def run(server, port, save_path):
client = ClientCNNAutoSplitter.new_basic_alpha_beta(server, port, 0.0, 1.0)
client.connect()
L_param_desc = client.read_param_desc_from_server()
D_param_values = {}
for param_desc in L_param_desc:
name = param_desc["name"]
value = client.pull_entire_param(name)
assert not np.any(np.isnan(value)), "The server parameter %s contains NaN values" % name
D_param_values[name] = value
print "Read %s of shape %s." % (name, str(param_desc["shape"]))
print "================================"
pickle.dump(D_param_values, open(save_path, "w"))
print "Wrote %s." % save_path
client.quit()
client.close()
def run():
server_host = "127.0.0.1"
port = 6000
(alpha, beta) = (0.0, 1.0)
client = ClientCNNAutoSplitter.new_basic_alpha_beta(server_host, port, alpha, beta)
client.connect()
print client.read_param_desc_from_server()
client.save_all_to_hdf5("test0")
client.load_all_from_hdf5("test0JSON", "test0")
# mini_training()
test_slice(client)
client.quit()
client.close()
def run(server, port, nclients, nreps, nsplits):
# we're not doing the clients in parallel or it'll be an insane mess
for _ in range(nclients):
(alpha, beta) = (np.random.rand(), np.random.rand())
print "Starting with new client. (alpha, beta) are (%0.2f, %0.2f)" % (alpha, beta)
client = ClientCNNAutoSplitter.new_basic_alpha_beta(server, port, alpha, beta)
client.connect()
print " Starting soak."
for __ in range(nreps):
soak(client, alpha, beta, nsplits)
print " Done with soak."
client.quit()
client.close()
print "Done with client."
def run(server, port, load_path):
client = ClientCNNAutoSplitter.new_basic_alpha_beta(server, port, 0.0, 1.0)
client.connect()
L_param_desc = client.read_param_desc_from_server()
# late on we might want to add support for hdf5,
# but currently we'll work only with pickle files
D_params = pickle.load(open(load_path, "r"))
print "Loaded %s." % load_path
# Before we commit stuff to the server, we'll make pretty damn sure
# that we're talking about the same thing.
A = set(D_params.keys())
B = set(e['name'] for e in L_param_desc)
for e in A - B:
print "D_params has parameter %s that the server does not have." % e
for e in B - A:
print "The server has parameter %s that D_params does not have." % e
assert A == B
for param_desc in L_param_desc:
name = param_desc['name']
shape = param_desc['shape']
assert D_params.has_key(name), "Server has parameter %s but the load_path %s does not contain a value for this parameter." % (name, load_path)
assert tuple(D_params[name].shape) == tuple(shape), "Loaded parameter %s from file. It has shape %s, but the server says that it should have shape %s." % (name, D_params[name].shape, shape)
assert not np.any(np.isnan(D_params[name])), "The saved parameter %s contains NaN values." % name
client.push_entire_param(name, D_params[name], 0.0, 1.0)
print "Pushed %s of shape %s." % (name, str(param_desc['shape']))
print "================================"
client.quit()
client.close()
def run(model_desc, train_desc, experiment_dir, saving_path, output_server_params_desc_path=None, force_quit_after_total_duration=None, server_params_desc=None):
# it's okay to not use the `experiment_dir` argument directly, for now
# If `output_server_params_desc_path` is used, then this function will terminate early
# after writing out the json file that the server will need.
# Conceptually, one can run this before the experiment, in order to obtain the
# file to be used for the server. Then we launch the server and we run the thing for real.
if output_server_params_desc_path is not None:
# we need to replace all the exo dropout values in order to generate the json file for the server config
set_all_exo_dropout_in_model_desc_to_zero(model_desc)
print "Setting all the exo dropout values in order to generate the json file for the server config."
want_ignore_endo_dropout = (train_desc.has_key('sync') and
train_desc['sync'].has_key('want_ignore_endo_dropout') and
train_desc['sync']['want_ignore_endo_dropout'] == True)
want_undo_exo_dropout = (train_desc.has_key('sync') and
train_desc['sync'].has_key('want_undo_exo_dropout') and
train_desc['sync']['want_undo_exo_dropout'] == True)
# When we're running the client in "observer" mode,
# we want to get rid of the exo and the endo dropout.
#
# There are multiple ways to go about doing this, but
# for the endo dropout we'll just check if the 'sync'
# component of `train_desc` has a 'want_ignore_endo_dropout'
# key that is set to `True`. If such is the case, we'll just
# mutate the values found in `model_desc` to set them to 0.0
# to achieve the desired effect.
#
# We don't have to scale the parameters to compensate for anything
# due to the way that dropout is implemented in Blocks.
# The implementation is such that the parameters are set to their
# proper values that they would have if we ignored the endo dropout,
# so we don't have to compute the equivalent of the `D_rescale_factor_exo_dropout`.
if want_ignore_endo_dropout:
print "Overriding ENDO dropout as requested by the train_desc."
for k in ["L_endo_dropout_conv_layers", "L_endo_dropout_full_layers"]:
if model_desc.has_key(k):
model_desc[k] = [0.0] * len(model_desc[k])
(cg, error_rate, cost,
D_params, D_kind,
L_exo_dropout,
D_dropout_probs, D_rescale_factor_exo_dropout) = build_model_with_endo_adjustments(model_desc, server_params_desc)
# This `D_rescale_factor_exo_dropout` will be used for the blocks extensions.
# The rest of the returned arguments will be used to setup the other parts of the training.
build_model.build_step_rule_parameters(train_desc['step_flavor'], D_params, D_kind)
(step_rule, D_additional_params, D_additional_kind) = build_model.build_step_rule_parameters(train_desc['step_flavor'], D_params, D_kind)
# merge the two dicts of parameters
D_params = dict(D_params.items() + D_additional_params.items())
D_kind = dict(D_kind.items() + D_additional_kind.items())
print "======================"
for (name, param_var) in sorted(D_params.items(), key=lambda e:e[0]):
print " %s has shape %s" % (name, param_var.get_value(borrow=True, return_internal_type=True).shape)
print "======================"
print ""
# We need to add the corresponding entries in `D_rescale_factor_exo_dropout`
# for all those additional variables.
for (name, param) in D_params.items():
if D_rescale_factor_exo_dropout.has_key(name):
print "Already a dropout entry for %s." % name
continue
for k in D_rescale_factor_exo_dropout.keys():
# check if `k` could be a prefix of `name`
if len(k) < len(name) and name[0:len(k)] == k:
print "D_rescale_factor_exo_dropout[%s] = D_rescale_factor_exo_dropout[%s]" % (name, k)
D_rescale_factor_exo_dropout[name] = D_rescale_factor_exo_dropout[k]
if D_rescale_factor_exo_dropout.has_key(name):
print "Failed to find the dropout entry for %s." % name
continue
if output_server_params_desc_path is not None:
L_server_params_desc = build_model.get_model_desc_for_server(D_params, D_kind)
json.dump(L_server_params_desc, open(output_server_params_desc_path, "w"), indent=4, separators=(',', ': '))
print "Wrote the json file for the server parameter description in %s. Now exiting." % output_server_params_desc_path
return
if train_desc.has_key('server'):
server_desc = train_desc['server']
else:
server_desc = None
client = None
if server_desc is not None:
if not server_desc.has_key('hostname'):
server_desc['hostname'] = "127.0.0.1"
assert server_desc.has_key('port')
assert server_desc.has_key('alpha')
if not server_desc.has_key('beta'):
server_desc['beta'] = 1.0 - server_desc['alpha']
print "(server, port, alpha, beta)"
print (server_desc['hostname'], server_desc['port'], server_desc['alpha'], server_desc['beta'])
client = ClientCNNAutoSplitter.new_basic_alpha_beta(server_desc['hostname'],
server_desc['port'],
server_desc['alpha'],
server_desc['beta'])
client.connect()
E = client.read_param_desc_from_server()
print ""
print "==== read_param_desc_from_server() ===="
for e in sorted(E, key=lambda e: e['name']):
print e
print "==== ===="
print ""
# Note that we don't need to get the parameters here.
# We use the `server_sync_initial_read_extension` to do this job.
sync_desc = train_desc['sync']
# At this point we strip away the keys in `sync_desc` that
# are not used by the extensions.
# This is not a great practice to do, but
# we're already aware that the is a bit of a conceptual
# mismatch in our choice of having those keys in `sync_desc`
# alongside the arguments to the extensions themselves.
for key in ['want_undo_exo_dropout',
'want_ignore_endo_dropout']:
if sync_desc.has_key(key):
del sync_desc[key]
for key in sync_desc:
assert key in [ 'want_read_only',
'max_time_ratio_spent',
'momentum_weights_scaling',
'want_sync_timing_log'], "Unrecognized key : %s" % key
if sync_desc.has_key('r'):
print "The 'r' value in the 'sync' dictionary is now called 'max_time_ratio_spent'."
print "Change your configuration file to reflect this."
print "Exiting."
exit()
dataset_desc = train_desc['dataset']
for key in dataset_desc:
assert key in ['hdf5_file', 'want_subset_valid', 'want_eval_on_valid',
'want_eval_on_test', 'want_subset_test']
# Run extension at every iteration, but that doesn't mean that we're updating at every iteration.
# It just means that we'll consider updating if the timing is good (in order to respect the
# ratio `max_time_ratio_spent` of time spend synching vs total).
server_sync_extension_auto_timing = ServerSyncAutoAdjustTiming( client, D_dropout_probs,
D_params,
every_n_batches=1, verbose=True,
D_rescale_factor_exo_dropout=D_rescale_factor_exo_dropout,
**sync_desc)
import copy
sync_desc_override_with_read_only = copy.copy(sync_desc)
sync_desc_override_with_read_only['want_read_only'] = True
server_sync_initial_read_extension = ServerSyncAutoAdjustTiming(client, D_dropout_probs,
D_params,
before_training=True, verbose=True,
D_rescale_factor_exo_dropout=D_rescale_factor_exo_dropout,
**sync_desc_override_with_read_only)
if client is None:
server_sync_initial_read_extension = None
server_sync_extension_auto_timing = None
print "WARNING : No client. Setting the sync extensions to be None."
print "Asked to run for force_quit_after_total_duration = %d seconds." % force_quit_after_total_duration
main_loop = build_training.build_training(cg, error_rate, cost, step_rule,
weight_decay_factor=train_desc['weight_decay_factor'],
hdf5_file=dataset_desc['hdf5_file'],
want_subset_valid=dataset_desc['want_subset_valid'],
want_eval_on_valid=dataset_desc['want_eval_on_valid'],
want_eval_on_test=dataset_desc['want_eval_on_test'],
want_subset_test=dataset_desc['want_subset_test'],
batch_size=train_desc['batch_size'],
nbr_epochs=train_desc['nbr_epochs'],
saving_path=saving_path,
server_sync_extension=server_sync_extension_auto_timing,
server_sync_initial_read_extension=server_sync_initial_read_extension,
monitor_interval_nbr_batches=train_desc['monitor_interval_nbr_batches'],
force_quit_after_total_duration=force_quit_after_total_duration)
main_loop.run()