def main(args):
if args.gpu is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
else:
try:
import py3nvml
py3nvml.grab_gpus(1, gpu_fraction=0.95)
except ImportError:
print("Could not import py3nvml")
test_agent = TestAgent(args)
if args.test:
# Get the config
with open(os.path.join('models', args.name, 'config.json')) as fp:
config_dict = json.load(fp)
args_dict = vars(args)
args_dict.update(config_dict)
test_wrapper(test_agent, args)
else:
agent = TrainAgent(args)
try:
agent.train(args.data,
args.max_grad_norm,
args.wd,
test_agent,
args=args)
except KeyboardInterrupt:
test_wrapper(test_agent, args)
def test_kernel():
py3nvml.grab_gpus(0)
kernel_module = tf.load_op_library(
os.path.join(LIB_BASE, 'kernel_example.so'))
with tf.Session() as sess:
result = kernel_module.example([5, 4, 3, 2, 1])
np.testing.assert_array_equal(result.eval(), np.array([10, 8, 6, 4,
2]))
def setup():
global barbara, barbara_t
global bshape, bshape_half
global ch
py3nvml.grab_gpus(1, gpu_fraction=0.5)
barbara = datasets.barbara()
barbara = (barbara/barbara.max()).astype('float32')
barbara = barbara.transpose([2, 0, 1])
bshape = list(barbara.shape)
bshape_half = bshape[:]
bshape_half[1] //= 2
barbara_t = torch.unsqueeze(
torch.tensor(barbara, dtype=torch.float32, device=dev), dim=0)
ch = barbara_t.shape[1]
def init_client(self, gpus):
gpus = [int(gpu.strip()) for gpu in gpus.split(",")]
if gpus:
py3nvml.grab_gpus(len(gpus), gpu_fraction=0, gpu_select=gpus)
ray.init(
num_gpus=len(gpus),
configure_logging=False,
include_dashboard=False,
namespace="ai.purplesmart.kernels",
_redis_max_memory=250 * 1024 * 1024,
)
import counter
self.counter = counter.create_counter()
self.client = serve.start(detached=True)
def setup():
global barbara, barbara_t, tf
global bshape, bshape_extracol
global ref_rowfilter, ch
py3nvml.grab_gpus(1, gpu_fraction=0.5)
barbara = datasets.barbara()
barbara = (barbara / barbara.max()).astype('float32')
barbara = barbara.transpose([2, 0, 1])
bshape = list(barbara.shape)
bshape_extracol = bshape[:]
bshape_extracol[2] += 1
barbara_t = torch.unsqueeze(torch.tensor(barbara, dtype=torch.float32),
dim=0).to(dev)
ch = barbara_t.shape[1]
# Some useful functions
ref_rowfilter = lambda x, h: np.stack([np_colfilter(s.T, h).T for s in x],
axis=0)
def setup():
global barbara, barbara_t
global bshape, bshape_half
global ref_rowdfilt, ch
py3nvml.grab_gpus(1, gpu_fraction=0.5, env_set_ok=True)
barbara = datasets.barbara()
barbara = (barbara / barbara.max()).astype('float32')
barbara = barbara.transpose([2, 0, 1])
bshape = list(barbara.shape)
bshape_half = bshape[:]
bshape_half[2] //= 2
barbara_t = torch.unsqueeze(torch.tensor(barbara, dtype=torch.float32),
dim=0).to(dev)
ch = barbara_t.shape[1]
# Some useful functions
ref_rowdfilt = lambda x, ha, hb: np.stack(
[np_coldfilt(s.T, ha, hb).T for s in x], axis=0)
# If we don't use a scheduler, just train 1 network in a simple loop
if args.no_scheduler:
# Create reporting objects
args.verbose = True
outdir = os.path.join(os.environ['HOME'], 'gainlayer_results', args.outdir)
tr_writer = SummaryWriter(os.path.join(outdir, 'train'))
val_writer = SummaryWriter(os.path.join(outdir, 'val'))
if not os.path.exists(outdir):
os.mkdir(outdir)
# Choose the model to run and build it
if args.type is None:
type_ = 'ref'
else:
type_ = args.type[0]
py3nvml.grab_gpus(ceil(args.num_gpus))
cfg = {'args': args, 'type': type_, 'num_gpus': args.num_gpus,
'dwt': args.dwt, 'C': args.C,
'lr': args.lr, 'lr1': args.lr1, 'mom': args.mom, 'mom1': args.mom1,
'wd': args.wd, 'q': args.q, 'wd1': args.wd1, 'opt1': args.opt1}
trn = TrainNET(cfg)
trn._final_epoch = args.epochs
# Copy this source file to the output directory for record keeping
if args.resume:
trn._restore(os.path.join(outdir, 'model_last.pth'))
else:
save_experiment_info(outdir, args.seed, args.no_comment, trn.model)
if args.seed is not None and trn.use_cuda:
torch.backends.cudnn.deterministic = True
# Author: Bichen Wu ([email protected]) 08/25/2016 """Evaluation""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import py3nvml py3nvml.grab_gpus(num_gpus=1, gpu_fraction=0.9) import cv2 from datetime import datetime import os.path import sys import time import numpy as np from six.moves import xrange import tensorflow as tf from config import * from dataset import pascal_voc, kitti from utils.util import bbox_transform, Timer from nets import * FLAGS = tf.app.flags.FLAGS tf.app.flags.DEFINE_string( 'dataset', 'KITTI', """Currently support PASCAL_VOC or KITTI dataset.""") tf.app.flags.DEFINE_string('data_path', '', """Root directory of data""") tf.app.flags.DEFINE_string( 'image_set', 'test', """Only used for VOC data."""
def test_dtcwt2(size, J, no_grad=False, dev='cuda'):
x = torch.randn(*size, requires_grad=(not no_grad)).to(dev)
h0a, h0b, _, _, h1a, h1b, _, _ = level1('farras')
cols, rows = lowlevel2.prep_filt_quad_afb2d(h0a, h1a, h0b, h1b, device=dev)
yh = []
for j in range(3):
x, y = lowlevel2.quad_afb2d(x, cols, rows, mode='zero')
yh.append(y)
return x, yh
if __name__ == "__main__":
args = parser.parse_args()
py3nvml.grab_gpus(1)
if args.size > 0:
size = (args.batch, 5, args.size, args.size)
else:
size = (args.batch, 5, 128, 128)
if args.ref:
print('Running dtcwt with FFTs')
reference_fftconv(size, args.j, args.no_grad, args.device)
elif args.convolution:
print('Running 11x11 convolution')
reference_conv(size, args.no_grad, args.device)
elif args.dwt:
print('Running separable dwt')
separable_dwt(size, args.j, args.no_grad, args.device)
elif args.fb:
# # Implementing a deep neural network using tensorflow
# In this notebok i will implement a 3 hidden layer neural network and feed in all the neccesary data. We will be focused on using S1_A1_E1.mat which means we are only concerned with one subject.
# ## 1. Import all the neccesary packages
import math
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
import scipy
import scipy.io as sio
import time
import py3nvml
py3nvml.grab_gpus(num_gpus=1, gpu_select=[1])
# ## 2. Load the data from .mat file and split it into a training (70%) and test (30%) data.
def load_dataset(path, NoZero):
X_path = path + 'emgShuffled.mat'
#Load the emg data
XData = sio.loadmat(X_path)
X_orig = (np.array((XData['emgShuffled']))).T
X_train_orig = X_orig[:,0:int(0.7*X_orig.shape[1])]
X_test_orig = X_orig[:,int(0.7*X_orig.shape[1])+1::]
#Flatten the EMG data
X_train_orig = X_train_orig/np.amax(X_train_orig)
X_test_orig = X_test_orig/np.amax(X_test_orig)
#Load the labels
Y_path = path + 'yShuffled.mat'
YData = sio.loadmat(Y_path)
Y_orig = (np.array((YData['yShuffled']))).T
if NoZero == True:
def run(self):
# def run(args, subprocess_func, subprocess_verbose=False):
list_of_configs = [
abs_path for abs_path in sorted(
glob.glob(self.path_to_configs + '/*%s' % (self.config_ext)))
]
list_of_gpus = self.available_gpus
max_utilization = self.max_gpu_utilization
max_jobs_per_gpu = self.max_jobs_per_gpu
queued = copy.deepcopy(list_of_configs)
if len(queued) == 0:
raise AssertionError('No tasks(configs) given!')
pools = {}
running = OrderedDict()
done = OrderedDict()
failed = OrderedDict()
curr_user = getpass.getuser()
last_task_time = -float('inf')
last_log_time = -float('inf')
alpha = np.exp(-3 / self.time_between_tasks)
total_gpu_utilization_filt = [0.0] * len(list_of_gpus)
user_gpu_utilization_filt = [0.0] * len(list_of_gpus)
while len(queued) + len(running) > 0:
time.sleep(0.01)
# allocate GPU (every log_refresh_rate seconds)
cand_gpu = []
cand_gpu_util = []
cand_gpumem_util = []
if time.time() - last_log_time >= 1.0:
total_compute_procs, user_compute_procs = get_num_procs(
allocated_gpus=list_of_gpus, username=curr_user)
total_gpu_utilization = get_gpu_utilization(
allocated_gpus=list_of_gpus)
total_gpumem_utilization = get_gpumem_utilization(
allocated_gpus=list_of_gpus)
user_gpu_utilization = [
ceil(x / (y + 1e-12) * z)
for x, y, z in zip(user_compute_procs, total_compute_procs,
total_gpu_utilization)
]
total_gpu_utilization_filt = [
(1 - alpha) * x + alpha * X for x, X in zip(
total_gpu_utilization, total_gpu_utilization_filt)
]
user_gpu_utilization_filt = [
(1 - alpha) * x + alpha * X for x, X in zip(
user_gpu_utilization, user_gpu_utilization_filt)
]
for i, gpuid, in enumerate(list_of_gpus):
tot_util_cond = total_gpu_utilization_filt[i] <= (
100 - self.utilization_margin)
tot_memutil_cond = total_gpumem_utilization[
i] <= 50 # (1 - gpu_fraction)*100
user_util_cond = user_gpu_utilization_filt[i] < floor(
max_utilization[i] *
(100 - self.utilization_margin) / 100)
user_numproc_cond = user_compute_procs[
i] < max_jobs_per_gpu[i] or max_jobs_per_gpu[i] == -1
if tot_util_cond and user_util_cond and user_numproc_cond and tot_memutil_cond:
cand_gpu.append(gpuid)
cand_gpu_util.append(total_gpu_utilization_filt[i])
cand_gpumem_util.append(total_gpumem_utilization[i])
# run task (every time_between_tasks seconds)
if len(queued) == 0 or len(cand_gpu) == 0 or time.time(
) - last_task_time < self.time_between_tasks: # no available GPUs or no queued tasks
pass
else:
min_util_idx = cand_gpu_util.index(min(cand_gpu_util))
if py3nvml.grab_gpus(num_gpus=1,
gpu_select=[cand_gpu[min_util_idx]],
gpu_fraction=0.5,
max_procs=-1) == 0:
# if for some reason cannot allocate gpu
# print('CUDA_VISIBLE_DEVICES = %s'%(os.environ.get('CUDA_VISIBLE_DEVICES')))
last_task_time = time.time()
continue
signal.signal(signal.SIGINT, signal.SIG_IGN)
if self.child_verbose:
p = multiprocessing.Pool(processes=1)
else:
p = multiprocessing.Pool(processes=1, initializer=mute)
pools[queued[0]] = p
with open(queued[0], 'r') as f:
# running[queued[0]] = p.map_async(self.child_process, self._get_child_process_args(f))
running[queued[0]] = p.apply_async(
self.child_process, self._get_child_process_args(f))
signal.signal(signal.SIGINT, self.default_handler)
queued.pop(0)
last_task_time = time.time()
# log (every log_refresh_rate seconds)
if time.time() - last_log_time >= 1.0:
# update thread status
ready = []
for key in running:
if running[key].ready(): # call has been executed
ready.append(key)
if running[key].successful(
): # process terminated successfully
done[key] = running[key]
else: # process terminated with errors
failed[key] = running[key]
for key in ready:
running.pop(key)
pools[key].close()
pools[key].terminate()
pools.pop(key)
entry_len = 150
print(''.center(entry_len, '+'))
print(
datetime.datetime.now(dateutil.tz.tzlocal()).strftime(
' %Y/%m/%d_%H:%M:%S ').center(entry_len, '-'))
print((
'+ USER: %s (process limit: %s, utilization limit: %s%%)' %
(curr_user, max_jobs_per_gpu, max_utilization)).ljust(
entry_len, ' '))
for i, gpuid in enumerate(list_of_gpus):
tup = (gpuid, )
tup += (user_compute_procs[i], )
tup += (total_compute_procs[i], )
tup += (user_gpu_utilization[i], )
tup += (total_gpu_utilization[i], )
tup += (total_gpumem_utilization[i], )
print((
'+ gpu%d compute processes (%d/%d) utilization rate (%d%%/%d%%) memory usage (--%%/%d%%)'
% tup).ljust(entry_len, ' '))
print((' %d QUEUED ' % (len(queued))).center(entry_len, '-'))
if self.kwargs.get('logging'):
print((' %d LOGGING ' % (len(running))).center(
entry_len, '-'))
else:
print((' %d RUNNING ' % (len(running))).center(
entry_len, '-'))
for key in running:
name_str = os.path.basename(key)
try:
tqdm_stat = pickle.load(
open(os.path.join('/tmp', name_str + '.tqdm'),
'rb'))
tqdm_str = 'gpu%s pid=%d |%d%%| %d/%d [%s<%s, %sit/s]' % tqdm_stat
except:
tqdm_str = ''
name_str = '+ ' + name_str
print(name_str + tqdm_str.rjust(entry_len - len(name_str)))
print((' %d FAILED ' % (len(failed))).center(entry_len, '-'))
for key in failed:
print(os.path.basename(key))
print((' %d DONE ' % (len(done))).center(entry_len, '-'))
for key in done:
print(os.path.basename(key))
print(''.center(entry_len, '+'))
print('+')
last_log_time = time.time()
if self.terminate:
self.resume = prompt_yes_or_no('Resume?')
if self.resume:
IPython.embed()
self.terminate = False
if self.terminate:
break
print('summary - done: %d, failed: %d, halted: %d, queued: %d' %
(len(done), len(failed), len(running), len(queued)))
def run(self):
# Access shared queues
shared_pending_job_q = self.get_pending_job_q()
shared_worker_status_q = self.get_worker_status_q()
# Worker state
self.worker_resume = True
self.worker_terminate = False
procs = {}
running = OrderedDict()
done = OrderedDict()
failed = OrderedDict()
last_job_time = -float('inf')
alpha = np.exp(-3 / self.limits.time_between_jobs)
total_gpu_utilization_filt = {
gpu_id: 0.0
for gpu_id in self.limits.available_gpus
}
user_gpu_utilization_filt = {
gpu_id: 0.0
for gpu_id in self.limits.available_gpus
}
worker_gpu_utilization_filt = {
gpu_id: 0.0
for gpu_id in self.limits.available_gpus
}
num_pending = shared_pending_job_q.qsize()
while num_pending + len(running):
curr_user = getpass.getuser()
list_of_gpus = self.limits.available_gpus
max_utilization = self.limits.gpu_utilization_limit
max_jobs_per_gpu = self.limits.gpu_job_limit
# 1. update candidate GPU
total_compute_procs, user_compute_procs, pid_compute_procs = \
get_num_procs(allocated_gpus=list_of_gpus, username=curr_user, version='v2')
worker_compute_procs = copy.deepcopy(user_compute_procs)
total_gpu_utilization = get_gpu_utilization(
allocated_gpus=list_of_gpus)
user_gpu_utilization = [
ceil(x / (y + 1e-12) * z)
for x, y, z in zip(user_compute_procs, total_compute_procs,
total_gpu_utilization)
]
total_gpumem_utilization, user_gpumem_utilization, pid_gpumem_utilization = \
get_gpumem_utilization(allocated_gpus=list_of_gpus, username=curr_user, version='v2')
total_gpu_utilization_filt = [
(1 - alpha) * x + alpha * X for x, X in zip(
total_gpu_utilization, total_gpu_utilization_filt)
]
user_gpu_utilization_filt = [
(1 - alpha) * x + alpha * X for x, X in zip(
user_gpu_utilization, user_gpu_utilization_filt)
]
cand_gpu, cand_gpu_util, cand_gpumem_util = [], [], []
for i, gpuid, in enumerate(list_of_gpus):
if gpuid < 0: # CPU mode
all_pid_compute_procs = [
item for sublist in pid_compute_procs
for item in sublist
]
worker_compute_procs[i] = sum([
running[key].pid not in all_pid_compute_procs
for key in running
])
user_compute_procs[i] = worker_compute_procs[i]
else:
worker_compute_procs[i] = sum([
running[key].pid in pid_compute_procs[i]
for key in running
])
tot_util_cond = total_gpu_utilization_filt[i] <= (
100 - self.limits.utilization_margin)
tot_memutil_cond = total_gpumem_utilization[
i] <= self.limits.max_gpu_mem_usage # (1 - gpu_fraction)*100
user_util_cond = user_gpu_utilization_filt[i] < floor(
max_utilization[i] *
(100 - self.limits.utilization_margin) / 100)
user_numproc_cond = user_compute_procs[i] < max_jobs_per_gpu[
i] or max_jobs_per_gpu[i] == -1
worker_numproc_cond = worker_compute_procs[
i] < max_jobs_per_gpu[i] or max_jobs_per_gpu[i] == -1
if self.limits.apply_limits == 'user':
is_cand = tot_util_cond and user_util_cond and user_numproc_cond and tot_memutil_cond
elif self.limits.apply_limits == 'worker':
is_cand = tot_util_cond and worker_numproc_cond and tot_memutil_cond
else:
is_cand = False
print(
"Invalid apply_limits. Available options are ['user', 'worker']"
)
if is_cand:
cand_gpu.append(gpuid)
cand_gpu_util.append(total_gpu_utilization_filt[i])
cand_gpumem_util.append(total_gpumem_utilization[i])
# 2. run job process
if len(cand_gpu) == 0 or time.time(
) - last_job_time < self.limits.time_between_jobs: # no available GPUs or no queued tasks
pass
else:
min_util_idx = cand_gpu_util.index(min(cand_gpu_util))
min_util_cand_gpu = cand_gpu[min_util_idx]
if min_util_cand_gpu < 0: # CPU mode
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
grab_device_success = True
else:
grab_device_success = py3nvml.grab_gpus(
num_gpus=1,
gpu_select=[cand_gpu[min_util_idx]],
gpu_fraction=(100 - self.limits.max_gpu_mem_usage) /
100,
max_procs=-1) > 0
if not grab_device_success:
# if for some reason cannot allocate gpu
# print('CUDA_VISIBLE_DEVICES = %s'%(os.environ.get('CUDA_VISIBLE_DEVICES')))
# last_job_time = time.time()
continue
try:
job = shared_pending_job_q.get_nowait(
) # {'tag': , 'config': , 'worker_args': , 'worker_kwargs': }
num_pending -= 1
# {'tag': path, 'config': json.load(f, object_hook=lambda d : SimpleNamespace(**d)),
# 'worker_args': worker_args, 'worker_kwargs': worker_kwargs}
signal.signal(signal.SIGINT, signal.SIG_IGN)
job['worker_kwargs'].update({
'config': job['config'],
'config_path': job['tag']
})
p = multiprocessing.Process(target=self.worker,
args=job['worker_args'],
kwargs=job['worker_kwargs'])
procs[job['tag']] = p
p.start()
running[job['tag']] = p
signal.signal(signal.SIGINT, self.default_handler)
last_job_time = time.time()
except queue.Empty:
pass
except (EOFError, BrokenPipeError) as e:
print('lost connection to server')
# update thread status
ready = []
for key in running:
if not running[key].is_alive(): # call has been executed
ready.append(key)
if running[
key].exitcode == 0: # process terminated successfully
done[key] = running[key]
else: # process terminated with errors
failed[key] = running[key]
for key in ready:
running.pop(key)
procs[key].terminate()
# procs[key].close()
procs.pop(key)
# 3. display status
entry_len = 150
print(''.center(entry_len, '+'))
print(
datetime.datetime.now(dateutil.tz.tzlocal()).strftime(
' %Y/%m/%d_%H:%M:%S ').center(entry_len, '-'))
# worker status
if self.limits.apply_limits == 'user':
print('+ WORKER: %s (apply limits on user %s)' %
(self.name, curr_user))
elif self.limits.apply_limits == 'worker':
print('+ WORKER: %s (apply limits on current worker)' %
(self.name))
else:
print(
"Invalid apply_limits. Available options are ['user', 'worker']"
)
print(('+ (gpu_ids=%s, job_limit=%s, util_limit=%s%%)' %
(list_of_gpus, max_jobs_per_gpu, max_utilization)).ljust(
entry_len, ' '))
for i, gpuid in enumerate(list_of_gpus):
tup = (gpuid, )
tup += (user_compute_procs[i], )
tup += (worker_compute_procs[i], )
tup += (total_compute_procs[i], )
tup += (user_gpu_utilization[i], )
tup += (total_gpu_utilization[i], )
tup += (user_gpumem_utilization[i], )
tup += (total_gpumem_utilization[i], )
print((
'+ gpu%d compute processes (%d(%d)/%d) utilization rate (%d%%/%d%%) memory usage (%d%%/%d%%)'
% tup).ljust(entry_len, ' '))
# job status
print((' %d PENDING ' % (num_pending)).center(entry_len, '-'))
# if self.kwargs.get('logging'):
# print((' %d LOGGING '%(len(running))).center(entry_len,'-'))
# else:
# print((' %d RUNNING '%(len(running))).center(entry_len,'-'))
print((' %d LOGGING/RUNNING ' % (len(running))).center(
entry_len, '-'))
tqdm_stats = []
for key in running:
name_str = os.path.basename(key)
try:
tqdm_stat = pickle.load(
open(os.path.join('/tmp', name_str + '.tqdm'), 'rb'))
tqdm_stats.append(tqdm_stat)
tqdm_str = 'gpu%s pid=%d |%d%%| %d/%d [%s<%s, %sit/s]' % tqdm_stat
except:
tqdm_stats.append(None)
tqdm_str = ''
name_str = '+ ' + name_str
print(name_str + tqdm_str.rjust(entry_len - len(name_str)))
print((' %d FAILED ' % (len(failed))).center(entry_len, '-'))
for key in failed:
print(os.path.basename(key))
print((' %d DONE ' % (len(done))).center(entry_len, '-'))
for key in done:
print(os.path.basename(key))
print(''.center(entry_len, '+'))
print('+')
# 4. report status to scheduler
try:
shared_worker_status_q.put({
self.name: {
'limit':
vars(self.limits),
'status': {
'worker_compute_procs': user_compute_procs,
'total_compute_procs': total_compute_procs,
'worker_gpu_utilization': user_gpu_utilization,
'total_gpu_utilization': total_gpu_utilization,
'worker_gpumem_utilization':
user_gpumem_utilization,
'total_gpumem_utilization':
total_gpumem_utilization
},
'running':
OrderedDict(
((key, tqdm_stat)
for key, tqdm_stat in zip(running, tqdm_stats))),
'done':
OrderedDict(((key, None) for key in done)),
'failed':
OrderedDict(((key, None) for key in failed)),
'last_updated':
time.time()
}
})
except (EOFError,
BrokenPipeError) as e: # lost connection to server
print('lost connection to server')
# 5. SIGINT(ctrl-c) handler
if self.worker_terminate:
self.worker_resume = prompt_yes_or_no('Resume?')
if self.worker_resume:
IPython.embed()
self.worker_terminate = False
if self.worker_terminate:
for key in running:
running[key].terminate()
break
# run while loop every second
self.rate.sleep()
try:
num_pending = shared_pending_job_q.qsize()
except (EOFError, BrokenPipeError) as e:
print('lost connection to server') # lost connection to server
print('summary - done: %d, failed: %d, halted: %d, pending: %d' %
(len(done), len(failed), len(running), num_pending))
time.sleep(wait_time_seconds)
with open('/proc/stat', 'r') as fh: # see https://linux.die.net/man/5/proc how to interpret this
lines = fh.readlines()
cpulines = [ line.strip().split(" ") for line in lines if line.startswith('cpu') ][1:]
idle_time_stop = [ safe_int(cpuinfo[4], idle_time_start[i]) for i, cpuinfo in enumerate(cpulines) ]
cpu_idle_info = [ ( idle_time_stop[i]-idle_time_start[i], cpu_id) for i, cpu_id in enumerate(cpu_ids) ]
cpu_idle_info.sort(reverse=True)
return cpu_idle_info
if __name__=='__main__':
if len(sys.argv)<3:
print("Missing arguments: [cpu-count] [gpu-count]")
sys.exit(1)
cpu_count = int(sys.argv[1])
gpu_count = int(sys.argv[2])
argv_rest = sys.argv[2:]
cpu_idle_info = find_idle_cpus(0.5)
my_pid = os.getpid()
cpu_list = ",".join([ cpu_idle_count[1] for cpu_idle_count in cpu_idle_info[:cpu_count] ])
argv = []+sys.argv
command = [ "taskset", "-a", "-c", cpu_list ] + argv[3:]
#print(command)
py3nvml.grab_gpus(num_gpus=gpu_count, gpu_fraction=0.95)
environ = dict(os.environ)
environ['LD_PRELOAD'] = os.path.abspath(os.path.dirname(__file__)) + "/limit_visible_cpus.so"
environ['OMP_NUM_THREADS'] = str(cpu_count)
# This call never returns.
os.execvpe(command[0], command, environ)
def setup():
py3nvml.grab_gpus(1, gpu_fraction=0.5)
h, m = divmod(m, 60)
return h, m, s
# If seed was not provided, create one and seed numpy and pytorch
if args.seed < 0:
args.seed = np.random.randint(1 << 16)
np.random.seed(args.seed)
random.seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.manual_seed(args.seed)
# Hyperparameter settings
py3nvml.grab_gpus(args.num_gpus,
gpu_select=args.gpu_select,
gpu_fraction=0.7,
max_procs=0)
use_cuda = torch.cuda.is_available()
best_acc = 0
start_epoch, batch_size = 1, args.batch_size
# ##############################################################################
# Model
print('\n[Phase 1] : Model setup')
if len(args.layers_per_scale) == 1:
args.layers_per_scale = args.layers_per_scale[0]
if args.resume:
# Load checkpoint
print('| Resuming from checkpoint...')
chkpt_dir = os.path.join(args.exp_dir, 'chkpt')
def setup():
global mode, o_dim, ri_dim
mode = mode_to_int('symmetric')
o_dim = 2
ri_dim = -1
py3nvml.grab_gpus(1, gpu_fraction=0.5, env_set_ok=True)
def setup():
py3nvml.grab_gpus(1, gpu_fraction=0.5, env_set_ok=True)
def test_zeroout():
py3nvml.grab_gpus(0)
zero_out_module = tf.load_op_library(os.path.join(LIB_BASE, 'zero_out.so'))
with tf.Session() as sess:
result = zero_out_module.zero_out([5, 4, 3, 2, 1])
np.testing.assert_array_equal(result.eval(), np.array([5, 0, 0, 0, 0]))