def __init__(self, devices, global_flags):
global _workerid
signal.signal(signal.SIGINT, signal.SIG_IGN) # Keyboard interrupts go up to main process
globals.set_devices(devices) # On windows we need to do this because we didn't actually fork
globals.flags.copy_from(global_flags)
process = multiprocessing.current_process()
if process.name == "MainProcess":
_workerid = 0
else:
process_type, process_id = process.name.split("-") # Get unique 1-base "process_id", which gets larger every time a new pool is created
_workerid = (int(process_id)-1) % len(devices) # Get unique 0-based "worker_id" index, always in range {0,...,nprocess-1}
# This function is the entry point of a worker process.
#logging.info("prediction %d on device %d" % (_workerid, globals._devices[_workerid]))
sm.set_backend_options(device=globals._devices[_workerid])
def __init__(self, devices, global_flags):
global _workerid
signal.signal(
signal.SIGINT,
signal.SIG_IGN) # Keyboard interrupts go up to main process
globals.set_devices(
devices
) # On windows we need to do this because we didn't actually fork
globals.flags.copy_from(global_flags)
process = multiprocessing.current_process()
if process.name == "MainProcess":
_workerid = 0
else:
process_type, process_id = process.name.split(
"-"
) # Get unique 1-base "process_id", which gets larger every time a new pool is created
_workerid = (int(process_id) - 1) % len(
devices
) # Get unique 0-based "worker_id" index, always in range {0,...,nprocess-1}
# This function is the entry point of a worker process.
#logging.info("prediction %d on device %d" % (_workerid, globals._devices[_workerid]))
sm.set_backend_options(device=globals._devices[_workerid])
parser.add_argument("-d",
"--device",
type=int,
default=None,
help="The device to use, e.g. CUDA device.")
parser.add_argument(
"-m",
"--method",
type=str,
default="L-BFGS-B",
help=
"The optimization algorithm to use. Valid values are COBYLA and L-BFGS-B.")
args = parser.parse_args()
if args.device is not None:
smat.set_backend_options(device=args.device)
print "Using device", smat.get_backend_info().device
print "Using method", args.method, "with float64"
# Load some sample bio data. Specifically this is a subset of the
# RNAcompete protein binding affinities from Ray et al., Nature, 2013.
y = numpy.load('data/rnac/rnac_subset.npz')['y']
n, m = y.shape
def objective_function(x, y, lib):
# The test objective function below happens to be that corresponding to
# "Variance Stabilization" (Huber et al., Bioinformatics, 2002).
# The specific objective is not important.
# The point is that the parameters can be sent to the GPU,
# http://tools.genes.toronto.edu/deepbind/
#
import numpy
import numpy.random
import smat
import smat.util
import argparse
import scipy.optimize
parser = argparse.ArgumentParser(description="Train a 784-1000-1000-10 neural net on MNIST and print out the error rates.")
parser.add_argument("-d","--device",type=int,default=None,help="The device to use, e.g. CUDA device.")
parser.add_argument("-m","--method",type=str,default="L-BFGS-B",help="The optimization algorithm to use. Valid values are COBYLA and L-BFGS-B.")
args = parser.parse_args()
if args.device is not None:
smat.set_backend_options(device=args.device)
print "Using device",smat.get_backend_info().device
print "Using method",args.method,"with float64"
# Load some sample bio data. Specifically this is a subset of the
# RNAcompete protein binding affinities from Ray et al., Nature, 2013.
y = numpy.load('data/rnac/rnac_subset.npz')['y']
n,m = y.shape
def objective_function(x,y,lib):
# The test objective function below happens to be that corresponding to
# "Variance Stabilization" (Huber et al., Bioinformatics, 2002).
# The specific objective is not important.
# The point is that the parameters can be sent to the GPU,
# evaluated, pulled back, and STILL be much faster than CPU.
