nvgpu - NVIDIA GPU tools
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.
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)
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
Metadata
2 maintainers
Release files for nvgpu 0.1.1
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