Start updating train script

spacy/cli/train.py

@@ -16,6 +16,7 @@ from ._util import import_code, get_sourced_components
 from ..language import Language
 from .. import util
 from ..training.example import Example
+from ..training.initialize import must_initialize, init_pipeline
 from ..errors import Errors
 from ..util import dot_to_object
 
@@ -31,8 +32,6 @@ def train_cli(
     code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
     verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
     use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
-    resume: bool = Opt(False, "--resume", "-R", help="Resume training"),
-    dave_path: Optional[Path] = Opt(None, "--dave", "-D", help="etc etc"),
     # fmt: on
 ):
     """
@@ -53,37 +52,36 @@ def train_cli(
     verify_cli_args(config_path, output_path)
     overrides = parse_config_overrides(ctx.args)
     import_code(code_path)
-    if prepared is None:
-        prepare(config_path, output_path / "prepared", config_overrides=overrides)
-    train(
-        config_path,
-        output_path=output_path,
-        dave_path=dave_path,
-        config_overrides=overrides,
-        use_gpu=use_gpu,
-        resume_training=resume,
-    )
-
-
-def train(
-    output_path: Path,
-    config_overrides: Dict[str, Any] = {},
-    use_gpu: int = -1,
-    resume_training: bool = False,
-) -> None:
     if use_gpu >= 0:
         msg.info(f"Using GPU: {use_gpu}")
         require_gpu(use_gpu)
     else:
         msg.info("Using CPU")
-    msg.info(f"Loading config and nlp from: {config_path}")
+    config = util.load_config(
-    # TODO: The details of this will change
+        config_path, overrides=config_overrides, interpolate=True
-    dave_path = output_path / "dave"
+    )
-    config_path = dave_path / "config.cfg"
+    if output_path is None:
-    with show_validation_error(config_path):
+        nlp = init_pipeline(config)
-        config = fill_config_etc_etc(config_path)
+    else:
-        nlp = make_and_load_nlp_etc_etc(config, dave_path)
+        init_path = output_path / "model-initial" 
-        optimizer, train_corpus, dev_corpus, score_weights, T_cfg = resolve_more_things_etc_etc(config)
+        if must_reinitialize(config, init_path):
+            nlp = init_pipeline(config)
+            nlp.to_disk(init_path)
+        else:
+            nlp = spacy.load(output_path / "model-initial")
+    msg.info("Start training")
+    train(nlp, config, output_path)
+
+
+def train(nlp: Language, output_path: Optional[Path]=None) -> None:
+    # Create iterator, which yields out info after each optimization step.
+    config = nlp.config
+    T_cfg = config["training"]
+    score_weights = T_cfg["score_weights"]
+    optimizer = T_cfg["optimizer"]
+    train_corpus = dot_to_object(config, T_cfg["train_corpus"])
+    dev_corpus = dot_to_object(config, T_cfg["dev_corpus"])
+    batcher = T_cfg["batcher"]
  
     training_step_iterator = train_while_improving(
         nlp,
@@ -142,6 +140,7 @@ def train(
             msg.good(f"Saved pipeline to output directory {final_model_path}")
 
 
+
 def add_vectors(nlp: Language, vectors: str) -> None:
     title = f"Config validation error for vectors {vectors}"
     desc = (