NVIDIA GPU tools
Project description
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)
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