Stack wizard CLI + Endpoints

examples/llm_finetuning/steps/finetune.py

@@ -102,8 +102,12 @@ def finetune(
     if should_print:
         logger.info("Loading datasets...")
     tokenizer = load_tokenizer(base_model_id, use_fast=use_fast)
-    tokenized_train_dataset = load_from_disk(dataset_dir / "train")
-    tokenized_val_dataset = load_from_disk(dataset_dir / "val")
+    tokenized_train_dataset = load_from_disk(
+        str((dataset_dir / "train").absolute())
+    )
+    tokenized_val_dataset = load_from_disk(
+        str((dataset_dir / "val").absolute())
+    )
 
     if should_print:
         logger.info("Loading base model...")
@@ -162,7 +166,6 @@ def finetune(
     if should_print:
         logger.info("Saving model...")
 
-    ft_model_dir = Path(ft_model_dir)
     if not use_accelerate or accelerator.is_main_process:
         ft_model_dir.mkdir(parents=True, exist_ok=True)
     if not use_accelerate:

src/zenml/cli/__init__.py

@@ -1411,6 +1411,34 @@
 Each corresponding argument should be the name, id or even the first few letters
  of the id that uniquely identify the artifact store or orchestrator.
 
+To create a new stack using the new service connector with a set of minimal components, 
+use the following command:
+
+```bash
+zenml stack register STACK_NAME \
+       -p CLOUD_PROVIDER
+```
+
+To create a new stack using the existing service connector with a set of minimal components, 
+use the following command:
+
+```bash
+zenml stack register STACK_NAME \
+       -sc SERVICE_CONNECTOR_NAME
+```
+
+To create a new stack using the existing service connector with existing components (
+important, that the components are already registered in the service connector), use the 
+following command:
+
+```bash
+zenml stack register STACK_NAME \
+       -sc SERVICE_CONNECTOR_NAME \
+       -a ARTIFACT_STORE_NAME \
+       -o ORCHESTRATOR_NAME \
+       ...
+```
+
 If you want to immediately set this newly created stack as your active stack,
 simply pass along the `--set` flag.