It provides information about GPUs and their availability for computation.
Often we want to train a ML model on one of GPUs installed on a multi-GPU machine. Since TensorFlow allocates all memory, only one such process can use the GPU at a time. Unfortunately nvidia-smi provides only a text interface with information about GPUs. This packages wraps it with an easier to use CLI and Python interface.
It’s a quick and dirty solution calling nvidia-smi and parsing its output. We can take one or more GPUs availabile for computation based on relative memory usage, ie. it is OK with Xorg taking a few MB.
In addition we have a fancy table of GPU with more information taken by python binding to NVML.
Installing
pip install nvgpu
Usage examples
Command-line interface:
# grab all available GPUs CUDA_VISIBLE_DEVICES=$(nvgpu available) # grab at most available GPU CUDA_VISIBLE_DEVICES=$(nvgpu available -l 1)
Print pretty colored table of devices, availability, users, processes:
$ nvgpu list
status type util. temp. MHz users since pids cmd
-- -------- ------------------- ------- ------- ----- ------- --------------- ------ --------
0 [ ] GeForce GTX 1070 0 % 44 139
1 [~] GeForce GTX 1080 Ti 0 % 44 139 alice 2 days ago 19028 jupyter
2 [~] GeForce GTX 1080 Ti 0 % 44 139 bob 14 hours ago 8479 jupyter
3 [~] GeForce GTX 1070 46 % 54 1506 bob 7 days ago 20883 train.py
4 [~] GeForce GTX 1070 35 % 64 1480 bob 7 days ago 26228 evaluate.py
5 [!] GeForce GTX 1080 Ti 0 % 44 139 ? 9305
6 [ ] GeForce GTX 1080 Ti 0 % 44 139
Or shortcut:
$ nvl
Python API:
import nvgpu
nvgpu.available_gpus()
# ['0', '2']
nvgpu.gpu_info()
[{'index': '0',
'mem_total': 8119,
'mem_used': 7881,
'mem_used_percent': 97.06860450794433,
'type': 'GeForce GTX 1070',
'uuid': 'GPU-3aa99ee6-4a9f-470e-3798-70aaed942689'},
{'index': '1',
'mem_total': 11178,
'mem_used': 10795,
'mem_used_percent': 96.57362676686348,
'type': 'GeForce GTX 1080 Ti',
'uuid': 'GPU-60410ded-5218-7b06-9c7a-124b77a22447'},
{'index': '2',
'mem_total': 11178,
'mem_used': 10789,
'mem_used_percent': 96.51994990159241,
'type': 'GeForce GTX 1080 Ti',
'uuid': 'GPU-d0a77bd4-cc70-ca82-54d6-4e2018cfdca6'},
...
]
TODO
order GPUs by priority (decreasing power, decreasing free memory)
2 maintainers
Release files for nvgpu 0.5.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| nvgpu-0.5.1.tar.gz | 5.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nvgpu-0.5.1-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 10.4 kB
Release files / nvgpu-0.5.1.tar.gz
| Download URL | nvgpu-0.5.1.tar.gz |
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| Size | 5.0 kB |
| Tags | Source |
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Release files / nvgpu-0.5.1-py2.py3-none-any.whl
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| Size | 5.5 kB |
| Tags | Python 2 Python 3 |
|
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