port

spacy/cli/train.py

@@ -132,7 +132,7 @@ def train_cli(
     verbose: bool = Opt(False, "--verbose", "-VV", help="Display more information for debugging purposes"),
     use_gpu: int = Opt(-1, "--use-gpu", "-g", help="Use GPU"),
     num_workers: int = Opt(None, "-j", help="Parallel Workers"),
-    strategy: str = Opt(None, "--strategy", help="Distributed training strategy (requires spacy_ray)"),
+    strategy: str = Opt("allreduce", "--strategy", help="Distributed training strategy (requires spacy_ray)"),
     ray_address: str = Opt(None, "--address", help="Address of the Ray cluster. Multi-node training (requires spacy_ray)"),
     tag_map_path: Optional[Path] = Opt(None, "--tag-map-path", "-tm", help="Location of JSON-formatted tag map"),
     omit_extra_lookups: bool = Opt(False, "--omit-extra-lookups", "-OEL", help="Don't include extra lookups in model"),
@@ -168,10 +168,7 @@ def train_cli(
     )
 
     if num_workers and num_workers > 1:
-        try:
-            from spacy_ray import distributed_setup_and_train
-        except ImportError:
-            msg.fail("Need to `pip install spacy_ray` to use distributed training!", exits=1)
+        from spacy_ray import distributed_setup_and_train
         distributed_setup_and_train(use_gpu, num_workers, strategy, ray_address, train_args)
     else:
         if use_gpu >= 0:
@@ -190,7 +187,7 @@ def train(
     weights_data: Optional[bytes] = None,
     omit_extra_lookups: bool = False,
     disable_tqdm: bool = False,
-    remote_optimizer: Optimizer = None,
+    remote_optimizer = None,
     randomization_index: int = 0
 ) -> None:
     msg.info(f"Loading config from: {config_path}")
@@ -321,6 +318,8 @@ def create_train_batches(nlp, corpus, cfg, randomization_index):
     while True:
         if len(train_examples) == 0:
             raise ValueError(Errors.E988)
+        # This is used when doing parallel training to
+        # ensure that the dataset is shuffled differently across all workers.
         for _ in range(randomization_index):
             random.random()
         random.shuffle(train_examples)
@@ -564,6 +563,9 @@ def verify_cli_args(
     raw_text=None,
     verbose=False,
     use_gpu=-1,
+    num_workers=None,
+    strategy=None,
+    ray_address=None,
     tag_map_path=None,
     omit_extra_lookups=False,
 ):
@@ -596,6 +598,12 @@ def verify_cli_args(
     if init_tok2vec is not None and not init_tok2vec.exists():
         msg.fail("Can't find pretrained tok2vec", init_tok2vec, exits=1)
 
+    if num_workers and num_workers > 1:
+        try:
+            from spacy_ray import distributed_setup_and_train
+        except ImportError:
+            msg.fail("Need to `pip install spacy_ray` to use distributed training!", exits=1)
+
 
 def verify_textcat_config(nlp, nlp_config):
     # if 'positive_label' is provided: double check whether it's in the data and