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__)
Metadata
Release files for gpubench 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gpubench-1.0.0.tar.gz | 79.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gpubench-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 85.9 kB
Release files / gpubench-1.0.0.tar.gz
| Download URL | gpubench-1.0.0.tar.gz |
|---|---|
| Size | 79.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.11
|
Release files / gpubench-1.0.0-py3-none-any.whl
| Download URL | gpubench-1.0.0-py3-none-any.whl |
|---|---|
| Size | 6.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.11
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