updates examples to use new queue and nfs methods, closes #659

06_gpu_and_ml/stable_diffusion/a1111_webui.py

@@ -15,7 +15,9 @@
 from modal import Image, Queue, Stub, forward
 
 stub = Stub("example-a1111-webui")
-stub.urls = Queue.new()  # TODO: FunctionCall.get() doesn't support generators.
+stub.urls = Queue.from_name(
+    "a1111-webui-example", create_if_missing=True
+)  # TODO: FunctionCall.get() doesn't support generators.
 
 
 def wait_for_port(port: int):
@@ -57,6 +59,7 @@ def wait_for_port(port: int):
     .run_commands(
         "cd /webui && . venv/bin/activate && "
         + "python -c 'from modules import shared_init, initialize; shared_init.initialize(); initialize.initialize()'",
+        gpu="a10g",
     ),
     gpu="a10g",
     cpu=2,

06_gpu_and_ml/stable_diffusion/stable_video_diffusion.py

@@ -7,7 +7,9 @@
 import modal
 
 stub = modal.Stub(name="example-stable-video-diffusion-streamlit")
-stub.q = modal.Queue.new()
+q = modal.Queue.from_name(
+    "stable-video-diffusion-streamlit", create_if_missing=True
+)
 
 session_timeout = 15 * 60
 
@@ -68,13 +70,13 @@ def run_streamlit(publish_url: bool = False):
     with modal.forward(8501) as tunnel:
         # Reload Streamlit config with information about Modal tunnel address.
         if publish_url:
-            stub.q.put(tunnel.url)
+            q.put(tunnel.url)
         load_config_options(
             {"browser.serverAddress": tunnel.host, "browser.serverPort": 443}
         )
         run(
             main_script_path="/sgm/scripts/demo/video_sampling.py",
-            command_line=None,
+            is_hello=False,
             args=["--timeout", str(session_timeout)],
             flag_options={},
         )
@@ -86,5 +88,5 @@ def share():
     from fastapi.responses import RedirectResponse
 
     run_streamlit.spawn(publish_url=True)
-    url = stub.q.get()
+    url = q.get()
     return RedirectResponse(url, status_code=303)

06_gpu_and_ml/tensorflow/tensorflow_tutorial.py

@@ -41,7 +41,7 @@
 # We want to run the web server for TensorBoard at the same time as we are training the TensorFlow model.
 # The easiest way to do this is to set up a shared filesystem between the training and the web server.
 
-fs = NetworkFileSystem.new()
+fs = NetworkFileSystem.from_name("tensorflow-tutorial", create_if_missing=True)
 logdir = "/tensorboard"
 
 # ## Training function

11_notebooks/jupyter_inside_modal.py

@@ -23,7 +23,7 @@
     )
 )
 # This volume is not persisted, so the data will be deleted when this demo app is stopped.
-volume = modal.NetworkFileSystem.new()
+volume = modal.NetworkFileSystem.ephemeral()
 
 CACHE_DIR = "/root/cache"
 JUPYTER_TOKEN = "1234"  # Change me to something non-guessable!

misc/queue_simple.py

@@ -15,7 +15,7 @@
 import modal.queue
 
 stub = modal.Stub("example-queue-simple")
-stub.q = modal.Queue.new()
+q = modal.Queue.ephemeral()
 
 
 @stub.function()
@@ -51,8 +51,8 @@ async def many_consumers(q: modal.Queue) -> None:
 
 async def main():
     with stub.run():
-        await run_async.remote.aio(stub.q)
-        await many_consumers.remote.aio(stub.q)
+        await run_async.remote.aio(q)
+        await many_consumers.remote.aio(q)
 
 
 if __name__ == "__main__":

pyproject.toml

@@ -1,7 +1,7 @@
 [tool.pytest.ini_options]
 filterwarnings = [
-    "ignore::DeprecationWarning",
-    "ignore::modal.exception.DeprecationError",
+    "error::DeprecationWarning",
+    "error::modal.exception.DeprecationError",
     "ignore::DeprecationWarning:pytest.*:",
 ]
 pythonpath = ["06_gpu_and_ml/spam-detect", "06_gpu_and_ml/text-to-pokemon"]