Created
March 28, 2019 06:54
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import subprocess, re | |
# Nvidia-smi GPU memory parsing. | |
# Tested on nvidia-smi 370.23 | |
def run_command(cmd): | |
"""Run command, return output as string.""" | |
output = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True).communicate()[0] | |
return output.decode("ascii") | |
def list_available_gpus(): | |
"""Returns list of available GPU ids.""" | |
output = run_command("nvidia-smi -L") | |
# lines of the form GPU 0: TITAN X | |
gpu_regex = re.compile(r"GPU (?P<gpu_id>\d+):") | |
result = [] | |
for line in output.strip().split("\n"): | |
m = gpu_regex.match(line) | |
assert m, "Couldnt parse "+line | |
result.append(int(m.group("gpu_id"))) | |
return result | |
def gpu_memory_map(): | |
"""Returns map of GPU id to memory allocated on that GPU.""" | |
output = run_command("nvidia-smi") | |
gpu_output = output[output.find("GPU Memory"):] | |
# lines of the form | |
# | 0 8734 C python 11705MiB | | |
memory_regex = re.compile(r"[|]\s+?(?P<gpu_id>\d+)\D+?(?P<pid>\d+).+[ ](?P<gpu_memory>\d+)MiB") | |
rows = gpu_output.split("\n") | |
result = {gpu_id: 0 for gpu_id in list_available_gpus()} | |
for row in gpu_output.split("\n"): | |
m = memory_regex.search(row) | |
if not m: | |
continue | |
gpu_id = int(m.group("gpu_id")) | |
gpu_memory = int(m.group("gpu_memory")) | |
result[gpu_id] += gpu_memory | |
return result | |
def pick_gpu_lowest_memory(): | |
"""Returns GPU with the least allocated memory""" | |
memory_gpu_map = [(memory, gpu_id) for (gpu_id, memory) in gpu_memory_map().items()] | |
best_memory, best_gpu = sorted(memory_gpu_map)[0] | |
return best_gpu |
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ref:Tensorflow on shared GPUs: how to automatically select the one that is unused