def evaluate(self,
block_material: BlockMaterial,
block_def,#: BlockDefinition,
training_datapair: ezData,
validation_datapair: ezData):
'''
stuff the old code has but unclear why
gpus = tf.config.experimental.list_physical_devices('GPU')
#tf.config.experimental.set_virtual_device_configuration(gpus[0],[
tf.config.experimental.VirtualDeviceConfiguration(memory_limit = 1024*3)
])
'''
ezLogging.info("%s - Start evaluating..." % (block_material.id))
try:
self.build_graph(block_material, block_def, training_datapair)
except Exception as err:
ezLogging.critical("%s - Build Graph; Failed: %s" % (block_material.id, err))
block_material.dead = True
import pdb; pdb.set_trace()
return
try:
output = self.train_graph(block_material, block_def, training_datapair, validation_datapair)
except Exception as err:
ezLogging.critical("%s - Train Graph; Failed: %s" % (block_material.id, err))
block_material.dead = True
import pdb; pdb.set_trace()
return
block_material.output = output # TODO make sure it is a list
def evaluate(self,
block_material: BlockMaterial,
block_def,#: BlockDefinition,
training_datapair: ezData,
validation_datapair: ezData=None):
ezLogging.info("%s - Start evaluating..." % (block_material.id))
output_list = self.standard_evaluate(block_material, block_def, training_datapair.x)
#training_datapair.x = output_list[0]
#block_material.output = training_datapair
block_material.output = output_list
def evaluate(self,
block_material: BlockMaterial,
block_def, #: BlockDefinition,
training_datapair: ezData,
validation_datapair: ezData):
ezLogging.info("%s - Start evaluating..." % (block_material.id))
output_list = self.standard_evaluate(block_material, block_def, [training_datapair.pipeline])
training_datapair.pipeline = output_list[0] #assuming only outputs the pipeline
block_material.output = [training_datapair, validation_datapair]
def evaluate(self,
block_material: BlockMaterial,
block_def,#: BlockDefinition,
training_datapair: ezData,
validation_datapair: ezData):
ezLogging.info("%s - Start evaluating..." % (block_material.id))
try:
self.build_graph(block_material, block_def, training_datapair)
except Exception as err:
ezLogging.critical("%s - Build Graph; Failed: %s" % (block_material.id, err))
block_material.dead = True
import pdb; pdb.set_trace()
return
block_material.output = [training_datapair, validation_datapair]
def evaluate(self,
block_material: BlockMaterial,
block_def,#: BlockDefinition,
training_datapair: ezData,
validation_datapair: ezData):
ezLogging.info("%s - Start evaluating..." % (block_material.id))
# going to treat training + validation as separate block_materials!
output = []
for datapair in [training_datapair, validation_datapair]:
single_output_list = self.standard_evaluate(block_material, block_def, [datapair.pipeline])
datapair.pipeline = single_output_list[0]
if block_material.dead:
return []
else:
output.append(datapair)
self.preprocess_block_evaluate(block_material) #prep for next loop through datapair
block_material.output = output
def evaluate(self,
block_material: BlockMaterial,
block_def,#: BlockDefinition,
training_datapair: ezData,
validation_datapair: ezData):
ezLogging.info("%s - Start evaluating..." % (block_material.id))
try:
# check if this fails our conditions
if block_def.main_count != 1:
raise self.TooManyMainNodes(block_def.main_count)
if 0 not in block_material.active_nodes:
raise self.NoActiveMainNodes(block_material.active_nodes)
# good to continue to build
self.build_graph(block_material, block_def, training_datapair)
except Exception as err:
ezLogging.critical("%s - Build Graph; Failed: %s" % (block_material.id, err))
block_material.dead = True
import pdb; pdb.set_trace()
return
block_material.output = [training_datapair, validation_datapair]
def evaluate(self,
block_material: BlockMaterial,
block_def,#: BlockDefinition,
training_datapair: ezData,
validation_datapair: ezData):
ezLogging.info("%s - Start evaluating..." % (block_material.id))
try:
self.build_graph(block_material, block_def, training_datapair.pipeline_wrapper)
except Exception as err:
ezLogging.critical("%s - Build Graph; Failed: %s" % (block_material.id, err))
block_material.dead = True
import pdb; pdb.set_trace()
return
try:
# outputs a list of the validation metrics
output = self.train_graph(block_material, block_def, training_datapair, validation_datapair)
except Exception as err:
ezLogging.critical("%s - Train Graph; Failed: %s" % (block_material.id, err))
block_material.dead = True
import pdb; pdb.set_trace()
return
block_material.output = [None, output] # TODO make sure it is a list