def get_gpu_memory(device_idx):
assert device_idx < NvidiaSmi.total_devices, "device index should {} less than total devices {}"\
.format(device_idx, NvidiaSmi.total_devices)
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(device_idx)
res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
M = 1024**2
return res.free / M, res.total / M, res.used / M
def get_gpu_memory():
import nvidia_smi
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
print("Used GPU memory: {}%".format((info.used * 100) // info.total))
nvidia_smi.nvmlShutdown()
def checkGPUsAvailability(n_gpus=1):
'''
Test that GPUs have free memory on 'n_gpus'.
OUT:
True: if they have
False: if not
'''
# For every gpu to check
for i_gpu in range(n_gpus):
# Access to the memory used by the i-th gpu
try:
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i_gpu)
mem_res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
except Exception:
print('Warning: GPU did not accessed')
break
# If more than 1GB is taken, then stop
if (mem_res.used/(1024.**3) > 1.0): # greater than 1GB of VRAM
# Report it
print('Memory used (gpu-%i): %.2f GB' % (i_gpu, mem_res.used/(1024**3)), end='')
print(' - on total: %.2f GB' % (mem_res.total/(1024**3)))
return False
return True
def Stop(self):
totalTime = time.perf_counter() - self.StartTime
mem_res = nvidia_smi.nvmlDeviceGetMemoryInfo(self.handle)
self.ElapsedTime.append(totalTime)
return totalTime, psutil.cpu_percent(), psutil.virtual_memory(
)[2], mem_res.used / mem_res.total, mem_res.used / mem_res.total
def get_gpu_mem(concat_string=''):
# res = nvidia_smi.nvmlDeviceGetUtilizationRates(handle)
# print(f'gpu: {res.gpu}%, gpu-mem: {res.memory}%')
res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
# print(f'mem: {res.used / (1024**2)} (GiB)') # usage in GiB
print(concat_string, f'mem: {100 * (res.used / res.total):.3f}%') # percentage
def show_memory_usage():
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0) # GPU number
mem_res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
# print('=' * 50)
# print(f'mem: {mem_res.used / (1024 ** 3)} (GiB)') # usage in GiB
print(f'mem usage: {100 * (mem_res.used / mem_res.total):.3f}%'
) # percentage
def print_gpu_info(idx=0):
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(idx)
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
print("Total memory:", info.total)
print("Free memory:", info.free)
print("Used memory:", info.used)
nvidia_smi.nvmlShutdown()
def get_mem_info(device_id):
gpu_list = [device_id]
nvidia_smi.nvmlInit()
handle = [nvidia_smi.nvmlDeviceGetHandleByIndex(i) for i in gpu_list]
res = [nvidia_smi.nvmlDeviceGetMemoryInfo(item) for item in handle]
res = [100 * item.used / item.total for item in res]
nvidia_smi.nvmlShutdown()
return res[0]
def Watch_fin():
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(1)
res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
time.sleep(1)
if res.used == 0:
return 0
else:
return 1
def memory_check():
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
# card id 0 hardcoded here, there is also a call to get all available card ids, so we could iterate
mem_res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
mbs = mem_res.used / (1024**2)
percent = mem_res.used / mem_res.total
return mbs, percent
def stats(self, n_iter):
if self.eps is not None:
self.log.add_scalar('eps', self.eps(), n_iter)
if self.handle is not None:
res = nvidia_smi.nvmlDeviceGetUtilizationRates(self.handle)
self.log.add_scalar('nvidia/load', res.gpu, n_iter)
res = nvidia_smi.nvmlDeviceGetMemoryInfo(self.handle)
self.log.add_scalar('nvidia/mem_gb', res.used / (1024**3), n_iter)
def Available_GPUs(self):
available = []
for i in range(self.total_gpus):
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i)
res = nvidia_smi.nvmlDeviceGetUtilizationRates(handle)
mem_res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
if res.gpu < 30 and (mem_res.used / mem_res.total * 100) < 30:
available.append(i)
return available
def gpu_memory_tracker():
"""returns nvidia gpu memory consumed"""
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
used = info.used
total = info.total
percent = used / total * 100
return percent
def get_usage(gpu_list=None, **kwargs):
""" Track GPU memory usage. """
_ = kwargs
gpu_list = gpu_list or [0]
nvidia_smi.nvmlInit()
handle = [nvidia_smi.nvmlDeviceGetHandleByIndex(i) for i in gpu_list]
res = [nvidia_smi.nvmlDeviceGetMemoryInfo(item) for item in handle]
res = [100 * item.used / item.total for item in res]
nvidia_smi.nvmlShutdown()
return res
def log_gpu_memory_to_tensorboard():
'''
Log every gpus current free memory level to tensorboard.
'''
for i in range(nvidia_smi.nvmlDeviceGetCount()):
info = nvidia_smi.nvmlDeviceGetMemoryInfo(gpus[i])
with loggers[i].as_default():
tl.summary({'free': np.array(info.free) / (1024**3)},
step=int(time.time()),
name='GPUs')
def use_gpu():
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
nvidia_smi.nvmlShutdown()
if info.used > 1000000000:
return True
else:
return False
def gpu_usage():
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
# card id 0 hardcoded here, there is also a call to get all available card ids, so we could iterate
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
usage = info.used
nvidia_smi.nvmlShutdown()
return usage
def measure_memory(fn, handle):
"""Measure memory to run fn. Returns the maximum allocated memory each run."""
try:
_ = fn()
except Exception as e:
print(e)
return np.nan
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
memory = info.used / 1000. / 1000. / 1000.
print('.', end='')
return memory
def available_GPUs(total_gpus):
available_gpus = []
for i in range(total_gpus):
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i)
res = nvidia_smi.nvmlDeviceGetUtilizationRates(handle)
mem_res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
if res.gpu < 30 and (
mem_res.used / mem_res.total * 100
) < 30: # Jon heuristically defines what it means for a GPU to be available
available_gpus.append(i)
return available_gpus
def check_cuda_memory():
nvidia_smi.nvmlInit()
deviceCount = nvidia_smi.nvmlDeviceGetCount()
for i in range(deviceCount):
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i)
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
print("Device {}: {}, Memory : ({:.2f}% free): {}(total), {} (free), {} (used)"\
.format(i, nvidia_smi.nvmlDeviceGetName(handle), 100*info.free/info.total, \
info.total, info.free, info.used))
nvidia_smi.nvmlShutdown()
return
def get_max_data_group_size():
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
total_memory = info.total
if total_memory >= 12 * (10 ** 9):
return 2 ** 12
elif total_memory >= 6 * (10 ** 9):
return 2 ** 11
else:
os.environ["CUDA_VISIBLE_DEVICES"] = ""
return 2 ** 12
def get_device(gpuID=False):
"""Checks available GPUs and selects the one with the most available memory
Parameters
----------
gpuID: bool or int
whether to use GPU, or the device ID of a specific GPU to use. If False,
use only CPU. If True, attempts to find the GPU with most available memory.
Returns
-------
device : jax.device
handle to gpu or cpu device selected
"""
import jax
if gpuID is False:
return jax.devices('cpu')[0]
try:
gpus = jax.devices('gpu')
# did the user request a specific GPU?
if isinstance(gpuID, int) and gpuID < len(gpus):
return gpus[gpuID]
if isinstance(gpuID, int):
from desc.backend import TextColors
# ID was not valid
warnings.warn(
TextColors.WARNING +
'gpuID did not match any found devices, trying default gpu option'
+ TextColors.ENDC)
# find all available options and see which has the most space
import nvidia_smi
nvidia_smi.nvmlInit()
maxmem = 0
gpu = gpus[0]
for i in range(len(gpus)):
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i)
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
if info.free > maxmem:
maxmem = info.free
gpu = gpus[i]
nvidia_smi.nvmlShutdown()
return gpu
except:
from desc.backend import TextColors
warnings.warn(TextColors.WARNING +
'No GPU found, falling back to CPU' + TextColors.ENDC)
return jax.devices('cpu')[0]
def on_train_batch_begin(self, batch, logs=None):
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
# card id 0 hardcoded here, there is also a call to get all available card ids, so we could iterate
res = nvidia_smi.nvmlDeviceGetUtilizationRates(handle)
res1 = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
#GPUs = GPU.getGPUs()
#gpu = GPUs[0]
print(f'gpu: {res.gpu}%, gpu-mem: {res.memory}%')
def get_gpu_info(gpu_id=None):
""" Get gpu-info regarding gpu_id
:param gpu_id: gpu bus id
:return mem_used: used memory in MiB
:return mem_total: total memory in MiB
"""
if gpu_id is None:
gpu_id = int(os.environ["CUDA_VISIBLE_DEVICES"])
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(int(gpu_id))
mem_res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
mem_used = mem_res.used / (1024**2)
mem_total = mem_res.total / (1024**2)
return mem_used, mem_total, gpu_id
def output(self, data_dict, n_iter):
if self.handle is not None:
res = nvidia_smi.nvmlDeviceGetUtilizationRates(self.handle)
self.log.add_scalar('nvidia/load', res.gpu, n_iter)
res = nvidia_smi.nvmlDeviceGetMemoryInfo(self.handle)
self.log.add_scalar(
'nvidia/mem_gb', res.used / (1024 ** 3), n_iter)
for key, val in data_dict.items():
if hasattr(val, 'shape') and np.prod(val.shape) > 1:
self.log.add_histogram(key, val, n_iter)
else:
self.log.add_scalar(key, val, n_iter)
def run(data_loader, engine):
batch_time = AverageMeter()
gpu = AverageMeter()
gpu_mem = AverageMeter()
# Allocate buffers and create a CUDA stream.
h_input, d_input, h_output, d_output, stream = allocate_buffers(engine)
# Contexts are used to perform inference.
input = torch.rand((args.batch_size, ) + ModelData.INPUT_SHAPE)
# if data_loader != 0:
with engine.create_execution_context() as context:
end = time.time()
# for i in range(args.loop):
for i, (input, target) in enumerate(data_loader):
if i == args.loop:
break
np.copyto(h_input, input.reshape(-1))
do_inference(context, h_input, d_input, h_output, d_output, stream)
batch_time.update(time.time() - end)
end = time.time()
# https://pypi.org/project/py3nvml/
util_rate = nvidia_smi.nvmlDeviceGetUtilizationRates(handle)
# print(util_rate.gpu, util_rate.memory)
gpu.update(util_rate.gpu)
mem_info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
gpu_mem.update(mem_info.used >> 20)
print("predict:", h_output)
print("predict: ", h_output.shape)
print("target:", target)
print("target:", target.shape)
if i % args.print_freq == 0 and not args.csv:
print(
'[{}/{}] batch time {batch_time.val:.3f} s (avg:{batch_time.avg:.3f})'
.format(i, args.loop, batch_time=batch_time))
# print summary
if args.csv:
print("{}, {:.3f}, {:.3f}, {:.3f}, {}".format(
args.batch_size, args.loop * args.batch_size / batch_time.sum,
batch_time.avg, gpu.avg, gpu_mem.avg))
else:
print("batchsize: {} ".format(args.batch_size))
print("throughput: {:.3f} img/sec".format(args.loop * args.batch_size /
batch_time.sum))
print("Latency: {:.3f} sec".format(batch_time.avg))
# see https://forums.fast.ai/t/show-gpu-utilization-metrics-inside-training-loop-without-subprocess-call/26594
# show gpu utilization metrics inside trianing loop
print("GPU util: {:.3f} %, GPU mem: {} MiB".format(
gpu.avg, gpu_mem.avg))
def predict_age(file_path="/media/original/data/vtps/sub-CC00050XX01_ses-7201_hemi-L_inflated_reduce50.vtp"):
torch.manual_seed(0)
if osp.isfile(file_path):
# mesh = read(file_path)
# reader = vtk.vtkPolyDataReader()
reader = vtk.vtkXMLPolyDataReader()
reader.SetFileName(file_path)
reader.Update()
# output = reader.GetOutput()
points = torch.tensor(np.array(reader.GetOutput().GetPoints().GetData()))
local_features = ['corrected_thickness', 'curvature', 'sulcal_depth']
x = get_features(local_features, reader)
transform = T.NormalizeScale()
# transform_samp = T.FixedPoints(10000)
data = Data(batch=torch.zeros_like(x[:, 0]).long(), x=x, pos=points)
data = transform(data)
# data = transform_samp(data)
# data = Data(batch=torch.zeros_like(x[:, 0]).long(), x=x, pos=points)
# data = Data(x=x, pos=points)
try:
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
mem_res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
free_mem = mem_res.free / 1024 ** 2
except:
free_mem = 0
device = torch.device('cuda' if torch.cuda.is_available() and free_mem >= GPU_MEM_LIMIT else 'cpu')
numb_local_features = x.size(1)
numb_global_features = 0
model = Net(numb_local_features, numb_global_features).to(device)
model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
model.eval()
# data_loader = DataLoader([data], batch_size=1, shuffle=False)
# print(len(data_loader))
# pred = model(next(iter(data_loader)).to(device))
pred = model(data.to(device))
return pred.item()
else:
return 'Unable to predict..'
def get_free_gpu_mem(gpu_index):
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
# card id 0 hardcoded here, there is also a call to get all available card ids, so we could iterate
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
# logging.info("Total GPU memory: {}".format(info.total))
# logging.info("Free GPU memory: {}".format(info.free))
# logging.info("Used GPU memory: {}".format(info.used))
nvidia_smi.nvmlShutdown()
return info.free
def watch(memory_max):
nvidia_smi.nvmlInit()
handle = nvidia_smi.nvmlDeviceGetHandleByIndex(1)
res = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
time.sleep(1)
if memory_max < res.used:
memory_max = res.used
with open(
"/mnt/mqs02/data/ogawa/BERT/preprocess-for-BERT/conbination/NUMAS/vocab_cost/recipe/32000/memory_cost_3.txt",
"a",
encoding="utf-8") as f:
result = str(memory_max) + "\n"
f.write(result)
print(memory_max)
return memory_max
def getMem():
data = psutil.virtual_memory()
total = data.total # byte
free = data.available
#
# print("total virtual memory" + str(total / 1024 / 1024 / 1024))
# print("free virtual memory" + str(free / 1024 / 1024 / 1024))
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
GPU_memory = "GPU Memory usage: " + str(
(info.used) / 1024 / 1024 / 1024) + "GB\n"
memory = "CPU Memory usage: " + str(
(total - free) / 1024 /
1024 / 1024) + "GB\nCPU Memory usage percent: %d" % (int(
round(data.percent))) + "%" + "\n"
cpu = "CPU usage percent:%0.2f" % psutil.cpu_percent(interval=1) + "%"
# return memory + cpu
return GPU_memory + memory + cpu
