fix: lazy imports in core/__init__ to prevent subprocess importing ML libs early

studio/backend/core/__init__.py

@@ -1,22 +1,12 @@
 """
 Unified core module for Unsloth backend
+
+Imports are LAZY (via __getattr__) so that training subprocesses can
+import core.training.worker without pulling in heavy ML dependencies
+like unsloth, transformers, or torch before the version activation
+code has a chance to run.
 """
 
-# Inference
-from .inference import InferenceBackend, get_inference_backend
-
-# Training
-from .training import TrainingBackend, get_training_backend, TrainingProgress
-
-# Configuration (from utils)
-from utils.models import is_vision_model, ModelConfig, scan_trained_loras, load_model_defaults, get_base_model_from_lora
-
-# Utilities (from utils)
-from utils.paths import normalize_path, is_local_path, is_model_cached
-from utils.utils import without_hf_auth, format_error_message
-from utils.hardware import get_device, is_apple_silicon, clear_gpu_cache, get_gpu_memory_info, log_gpu_memory, DeviceType
-from utils.datasets import format_and_template_dataset
-
 __all__ = [
     # Inference
     'InferenceBackend',
@@ -48,3 +38,72 @@ __all__ = [
     'clear_gpu_cache',
     'DeviceType',
 ]
+
+
+def __getattr__(name):
+    # Inference
+    if name in ('InferenceBackend', 'get_inference_backend'):
+        from .inference import InferenceBackend, get_inference_backend
+        globals()['InferenceBackend'] = InferenceBackend
+        globals()['get_inference_backend'] = get_inference_backend
+        return globals()[name]
+
+    # Training
+    if name in ('TrainingBackend', 'get_training_backend', 'TrainingProgress'):
+        from .training import TrainingBackend, get_training_backend, TrainingProgress
+        globals()['TrainingBackend'] = TrainingBackend
+        globals()['get_training_backend'] = get_training_backend
+        globals()['TrainingProgress'] = TrainingProgress
+        return globals()[name]
+
+    # Config (from utils.models)
+    if name in ('is_vision_model', 'ModelConfig', 'scan_trained_loras',
+                'load_model_defaults', 'get_base_model_from_lora'):
+        from utils.models import (
+            is_vision_model, ModelConfig, scan_trained_loras,
+            load_model_defaults, get_base_model_from_lora,
+        )
+        globals()['is_vision_model'] = is_vision_model
+        globals()['ModelConfig'] = ModelConfig
+        globals()['scan_trained_loras'] = scan_trained_loras
+        globals()['load_model_defaults'] = load_model_defaults
+        globals()['get_base_model_from_lora'] = get_base_model_from_lora
+        return globals()[name]
+
+    # Paths
+    if name in ('normalize_path', 'is_local_path', 'is_model_cached'):
+        from utils.paths import normalize_path, is_local_path, is_model_cached
+        globals()['normalize_path'] = normalize_path
+        globals()['is_local_path'] = is_local_path
+        globals()['is_model_cached'] = is_model_cached
+        return globals()[name]
+
+    # Utils
+    if name in ('without_hf_auth', 'format_error_message'):
+        from utils.utils import without_hf_auth, format_error_message
+        globals()['without_hf_auth'] = without_hf_auth
+        globals()['format_error_message'] = format_error_message
+        return globals()[name]
+
+    # Hardware
+    if name in ('get_device', 'is_apple_silicon', 'clear_gpu_cache',
+                'get_gpu_memory_info', 'log_gpu_memory', 'DeviceType'):
+        from utils.hardware import (
+            get_device, is_apple_silicon, clear_gpu_cache,
+            get_gpu_memory_info, log_gpu_memory, DeviceType,
+        )
+        globals()['get_device'] = get_device
+        globals()['is_apple_silicon'] = is_apple_silicon
+        globals()['clear_gpu_cache'] = clear_gpu_cache
+        globals()['get_gpu_memory_info'] = get_gpu_memory_info
+        globals()['log_gpu_memory'] = log_gpu_memory
+        globals()['DeviceType'] = DeviceType
+        return globals()[name]
+
+    # Datasets
+    if name == 'format_and_template_dataset':
+        from utils.datasets import format_and_template_dataset
+        globals()['format_and_template_dataset'] = format_and_template_dataset
+        return format_and_template_dataset
+
+    raise AttributeError(f"module 'core' has no attribute {name!r}")