def experiment(state, channel):
"""
Experiment function.
Used by jobman to run jobs. Must be loaded externally.
Parameters
----------
state: WRITEME
channel: WRITEME
"""
yaml_template = open(yaml_file).read()
hyper_parameters = expand(flatten(state.hyper_parameters), dict_type=ydict)
file_params = expand(flatten(state.file_parameters), dict_type=ydict)
# Hack to fill in file parameter strings first
for param in file_params:
yaml_template = yaml_template.replace("%%(%s)s" % param,
file_params[param])
yaml = yaml_template % hyper_parameters
# with open("/na/homes/dhjelm/pylearn2/pylearn2/jobman/nice_lr_search/%d.yaml"
# % state.id, "w") as f:
# f.write(yaml)
train_object = yaml_parse.load(yaml)
train_object.main_loop()
state.results = extract_results(train_object.model)
return channel.COMPLETE
def main(args):
# Create a new database
db = sql.db('%s?table=%s' % (args.database, args.table_name))
# Create a jobman state
state = DD()
# Loop over the search space and schedule jobs
config_generator = gen_configurations(args.configurations)
for i, params in enumerate(config_generator):
# Complete parameters dictionary and add to the state
state.parameters = params
state.parameters['model'] = args.model
state.parameters['dataset'] = args.dataset
state.parameters['nb_epoch'] = args.n_epochs
state.parameters['batch_size'] = args.batch_size
# Insert the job into the database
if args.model == 'ADIOS':
state.parameters['labels_order'] = args.labels_order
state.parameters['n_label_splits'] = args.n_label_splits
for i in xrange(args.n_label_splits):
state.parameters['label_split'] = i + 1
sql.insert_job(train_adios, flatten(state), db)
else: # args.model == 'MLP'
sql.insert_job(train_mlp, flatten(state), db)
# Create a view for the new database table
db.createView(args.table_name + '_view')
def experiment(state, channel):
"""
Experiment function.
Used by jobman to run jobs. Must be loaded externally.
Parameters
----------
state: WRITEME
channel: WRITEME
"""
yaml_template = open(yaml_file).read()
hyper_parameters = expand(flatten(state.hyper_parameters), dict_type=ydict)
file_params = expand(flatten(state.file_parameters), dict_type=ydict)
# Hack to fill in file parameter strings first
for param in file_params:
yaml_template = yaml_template.replace("%%(%s)s" % param, file_params[param])
yaml = yaml_template % hyper_parameters
train_object = yaml_parse.load(yaml)
state.pid = os.getpid()
channel.save()
train_object.main_loop()
state.results = extract_results(train_object.model)
return channel.COMPLETE
def run_experiment_jobman(state, channel):
"""
Main jobman experiment function, called by all jobs.
"""
experiment_module = state["experiment&module"]
experiment = imp.load_source("module.name", experiment_module)
yaml_template = open(experiment.yaml_file).read()
hyperparams = expand(flatten(translate(state.hyperparams, "pylearn2")),
dict_type=ydict)
if not state["out&path"].endswith("job_%d" % state["jobman.id"]):
state["out&path"] = path.join(state["out&path"],
"job_%d" % state["jobman.id"])
channel.save()
out_path = path.join(state["out&path"])
try:
run_experiment(experiment,
hyperparams,
ask=False,
out_path=out_path,
dbdescr=state["dbdescr"],
job_id=state["jobman.id"])
except ValueError as e:
if str(e) == "KILLED":
return channel.CANCELED
else:
return channel.ERR_RUN
except:
return channel.ERR_RUN
print "Ending experiment"
return channel.COMPLETE
def create_jobman_jobs():
#Database operations
TABLE_NAME = "arcade_post_mlp_cv_binary_8x8_40k"
db = api0.open_db('postgresql://[email protected]/gulcehrc_db?table=' + TABLE_NAME)
ri = numpy.random.random_integers
# Default values
state = DD()
state.dataset = \
"/home/gulcehre/dataset/pentomino/experiment_data/pento64x64_40k_seed_23112222.npy"
state.no_of_folds = 5
state.exid = 0
state.n_hiddens = [100, 200, 300]
state.n_hidden_layers = 3
state.learning_rate = 0.001
state.l1_reg = 1e-5
state.l2_reg = 1e-3
state.n_epochs = 2
state.batch_size = 120
state.save_exp_data = True
self.no_of_patches = 64
state.cost_type = "crossentropy"
state.n_in = 8*8
state.n_out = 1
state.best_valid_error = 0.0
state.best_test_error = 0.0
state.valid_obj_path_error = 0.0
state.test_obj_path_error = 0.0
l1_reg_values = [0., 1e-6, 1e-5, 1e-4]
l2_reg_values = [0., 1e-5, 1e-4]
learning_rates = numpy.logspace(numpy.log10(0.0001), numpy.log10(1), 36)
num_hiddens = numpy.logspace(numpy.log10(256), numpy.log10(2048), 24)
for i in xrange(NO_OF_TRIALS):
state.exid = i
state.n_hidden_layers = ri(4)
n_hiddens = []
for i in xrange(state.n_hidden_layers):
n_hiddens.append(int(num_hiddens[ri(num_hiddens.shape[0]) - 1]))
state.n_hiddens = n_hiddens
state.learning_rate = learning_rates[ri(learning_rates.shape[0]) - 1]
state.l1_reg = l1_reg_values[ri(len(l1_reg_values)) - 1]
state.l2_reg = l2_reg_values[ri(len(l2_reg_values)) - 1]
sql.insert_job(experiment, flatten(state), db)
db.createView(TABLE_NAME + "_view")
def _load_in_safe_session(self,
db,
safe_session,
filter_eq_dct=None,
row_id=None,
hash_of=None):
with safe_session.set_timer(60 * 5):
logger.debug("Query session...")
q = db.query(safe_session.session)
if row_id is not None:
sql_row = q._query.get(row_id)
if sql_row is None:
raise OperationalError("There is no rows with id \"%d\"" %
row_id)
sql_rows = [sql_row]
elif hash_of is not None:
hashcode = sql.hash_state(flatten(hash_of))
sql_rows = q._query.filter(db._Dict.hash == hashcode).all()
elif filter_eq_dct is not None:
sql_rows = q.filter_eq_dct(filter_eq_dct).all()
else:
sql_rows = q.all()
logger.debug("Query done")
return sql_rows
def main(args):
dataset_name = args.dataset_name
logger.info("Getting dataset info for %s" % dataset_name)
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + dataset_name)
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
user = path.expandvars("$USER")
save_path = serial.preprocess("/export/mialab/users/%s/pylearn2_outs/%s"
% (user, "rbm_simple_test"))
# File parameters are path specific ones (not model specific).
file_params = {"save_path": save_path,
}
yaml_template = open(yaml_file).read()
hyperparams = expand(flatten(experiment.default_hyperparams(input_dim=input_dim)),
dict_type=ydict)
# Set additional hyperparams from command line args
if args.learning_rate is not None:
hyperparams["learning_rate"] = args.learning_rate
if args.batch_size is not None:
hyperparams["batch_size"] = args.batch_size
for param in file_params:
yaml_template = yaml_template.replace("%%(%s)s" % param, file_params[param])
yaml = yaml_template % hyperparams
logger.info("Training")
train = yaml_parse.load(yaml)
train.main_loop()
def run_experiment_jobman(state, channel):
"""
Main jobman experiment function, called by all jobs.
"""
experiment_module = state["experiment&module"]
experiment = imp.load_source("module.name", experiment_module)
yaml_template = open(experiment.yaml_file).read()
hyperparams = expand(flatten(translate(state.hyperparams, "pylearn2")),
dict_type=ydict)
if not state["out&path"].endswith("job_%d" % state["jobman.id"]):
state["out&path"] = path.join(state["out&path"],
"job_%d" % state["jobman.id"])
channel.save()
out_path = path.join(state["out&path"])
try:
run_experiment(experiment, hyperparams, ask=False, out_path=out_path,
dbdescr=state["dbdescr"], job_id=state["jobman.id"])
except ValueError as e:
if str(e) == "KILLED":
return channel.CANCELED
else:
return channel.ERR_RUN
except:
return channel.ERR_RUN
print "Ending experiment"
return channel.COMPLETE
def save(self, table_name, row):
db = self._open_db(table_name)
with SafeSession(db) as safe_session:
if "id" in row:
row_id = row["id"]
del row["id"]
sql_row = self._load_in_safe_session(db=db,
safe_session=safe_session,
row_id=row_id)[0]
logger.debug("update row %d" % row_id)
with safe_session.set_timer(60 * 5):
sql_row.update_simple(flatten(row), safe_session.session)
else:
logger.debug("insert new row")
with safe_session.set_timer(60 * 5):
sql_row = sql.insert_dict(flatten(row),
db,
session=safe_session.session)
if sql_row is None:
raise OperationalError("Identical row already exists")
sql_row._set_in_session(sql.JOBID, sql_row.id,
safe_session.session)
row_id = sql_row.id
with safe_session.set_timer(60 * 5):
safe_session.session.commit()
logger.debug("session commited")
# load in new session otherwise lazy attribute selection hangs
# on forever... why is that!?
with SafeSession(db) as safe_session:
sql_row = self._load_in_safe_session(db,
safe_session,
row_id=row_id)[0]
logger.debug("Fetch all row attributes...")
eager_dict = self._eager_dicts([sql_row], safe_session)[0]
logger.debug("Fetch done")
eager_dict['id'] = row_id
return eager_dict
def set_hyperparams(job, db):
dbjob = db.get(job.job_id)
job.file_prefix = dbjob["file_parameters.save_path"]
job.out_dir = path.join(job.table_dir, job.file_prefix.split("/")[-1])
hyperparams = experiment.default_hyperparams()
model_keys = flatten(hyperparams).keys()
job.hyperparams = dict((" ".join(k.replace(".__builder__", "").split(".")),
dbjob.get("hyper_parameters." + k, None))
for k in model_keys)
def save(self, table_name, row):
db = self._open_db(table_name)
with SafeSession(db) as safe_session:
if "id" in row:
row_id = row["id"]
del row["id"]
sql_row = self._load_in_safe_session(
db=db, safe_session=safe_session, row_id=row_id)[0]
logger.debug("update row %d" % row_id)
with safe_session.set_timer(60 * 5):
sql_row.update_simple(flatten(row), safe_session.session)
else:
logger.debug("insert new row")
with safe_session.set_timer(60 * 5):
sql_row = sql.insert_dict(flatten(row), db,
session=safe_session.session)
if sql_row is None:
raise OperationalError("Identical row already exists")
sql_row._set_in_session(sql.JOBID, sql_row.id,
safe_session.session)
row_id = sql_row.id
with safe_session.set_timer(60 * 5):
safe_session.session.commit()
logger.debug("session commited")
# load in new session otherwise lazy attribute selection hangs
# on forever... why is that!?
with SafeSession(db) as safe_session:
sql_row = self._load_in_safe_session(db, safe_session,
row_id=row_id)[0]
logger.debug("Fetch all row attributes...")
eager_dict = self._eager_dicts([sql_row], safe_session)[0]
logger.debug("Fetch done")
eager_dict['id'] = row_id
return eager_dict
def set_hyperparams(job, db):
dbjob = db.get(job.job_id)
job.file_prefix = dbjob["file_parameters.save_path"]
job.out_dir = path.join(job.table_dir,
job.file_prefix.split("/")[-1])
hyperparams = experiment.default_hyperparams()
model_keys = flatten(hyperparams).keys()
job.hyperparams = dict(
(" ".join(k.replace(".__builder__", "").split(".")),
dbjob.get("hyper_parameters." + k, None))
for k in model_keys)
def insert_jobexp(exp_args, jobman_args):
"""
Insert jobman experiment jobs in a sql database.
Remarks: We have this in a separate module since we can't refer to the jobexp.run function
from within the jobexp module by it's absolute name (else jobman won't find the experiment when run)
:param exp_args:
:param jobman_args:
:return:
"""
table_name = jobman_args.get("table_name", "experiment")
db = api0.open_db('postgres://*****:*****@127.0.0.1/jobbase?table='+table_name)
for arg in jobman_args:
sql.insert_job(predictive_rl.rlglueexp.jobexp.run, flatten(arg), db)
def update(self, table_name, rows, update_dict):
db = self._open_db(table_name)
with SafeSession(db) as safe_session:
with safe_session.set_timer(60 * 5):
for row in rows:
sql_row = self._load_in_safe_session(
db=db, safe_session=safe_session, row_id=row['id'])[0]
try:
sql_row.update_simple(flatten(update_dict), safe_session.session)
except:
sys.stderr.write("Failed for row: %d\n" % row['id'])
safe_session.session.commit()
def update(self, table_name, rows, update_dict):
db = self._open_db(table_name)
with SafeSession(db) as safe_session:
with safe_session.set_timer(60 * 5):
for row in rows:
sql_row = self._load_in_safe_session(
db=db, safe_session=safe_session, row_id=row['id'])[0]
try:
sql_row.update_simple(flatten(update_dict),
safe_session.session)
except:
sys.stderr.write("Failed for row: %d\n" % row['id'])
safe_session.session.commit()
def train_experiment(state, channel):
"""
Train a model specified in state, and extract required results.
This function builds a YAML string from ``state.yaml_template``, taking
the values of hyper-parameters from ``state.hyper_parameters``, creates
the corresponding object and trains it (like train.py), then run the
function in ``state.extract_results`` on it, and store the returned values
into ``state.results``.
To know how to use this function, you can check the example in tester.py
(in the same directory).
"""
yaml_template = state.yaml_template
# Convert nested DD into nested ydict.
hyper_parameters = expand(flatten(state.hyper_parameters), dict_type=ydict)
# This will be the complete yaml string that should be executed
final_yaml_str = yaml_template % hyper_parameters
# Instantiate an object from YAML string
train_obj = pylearn2.config.yaml_parse.load(final_yaml_str)
for ext in train_obj.extensions:
if hasattr(ext, 'set_train_obj'):
ext.set_train_obj(train_obj)
if hasattr(ext, 'set_jobman_channel'):
ext.set_jobman_channel(channel)
if hasattr(ext, 'set_jobman_state'):
ext.set_jobman_state(state)
try:
iter(train_obj)
iterable = True
except TypeError:
iterable = False
if iterable:
raise NotImplementedError(
('Current implementation does not support running multiple '
'models in one yaml string. Please change the yaml template '
'and parameters to contain only one single model.'))
else:
# print "Executing the model."
train_obj.main_loop()
# This line will call a function defined by the user and pass train_obj
# to it.
state.results = jobman.tools.resolve(state.extract_results)(train_obj)
return channel.COMPLETE
def train_experiment(state, channel):
"""
Train a model specified in state, and extract required results.
This function builds a YAML string from ``state.yaml_template``, taking
the values of hyper-parameters from ``state.hyper_parameters``, creates
the corresponding object and trains it (like train.py), then run the
function in ``state.extract_results`` on it, and store the returned values
into ``state.results``.
To know how to use this function, you can check the example in tester.py
(in the same directory).
"""
yaml_template = state.yaml_template
# Convert nested DD into nested ydict.
hyper_parameters = expand(flatten(state.hyper_parameters), dict_type=ydict)
# This will be the complete yaml string that should be executed
final_yaml_str = yaml_template % hyper_parameters
# Instantiate an object from YAML string
train_obj = pylearn2.config.yaml_parse.load(final_yaml_str)
for ext in train_obj.extensions:
if hasattr(ext, 'set_train_obj'):
ext.set_train_obj( train_obj )
if hasattr(ext, 'set_jobman_channel'):
ext.set_jobman_channel( channel )
if hasattr(ext, 'set_jobman_state'):
ext.set_jobman_state( state )
try:
iter(train_obj)
iterable = True
except TypeError:
iterable = False
if iterable:
raise NotImplementedError(
('Current implementation does not support running multiple '
'models in one yaml string. Please change the yaml template '
'and parameters to contain only one single model.'))
else:
# print "Executing the model."
train_obj.main_loop()
# This line will call a function defined by the user and pass train_obj
# to it.
state.results = jobman.tools.resolve(state.extract_results)(train_obj)
return channel.COMPLETE
def run_experiment(experiment, **kwargs):
"""
Experiment function.
Used by jobman to run jobs. Must be loaded externally.
TODO: add sigint handling.
Parameters
----------
experiment: module
Experiment module.
kwargs: dict
Typically hyperparameters.
"""
hyper_parameters = experiment.default_hyperparams()
set_hyper_parameters(hyper_parameters, **kwargs)
file_parameters = experiment.fileparams
set_hyper_parameters(file_parameters, **kwargs)
hyper_parameters.update(file_parameters)
ih = MRIInputHandler()
input_dim, variance_map_file = ih.get_input_params(hyper_parameters)
hyper_parameters["nvis"] = input_dim
hyper_parameters["variance_map_file"] = variance_map_file
pid = os.getpid()
out_path = serial.preprocess(
hyper_parameters.get("out_path", "${PYLEARN2_OUTS}"))
if not path.isdir(out_path):
os.mkdir(out_path)
if not path.isdir(path.join(out_path, "logs")):
os.mkdir(path.join(out_path, "logs"))
hyper_parameters = expand(flatten(hyper_parameters), dict_type=ydict)
lh = LogHandler(experiment, hyper_parameters, out_path, pid)
h = logging.StreamHandler(lh)
monitor.log.addHandler(h)
yaml_template = open(experiment.yaml_file).read()
yaml = yaml_template % hyper_parameters
train_object = yaml_parse.load(yaml)
try:
train_object.main_loop()
lh.finish("COMPLETED")
except KeyboardInterrupt:
print("Quitting...")
lh.finish("KILLED")
def run_experiment(experiment, **kwargs):
"""
Experiment function.
Used by jobman to run jobs. Must be loaded externally.
TODO: add sigint handling.
Parameters
----------
experiment: module
Experiment module.
kwargs: dict
Typically hyperparameters.
"""
hyper_parameters = experiment.default_hyperparams()
set_hyper_parameters(hyper_parameters, **kwargs)
file_parameters = experiment.fileparams
set_hyper_parameters(file_parameters, **kwargs)
hyper_parameters.update(file_parameters)
ih = MRIInputHandler()
input_dim, variance_map_file = ih.get_input_params(hyper_parameters)
hyper_parameters["nvis"] = input_dim
hyper_parameters["variance_map_file"] = variance_map_file
pid = os.getpid()
out_path = serial.preprocess(
hyper_parameters.get("out_path", "${PYLEARN2_OUTS}"))
if not path.isdir(out_path):
os.mkdir(out_path)
if not path.isdir(path.join(out_path, "logs")):
os.mkdir(path.join(out_path, "logs"))
hyper_parameters = expand(flatten(hyper_parameters), dict_type=ydict)
lh = LogHandler(experiment, hyper_parameters, out_path, pid)
h = logging.StreamHandler(lh)
monitor.log.addHandler(h)
yaml_template = open(experiment.yaml_file).read()
yaml = yaml_template % hyper_parameters
train_object = yaml_parse.load(yaml)
try:
train_object.main_loop()
lh.finish("COMPLETED")
except KeyboardInterrupt:
print("Quitting...")
lh.finish("KILLED")
def train_experiment(state, channel):
"""
Train a model specified in state, and extract required results.
This function builds a YAML string from ``state.yaml_template``, taking
the values of hyper-parameters from ``state.hyper_parameters``, creates
the corresponding object and trains it (like train.py), then run the
function in ``state.extract_results`` on it, and store the returned values
into ``state.results``.
To know how to use this function, you can check the example in tester.py
(in the same directory).
"""
yaml_template = state.yaml_template
# Convert nested DD into nested ydict.
hyper_parameters = expand(flatten(state.hyper_parameters), dict_type=ydict)
# This will be the complete yaml string that should be executed
final_yaml_str = yaml_template % hyper_parameters
# write to .yaml file for ease of reproducibility
fp = open('experiment.yaml', 'w')
fp.write('%s' % final_yaml_str[2:])
fp.close()
# Instantiate an object from YAML string
train_obj = pylearn2.config.yaml_parse.load(final_yaml_str)
try:
iter(train_obj)
iterable = True
except TypeError:
iterable = False
if iterable:
raise NotImplementedError(
('Current implementation does not support running multiple '
'models in one yaml string. Please change the yaml template '
'and parameters to contain only one single model.'))
else:
# print "Executing the model."
# (GD) HACK HACK
train_obj.model.jobman_channel = channel
train_obj.model.jobman_state = state
train_obj.main_loop()
return channel.COMPLETE
def train_experiment(state, channel):
"""
Train a model specified in state, and extract required results.
This function builds a YAML string from ``state.yaml_template``, taking
the values of hyper-parameters from ``state.hyper_parameters``, creates
the corresponding object and trains it (like train.py), then run the
function in ``state.extract_results`` on it, and store the returned values
into ``state.results``.
To know how to use this function, you can check the example in tester.py
(in the same directory).
"""
yaml_template = state.yaml_template
# Convert nested DD into nested ydict.
hyper_parameters = expand(flatten(state.hyper_parameters), dict_type=ydict)
# This will be the complete yaml string that should be executed
final_yaml_str = yaml_template % hyper_parameters
# write to .yaml file for ease of reproducibility
fp = open('experiment.yaml', 'w')
fp.write('%s' % final_yaml_str[2:])
fp.close()
# Instantiate an object from YAML string
train_obj = pylearn2.config.yaml_parse.load(final_yaml_str)
try:
iter(train_obj)
iterable = True
except TypeError:
iterable = False
if iterable:
raise NotImplementedError(
('Current implementation does not support running multiple '
'models in one yaml string. Please change the yaml template '
'and parameters to contain only one single model.'))
else:
# print "Executing the model."
# (GD) HACK HACK
train_obj.model.jobman_channel = channel
train_obj.model.jobman_state = state
train_obj.main_loop()
return channel.COMPLETE
def load_experiments_jobman(experiment_module, jobargs):
"""
Load jobs from experiment onto postgresql database table.
"""
dbdescr = get_desc(jobargs)
db = api0.open_db(dbdescr)
experiment = imp.load_source("module.name", experiment_module)
for i, items in enumerate(experiment.generator):
hyperparams = experiment.default_hyperparams
state = DD()
set_hyper_parameters(hyperparams, **dict((k, v) for k, v in items))
state.hyperparams = translate(hyperparams, "knex")
state["out&path"] = path.abspath(jobargs["out_path"])
state["experiment&module"] = path.abspath(experiment_module)
state["dbdescr"] = dbdescr
sql.insert_job(run_experiment_jobman, flatten(state), db)
db.createView("%s" % jobargs["table"])
def main(dataset_name="smri"):
logger.info("Getting dataset info for %s" % args.dataset_name)
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + args.dataset_name)
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
if input_dim % 2 == 1:
input_dim -= 1
mri = MRI.MRI_Standard(which_set="full",
dataset_name=args.dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
variance_map_file = path.join(data_path, "variance_map.npy")
mri_nifti.save_variance_map(mri, variance_map_file)
user = path.expandvars("$USER")
save_path = serial.preprocess("/export/mialab/users/%s/pylearn2_outs/%s"
% (user, "jobman_test"))
file_params = {"save_path": save_path,
"variance_map_file": variance_map_file
}
yaml_template = open(yaml_file).read()
hyperparams = expand(flatten(mlp_experiment.default_hyperparams(input_dim=input_dim)),
dict_type=ydict)
for param in hyperparams:
if hasattr(args, param) and getattr(args, param):
val = getattr(args, param)
logger.info("Filling %s with %r" % (param, val))
hyperparams[param] = type(hyperparams[param])(val)
for param in file_params:
yaml_template = yaml_template.replace("%%(%s)s" % param, file_params[param])
yaml = yaml_template % hyperparams
print yaml
logger.info("Training")
train = yaml_parse.load(yaml)
train.main_loop()
def load_experiments_jobman(experiment_module, jobargs):
"""
Load jobs from experiment onto postgresql database table.
"""
dbdescr = get_desc(jobargs)
db = api0.open_db(dbdescr)
experiment = imp.load_source("module.name", experiment_module)
for i, items in enumerate(experiment.generator):
hyperparams = experiment.default_hyperparams
state = DD()
set_hyper_parameters(hyperparams, **dict((k, v) for k, v in items))
state.hyperparams = translate(hyperparams, "knex")
state["out&path"] = path.abspath(jobargs["out_path"])
state["experiment&module"] = path.abspath(experiment_module)
state["dbdescr"] = dbdescr
sql.insert_job(run_experiment_jobman,
flatten(state),
db)
db.createView("%s" % jobargs["table"])
def _load_in_safe_session(self, db, safe_session,
filter_eq_dct=None, row_id=None, hash_of=None):
with safe_session.set_timer(60 * 5):
logger.debug("Query session...")
q = db.query(safe_session.session)
if row_id is not None:
sql_row = q._query.get(row_id)
if sql_row is None:
raise OperationalError("There is no rows with id \"%d\"" % row_id)
sql_rows = [sql_row]
elif hash_of is not None:
hashcode = sql.hash_state(flatten(hash_of))
sql_rows = q._query.filter(db._Dict.hash == hashcode).all()
elif filter_eq_dct is not None:
sql_rows = q.filter_eq_dct(filter_eq_dct).all()
else:
sql_rows = q.all()
logger.debug("Query done")
return sql_rows
def main(args):
dataset_name = args.dataset_name
logger.info("Getting dataset info for %s" % dataset_name)
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + dataset_name)
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
user = path.expandvars("$USER")
save_path = serial.preprocess("/export/mialab/users/%s/pylearn2_outs/%s" %
(user, "rbm_simple_test"))
# File parameters are path specific ones (not model specific).
file_params = {
"save_path": save_path,
}
yaml_template = open(yaml_file).read()
hyperparams = expand(flatten(
experiment.default_hyperparams(input_dim=input_dim)),
dict_type=ydict)
# Set additional hyperparams from command line args
if args.learning_rate is not None:
hyperparams["learning_rate"] = args.learning_rate
if args.batch_size is not None:
hyperparams["batch_size"] = args.batch_size
for param in file_params:
yaml_template = yaml_template.replace("%%(%s)s" % param,
file_params[param])
yaml = yaml_template % hyperparams
logger.info("Training")
train = yaml_parse.load(yaml)
train.main_loop()
state.hyper_parameters = {
'trainfile': 'train_gray_uvd_rot_1562_31.h5',
'N': 10 * 64,
'batch_size': 64 * 2,
'c1': c1,
'kernel_c1': kernel_c1,
'pool_c1': pool_c1,
'c2': c2,
'kernel_c2': kernel_c2,
'pool_c2': pool_c2,
'irange_c1': irange_c1,
'irange_c2': irange_c2,
'irange_hd1': irange_hd1,
'irange_hd2': irange_hd2,
'irange_out': irange_out,
'hd1': hd1,
'hd2': hd2,
'output_dim': outdim,
'lamda': lamda,
'decay': decay,
'max_epochs': 100,
'save_best_path': save_best_path
}
yaml_template = state.yaml_template
hyper_parameters = expand(flatten(state.hyper_parameters), dict_type=ydict)
# This will be the complete yaml string that should be executed
final_yaml_str = yaml_template % hyper_parameters
train_obj = pylearn2.config.yaml_parse.load(final_yaml_str)
train_obj.main_loop()
'n': 2*64,
'batch_size': 64,
'c1': c1,
'kernel_c1':kernel_c1,
'pool_c1':pool_c1,
'c2': c2,
'kernel_c2':kernel_c2,
'pool_c2':pool_c2,
'irange_c1':irange_c1,
'irange_c2':irange_c2,
'irange_hd1':irange_hd1,
'irange_hd2':irange_hd2,
'irange_out':irange_out,
'hd1': 2592,
'hd2': 36,
'output_dim':constants.NUM_JNTS * 3,
'lamda':lamda,
'decay':decay,
'max_epochs': 50,
'save_best_path': save_best_path
}
yaml_template = state.yaml_template
hyper_parameters = expand(flatten(state.hyper_parameters), dict_type=ydict)
# This will be the complete yaml string that should be executed
final_yaml_str = yaml_template % hyper_parameters
train_obj = pylearn2.config.yaml_parse.load(final_yaml_str)
train_obj.main_loop()
# hadag
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db("postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s"
% {"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
logger.info("Getting dataset info for %s%s"
% (dataset_name, ", transposed" if args.transposed else ""))
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + args.dataset_name)
if args.transposed:
logger.info("Data in transpose...")
mri = MRI.MRI_Transposed(dataset_name=args.dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
input_dim = mri.X.shape[1]
variance_map_file = path.join(data_path, "transposed_variance_map.npy")
else:
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
if input_dim % 2 == 1:
input_dim -= 1
mri = MRI.MRI_Standard(which_set="full",
dataset_name=args.dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
variance_map_file = path.join(data_path, "variance_map.npy")
mri_nifti.save_variance_map(mri, variance_map_file)
for items in nested_generator(layer_depth_generator("encoder.layer_depths",
xrange(4, 6), 5),
hidden_generator("encoder.nhid", 4),
float_generator("weight_decay.coeffs.z", 3, 0.1, 0.001, log_scale=True)):
# logger.info("Adding NICE experiment with hyperparameters %s" % (items, ))
state = DD()
experiment_hyperparams = nice_experiment.default_hyperparams(input_dim)
if args.transposed:
experiment_hyperparams["data_class"] = "MRI_Transposed"
if args.logistic:
experiment_hyperparams["prior"]["__builder__"] =\
"nice.pylearn2.models.nice.StandardLogistic"
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list) else v for v in flatten(experiment_hyperparams).values()])))
user = path.expandvars("$USER")
save_path = serial.preprocess("/export/mialab/users/%s/pylearn2_outs/%d"
% (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
sql.insert_job(
nice_experiment.experiment,
flatten(state),
db
)
db.createView("%s_view" % args.table)
def load_experiments(args):
dataset_name = args.dataset_name
# Load the database and table.
db = sql.db(
"postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s" %
{
"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
# Don't worry about this yet.
input_handler = InputHandler()
# For generating models, we use a special set of jobman generators, made
# for convenience.
for items in jg.nested_generator(
jg.float_generator("learning_rate",
3,
0.01,
0.0001,
log_scale=True),
jg.list_generator("nhid", [50, 100, 200, 300]),
):
logger.info("Adding RBM experiment across hyperparameters %s" %
(items, ))
state = DD()
# Load experiment hyperparams from experiment
experiment_hyperparams = experiment.default_hyperparams()
# Set them with values in our loop.
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
assert split_keys[-1] in entry, (
"Key not found in hyperparams: %s" % split_keys[-1])
entry[split_keys[-1]] = value
# Set the dataset name
experiment_hyperparams["dataset_name"] = dataset_name
# Get the input dim and variance map. Don't worry about variance maps right now,
# they aren't used here.
input_dim, variance_map_file = input_handler.get_input_params(
args, experiment_hyperparams)
logger.info("%s\n%s\n" % (input_dim, variance_map_file))
# Set the input dimensionality by the data
experiment_hyperparams["nvis"] = input_dim
# Set the minimum learning rate relative to the initial learning rate.
experiment_hyperparams[
"min_lr"] = experiment_hyperparams["learning_rate"] / 10
# Make a unique hash for experiments. Remember that lists, dicts, and other data
# types may not be hashable, so you may need to do some special processing. In
# this case we convert the lists to tuples.
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list)
else v
for v in flatten(experiment_hyperparams).values()])))
# Save path for the experiments. In this case we are sharing a directory in my
# export directory so IT can blame me.
save_path = serial.preprocess(
"/export/mialab/users/dhjelm/pylearn2_outs/rbm_demo/%d" % h)
# We save file params separately as they aren't model specific.
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
user = path.expandvars("$USER")
state.created_by = user
# Finally we add the experiment to the table.
sql.insert_job(experiment.experiment, flatten(state), db)
# A view can be used when querying the database using psql. May not be needed in future.
db.createView("%s_view" % args.table)
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db(
"postgres://%(user)s@%(host)s:"
"%(port)d/%(database)s?table=%(table)s" % {
"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
input_handler = InputHandler()
for items in jg.nested_generator(
jg.list_generator(
"encoder.layer_depths",
[[3, 5, 5, 5, 3], [5, 5, 5, 5, 5], [2, 4, 4, 2]]),
jg.list_generator("variance_normalize", [False, 2]),
jg.float_generator("weight_decay.coeff",
4,
0.1,
0.0001,
log_scale=True),
jg.list_generator("prior.__builder__", [
"nice.pylearn2.models.nice.StandardNormal",
"nice.pylearn2.models.nice.StandardLogistic"
])):
logger.info("Adding NICE experiment across hyperparameters %s" %
(items, ))
state = DD()
experiment_hyperparams = nice_experiment.default_hyperparams()
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
assert split_keys[-1] in entry,\
("Key not found in hyperparams: %s, "
"found: %s" % (split_keys[-1], entry.keys()))
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
input_dim, variance_map_file = input_handler.get_input_params(
args, experiment_hyperparams)
logger.info("%s\n%s\n" % (input_dim, variance_map_file))
experiment_hyperparams["nvis"] = input_dim
experiment_hyperparams["encoder"]["nvis"] = input_dim
h = abs(hash(frozenset(
flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list)
else v for v in flatten(experiment_hyperparams).values()])))
user = path.expandvars("$USER")
save_path = serial.preprocess(
"/export/mialab/users/%s/pylearn2_outs/%d" % (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
sql.insert_job(nice_experiment.experiment, flatten(state), db)
db.createView("%s_view" % args.table)
from jobman.tools import DD, flatten
from jobman import api0, sql
from jobman.examples.def_addition import addition_example
TABLE_NAME = 'test_add_'
# DB path...
db = api0.open_db('postgres://<user>:<pass>@<server>/<database>?table=' +
TABLE_NAME)
state = DD()
for first in 0, 2, 4, 6, 8, 10:
state.first = first
for second in 1, 3, 5, 7, 9:
state.second = second
sql.insert_job(addition_example, flatten(state), db)
def create_jobman_jobs():
#Database operations
TABLE_NAME = "arcade_multi_prmlp_cv_binary_8x8_40k"
db = api0.open_db(
'postgresql://[email protected]/gulcehrc_db?table=' +
TABLE_NAME)
ri = numpy.random.random_integers
# Default values
state = DD()
state.dataset = \
"/home/gulcehre/dataset/pentomino/experiment_data/pento64x64_40k_seed_23112222.npy"
state.no_of_folds = 5
state.exid = 0
state.n_hiddens = [100, 200, 300]
state.n_hidden_layers = 3
state.learning_rate = 0.001
state.l1_reg = 1e-5
state.l2_reg = 1e-3
state.n_epochs = 2
state.batch_size = 120
state.save_exp_data = True
self.no_of_patches = 64
state.cost_type = "crossentropy"
state.n_in = 8 * 8
state.n_out = 1
state.best_valid_error = 0.0
state.best_test_error = 0.0
state.valid_obj_path_error = 0.0
state.test_obj_path_error = 0.0
l1_reg_values = [0., 1e-6, 1e-5, 1e-4]
l2_reg_values = [0., 1e-5, 1e-4]
learning_rates = numpy.logspace(numpy.log10(0.0001), numpy.log10(1), 36)
num_hiddens = numpy.logspace(numpy.log10(256), numpy.log10(2048), 24)
for i in xrange(NO_OF_TRIALS):
state.exid = i
state.n_hidden_layers = ri(4)
n_hiddens = []
for i in xrange(state.n_hidden_layers):
n_hiddens.append(int(num_hiddens[ri(num_hiddens.shape[0]) - 1]))
state.n_hiddens = n_hiddens
state.learning_rate = learning_rates[ri(learning_rates.shape[0]) - 1]
state.l1_reg = l1_reg_values[ri(len(l1_reg_values)) - 1]
state.l2_reg = l2_reg_values[ri(len(l2_reg_values)) - 1]
sql.insert_job(experiment, flatten(state), db)
db.createView(TABLE_NAME + "_view")
def run_experiment(experiment,
hyper_parameters=None,
ask=True,
keep=False,
dbdescr=None,
job_id=None,
debug=False,
dataset_root="${PYLEARN2_NI_PATH}",
**kwargs):
"""
Experiment function.
Used by jobman to run jobs. Must be loaded externally.
TODO: add sigint handling.
Parameters
----------
experiment: module
Experiment module.
kwargs: dict
Typically hyperparameters.
"""
# Fill the hyperparameter values.
if hyper_parameters is None:
hyper_parameters = experiment.default_hyperparams
set_hyper_parameters(hyper_parameters, **kwargs)
file_parameters = experiment.fileparams
set_hyper_parameters(file_parameters, **kwargs)
hyper_parameters.update(file_parameters)
# Set the output path, default from environment variable $PYLEARN2_OUTS
out_path = serial.preprocess(
hyper_parameters.get("out_path", "${PYLEARN2_OUTS}"))
if not path.isdir(out_path):
os.makedirs(out_path)
processing_flag = mp.Value("b", False)
mem = mp.Value("f", 0.0)
cpu = mp.Value("f", 0.0)
last_processed = mp.Manager().dict()
last_processed["value"] = "Never"
lh = LogHandler(experiment, out_path, processing_flag, mem, cpu,
last_processed, dbdescr, job_id)
h = logging.StreamHandler(lh)
lh.logger.info("Hijacking pylearn2 logger (sweet)...")
monitor.log.addHandler(h)
try:
# HACK TODO: fix this. For some reason knex formatted strings are
# sometimes getting in.
hyper_parameters = translate(hyper_parameters, "pylearn2")
# Use the input hander to get input information.
ih = input_handler.MRIInputHandler()
input_dim, variance_map_file = ih.get_input_params(
hyper_parameters, dataset_root=dataset_root)
if hyper_parameters["nvis"] is None:
hyper_parameters["nvis"] = input_dim
# Hack for NICE. Need to rethink inner-dependencies of some model params.
if ("encoder" in hyper_parameters.keys()
and "nvis" in hyper_parameters["encoder"].keys()
and hyper_parameters["encoder"]["nvis"] is None):
hyper_parameters["encoder"]["nvis"] = input_dim
# If there's min_lr, make it 1/10 learning_rate
if "min_lr" in hyper_parameters.keys():
hyper_parameters["min_lr"] = hyper_parameters["learning_rate"] / 10
# Corruptor is a special case of hyper parameters that depends on input
# file: variance_map. So we hack it in here.
if "corruptor" in hyper_parameters.keys():
if "variance_map" in hyper_parameters["corruptor"].keys():
hyper_parameters["corruptor"]["variance_map"] =\
"!pkl: %s" % variance_map_file
else:
hyper_parameters["variance_map_file"] = variance_map_file
lh.write_json()
# The Process id
pid = os.getpid()
lh.logger.info("Proces id is %d" % pid)
# If any pdfs are in out_path, kill or quit
json_file = path.join(out_path, "analysis.json")
if (ask and (path.isfile(json_file)
or len(glob.glob(path.join(out_path, "*.pkl"))) > 0)):
print("Results found in %s " "Proceeding will erase." % out_path)
command = None
while not command in ["yes", "no", "y", "n"]:
command = raw_input("%s: " % "Proceed?")
if command in ["yes", "y"]:
break
elif command in ["no", "n"]:
exit()
else:
print("Please enter yes(y) or no(n)")
if path.isfile(json_file):
with open(json_file) as f:
models = json.load(f)
for model in models.keys():
lh.logger.info("Removing results for model %s" % model)
try:
os.rmdir(path.join(out_path, "%s_images" % model))
except:
pass
os.remove(json_file)
for pkl in glob.glob(path.join(out_path, "*.pkl")):
lh.logger.info("Removing %s" % pkl)
os.remove(pkl)
lh.logger.info("Making the train object")
hyper_parameters = expand(flatten(hyper_parameters), dict_type=ydict)
yaml_template = open(experiment.yaml_file).read()
yaml = yaml_template % hyper_parameters
train_object = yaml_parse.load(yaml)
if debug:
return train_object
lh.write_json()
lh.logger.info("Seting up subprocesses")
lh.logger.info("Setting up listening socket")
mp_ep, s_ep = mp.Pipe()
p = mp.Process(target=server, args=(pid, s_ep))
p.start()
port = mp_ep.recv()
lh.logger.info("Listening on port %d" % port)
lh.logger.info("Starting model processor")
model_processor = ModelProcessor(experiment, train_object.save_path,
mp_ep, processing_flag,
last_processed)
model_processor.start()
lh.logger.info("Model processor started")
lh.logger.info("Starting stat processor")
stat_processor = StatProcessor(pid, mem, cpu)
stat_processor.start()
lh.logger.info("Stat processor started")
lh.update(hyperparams=hyper_parameters, yaml=yaml, pid=pid, port=port)
lh.write_json()
except Exception as e:
lh.logger.exception(e)
lh.finish("FAILED")
raise e
# Clean the model after running
def clean():
p.terminate()
lh.logger.info("waiting for server...")
p.join()
model_processor.terminate()
lh.logger.info("waiting for model processor...")
model_processor.join()
stat_processor.terminate()
lh.logger.info("waiting for stat processor...")
stat_processor.join()
if keep:
lh.logger.info("Keeping checkpoints")
else:
lh.logger.info("Cleaning checkpoints")
try:
os.remove(model_processor.best_checkpoint)
except:
pass
try:
os.remove(model_processor.checkpoint)
except:
pass
# A signal handler so processes kill cleanly.
def signal_handler(signum, frame):
lh.logger.info("Forced quitting...")
clean()
lh.finish("KILLED")
if dbdescr is None:
exit()
else:
raise ValueError("KILLED")
signal.signal(signal.SIGINT, signal_handler)
# Main loop.
try:
lh.logger.info("Training...")
train_object.main_loop()
lh.logger.info("Training quit without exception")
except Exception as e:
lh.logger.exception(e)
clean()
lh.finish("FAILED")
raise (e)
# After complete, process model.
lh.logger.info("Processing model results...")
try:
experiment.analyze_fn(model_processor.best_checkpoint,
model_processor.out_path)
except IOError:
experiment.analyze_fn(model_processor.checkpoint,
model_processor.out_path)
except Exception as e:
lh.logger.error(e)
# Clean checkpoints.
clean()
lh.logger.info("Finished experiment.")
lh.finish("COMPLETED")
return
state = DD()
state.learning_rate = 0.01
state.L1_reg = 0.00
state.L2_reg = 0.0001
state.n_iter = 50
state.batch_size = 20
state.n_hidden = 10
# Hyperparameter exploration
for n_hidden in 20, 30:
state.n_hidden = n_hidden
# Explore L1 regularization w/o L2
state.L2_reg = 0.
for L1_reg in 0., 1e-6, 1e-5, 1e-4:
state.L1_reg = L1_reg
# Insert job
sql.insert_job(experiment, flatten(state), db)
# Explore L2 regularization w/o L1
state.L1_reg = 0.
for L2_reg in 1e-5, 1e-4:
state.L1_reg = L1_reg
# Insert job
sql.insert_job(experiment, flatten(state), db)
# Create the view
db.createView(TABLE_NAME + '_view')
state.batch_size = 20
state.n_hidden = 10
# Hyperparameter exploration
for n_hidden in 20, 30:
print "h_hidden =",h_hidden
state.n_hidden = n_hidden
# Explore L1 regularization w/o L2
state.L2_reg = 0.
for L1_reg in 0., 1e-6, 1e-5, 1e-4:
print "L1_reg =",L1_reg
state.L1_reg = L1_reg
# Insert job
sql.insert_job(experiment, flatten(state), db)
# Explore L2 regularization w/o L1
state.L1_reg = 0.
for L2_reg in 1e-5, 1e-4:
print "L2_reg =",L2_reg
state.L1_reg = L1_reg
# Insert job
sql.insert_job(experiment, flatten(state), db)
# Create the view
db.createView(TABLE_NAME+'_view')
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db("postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s"
% {"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
logger.info("Getting dataset info for %s" % dataset_name)
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + dataset_name)
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
if input_dim % 2 == 1:
input_dim -= 1
mri = MRI.MRI_Standard(which_set="full",
dataset_name=dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
variance_map_file = path.join(data_path, "variance_map.npy")
mri_nifti.save_variance_map(mri, variance_map_file)
for items in jg.nested_generator(jg.hidden_generator("nhid1", 1),
jg.hidden_generator("nhid2", 1),
):
state = DD()
experiment_hyperparams = mlp_experiment.default_hyperparams(input_dim)
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
flatten(experiment_hyperparams).values())))
user = path.expandvars("$USER")
save_path = serial.preprocess("/export/mialab/users/%s/pylearn2_outs/%d"
% (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
state.pid = 0
sql.insert_job(
mlp_experiment.experiment,
flatten(state),
db
)
db.createView("%s_view" % args.table)
def load_experiments(args):
dataset_name = args.dataset_name
# Load the database and table.
db = sql.db("postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s"
% {"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
# Don't worry about this yet.
input_handler = InputHandler()
# For generating models, we use a special set of jobman generators, made
# for convenience.
for items in jg.nested_generator(
jg.float_generator("learning_rate", 3, 0.01, 0.0001, log_scale=True),
jg.list_generator("nhid", [50, 100, 200, 300]),
):
logger.info("Adding RBM experiment across hyperparameters %s" % (items, ))
state = DD()
# Load experiment hyperparams from experiment
experiment_hyperparams = experiment.default_hyperparams()
# Set them with values in our loop.
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
assert split_keys[-1] in entry, ("Key not found in hyperparams: %s"
% split_keys[-1])
entry[split_keys[-1]] = value
# Set the dataset name
experiment_hyperparams["dataset_name"] = dataset_name
# Get the input dim and variance map. Don't worry about variance maps right now,
# they aren't used here.
input_dim, variance_map_file = input_handler.get_input_params(args,
experiment_hyperparams)
logger.info("%s\n%s\n" % (input_dim, variance_map_file))
# Set the input dimensionality by the data
experiment_hyperparams["nvis"] = input_dim
# Set the minimum learning rate relative to the initial learning rate.
experiment_hyperparams["min_lr"] = experiment_hyperparams["learning_rate"] / 10
# Make a unique hash for experiments. Remember that lists, dicts, and other data
# types may not be hashable, so you may need to do some special processing. In
# this case we convert the lists to tuples.
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list)
else v
for v in flatten(experiment_hyperparams).values()])))
# Save path for the experiments. In this case we are sharing a directory in my
# export directory so IT can blame me.
save_path = serial.preprocess("/export/mialab/users/dhjelm/pylearn2_outs/rbm_demo/%d"
% h)
# We save file params separately as they aren't model specific.
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
user = path.expandvars("$USER")
state.created_by = user
# Finally we add the experiment to the table.
sql.insert_job(
experiment.experiment,
flatten(state),
db
)
# A view can be used when querying the database using psql. May not be needed in future.
db.createView("%s_view" % args.table)
# tested didn't change at all. This can be confusing.
from jobman.examples.def_addition import addition_example
# here we build a list of dictionaries, each of which specifies a setting of
# parameters corresponding to one job.
#
# We will pass this list to mydriver.main, so that it can potentially insert
# them, or trim down the database to match the current list, and other sorts of
# things.
state = DD()
jobs = []
for first in 0, 2, 4, 6, 8, 10:
state.first = first
for second in 1, 3, 5, 7, 9:
state.second = second
jobs.append(dict(flatten(state)))
#
# mydriver.main defines a few commands that are generally useful (insert,
# clear_db, summary, etc.) but if you want to analyse your results you can add
# additional commands.
#
# For example, if you type 'python additional_driver.py list_all', then this little function will run, because its function name is 'list_all'.
#
# For help on the meaning of the various elements of `kwargs`, see <WRITEME>.
#
@mydriver.mydriver_cmd_desc('list the contents of the database')
def list_all(db, **kwargs):
for d in db:
def run_experiment(experiment, hyper_parameters=None, ask=True, keep=False,
dbdescr=None, job_id=None, debug=False,
dataset_root="${PYLEARN2_NI_PATH}", **kwargs):
"""
Experiment function.
Used by jobman to run jobs. Must be loaded externally.
TODO: add sigint handling.
Parameters
----------
experiment: module
Experiment module.
kwargs: dict
Typically hyperparameters.
"""
# Fill the hyperparameter values.
if hyper_parameters is None:
hyper_parameters = experiment.default_hyperparams
set_hyper_parameters(hyper_parameters, **kwargs)
file_parameters = experiment.fileparams
set_hyper_parameters(file_parameters, **kwargs)
hyper_parameters.update(file_parameters)
# Set the output path, default from environment variable $PYLEARN2_OUTS
out_path = serial.preprocess(
hyper_parameters.get("out_path", "${PYLEARN2_OUTS}"))
if not path.isdir(out_path):
os.makedirs(out_path)
processing_flag = mp.Value("b", False)
mem = mp.Value("f", 0.0)
cpu = mp.Value("f", 0.0)
last_processed = mp.Manager().dict()
last_processed["value"] = "Never"
lh = LogHandler(experiment, out_path, processing_flag, mem, cpu,
last_processed, dbdescr, job_id)
h = logging.StreamHandler(lh)
lh.logger.info("Hijacking pylearn2 logger (sweet)...")
monitor.log.addHandler(h)
try:
# HACK TODO: fix this. For some reason knex formatted strings are
# sometimes getting in.
hyper_parameters = translate(hyper_parameters, "pylearn2")
# Use the input hander to get input information.
ih = input_handler.MRIInputHandler()
input_dim, variance_map_file = ih.get_input_params(
hyper_parameters, dataset_root=dataset_root)
if hyper_parameters["nvis"] is None:
hyper_parameters["nvis"] = input_dim
# Hack for NICE. Need to rethink inner-dependencies of some model params.
if ("encoder" in hyper_parameters.keys()
and "nvis" in hyper_parameters["encoder"].keys()
and hyper_parameters["encoder"]["nvis"] is None):
hyper_parameters["encoder"]["nvis"] = input_dim
# If there's min_lr, make it 1/10 learning_rate
if "min_lr" in hyper_parameters.keys():
hyper_parameters["min_lr"] = hyper_parameters["learning_rate"] / 10
# Corruptor is a special case of hyper parameters that depends on input
# file: variance_map. So we hack it in here.
if "corruptor" in hyper_parameters.keys():
if "variance_map" in hyper_parameters["corruptor"].keys():
hyper_parameters["corruptor"]["variance_map"] =\
"!pkl: %s" % variance_map_file
else:
hyper_parameters["variance_map_file"] = variance_map_file
lh.write_json()
# The Process id
pid = os.getpid()
lh.logger.info("Proces id is %d" % pid)
# If any pdfs are in out_path, kill or quit
json_file = path.join(out_path, "analysis.json")
if (ask and (path.isfile(json_file) or
len(glob.glob(path.join(out_path, "*.pkl"))) > 0)):
print ("Results found in %s "
"Proceeding will erase." % out_path)
command = None
while not command in ["yes", "no", "y", "n"]:
command = raw_input("%s: " % "Proceed?")
if command in ["yes", "y"]:
break
elif command in ["no", "n"]:
exit()
else:
print ("Please enter yes(y) or no(n)")
if path.isfile(json_file):
with open(json_file) as f:
models = json.load(f)
for model in models.keys():
lh.logger.info("Removing results for model %s" % model)
try:
os.rmdir(path.join(out_path, "%s_images" % model))
except:
pass
os.remove(json_file)
for pkl in glob.glob(path.join(out_path, "*.pkl")):
lh.logger.info("Removing %s" % pkl)
os.remove(pkl)
lh.logger.info("Making the train object")
hyper_parameters = expand(flatten(hyper_parameters), dict_type=ydict)
yaml_template = open(experiment.yaml_file).read()
yaml = yaml_template % hyper_parameters
train_object = yaml_parse.load(yaml)
if debug:
return train_object
lh.write_json()
lh.logger.info("Seting up subprocesses")
lh.logger.info("Setting up listening socket")
mp_ep, s_ep = mp.Pipe()
p = mp.Process(target=server, args=(pid, s_ep))
p.start()
port = mp_ep.recv()
lh.logger.info("Listening on port %d" % port)
lh.logger.info("Starting model processor")
model_processor = ModelProcessor(experiment, train_object.save_path,
mp_ep, processing_flag, last_processed)
model_processor.start()
lh.logger.info("Model processor started")
lh.logger.info("Starting stat processor")
stat_processor = StatProcessor(pid, mem, cpu)
stat_processor.start()
lh.logger.info("Stat processor started")
lh.update(hyperparams=hyper_parameters,
yaml=yaml,
pid=pid,
port=port)
lh.write_json()
except Exception as e:
lh.logger.exception(e)
lh.finish("FAILED")
raise e
# Clean the model after running
def clean():
p.terminate()
lh.logger.info("waiting for server...")
p.join()
model_processor.terminate()
lh.logger.info("waiting for model processor...")
model_processor.join()
stat_processor.terminate()
lh.logger.info("waiting for stat processor...")
stat_processor.join()
if keep:
lh.logger.info("Keeping checkpoints")
else:
lh.logger.info("Cleaning checkpoints")
try:
os.remove(model_processor.best_checkpoint)
except:
pass
try:
os.remove(model_processor.checkpoint)
except:
pass
# A signal handler so processes kill cleanly.
def signal_handler(signum, frame):
lh.logger.info("Forced quitting...")
clean()
lh.finish("KILLED")
if dbdescr is None:
exit()
else:
raise ValueError("KILLED")
signal.signal(signal.SIGINT, signal_handler)
# Main loop.
try:
lh.logger.info("Training...")
train_object.main_loop()
lh.logger.info("Training quit without exception")
except Exception as e:
lh.logger.exception(e)
clean()
lh.finish("FAILED")
raise(e)
# After complete, process model.
lh.logger.info("Processing model results...")
try:
experiment.analyze_fn(model_processor.best_checkpoint,
model_processor.out_path)
except IOError:
experiment.analyze_fn(model_processor.checkpoint,
model_processor.out_path)
except Exception as e:
lh.logger.error(e)
# Clean checkpoints.
clean()
lh.logger.info("Finished experiment.")
lh.finish("COMPLETED")
return
from jobman.examples.def_addition import addition_example
# here we build a list of dictionaries, each of which specifies a setting of
# parameters corresponding to one job.
#
# We will pass this list to mydriver.main, so that it can potentially insert
# them, or trim down the database to match the current list, and other sorts of
# things.
state = DD()
jobs = []
for first in 0, 2, 4, 6, 8, 10:
state.first = first
for second in 1, 3, 5, 7, 9:
state.second = second
jobs.append(dict(flatten(state)))
#
# mydriver.main defines a few commands that are generally useful (insert,
# clear_db, summary, etc.) but if you want to analyse your results you can add
# additional commands.
#
# For example, if you type 'python additional_driver.py list_all', then this little function will run, because its function name is 'list_all'.
#
# For help on the meaning of the various elements of `kwargs`, see <WRITEME>.
#
@mydriver.mydriver_cmd_desc('list the contents of the database')
def list_all(db, **kwargs):
for d in db:
from jobman.tools import DD, flatten
from jobman import api0, sql
from jobman.examples.def_addition import addition_example
TABLE_NAME = 'test_add_'
# DB path...
db = api0.open_db(
'postgres://<user>:<pass>@<server>/<database>?table=' + TABLE_NAME)
state = DD()
for first in 0, 2, 4, 6, 8, 10:
state.first = first
for second in 1, 3, 5, 7, 9:
state.second = second
sql.insert_job(addition_example, flatten(state), db)
from jobman import sql
from jobman.parse import filemerge
from Experimentsbatchpretrain import *
import numpy
db = sql.db('postgres://[email protected]/glorotxa_db/pretrainexpe')
state = DD()
state.curridata = DD(filemerge('Curridata.conf'))
state.depth = 3
state.tie = True
state.n_hid = 1000 #nb of unit per layer
state.act = 'tanh'
state.sup_lr = 0.01
state.unsup_lr = 0.001
state.noise = 0.25
state.seed = 1
state.nbepochs_unsup = 30 #maximal number of supervised updates
state.nbepochs_sup = 1000 #maximal number of unsupervised updates per layer
state.batchsize = 10
for i in ['MNIST','CIFAR10','ImageNet','shapesetbatch']:
state.dat =i
sql.insert_job(pretrain, flatten(state), db)
db.createView('pretrainexpeview')
# decoding activation function is for the first layer
# e.g. inputtype 'tfidf' ('tf*idf'?) uses activation function softplus
# to decode the tf*idf.
state.inputtype = 'binary'
state.seed = 123
#here is the for loops that does the grid:
for i in [0.01, 0.001, 0.0001]:
state.lr = [i]
for j in [(0.7, 0.0041), (0.5, 0.003), (0.8, 0.005)]:
state.noise_lvl = [j]
for k in [0.001, 0.00001, 0.0]:
state.activation_regularization_coeff = [k]
sql.insert_job(
NLPSDAE, flatten(state), db
) #this submit the current state DD to the db, if it already exist in the db no additionnal job is added.
db.createView('opentablegpuview')
#in order to access the db from a compute node you need to create an tunnel ssh connection on ang23:
#(to do one time, I think you should keep the shell open or you can create the tunnel on a screen and detached it)
#ssh -v -f -o ServerAliveInterval=60 -o ServerAliveCountMax=60 -N -L *:5432:localhost:5432 gershwin.iro.umontreal.ca
#you will need to give your LISA password.
#here is the command you use to launch 1 jobs of the db.
#THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 sqsub -q gpu -r 2d -n 1 --gpp=1 --memperproc=2.5G -o the_output_you_want jobman sql 'postgres://glorotxa@ang23/glorotxa_db/opentablegpu' /scratch/glorotxa/
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db(
"postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s" %
{
"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
logger.info("Getting dataset info for %s" % dataset_name)
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + dataset_name)
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
if input_dim % 2 == 1:
input_dim -= 1
mri = MRI.MRI_Standard(which_set="full",
dataset_name=dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
variance_map_file = path.join(data_path, "variance_map.npy")
mri_nifti.save_variance_map(mri, variance_map_file)
for items in jg.nested_generator(
jg.hidden_generator("nhid1", 1),
jg.hidden_generator("nhid2", 1),
):
state = DD()
experiment_hyperparams = mlp_experiment.default_hyperparams(input_dim)
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
flatten(experiment_hyperparams).values())))
user = path.expandvars("$USER")
save_path = serial.preprocess(
"/export/mialab/users/%s/pylearn2_outs/%d" % (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
state.pid = 0
sql.insert_job(mlp_experiment.experiment, flatten(state), db)
db.createView("%s_view" % args.table)
# to decode the tf*idf.
state.inputtype = 'binary'
state.seed = 123
state.activation_regularization_coeff = [0]
#here is the for loops that does the grid:
for i in [0.01,0.001]:
state.lr = [i]
for j in [0.5,0.25,0.125,0.05]:
state.noise_lvl=[j]
for k in [1400,2500,5000]:
state.n_hid = [k]
sql.insert_job(NLPSDAE, flatten(state), db) #this submit the current state DD to the db, if it already exist in the db no additionnal job is added.
db.createView('opentablegpuview')
# First run this script
# PYTHONPATH=$PYTHONPATH:.. python DARPAjobs.py
# Test the jobs are in the database:
# psql -d ift6266h10_sandbox_db -h gershwin.iro.umontreal.ca -U ift6266h10
# select id,lr,noiselvl,nhid as reg,jobman_status from opentablegpuview;
# password: f0572cd63b
# Set some values
# update opentablegpukeyval set ival=0 where name='jobman.status';
# update opentablegpukeyval set ival=0 where name='jobman.status' where dict_id=20;
method_name: contraction_penalty
},
coefficient: %(coefficient)f
}
],
"termination_criterion" : %(term_crit)s,
}
}
'''
state.hyper_parameters = {
"file": "${PYLEARN2_DATA_PATH}/UTLC/pca/sylvester_train_x_pca32.npy",
"nvis": 32,
"nhid": 6,
"learning_rate": 0.1,
"batch_size": 10,
"coefficient": 0.5,
"term_crit": {
"__builder__": "pylearn2.training_algorithms.sgd.EpochCounter",
"max_epochs": 2
}
}
state.extract_results = "pylearn2.scripts.jobman.tester.result_extractor"
sql.insert_job(
experiment.train_experiment,
flatten(state),
db,
force_dup=True)
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db(
"postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s" %
{
"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
logger.info("Getting dataset info for %s%s" %
(dataset_name, ", transposed" if args.transposed else ""))
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + args.dataset_name)
if args.transposed:
logger.info("Data in transpose...")
mri = MRI.MRI_Transposed(dataset_name=args.dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
input_dim = mri.X.shape[1]
variance_map_file = path.join(data_path, "transposed_variance_map.npy")
else:
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
if input_dim % 2 == 1:
input_dim -= 1
mri = MRI.MRI_Standard(which_set="full",
dataset_name=args.dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
variance_map_file = path.join(data_path, "variance_map.npy")
mri_nifti.save_variance_map(mri, variance_map_file)
for items in nested_generator(
layer_depth_generator("encoder.layer_depths", xrange(4, 6), 5),
hidden_generator("encoder.nhid", 4),
float_generator("weight_decay.coeffs.z",
3,
0.1,
0.001,
log_scale=True)):
# logger.info("Adding NICE experiment with hyperparameters %s" % (items, ))
state = DD()
experiment_hyperparams = nice_experiment.default_hyperparams(input_dim)
if args.transposed:
experiment_hyperparams["data_class"] = "MRI_Transposed"
if args.logistic:
experiment_hyperparams["prior"]["__builder__"] =\
"nice.pylearn2.models.nice.StandardLogistic"
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list) else v for v in flatten(experiment_hyperparams).values()])))
user = path.expandvars("$USER")
save_path = serial.preprocess(
"/export/mialab/users/%s/pylearn2_outs/%d" % (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
sql.insert_job(nice_experiment.experiment, flatten(state), db)
db.createView("%s_view" % args.table)