def train_and_eval(config):
"""
test_train_eval
"""
np.random.seed(1000)
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path, train_mode=True, epochs=epochs,
batch_size=batch_size, rank_id=get_rank(), rank_size=get_group_size())
ds_eval = create_dataset(data_path, train_mode=False, epochs=epochs + 1,
batch_size=batch_size, rank_id=get_rank(), rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path, config=ckptconfig)
out = model.eval(ds_eval)
print("=====" * 5 + "model.eval() initialized: {}".format(out))
model.train(epochs, ds_train,
callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb])
def test_train_eval():
"""
test_train_eval
"""
config = WideDeepConfig()
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path, train_mode=True, epochs=epochs, batch_size=batch_size,
data_type=DataType.MINDRECORD, rank_id=get_rank(), rank_size=get_group_size())
ds_eval = create_dataset(data_path, train_mode=False, epochs=epochs + 1, batch_size=batch_size,
data_type=DataType.MINDRECORD, rank_id=get_rank(), rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
context.set_auto_parallel_context(strategy_ckpt_save_file="./strategy_train.ckpt")
model.train(epochs, ds_train,
callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback])
eval_values = list(eval_callback.eval_values)
assert eval_values[0] > 0.78
def test_eval(config):
"""
test evaluate
"""
data_path = config.data_path
batch_size = config.batch_size
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size, data_type=dataset_type)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
param_dict = load_checkpoint(config.ckpt_path)
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
def train_and_eval(config):
"""
train_and_eval.
"""
data_path = config.data_path
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=config.batch_size, is_tf_dataset=config.is_tf_dataset)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
param_dict = load_checkpoint(config.ckpt_path)
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
def test_train_eval():
"""
test_train_eval
"""
np.random.seed(1000)
config = WideDeepConfig()
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size())
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
out = model.eval(ds_eval)
print("=====" * 5 + "model.eval() initialized: {}".format(out))
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), eval_callback,
callback, ckpoint_cb
])
expect_out0 = [0.792634, 0.799862, 0.803324]
expect_out6 = [0.796580, 0.803908, 0.807262]
if get_rank() == 0:
assert np.allclose(eval_callback.eval_values, expect_out0)
if get_rank() == 6:
assert np.allclose(eval_callback.eval_values, expect_out6)
def test_train_eval(config):
"""
test_train_eval
"""
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
sparse = config.sparse
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
out = model.eval(ds_eval)
print("=====" * 5 + "model.eval() initialized: {}".format(out))
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), eval_callback,
callback, ckpoint_cb
],
dataset_sink_mode=(not sparse))
def train_and_eval(config):
"""
train_and_eval
"""
set_seed(1000)
data_path = config.data_path
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=config.batch_size,
is_tf_dataset=config.is_tf_dataset,
rank_id=get_rank(),
rank_size=get_group_size())
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=config.batch_size,
is_tf_dataset=config.is_tf_dataset,
rank_id=get_rank(),
rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config)
# Only save the last checkpoint at the last epoch. For saving epochs at each epoch, please
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size() * config.epochs,
keep_checkpoint_max=10)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path + '/ckpt_' +
str(get_rank()) + '/',
config=ckptconfig)
callback_list = [
TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback
]
if int(get_rank()) == 0:
callback_list.append(ckpoint_cb)
model.train(epochs,
ds_train,
callbacks=callback_list,
sink_size=ds_train.get_dataset_size())
def test_eval(config):
"""
test evaluate
"""
data_path = config.data_path
batch_size = config.batch_size
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
ckpt_path = config.ckpt_path
if ";" in ckpt_path:
ckpt_paths = ckpt_path.split(';')
param_list_dict = {}
strategy = build_searched_strategy(config.stra_ckpt)
for slice_path in ckpt_paths:
param_slice_dict = load_checkpoint(slice_path)
for key, value in param_slice_dict.items():
if 'optimizer' in key:
continue
if key not in param_list_dict:
param_list_dict[key] = []
param_list_dict[key].append(value)
param_dict = {}
for key, value in param_list_dict.items():
if key in strategy:
merged_parameter = merge_sliced_parameter(value, strategy)
else:
merged_parameter = merge_sliced_parameter(value)
param_dict[key] = merged_parameter
else:
param_dict = load_checkpoint(ckpt_path)
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
def train_and_eval(config):
"""
test_train_eval
"""
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
host_device_mix = bool(config.host_device_mix)
print("epochs is {}".format(epochs))
if config.full_batch:
context.set_auto_parallel_context(full_batch=True)
de.config.set_seed(1)
ds_train = create_dataset(data_path, train_mode=True, epochs=1,
batch_size=batch_size*get_group_size(), data_type=dataset_type)
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size*get_group_size(), data_type=dataset_type)
else:
ds_train = create_dataset(data_path, train_mode=True, epochs=1,
batch_size=batch_size, rank_id=get_rank(),
rank_size=get_group_size(), data_type=dataset_type)
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size, rank_id=get_rank(),
rank_size=get_group_size(), data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config, host_device_mix=host_device_mix)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path, config=ckptconfig)
context.set_auto_parallel_context(strategy_ckpt_save_file="./strategy_train.ckpt")
callback_list = [TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback]
if not host_device_mix:
callback_list.append(ckpoint_cb)
model.train(epochs, ds_train, callbacks=callback_list, dataset_sink_mode=(not host_device_mix))
def train_and_eval(config):
"""
train_and_eval.
"""
data_path = config.data_path
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=config.batch_size,
is_tf_dataset=config.is_tf_dataset)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=config.batch_size,
is_tf_dataset=config.is_tf_dataset)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=10)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), eval_callback,
callback, ckpoint_cb
])
def train_and_eval(config):
"""
test_train_eval
"""
set_seed(1000)
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path, train_mode=True, epochs=1,
batch_size=batch_size, rank_id=get_rank(),
rank_size=get_group_size(), data_type=dataset_type)
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size, rank_id=get_rank(),
rank_size=get_group_size(), data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path + '/ckpt_' + str(get_rank()) + '/',
config=ckptconfig)
out = model.eval(ds_eval)
print("=====" * 5 + "model.eval() initialized: {}".format(out))
callback_list = [TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback]
if get_rank() == 0:
callback_list.append(ckpoint_cb)
model.train(epochs, ds_train,
callbacks=callback_list,
sink_size=ds_train.get_dataset_size())
def test_eval(config):
"""
test evaluate
"""
data_path = config.data_path
ckpt_path = config.ckpt_path
batch_size = config.batch_size
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
# data upload
print('Upload data from obs to modelarts server.')
mox.file.copy_parallel(src_url=config.data_url, dst_url=data_path)
mox.file.copy_parallel(src_url=config.ckpt_url, dst_url=ckpt_path)
tar_file = data_path + "train_demo.tar.gz"
untar(tar_file, data_path)
data_path = data_path + config.dataset_type
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
param_dict = load_checkpoint(find_ckpt(ckpt_path))
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
def train_and_eval(config):
"""
test_train_eval
"""
set_seed(1000)
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
parameter_server = bool(config.parameter_server)
cache_enable = config.vocab_cache_size > 0
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path, train_mode=True, epochs=1,
batch_size=batch_size, data_type=dataset_type)
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size, data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=config.ckpt_path, config=ckptconfig)
callback_list = [TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb]
model.train(epochs, ds_train,
callbacks=callback_list,
dataset_sink_mode=(parameter_server and cache_enable))
def train_and_eval(config):
"""
test_train_eval
"""
set_seed(1000)
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
parameter_server = bool(config.parameter_server)
if cache_enable:
config.full_batch = True
print("epochs is {}".format(epochs))
if config.full_batch:
context.set_auto_parallel_context(full_batch=True)
ds.config.set_seed(1)
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type)
else:
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size(),
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size(),
data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
if cache_enable:
config.stra_ckpt = os.path.join(
config.stra_ckpt + "-{}".format(get_rank()), "strategy.ckpt")
context.set_auto_parallel_context(
strategy_ckpt_save_file=config.stra_ckpt)
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
if _is_role_worker():
if cache_enable:
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size() * epochs,
keep_checkpoint_max=1,
integrated_save=False)
else:
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
else:
ckptconfig = CheckpointConfig(save_checkpoint_steps=1,
keep_checkpoint_max=1)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path + '/ckpt_' +
str(get_rank()) + '/',
config=ckptconfig)
callback_list = [
TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback
]
if get_rank() == 0:
callback_list.append(ckpoint_cb)
model.train(epochs,
ds_train,
callbacks=callback_list,
dataset_sink_mode=bool(parameter_server and cache_enable))
def train_and_eval(config):
"""
test_train_eval
"""
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
print("epochs is {}".format(epochs))
if config.full_batch:
context.set_auto_parallel_context(full_batch=True)
de.config.set_seed(1)
ds_train = create_dataset(data_path,
train_mode=True,
epochs=epochs,
batch_size=batch_size * get_group_size())
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=epochs + 1,
batch_size=batch_size * get_group_size())
else:
ds_train = create_dataset(data_path,
train_mode=True,
epochs=epochs,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size())
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=epochs + 1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
context.set_auto_parallel_context(
strategy_ckpt_save_file="./strategy_train.ckpt")
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), eval_callback,
callback, ckpoint_cb
])
def train_and_eval(config):
"""
test_train_eval
"""
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
host_device_mix = bool(config.host_device_mix)
sparse = config.sparse
print("epochs is {}".format(epochs))
if config.full_batch:
context.set_auto_parallel_context(full_batch=True)
ds.config.set_seed(1)
if config.field_slice:
compute_manual_shape(config, get_group_size())
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type,
manual_shape=config.manual_shape,
target_column=config.field_size)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type,
manual_shape=config.manual_shape,
target_column=config.field_size)
else:
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type)
else:
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size(),
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size(),
data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
# Save strategy ckpts according to the rank id, this must be done before initializing the callbacks.
config.stra_ckpt = os.path.join(
config.stra_ckpt + "-{}".format(get_rank()), "strategy.ckpt")
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config, per_print_times=20)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size() * epochs,
keep_checkpoint_max=5,
integrated_save=False)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=os.path.join(
config.ckpt_path,
'ckpt_' + str(get_rank())),
config=ckptconfig)
context.set_auto_parallel_context(strategy_ckpt_save_file=config.stra_ckpt)
callback_list = [
TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback
]
if not host_device_mix:
callback_list.append(ckpoint_cb)
model.train(epochs,
ds_train,
callbacks=callback_list,
dataset_sink_mode=(not sparse))
def train_eval(config):
"""
test evaluate
"""
data_path = config.data_path + config.dataset_type
ckpt_path = config.ckpt_path
epochs = config.epochs
batch_size = config.batch_size
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
train_model = Model(train_net)
train_callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=1)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
train_model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()),
train_callback, ckpoint_cb
])
# data download
print('Download data from modelarts server to obs.')
mox.file.copy_parallel(src_url=config.ckpt_path, dst_url=config.train_url)
param_dict = load_checkpoint(find_ckpt(ckpt_path))
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
eval_model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(eval_model, ds_eval, auc_metric, config)
eval_model.eval(ds_eval, callbacks=eval_callback)