pip install -U gpucompare
or install with Poetry
poetry add gpucompare
Then you can run
gpucompare --help
or with Poetry
:
poetry run gpucompare --help
$ gpucompare --help
Usage: gpucompare [OPTIONS]
Compare GPUs
╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --csv-data TEXT CSV file containing row-wise GPU data [default: None] [required] │
│ │
│ Possible columns: │
│ gpu_name (str): name of gpu [required] │
│ architecture (str): GPU architecture │
│ cuda_cores (int): number of cuda cores │
│ fp32_perf (float): fp32 performance in TFLOPS │
│ fp16_perf (float): fp16 performance in TFLOPS │
│ int8_perf (float): int8 performance in TOPS │
│ mem (float): gpu memory in GiB │
│ --output TEXT Output in 'concise' or 'detailed' format. [default: concise] |
│ mem_bandwidth (float): memory bandwidth in GB/s │
│ --version -v Prints the version of the gpucompare package. │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────╯
$ gpucompare --csv-data assets/gpu_data.csv
# gpu_data.csv
# gpu_name,architecture,int8_perf,mem_bandwidth
# A2,ampere,36,200
# A10,ampere,250,600
# A30,ampere,330,933
# V100,volta,112,900
{'A10/A2': '3.0x', 'A30/A2': '4.67x', 'V100/A2': '3.01x'}
$ gpucompare --csv-data assets/gpu_data.csv --output=detailed
gpu_name | architecture | int8_perf | mem_bandwidth | performance
-------- | ------------ | --------- | ------------- | -----------
A2 | ampere | 36 | 200 | 1x
A10 | ampere | 250 | 600 | 3.0x
A30 | ampere | 330 | 933 | 4.67x
V100 | volta | 112 | 900 | 3.01x
Thanks for considering contributing to this project. Please follow Contributing guidelines.
This project is licensed under the terms of the MIT
license. See LICENSE for more details.
@misc{gpucompare,
author = {kHarshit},
title = {Compare GPUs},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/kHarshit/gpucompare}}
}
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