UPDATE: load components from pipeline

src/itwinai/components.py

@@ -344,23 +344,27 @@ def _pack_args(self, args) -> Tuple:
         return args
 
 
-def recursive_replace(config: Dict, target_field: str, new_value: Any) -> None:
-    def _recursive_replace_key(sub_dict: Dict):
-        if not isinstance(sub_dict, dict):
-            return
-        for k, v in sub_dict.items():
-            if k == target_field:
-                sub_dict[k] = new_value
-                return
-            else:
-                _recursive_replace_key(v)
-    _recursive_replace_key(config)
+def add_replace_field(
+    config: Dict,
+    key_chain: str,
+    value: Any
+) -> None:
+    sub_config = config
+    for idx, k in enumerate(key_chain.split('.')):
+        if idx >= len(key_chain.split('.')) - 1:
+            # Last key reached
+            break
+        if not isinstance(sub_config.get(k), dict):
+            sub_config[k] = dict()
+        sub_config = sub_config[k]
+    sub_config[k] = value
 
 
 def load_pipeline_step(
     pipe: Union[str, Dict],
     step_id: Union[str, int],
-    override_keys: Optional[Dict[str, Any]] = None
+    override_keys: Optional[Dict[str, Any]] = None,
+    verbose: bool = False
 ) -> Executable:
     if isinstance(pipe, str):
         # Load pipe from YAML file path
@@ -369,8 +373,12 @@ def load_pipeline_step(
 
     # Override fields
     if override_keys is not None:
-        for key, value in override_keys.items():
-            recursive_replace(step_dict_config, key, value)
+        for key_chain, value in override_keys.items():
+            add_replace_field(step_dict_config, key_chain, value)
+    if verbose:
+        import json
+        print(f"NEW STEP <ID:{step_id}> CONFIG:")
+        print(json.dumps(step_dict_config, indent=4))
 
     # Wrap config under "step" field and parse it
     step_dict_config = dict(step=step_dict_config)

use-cases/3dgan/pipeline.yaml

@@ -2,12 +2,14 @@ executor:
   class_path: itwinai.components.Executor
   init_args:
     steps:
-      - class_path: dataloader.Lightning3DGANDownloader
+      dataloading_step:
+        class_path: dataloader.Lightning3DGANDownloader
         init_args:
           data_path: exp_data/ # Set to null to skip dataset download
           data_url: https://drive.google.com/drive/folders/1uPpz0tquokepptIfJenTzGpiENfo2xRX
 
-      - class_path: trainer.Lightning3DGANTrainer
+      training_step:
+        class_path: trainer.Lightning3DGANTrainer
         init_args:
           # Pytorch lightning config for training
           config:
@@ -49,7 +51,7 @@ executor:
               limit_test_batches: null
               limit_train_batches: null
               limit_val_batches: null
-              log_every_n_steps: 2
+              log_every_n_steps: 1
               logger: 
                 # - class_path: lightning.pytorch.loggers.CSVLogger
                 #   init_args:
@@ -59,8 +61,8 @@ executor:
                   experiment_name: 3DGAN
                   save_dir: ml_logs/mlflow_logs
                   log_model: all
-              max_epochs: 1
-              max_steps: 20
+              max_epochs: 5
+              # max_steps: 2000
               max_time: null
               min_epochs: null
               min_steps: null
@@ -69,7 +71,7 @@ executor:
               plugins: null
               profiler: null
               reload_dataloaders_every_n_epochs: 0
-              strategy: ddp_find_unused_parameters_true #auto
+              strategy: auto #ddp_find_unused_parameters_true #auto
               sync_batchnorm: false
               use_distributed_sampler: true
               val_check_interval: null
@@ -79,7 +81,7 @@ executor:
               class_path: model.ThreeDGAN
               init_args:
                 latent_size: 256
-                batch_size: 64
+                batch_size: 4
                 loss_weights: [3, 0.1, 25, 0.1]
                 power: 0.85
                 lr: 0.001
@@ -90,4 +92,6 @@ executor:
               class_path: dataloader.ParticlesDataModule
               init_args:
                 datapath: exp_data/*/*.h5
-                batch_size: 64
+                batch_size: 4
+                num_workers: 0
+                max_samples: 12