def __init__(
self,
paths_or_dataset,
batch_size,
label_names,
feature_columns=None,
cat_names=None,
cont_names=None,
engine=None,
shuffle=True,
seed_fn=None,
buffer_size=0.1,
device=None,
parts_per_chunk=1,
reader_kwargs=None,
global_size=None,
global_rank=None,
drop_last=False,
sparse_names=None,
sparse_max=None,
sparse_as_dense=False,
):
dataset = _validate_dataset(
paths_or_dataset, batch_size, buffer_size, engine, reader_kwargs
)
cat_names, cont_names = _validate_schema(feature_columns, cat_names, cont_names)
# sort the ccolumns to avoid getting incorrect output
# (https://github.com/NVIDIA/NVTabular/issues/412)
cat_names = _get_embedding_order(cat_names)
cont_names = _get_embedding_order(cont_names)
device = device or 0
DataLoader.__init__(
self,
dataset,
cat_names,
cont_names,
label_names,
batch_size,
shuffle,
seed_fn=seed_fn,
parts_per_chunk=parts_per_chunk,
device=device,
global_size=global_size,
global_rank=global_rank,
drop_last=drop_last,
sparse_names=sparse_names,
sparse_max=sparse_max,
sparse_as_dense=sparse_as_dense,
)
self._map_fns = []
def __init__(
self,
paths_or_dataset,
batch_size,
label_names=None,
feature_columns=None,
cat_names=None,
cont_names=None,
engine=None,
shuffle=True,
seed_fn=None,
buffer_size=0.1,
device=None,
parts_per_chunk=1,
reader_kwargs=None,
global_size=None,
global_rank=None,
drop_last=False,
sparse_names=None,
sparse_max=None,
sparse_as_dense=False,
schema=None,
):
dataset = _validate_dataset(paths_or_dataset, batch_size, buffer_size,
engine, reader_kwargs)
schema = _get_schema(dataset) if not schema else schema
cat_names, cont_names = _validate_schema(feature_columns,
cat_names,
cont_names,
schema=schema)
device = device or 0
device = "cpu" if not HAS_GPU else device
DataLoader.__init__(
self,
dataset,
batch_size,
shuffle,
cat_names=cat_names,
cont_names=cont_names,
label_names=label_names,
seed_fn=seed_fn,
parts_per_chunk=parts_per_chunk,
device=device,
global_size=global_size,
global_rank=global_rank,
drop_last=drop_last,
sparse_names=sparse_names,
sparse_max=sparse_max,
sparse_as_dense=sparse_as_dense,
)
self._map_fns = []
def __init__(
self,
paths_or_dataset,
batch_size,
label_names,
feature_columns=None,
cat_names=None,
cont_names=None,
engine=None,
shuffle=True,
buffer_size=0.1,
workflows=None,
devices=None,
parts_per_chunk=1,
reader_kwargs=None,
):
dataset = _validate_dataset(paths_or_dataset, batch_size, buffer_size,
engine, reader_kwargs)
cat_names, cont_names = _validate_schema(feature_columns, cat_names,
cont_names)
# sort the ccolumns to avoid getting incorrect output
# (https://github.com/NVIDIA/NVTabular/issues/412)
cat_names = _get_embedding_order(cat_names)
cont_names = _get_embedding_order(cont_names)
assert devices is None or len(
devices) == 1 # TODO: figure out multi-gpu support
devices = devices or [0]
DataLoader.__init__(
self,
dataset,
cat_names,
cont_names,
label_names,
batch_size,
shuffle,
parts_per_chunk=parts_per_chunk,
workflows=workflows,
devices=devices,
)