def train(datasource,
estimator_string,
select,
validation_select,
feature_columns,
feature_column_names,
feature_metas={},
label_meta={},
model_params={},
train_params={},
validation_metrics=["Accuracy"],
disk_cache=False,
save="",
batch_size=None,
epoch=1,
validation_steps=1,
verbose=0,
max_steps=None,
validation_start_delay_secs=0,
validation_throttle_secs=0,
save_checkpoints_steps=100,
log_every_n_iter=10,
load_pretrained_model=False,
is_pai=True,
pai_table="",
pai_val_table="",
feature_columns_code="",
model_repo_image="",
original_sql="",
oss_model_dir_to_load="",
feature_column_names_map=None):
FLAGS = define_tf_flags()
num_workers = len(FLAGS.worker_hosts.split(","))
is_dist_train = num_workers > 1
oss_model_dir = FLAGS.sqlflow_oss_modeldir
if load_pretrained_model:
oss.load_file(oss_model_dir_to_load, "my_model")
# NOTE: in the current implementation, we are generating a transform_fn
# from COLUMN clause. The transform_fn is executed during the process of
# dumping the original data into DMatrix SVM file.
transform_fn = ComposedColumnTransformer(feature_column_names,
*feature_columns)
if is_dist_train:
dist_train(flags=FLAGS,
datasource=datasource,
select=select,
model_params=model_params,
train_params=train_params,
feature_metas=feature_metas,
feature_column_names=feature_column_names,
label_meta=label_meta,
validation_select=validation_select,
disk_cache=disk_cache,
batch_size=batch_size,
epoch=epoch,
load_pretrained_model=load_pretrained_model,
is_pai=True,
pai_train_table=pai_table,
pai_validate_table=pai_val_table,
oss_model_dir=oss_model_dir,
transform_fn=transform_fn,
feature_column_code=feature_columns_code,
model_repo_image=model_repo_image,
original_sql=original_sql)
else:
local_train(datasource=datasource,
select=select,
model_params=model_params,
train_params=train_params,
feature_metas=feature_metas,
feature_column_names=feature_column_names,
label_meta=label_meta,
validation_select=validation_select,
disk_cache=disk_cache,
batch_size=batch_size,
epoch=epoch,
load_pretrained_model=load_pretrained_model,
is_pai=True,
pai_train_table=pai_table,
pai_validate_table=pai_val_table,
rank=0,
nworkers=1,
oss_model_dir=oss_model_dir,
transform_fn=transform_fn,
feature_column_code=feature_columns_code,
model_repo_image=model_repo_image,
original_sql=original_sql)
def train(original_sql,
model_image,
estimator_string,
datasource,
select,
validation_select,
model_params,
train_params,
validation_params,
feature_column_map,
label_column,
save,
load=None,
pai_table="",
pai_val_table=""):
is_pai = True if pai_table != "" else False
is_dist_train = False
FLAGS = None
oss_model_dir = ""
if is_pai:
FLAGS = define_tf_flags()
num_workers = len(FLAGS.worker_hosts.split(","))
is_dist_train = num_workers > 1
oss_model_dir = FLAGS.sqlflow_oss_modeldir
try:
oss_path_to_load = train_params.pop("oss_path_to_load")
if load:
oss.load_file(oss_path_to_load, "my_model")
except: # noqa: E722
pass
feature_columns = compile_ir_feature_columns(feature_column_map,
EstimatorType.XGBOOST)
field_descs = get_ordered_field_descs(feature_column_map)
feature_column_names = [fd.name for fd in field_descs]
feature_metas = dict([(fd.name, fd.to_dict(dtype_to_string=True))
for fd in field_descs])
label_meta = label_column.get_field_desc()[0].to_dict(dtype_to_string=True)
transform_fn = ComposedColumnTransformer(
feature_column_names, *feature_columns["feature_columns"])
batch_size = train_params.pop("batch_size", None)
epoch = train_params.pop("epoch", 1)
load_pretrained_model = True if load else False
disk_cache = train_params.pop("disk_cache", False)
if is_dist_train:
# NOTE(typhoonzero): dist_train returns None
dist_train(flags=FLAGS,
datasource=datasource,
select=select,
model_params=model_params,
train_params=train_params,
feature_metas=feature_metas,
feature_column_names=feature_column_names,
label_meta=label_meta,
validation_select=validation_select,
disk_cache=disk_cache,
batch_size=batch_size,
epoch=epoch,
load_pretrained_model=load_pretrained_model,
is_pai=True,
pai_train_table=pai_table,
pai_validate_table=pai_val_table,
oss_model_dir=oss_model_dir,
transform_fn=transform_fn,
feature_column_code=feature_column_map,
model_repo_image=model_image,
original_sql=original_sql)
else:
return local_train(original_sql,
model_image,
estimator_string,
datasource,
select,
validation_select,
model_params,
train_params,
feature_metas,
feature_column_names,
feature_column_map,
label_column,
transform_fn,
save,
load=load,
is_pai=is_pai,
oss_model_dir=oss_model_dir)
def train_step(original_sql,
model_image,
estimator_string,
datasource,
select,
validation_select,
pai_table,
pai_val_table,
model_params,
train_params,
feature_column_map,
label_column,
save,
load=None):
FLAGS = define_tf_flags()
num_workers = len(FLAGS.worker_hosts.split(","))
is_dist_train = num_workers > 1
oss_model_dir = FLAGS.sqlflow_oss_modeldir
oss_path_to_load = train_params.pop("oss_path_to_load")
if load:
oss.load_file(oss_path_to_load, "my_model")
conn = db.connect_with_data_source(datasource)
fc_map_ir, fc_label_ir = infer_feature_columns(conn,
select,
feature_column_map,
label_column,
n=1000)
feature_columns = compile_ir_feature_columns(fc_map_ir,
EstimatorType.XGBOOST)
field_descs = get_ordered_field_descs(fc_map_ir)
feature_column_names = [fd.name for fd in field_descs]
feature_metas = dict([(fd.name, fd.to_dict()) for fd in field_descs])
label_meta = label_column.get_field_desc()[0].to_dict()
transform_fn = ComposedColumnTransformer(
feature_column_names, *feature_columns["feature_columns"])
batch_size = train_params.pop("batch_size", None)
epoch = train_params.pop("epoch", 1)
load_pretrained_model = True if load else False
disk_cache = train_params.pop("disk_cache", False)
if is_dist_train:
dist_train(flags=FLAGS,
datasource=datasource,
select=select,
model_params=model_params,
train_params=train_params,
feature_metas=feature_metas,
feature_column_names=feature_column_names,
label_meta=label_meta,
validation_select=validation_select,
disk_cache=disk_cache,
batch_size=batch_size,
epoch=epoch,
load_pretrained_model=load_pretrained_model,
is_pai=True,
pai_train_table=pai_table,
pai_validate_table=pai_val_table,
oss_model_dir=oss_model_dir,
transform_fn=transform_fn,
feature_column_code=fc_map_ir,
model_repo_image=model_image,
original_sql=original_sql)
else:
local_train(datasource=datasource,
select=select,
model_params=model_params,
train_params=train_params,
feature_metas=feature_metas,
feature_column_names=feature_column_names,
label_meta=label_meta,
validation_select=validation_select,
disk_cache=disk_cache,
batch_size=batch_size,
epoch=epoch,
load_pretrained_model=load_pretrained_model,
is_pai=True,
pai_train_table=pai_table,
pai_validate_table=pai_val_table,
rank=0,
nworkers=1,
oss_model_dir=oss_model_dir,
transform_fn=transform_fn,
feature_column_code=fc_map_ir,
model_repo_image=model_image,
original_sql=original_sql)