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| import os | |
| import gc | |
| import random | |
| import time | |
| import numpy as np | |
| import pandas as pd | |
| import torch | |
| def set_seed(seed: int): | |
| """ | |
| Sets the seed of the entire notebook so results are the same every time we run. | |
| This is for REPRODUCIBILITY. | |
| """ | |
| np.random.seed(seed) | |
| random_state = np.random.RandomState(seed) | |
| random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) | |
| torch.backends.cudnn.deterministic = True | |
| torch.backends.cudnn.benchmark = False | |
| os.environ['PYTHONHASHSEED'] = str(seed) | |
| return random_state | |
| def flatten_list(lis): | |
| """Given a list, possibly nested to any level, return it flattened.""" | |
| new_lis = [] | |
| for item in lis: | |
| if type(item) == type([]): | |
| new_lis.extend(flatten_list(item)) | |
| else: | |
| new_lis.append(item) | |
| return new_lis | |
| def clear_torch_cache(): | |
| if torch.cuda.is_available: | |
| torch.cuda.empty_cache() | |
| torch.cuda.ipc_collect() | |
| gc.collect() | |
| def system_info(): | |
| import psutil | |
| system = {} | |
| # https://stackoverflow.com/questions/48951136/plot-multiple-graphs-in-one-plot-using-tensorboard | |
| # https://arshren.medium.com/monitoring-your-devices-in-python-5191d672f749 | |
| temps = psutil.sensors_temperatures(fahrenheit=False) | |
| if 'coretemp' in temps: | |
| coretemp = temps['coretemp'] | |
| temp_dict = {k.label: k.current for k in coretemp} | |
| for k, v in temp_dict.items(): | |
| system['CPU_C/%s' % k] = v | |
| # https://github.com/gpuopenanalytics/pynvml/blob/master/help_query_gpu.txt | |
| from pynvml.smi import nvidia_smi | |
| nvsmi = nvidia_smi.getInstance() | |
| gpu_power_dict = {'W_gpu%d' % i: x['power_readings']['power_draw'] for i, x in | |
| enumerate(nvsmi.DeviceQuery('power.draw')['gpu'])} | |
| for k, v in gpu_power_dict.items(): | |
| system['GPU_W/%s' % k] = v | |
| gpu_temp_dict = {'C_gpu%d' % i: x['temperature']['gpu_temp'] for i, x in | |
| enumerate(nvsmi.DeviceQuery('temperature.gpu')['gpu'])} | |
| for k, v in gpu_temp_dict.items(): | |
| system['GPU_C/%s' % k] = v | |
| gpu_memory_free_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['free'] for i, x in | |
| enumerate(nvsmi.DeviceQuery('memory.free')['gpu'])} | |
| gpu_memory_total_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['total'] for i, x in | |
| enumerate(nvsmi.DeviceQuery('memory.total')['gpu'])} | |
| gpu_memory_frac_dict = {k: gpu_memory_free_dict[k] / gpu_memory_total_dict[k] for k in gpu_memory_total_dict} | |
| for k, v in gpu_memory_frac_dict.items(): | |
| system[f'GPU_M/%s' % k] = v | |
| return system | |
| def system_info_print(): | |
| try: | |
| df = pd.DataFrame.from_dict(system_info(), orient='index') | |
| # avoid slamming GPUs | |
| time.sleep(1) | |
| return df.to_markdown() | |
| except Exception as e: | |
| return "Error: %s" % str(e) | |