def train_partition(idx, iterator):
port = 50000 + idx % 256
main = SparkFiles.get("main.py")
architecture = SparkFiles.get("train_val.prototxt")
model = SparkFiles.get("deepq16.caffemodel")
solver = SparkFiles.get("solver.prototxt")
root = SparkFiles.getRootDirectory()
dset = os.path.join(root, "dset-%02d.hdf5" % idx)
flag_file = "flags/__BARISTA_READY__.%d" % port
if os.path.isfile(flag_file):
os.remove(flag_file)
# out = open(os.path.join(root, "barista.log"), 'w')
subprocess.Popen(["python", main, architecture, model,
"--dataset", dset,
"--solver", solver,
"--dset-size", "30000",
"--initial-replay", "20000",
"--debug",
"--overwrite",
"--port", str(port)])
while not os.path.isfile(flag_file):
pass
for step in iterator:
dc = DummyClient("127.0.0.1", port)
dc.send(barista.GRAD_UPDATE)
response = dc.recv()
yield response
def train_partition(idx, iterator):
port = 50000 + idx % 256
main = SparkFiles.get("main.py")
architecture = SparkFiles.get("train_val.prototxt")
model = SparkFiles.get("deepq16.caffemodel")
solver = SparkFiles.get("solver.prototxt")
root = SparkFiles.getRootDirectory()
dset = os.path.join(root, "dset-%02d.hdf5" % idx)
flag_file = "flags/__BARISTA_READY__.%d" % port
if os.path.isfile(flag_file):
os.remove(flag_file)
# out = open(os.path.join(root, "barista.log"), 'w')
subprocess.Popen([
"python", main, architecture, model, "--dataset", dset, "--solver",
solver, "--dset-size", "30000", "--initial-replay", "20000", "--debug",
"--overwrite", "--port",
str(port)
])
while not os.path.isfile(flag_file):
pass
for step in iterator:
dc = DummyClient("127.0.0.1", port)
dc.send(barista.GRAD_UPDATE)
response = dc.recv()
yield response
def sgd_step(step_num):
dc = DummyClient("127.0.0.1", 50001)
dc.send(barista.GRAD_UPDATE)
response = dc.recv()
return response
def sgd_step(step_num):
dc = DummyClient("127.0.0.1", 50001)
dc.send(barista.GRAD_UPDATE)
response = dc.recv()
return response
