# ⏱ pytorch-benchmark
__Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption__
<div align="left">
<a href="https://pypi.org/project/pytorch-benchmark/">
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</a>
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</a>
<a href="https://pepy.tech/project/pytorch-benchmark">
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<a href="https://codecov.io/gh/LukasHedegaard/pytorch-benchmark">
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</a>
<sup>*</sup>
</div>
###### \*Actual coverage is higher as GPU-related code is skipped by Codecov
## Install
```bash
pip install pytorch-benchmark
```
## Usage
```python
import torch
from torchvision.models import efficientnet_b0
from pytorch_benchmark import benchmark
model = efficientnet_b0().to("cpu") # Model device sets benchmarking device
sample = torch.randn(8, 3, 224, 224) # (B, C, H, W)
results = benchmark(model, sample, num_runs=100)
```
### Sample results 💻
<details>
<summary>Macbook Pro (16-inch, 2019), 2.6 GHz 6-Core Intel Core i7</summary>
```
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
```
</details>
<details>
<summary>Server with NVIDIA GeForce RTX 2080 and Intel Xeon 2.10GHz CPU</summary>
```
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
```
</details>
... Your turn
## How we benchmark
The overall flow can be summarized with the diagram shown below (best viewed on GitHub):
```mermaid
flowchart TB;
A([Start]) --> B
B(prepare_samples)
B --> C[get_machine_info]
C --> D[measure_params]
D --> E[warm_up, batch_size=1]
E --> F[measure_flops]
subgraph SG[Repeat for batch_size 1 and x]
direction TB
G[measure_allocated_memory]
G --> H[warm_up, given batch_size]
H --> I[measure_detailed_inference_timing]
I --> J[measure_repeated_inference_timing]
J --> K[measure_energy]
end
F --> SG
SG --> END([End])
```
Usually, the sample and model don't reside on the same device initially (e.g., a GPU holds the model while the sample is on CPU after being loaded from disk or collected as live data). Accordingly, we measure timing in three parts: `cpu_to_gpu`, `on_device_inference`, and `gpu_to_cpu`, as well as a sum of the three, `total`. Note that the `model.device()` determines the execution device. The inference flow is shown below:
```mermaid
flowchart LR;
A([sample])
A --> B[cpu -> gpu]
B --> C[model __call__]
C --> D[gpu -> cpu]
D --> E([result])
```
## Advanced use
Trying to benchmark a custom class, which is not a `torch.nn.Module`?
You can pass custom functions to `benchmark` as seen in [this example](tests/test_custom_class.py).
## Limitations
- Allocated memory measurements are only available on CUDA devices.
- Energy consumption can only be measured on NVIDIA Jetson platforms at the moment.
- FLOPs and parameter count is not support for custom classes.
## Acknowledgement
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR).
It was developed for benchmarking tools in [OpenDR](https://github.com/opendr-eu/opendr), a non-proprietary toolkit for deep learning based functionalities for robotics and vision.
## Citation
If you like the tool and use it in research, please consider citing it:
```bibtex
@software{hedegaard2022pytorchbenchmark,
author = {Hedegaard, Lukas},
doi = {10.5281/zenodo.7223585},
month = {10},
title = {{PyTorch-Benchmark}},
version = {0.3.5},
year = {2022}
}
```
Raw data
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"maintainer": "",
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"requires_python": "",
"maintainer_email": "",
"keywords": "deep learning,pytorch,AI,benchmark,speed,energy,memory",
"author": "Lukas Hedegaard",
"author_email": "lukasxhedegaard@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/91/9e/597879f4df381ae4e8cc2bb02c7ec6e0dc4a3f226fd967e07a6a4e29c667/pytorch-benchmark-0.3.6.tar.gz",
"platform": null,
"description": "# \u23f1 pytorch-benchmark\n__Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption__\n<div align=\"left\">\n <a href=\"https://pypi.org/project/pytorch-benchmark/\">\n <img src=\"https://img.shields.io/pypi/pyversions/pytorch-benchmark\" height=\"20\" >\n </a>\n <a href=\"https://badge.fury.io/py/pytorch-benchmark\">\n <img src=\"https://badge.fury.io/py/pytorch-benchmark.svg\" height=\"20\" >\n </a>\n <a href=\"https://pepy.tech/project/pytorch-benchmark\">\n <img src=\"https://pepy.tech/badge/pytorch-benchmark\" height=\"20\">\n </a>\n <a href=\"https://www.codefactor.io/repository/github/lukashedegaard/pytorch-benchmark/overview/main\">\n <img src=\"https://www.codefactor.io/repository/github/lukashedegaard/pytorch-benchmark/badge/main\" alt=\"CodeFactor\" />\n </a>\n <a href=\"https://opensource.org/licenses/Apache-2.0\">\n <img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" height=\"20\">\n </a>\n <a href=\"https://github.com/psf/black\">\n <img src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" height=\"20\">\n </a>\n <a href=\"https://codecov.io/gh/LukasHedegaard/pytorch-benchmark\">\n <img src=\"https://codecov.io/gh/LukasHedegaard/pytorch-benchmark/branch/main/graph/badge.svg?token=B91XGSKSFJ\"/>\n </a>\n <sup>*</sup>\n</div>\n\n###### \\*Actual coverage is higher as GPU-related code is skipped by Codecov\n\n## Install \n```bash\npip install pytorch-benchmark\n```\n\n## Usage \n```python\nimport torch\nfrom torchvision.models import efficientnet_b0\nfrom pytorch_benchmark import benchmark\n\n\nmodel = efficientnet_b0().to(\"cpu\") # Model device sets benchmarking device\nsample = torch.randn(8, 3, 224, 224) # (B, C, H, W)\nresults = benchmark(model, sample, num_runs=100)\n```\n\n### Sample results \ud83d\udcbb\n<details>\n <summary>Macbook Pro (16-inch, 2019), 2.6 GHz 6-Core Intel Core i7</summary>\n \n ```\n device: cpu\n flops: 401669732\n machine_info:\n cpu:\n architecture: x86_64\n cores:\n physical: 6\n total: 12\n frequency: 2.60 GHz\n model: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz\n gpus: null\n memory:\n available: 5.86 GB\n total: 16.00 GB\n used: 7.29 GB\n system:\n node: d40049\n release: 21.2.0\n system: Darwin\n params: 5288548\n timing:\n batch_size_1:\n on_device_inference:\n human_readable:\n batch_latency: 74.439 ms +/- 6.459 ms [64.604 ms, 96.681 ms]\n batches_per_second: 13.53 +/- 1.09 [10.34, 15.48]\n metrics:\n batches_per_second_max: 15.478907181264278\n batches_per_second_mean: 13.528026359855625\n batches_per_second_min: 10.343281300091244\n batches_per_second_std: 1.0922382209314958\n seconds_per_batch_max: 0.09668111801147461\n seconds_per_batch_mean: 0.07443853378295899\n seconds_per_batch_min: 0.06460404396057129\n seconds_per_batch_std: 0.006458734193132054\n batch_size_8:\n on_device_inference:\n human_readable:\n batch_latency: 509.410 ms +/- 30.031 ms [405.296 ms, 621.773 ms]\n batches_per_second: 1.97 +/- 0.11 [1.61, 2.47]\n metrics:\n batches_per_second_max: 2.4673319862230025\n batches_per_second_mean: 1.9696935126370148\n batches_per_second_min: 1.6083039834656554\n batches_per_second_std: 0.11341204895590185\n seconds_per_batch_max: 0.6217730045318604\n seconds_per_batch_mean: 0.509410228729248\n seconds_per_batch_min: 0.40529608726501465\n seconds_per_batch_std: 0.030031445467788704\n ```\n</details>\n\n<details>\n <summary>Server with NVIDIA GeForce RTX 2080 and Intel Xeon 2.10GHz CPU</summary>\n \n ```\n device: cuda\n flops: 401669732\n machine_info:\n cpu:\n architecture: x86_64\n cores:\n physical: 16\n total: 32\n frequency: 3.00 GHz\n model: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz\n gpus:\n - memory: 8192.0 MB\n name: NVIDIA GeForce RTX 2080\n - memory: 8192.0 MB\n name: NVIDIA GeForce RTX 2080\n - memory: 8192.0 MB\n name: NVIDIA GeForce RTX 2080\n - memory: 8192.0 MB\n name: NVIDIA GeForce RTX 2080\n memory:\n available: 119.98 GB\n total: 125.78 GB\n used: 4.78 GB\n system:\n node: monster\n release: 4.15.0-167-generic\n system: Linux\n max_inference_memory: 736250368\n params: 5288548\n post_inference_memory: 21402112\n pre_inference_memory: 21402112\n timing:\n batch_size_1:\n cpu_to_gpu:\n human_readable:\n batch_latency: \"144.815 \\xB5s +/- 16.103 \\xB5s [136.614 \\xB5s, 272.751 \\xB5\\\n s]\"\n batches_per_second: 6.96 K +/- 535.06 [3.67 K, 7.32 K]\n metrics:\n batches_per_second_max: 7319.902268760908\n batches_per_second_mean: 6962.865857677197\n batches_per_second_min: 3666.3496503496503\n batches_per_second_std: 535.0581873859935\n seconds_per_batch_max: 0.0002727508544921875\n seconds_per_batch_mean: 0.00014481544494628906\n seconds_per_batch_min: 0.0001366138458251953\n seconds_per_batch_std: 1.6102982159292097e-05\n gpu_to_cpu:\n human_readable:\n batch_latency: \"106.168 \\xB5s +/- 17.829 \\xB5s [53.167 \\xB5s, 248.909 \\xB5\\\n s]\"\n batches_per_second: 9.64 K +/- 1.60 K [4.02 K, 18.81 K]\n metrics:\n batches_per_second_max: 18808.538116591928\n batches_per_second_mean: 9639.942102368092\n batches_per_second_min: 4017.532567049808\n batches_per_second_std: 1595.7983033708472\n seconds_per_batch_max: 0.00024890899658203125\n seconds_per_batch_mean: 0.00010616779327392578\n seconds_per_batch_min: 5.316734313964844e-05\n seconds_per_batch_std: 1.7829135190772566e-05\n on_device_inference:\n human_readable:\n batch_latency: \"15.567 ms +/- 546.154 \\xB5s [15.311 ms, 19.261 ms]\"\n batches_per_second: 64.31 +/- 1.96 [51.92, 65.31]\n metrics:\n batches_per_second_max: 65.31149174711928\n batches_per_second_mean: 64.30692850265713\n batches_per_second_min: 51.918698784442846\n batches_per_second_std: 1.9599322351815833\n seconds_per_batch_max: 0.019260883331298828\n seconds_per_batch_mean: 0.015567030906677246\n seconds_per_batch_min: 0.015311241149902344\n seconds_per_batch_std: 0.0005461537255227954\n total:\n human_readable:\n batch_latency: \"15.818 ms +/- 549.873 \\xB5s [15.561 ms, 19.461 ms]\"\n batches_per_second: 63.29 +/- 1.92 [51.38, 64.26]\n metrics:\n batches_per_second_max: 64.26476266356143\n batches_per_second_mean: 63.28565696640637\n batches_per_second_min: 51.38378232692614\n batches_per_second_std: 1.9198343850767468\n seconds_per_batch_max: 0.019461393356323242\n seconds_per_batch_mean: 0.01581801414489746\n seconds_per_batch_min: 0.015560626983642578\n seconds_per_batch_std: 0.0005498731526138171\n batch_size_8:\n cpu_to_gpu:\n human_readable:\n batch_latency: \"805.674 \\xB5s +/- 157.254 \\xB5s [773.191 \\xB5s, 2.303 ms]\"\n batches_per_second: 1.26 K +/- 97.51 [434.24, 1.29 K]\n metrics:\n batches_per_second_max: 1293.3407338883749\n batches_per_second_mean: 1259.5653105357776\n batches_per_second_min: 434.23791282741485\n batches_per_second_std: 97.51424036939879\n seconds_per_batch_max: 0.002302885055541992\n seconds_per_batch_mean: 0.000805673599243164\n seconds_per_batch_min: 0.0007731914520263672\n seconds_per_batch_std: 0.0001572538140613121\n gpu_to_cpu:\n human_readable:\n batch_latency: \"104.215 \\xB5s +/- 12.658 \\xB5s [59.605 \\xB5s, 128.031 \\xB5\\\n s]\"\n batches_per_second: 9.81 K +/- 1.76 K [7.81 K, 16.78 K]\n metrics:\n batches_per_second_max: 16777.216\n batches_per_second_mean: 9806.840626578907\n batches_per_second_min: 7810.621973929236\n batches_per_second_std: 1761.6008872740726\n seconds_per_batch_max: 0.00012803077697753906\n seconds_per_batch_mean: 0.00010421514511108399\n seconds_per_batch_min: 5.9604644775390625e-05\n seconds_per_batch_std: 1.2658293070174213e-05\n on_device_inference:\n human_readable:\n batch_latency: \"16.623 ms +/- 759.017 \\xB5s [16.301 ms, 22.584 ms]\"\n batches_per_second: 60.26 +/- 2.22 [44.28, 61.35]\n metrics:\n batches_per_second_max: 61.346243290283894\n batches_per_second_mean: 60.25881046175457\n batches_per_second_min: 44.27827629162004\n batches_per_second_std: 2.2193085956672296\n seconds_per_batch_max: 0.02258443832397461\n seconds_per_batch_mean: 0.01662288188934326\n seconds_per_batch_min: 0.01630091667175293\n seconds_per_batch_std: 0.0007590167680596548\n total:\n human_readable:\n batch_latency: \"17.533 ms +/- 836.015 \\xB5s [17.193 ms, 23.896 ms]\"\n batches_per_second: 57.14 +/- 2.20 [41.85, 58.16]\n metrics:\n batches_per_second_max: 58.16374528511205\n batches_per_second_mean: 57.140338855126565\n batches_per_second_min: 41.84762740950632\n batches_per_second_std: 2.1985066663972677\n seconds_per_batch_max: 0.023896217346191406\n seconds_per_batch_mean: 0.01753277063369751\n seconds_per_batch_min: 0.017192840576171875\n seconds_per_batch_std: 0.0008360147274630088\n ```\n</details>\n\n... Your turn\n\n## How we benchmark\nThe overall flow can be summarized with the diagram shown below (best viewed on GitHub):\n```mermaid\nflowchart TB;\n A([Start]) --> B\n B(prepare_samples)\n B --> C[get_machine_info]\n C --> D[measure_params]\n D --> E[warm_up, batch_size=1]\n E --> F[measure_flops]\n \n subgraph SG[Repeat for batch_size 1 and x]\n direction TB\n G[measure_allocated_memory]\n G --> H[warm_up, given batch_size]\n H --> I[measure_detailed_inference_timing]\n I --> J[measure_repeated_inference_timing]\n J --> K[measure_energy]\n end\n\n F --> SG\n SG --> END([End])\n```\n\nUsually, the sample and model don't reside on the same device initially (e.g., a GPU holds the model while the sample is on CPU after being loaded from disk or collected as live data). Accordingly, we measure timing in three parts: `cpu_to_gpu`, `on_device_inference`, and `gpu_to_cpu`, as well as a sum of the three, `total`. Note that the `model.device()` determines the execution device. The inference flow is shown below:\n\n```mermaid\nflowchart LR;\n A([sample])\n A --> B[cpu -> gpu]\n B --> C[model __call__]\n C --> D[gpu -> cpu]\n D --> E([result])\n```\n\n## Advanced use\nTrying to benchmark a custom class, which is not a `torch.nn.Module`?\nYou can pass custom functions to `benchmark` as seen in [this example](tests/test_custom_class.py).\n\n\n## Limitations\n- Allocated memory measurements are only available on CUDA devices.\n- Energy consumption can only be measured on NVIDIA Jetson platforms at the moment.\n- FLOPs and parameter count is not support for custom classes.\n\n\n## Acknowledgement\nThis work has received funding from the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR).\nIt was developed for benchmarking tools in [OpenDR](https://github.com/opendr-eu/opendr), a non-proprietary toolkit for deep learning based functionalities for robotics and vision.\n\n\n## Citation\nIf you like the tool and use it in research, please consider citing it:\n```bibtex\n@software{hedegaard2022pytorchbenchmark,\n author = {Hedegaard, Lukas},\n doi = {10.5281/zenodo.7223585},\n month = {10},\n title = {{PyTorch-Benchmark}},\n version = {0.3.5},\n year = {2022}\n}\n```\n\n\n",
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