def load_and_analyze(args_tuple):
fn, args, dataset, entrystart, entrystop, ismc, ichunk = args_tuple
this_worker = get_worker_wrapper()
NUMPY_LIB, backend = hepaccelerate.choose_backend(args.use_cuda)
print("Loading {0}".format(fn))
ds, timing_results = load_dataset(
args.datapath,
fn,
ismc,
args.nthreads,
args.skim,
NUMPY_LIB,
backend,
entrystart,
entrystop,
)
t0 = time.time()
ret = run_analysis(ds, "{0}_{1}".format(dataset, ichunk),
this_worker.dnnmodel, args.use_cuda, ismc)
t1 = time.time()
ret["timing"] = Results(timing_results)
ret["timing"]["run_analysis"] = t1 - t0
ret["timing"]["num_events"] = ds.numevents()
return ret
def multiprocessing_initializer(args, gpu_id=None):
this_worker = get_worker_wrapper()
# Set up tensorflow
import tensorflow as tf
config = tf.compat.v1.ConfigProto()
config.intra_op_parallelism_threads = args.nthreads
config.inter_op_parallelism_threads = args.nthreads
os.environ["NUMBA_NUM_THREADS"] = str(args.nthreads)
os.environ["OMP_NUM_THREADS"] = str(args.nthreads)
if not args.use_cuda:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
else:
if not gpu_id is None:
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
config.gpu_options.allow_growth = False
gpu_memory_fraction = 0.2
config.gpu_options.per_process_gpu_memory_fraction = gpu_memory_fraction
from keras.backend.tensorflow_backend import set_session
this_worker.session = tf.Session(config=config)
set_session(this_worker.session)
NUMPY_LIB, backend = hepaccelerate.choose_backend(args.use_cuda)
this_worker.dnnmodel = DNNModel(NUMPY_LIB)
this_worker.NUMPY_LIB = NUMPY_LIB
this_worker.backend = backend
# Import kernels that are specific to this analysis
if args.use_cuda:
import cuda_kernels as kernels
else:
import cpu_kernels as kernels
this_worker.kernels = kernels
this_worker.graph = tf.get_default_graph()
# Create random vectors as placeholders of lepton pt event weights
this_worker.electron_weights = NUMPY_LIB.zeros((100, 2),
dtype=NUMPY_LIB.float32)
this_worker.electron_weights[:, 0] = NUMPY_LIB.linspace(
0, 200, this_worker.electron_weights.shape[0])[:]
this_worker.electron_weights[:, 1] = 1.0
# Create random vectors as placeholders of pt-dependent jet energy corrections
this_worker.jecs_bins = NUMPY_LIB.zeros(100, dtype=NUMPY_LIB.float32)
this_worker.jecs_up = NUMPY_LIB.zeros((99, args.njec),
dtype=NUMPY_LIB.float32)
this_worker.jecs_down = NUMPY_LIB.zeros((99, args.njec),
dtype=NUMPY_LIB.float32)
this_worker.jecs_bins[:] = NUMPY_LIB.linspace(
0, 200, this_worker.jecs_bins.shape[0])[:]
for i in range(args.njec):
this_worker.jecs_up[:, i] = 0.3 * (float(i + 1) / float(args.njec))
this_worker.jecs_down[:, i] = -0.3 * (float(i + 1) / float(args.njec))