run_inside -> imports

02_building_containers/import_sklearn.py

@@ -18,11 +18,11 @@
     .pip_install("scikit-learn"),
 )
 
-# The `stub.image.run_inside()` lets us conditionally run code in the global scope.
+# The `stub.image.imports()` lets us conditionally import in the global scope.
 # This is needed because we might not have sklearn and numpy installed locally,
 # but we know they are installed inside the custom image.
 
-with stub.image.run_inside():
+with stub.image.imports():
     import numpy as np
     from sklearn import datasets, linear_model
 

03_scaling_out/youtube_face_detection.py

@@ -52,14 +52,14 @@
 )
 stub = modal.Stub("example-youtube-face-detection", image=image)
 
-with stub.image.run_inside():
+with image.imports():
     import cv2
     import moviepy.editor
     import pytube
 
 # For temporary storage and sharing of downloaded movie clips, we use a network file system.
 
-stub.net_file_system = modal.NetworkFileSystem.new()
+net_file_system = modal.NetworkFileSystem.new()
 
 # ### Face detection function
 #
@@ -73,9 +73,7 @@
 # and stores the resulting video back to the shared storage.
 
 
-@stub.function(
-    network_file_systems={"/clips": stub.net_file_system}, timeout=600
-)
+@stub.function(network_file_systems={"/clips": net_file_system}, timeout=600)
 def detect_faces(fn, start, stop):
     # Extract the subclip from the video
     clip = moviepy.editor.VideoFileClip(fn).subclip(start, stop)
@@ -108,7 +106,7 @@ def detect_faces(fn, start, stop):
 # 3. Stitch the results back into a new video
 
 
-@stub.function(network_file_systems={"/clips": stub.net_file_system}, retries=1)
+@stub.function(network_file_systems={"/clips": net_file_system}, retries=1)
 def process_video(url):
     print(f"Downloading video from '{url}'")
     yt = pytube.YouTube(url)

06_gpu_and_ml/embeddings/text_embeddings_inference.py

@@ -1,3 +1,6 @@
+import json
+import os
+import socket
 import subprocess
 from pathlib import Path
 
@@ -23,8 +26,6 @@
 
 
 def spawn_server() -> subprocess.Popen:
-    import socket
-
     process = subprocess.Popen(["text-embeddings-router"] + LAUNCH_FLAGS)
 
     # Poll until webserver at 127.0.0.1:8000 accepts connections before running inputs.
@@ -64,8 +65,9 @@ def download_model():
 )
 
 
-with tei_image.run_inside():
+with tei_image.imports():
     import numpy as np
+    from httpx import AsyncClient
 
 
 @stub.cls(
@@ -79,8 +81,6 @@ def download_model():
 )
 class TextEmbeddingsInference:
     def __enter__(self):
-        from httpx import AsyncClient
-
         self.process = spawn_server()
         self.client = AsyncClient(base_url="http://127.0.0.1:8000")
 
@@ -101,12 +101,6 @@ async def embed(self, inputs_with_ids: list[tuple[int, str]]):
 
 
 def download_data():
-    import json
-    import os
-
-    from google.cloud import bigquery
-    from google.oauth2 import service_account
-
     service_account_info = json.loads(os.environ["SERVICE_ACCOUNT_JSON"])
     credentials = service_account.Credentials.from_service_account_info(
         service_account_info
@@ -131,16 +125,21 @@ def download_data():
     volume.commit()
 
 
+image = Image.debian_slim().pip_install(
+    "google-cloud-bigquery", "pandas", "db-dtypes", "tqdm"
+)
+
+with image.imports():
+    from google.cloud import bigquery
+    from google.oauth2 import service_account
+
+
 @stub.function(
-    image=Image.debian_slim().pip_install(
-        "google-cloud-bigquery", "pandas", "db-dtypes", "tqdm"
-    ),
+    image=image,
     secrets=[Secret.from_name("bigquery")],
     volumes={DATA_PATH.parent: volume},
 )
 def embed_dataset():
-    import json
-
     model = TextEmbeddingsInference()
 
     if not DATA_PATH.exists():

07_web_endpoints/chatbot_spa.py

@@ -48,7 +48,7 @@ def load_tokenizer_and_model():
 )
 
 
-with gpu_image.run_inside():
+with gpu_image.imports():
     import torch
 
     tokenizer, model = load_tokenizer_and_model()