minimal-changes

spacy/cli/train_from_config.py

@@ -200,9 +200,13 @@ def train_cli(
         except ImportError:
             msg.fail("Need to install ray_spacy to use distributed training!", exits=1)
         distributed_setup_and_train(use_gpu, num_workers, strategy, train_args)
-
     else:
-        setup_and_train(use_gpu, train_args)
+        if use_gpu >= 0:
+            msg.info(f"Using GPU: {use_gpu}")
+            util.use_gpu(use_gpu)
+        else:
+            msg.info("Using CPU")
+        train(**train_args)
 
 def train(
     config_path,
@@ -213,7 +217,8 @@ def train(
     weights_data=None,
     omit_extra_lookups=False,
     disable_tqdm=False,
-    remote_optimizer=None
+    remote_optimizer=None,
+    randomization_index=0
 ):
     msg.info(f"Loading config from: {config_path}")
     # Read the config first without creating objects, to get to the original nlp_config
@@ -327,7 +332,7 @@ def train(
             )
         tok2vec.from_bytes(weights_data)
 
-    train_batches = create_train_batches(nlp, corpus, training)
+    train_batches = create_train_batches(nlp, corpus, training, randomization_index)
     evaluate = create_evaluation_callback(nlp, optimizer, corpus, training)
 
     # Create iterator, which yields out info after each optimization step.
@@ -382,7 +387,7 @@ def train(
             msg.good(f"Saved model to output directory {final_model_path}")
 
 
-def create_train_batches(nlp, corpus, cfg):
+def create_train_batches(nlp, corpus, cfg, randomization_index):
     epochs_todo = cfg.get("max_epochs", 0)
     while True:
         train_examples = list(
@@ -397,8 +402,9 @@ def create_train_batches(nlp, corpus, cfg):
         )
         if len(train_examples) == 0:
             raise ValueError(Errors.E988)
+        for _ in range(randomization_index):
+            random.random()
         random.shuffle(train_examples)
-
         batches = util.minibatch_by_words(
             train_examples,
             size=cfg["batch_size"],