Replace gluster paths with local file paths for NLG configs

pytext/data/data_handler.py

@@ -29,6 +29,7 @@
 from pytext.fields import Field, FieldMeta, RawField, VocabUsingField
 from pytext.utils import cuda, distributed, embeddings as embeddings_utils
 from pytext.utils.data import parse_json_array
+from pytext.utils.path import get_absolute_path
 from torchtext import data as textdata
 
 from .utils import align_target_labels
@@ -249,6 +250,7 @@ def load_vocab(self, vocab_file, vocab_size, lowercase_tokens: bool = False):
             lowercase_tokens (bool): if the tokens should be lowercased
         """
         vocab: Set[str] = set()
+        vocab_file = get_absolute_path(vocab_file)
         if os.path.isfile(vocab_file):
             with open(vocab_file, "r") as f:
                 for i, line in enumerate(f):
@@ -727,6 +729,7 @@ def read_from_file(
             columns_to_use (Union[Dict[str, int], List[str]]): either a list of
                 column names or a dict of column name -> column index in the file
         """
+        file_name = get_absolute_path(file_name)
         print("reading data from {}".format(file_name))
         if isinstance(columns_to_use, list):
             columns_to_use = {

pytext/utils/embeddings.py

@@ -9,6 +9,7 @@
 from pytext.common.constants import PackageFileName
 from pytext.config.field_config import EmbedInitStrategy
 from pytext.utils.file_io import PathManager
+from pytext.utils.path import get_absolute_path
 
 
 class PretrainedEmbedding(object):
@@ -24,6 +25,7 @@ def __init__(
         delimiter: str = " ",
     ) -> None:
         if embeddings_path:
+            embeddings_path = get_absolute_path(embeddings_path)
             if PathManager.isdir(embeddings_path):
                 serialized_embed_path = os.path.join(
                     embeddings_path, PackageFileName.SERIALIZED_EMBED