gpubench 1.0.0

A tool for reporting GPU benchmark results and information

gpu-test

Reporting GPU benchmark results and information.

Installation

Just pip install gpubench !

Usage

> gpubench

example output:

Distributor ID:	Ubuntu
Description:	Ubuntu 22.04.3 LTS
Release:	22.04
Codename:	jammy
┌──────────────────────────┐
│ Experimental Environment │
└──────────────────────────┘
platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.35
node: DESKTOP-CJIIOBE
time: 2023-08-21 00:15:32.669387
python interpreter: /home/m/miniconda3/bin/python
python version: 3.11.4 (main, Jul  5 2023, 13:45:01) [GCC 11.2.0]
device: gpu
CUDA version: 11.8
driver version: 536.40
cuDNN version: 8700
nccl version: 2.14.3
gpu usable count: 1
gpu total count: 1
    gpu 0: NVIDIA GeForce RTX 4070, [mem]   844M / 12282M,  7%,  33°C, 🔋 8
gpu direct communication matrix:
	    GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 				N/A
cpu: [logical] 24, [physical] 12, [usage] 3.0%
virtual memory: [total] 7.7GB, [avail] 7.0GB, [used] 424.9MB 8.4%
disk usage: [total] 251.0GB, [free] 226.9GB, [used] 11.2GB 4.7%
current dir: /mnt/d
user: m
shell: /bin/bash
python packages version:
    torch: 2.0.1
    transformers: 4.31.0
    triton: 2.0.0
┌─────────────────────────────────┐
│ Matrix Multiplication Benchmark │
└─────────────────────────────────┘
Matrix: A [16384 x 16384], B [16384 x 16384]
Operation: A @ B
Experiment: 50
Tensor:
    - torch.float16 | 0.13958s (median) | 63.0187 TFLOPS | GPU mem allocated 1.5GB, reserved 1.5GB
    - torch.float32 | 0.45894s (median) | 19.1661 TFLOPS | GPU mem allocated 3.0GB, reserved 4.5GB
┌─────────────────────────────┐
│ Resnet18 Inference Profiler │
└─────────────────────────────┘
---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
                             Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg       CPU Mem  Self CPU Mem      CUDA Mem  Self CUDA Mem    # of Calls  
---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
                  model_inference         0.55%       3.315ms       100.00%     602.416ms     602.416ms           0 b           0 b           0 b    -116.00 Mb             1  
                     aten::conv2d         0.02%     130.000us        91.05%     548.511ms      27.426ms           0 b           0 b      47.51 Mb       5.74 Mb            20  
                aten::convolution         0.03%     167.000us        91.04%     548.439ms      27.422ms           0 b           0 b      47.51 Mb           0 b            20  
               aten::_convolution         0.02%     100.000us        91.01%     548.272ms      27.414ms           0 b           0 b      47.51 Mb           0 b            20  
          aten::cudnn_convolution        91.00%     548.172ms        91.00%     548.172ms      27.409ms           0 b           0 b      47.51 Mb      47.51 Mb            20  
                       aten::add_         0.08%     487.000us         0.08%     487.000us      17.393us           0 b           0 b           0 b           0 b            28  
                 aten::batch_norm         0.01%      54.000us         7.46%      44.938ms       2.247ms           0 b           0 b      47.41 Mb       3.83 Mb            20  
     aten::_batch_norm_impl_index         0.01%      58.000us         7.45%      44.909ms       2.245ms           0 b           0 b      47.41 Mb           0 b            20  
           aten::cudnn_batch_norm         7.15%      43.096ms         7.45%      44.851ms       2.243ms           0 b           0 b      47.41 Mb       2.00 Kb            20  
                 aten::empty_like         0.01%      81.000us         0.28%       1.694ms      84.700us           0 b           0 b      47.37 Mb           0 b            20  
---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
Self CPU time total: 602.416ms

from gpuinfo.nvidia import get_gpus

for gpu in get_gpus():
	print(gpu.__dict__)
	print(gpu.get_max_clock_speeds())
	print(gpu.get_clock_speeds())
	print(gpu.get_memory_details())

from gpuinfo.windows import get_gpus

for gpu in get_gpus():
	print(gpu.__dict__)