Tutorial

How to use fasterbench

from fasterbench.benchmark import benchmark
from fasterbench.plot import *
from torchvision.models import resnet18, mobilenet_v3_large
import torch

dummy = torch.randn(8,3,224,224)

resnet   = benchmark(resnet18(),            dummy,
                     metrics=("size","speed","compute","energy"))
mobilenet= benchmark(mobilenet_v3_large(),  dummy,
                     metrics=("size","speed","compute","energy"))

/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/torch/cuda/__init__.py:129: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 11040). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
  return torch._C._cuda_getDeviceCount() > 0
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'

[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
[INFO] Register count_adap_avgpool() for <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>.
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.

[codecarbon INFO @ 13:54:03] offline tracker init
[codecarbon WARNING @ 13:54:03] Multiple instances of codecarbon are allowed to run at the same time.
[W430 13:54:29.057534160 NNPACK.cpp:61] Could not initialize NNPACK! Reason: Unsupported hardware.

[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
[INFO] Register count_adap_avgpool() for <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>.
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.

fig = create_radar_plot([resnet, mobilenet],
                        model_names=["ResNet-18", "MobileNet-V3"])
fig.show()