 2022
Predicting 10 different clothing types using Xception pre-trained model.

Predicting-Clothing-Types Predicting 10 different clothing types using Xception pre-trained model from Keras library. It is reimplemented version from

AbdAssalam Ahmad 3 Dec 29, 2021
Tensorflow implementation of MIRNet for Low-light image enhancement

MIRNet Tensorflow implementation of the MIRNet architecture as proposed by Learning Enriched Features for Real Image Restoration and Enhancement. Lanu

Soumik Rakshit 91 Jan 06, 2023
[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias

Counterfactual VQA (CF-VQA) This repository is the Pytorch implementation of our paper "Counterfactual VQA: A Cause-Effect Look at Language Bias" in C

Yulei Niu 94 Dec 03, 2022
[CVPR 2022] "The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy" by Tianlong Chen, Zhenyu Zhang, Yu Cheng, Ahmed Awadallah, Zhangyang Wang

The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy Codes for this paper: [CVPR 2022] The Pr

VITA 16 Nov 26, 2022
Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

youceF 1 Nov 12, 2021
A best practice for tensorflow project template architecture.

A best practice for tensorflow project template architecture.

Mahmoud Gamal Salem 3.6k Dec 22, 2022
Jittor implementation of PCT:Point Cloud Transformer

PCT: Point Cloud Transformer This is a Jittor implementation of PCT: Point Cloud Transformer.

MenghaoGuo 547 Jan 03, 2023
DeLighT: Very Deep and Light-Weight Transformers

DeLighT: Very Deep and Light-weight Transformers This repository contains the source code of our work on building efficient sequence models: DeFINE (I

Sachin Mehta 440 Dec 18, 2022
Colar: Effective and Efficient Online Action Detection by Consulting Exemplars, CVPR 2022.

Colar: Effective and Efficient Online Action Detection by Consulting Exemplars This repository is the official implementation of Colar. In this work,

LeYang 246 Dec 13, 2022
Official Pytorch implementation of Meta Internal Learning

Official Pytorch implementation of Meta Internal Learning

10 Aug 24, 2022
TransferNet: Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

TransferNet: Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network Created by Seunghoon Hong, Junhyuk Oh,

42 Jun 29, 2022
LAMDA: Label Matching Deep Domain Adaptation

LAMDA: Label Matching Deep Domain Adaptation This is the implementation of the paper LAMDA: Label Matching Deep Domain Adaptation which has been accep

Tuan Nguyen 9 Sep 06, 2022
buildseg is a building extraction plugin of QGIS based on PaddlePaddle.

buildseg buildseg is a Building Extraction plugin for QGIS based on PaddlePaddle. How to use Download and install QGIS and clone the repo : git clone

39 Dec 09, 2022
Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

PackNet: https://arxiv.org/abs/1711.05769 Pretrained models are available here: https://uofi.box.com/s/zap2p03tnst9dfisad4u0sfupc0y1fxt Datasets in Py

Arun Mallya 216 Jan 05, 2023
Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting This repository is the official implementation of Spectral Temporal Gr

Microsoft 306 Dec 29, 2022