map & operator: Add lightning.ai account creation info

src/lightning/data/streaming/functions.py

@@ -23,7 +23,7 @@
 import torch
 
 from lightning.data.processing.readers import BaseReader
-from lightning.data.streaming.constants import _TORCH_GREATER_EQUAL_2_1_0
+from lightning.data.streaming.constants import _IS_IN_STUDIO, _TORCH_GREATER_EQUAL_2_1_0
 from lightning.data.streaming.data_processor import DataChunkRecipe, DataProcessor, DataTransformRecipe
 from lightning.data.streaming.resolver import (
     Dir,
@@ -169,8 +169,8 @@ def map(
         output_dir: The folder where the processed data should be stored.
         num_workers: The number of workers to use during processing
         fast_dev_run: Whether to use process only a sub part of the inputs
-        num_nodes: When doing remote execution, the number of nodes to use.
-        machine: When doing remote execution, the machine to use.
+        num_nodes: When doing remote execution, the number of nodes to use. Only supported on https://lightning.ai/.
+        machine: When doing remote execution, the machine to use. Only supported on https://lightning.ai/.
         num_downloaders: The number of downloaders per worker.
         reorder_files: By default, reorders the files by file size to distribute work equally among all workers.
             Set this to ``False`` if the order in which samples are processed should be preserved.
@@ -183,6 +183,17 @@ def map(
     if len(inputs) == 0:
         raise ValueError(f"The provided inputs should be non empty. Found {inputs}.")
 
+    if not _IS_IN_STUDIO and (machine is not None or num_nodes is not None):
+        raise ValueError(
+            "Only https://lightning.ai/ supports multiple nodes or selecting a machine."
+            " Create an account to try it out.")
+
+    if not _IS_IN_STUDIO:
+        print(
+            "Create an account on https://lightning.ai/ to transform your data faster using "
+            "multiple nodes and large machines."
+        )
+
     if num_nodes is None or int(os.getenv("DATA_OPTIMIZER_NUM_NODES", 0)) > 0:
         _output_dir: Dir = _resolve_dir(output_dir)
 
@@ -242,8 +253,8 @@ def optimize(
         compression: The compression algorithm to use over the chunks.
         num_workers: The number of workers to use during processing
         fast_dev_run: Whether to use process only a sub part of the inputs
-        num_nodes: When doing remote execution, the number of nodes to use.
-        machine: When doing remote execution, the machine to use.
+        num_nodes: When doing remote execution, the number of nodes to use. Only supported on https://lightning.ai/.
+        machine: When doing remote execution, the machine to use. Only supported on https://lightning.ai/.
         num_downloaders: The number of downloaders per worker.
         reorder_files: By default, reorders the files by file size to distribute work equally among all workers.
             Set this to ``False`` if the order in which samples are processed should be preserved.
@@ -258,6 +269,18 @@ def optimize(
     if chunk_size is None and chunk_bytes is None:
         raise ValueError("Either `chunk_size` or `chunk_bytes` needs to be defined.")
 
+    if not _IS_IN_STUDIO and (machine is not None or num_nodes is not None):
+        raise ValueError(
+            "Only https://lightning.ai/ supports multiple nodes or selecting a machine."
+            "Create an account to try it out."
+        )
+
+    if not _IS_IN_STUDIO:
+        print(
+            "Create an account on https://lightning.ai/ to optimize your data faster "
+            "using multiple nodes and large machines."
+        )
+
     if num_nodes is None or int(os.getenv("DATA_OPTIMIZER_NUM_NODES", 0)) > 0:
         _output_dir: Dir = _resolve_dir(output_dir)
 
@@ -312,6 +335,9 @@ def __init__(self, folder: str, max_workers: Optional[int] = os.cpu_count()) ->
         self.max_workers = max_workers or 1
         self.futures: List[concurrent.futures.Future] = []
 
+        if not _IS_IN_STUDIO:
+            print("This method is optimized to run on https://lightning.ai/. Don't use it otherwise.")
+
     def __iter__(self) -> Any:
         """This function queues the folders to perform listdir across multiple workers."""
         with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor: