Avoid using the same port number for autoscaler works (#15966)

* dont hardcode port in python server
* add another chglog

examples/app_server_with_auto_scaler/app.py

@@ -1,3 +1,4 @@
+# ! pip install torch torchvision
 from typing import Any, List
 
 import torch
@@ -22,10 +23,10 @@ class BatchResponse(BaseModel):
 class PyTorchServer(L.app.components.PythonServer):
     def __init__(self, *args, **kwargs):
         super().__init__(
-            port=L.app.utilities.network.find_free_network_port(),
             input_type=BatchRequestModel,
             output_type=BatchResponse,
-            cloud_compute=L.CloudCompute("gpu"),
+            *args,
+            **kwargs,
         )
 
     def setup(self):
@@ -57,30 +58,32 @@ def scale(self, replicas: int, metrics: dict) -> int:
         """The default scaling logic that users can override."""
         # scale out if the number of pending requests exceeds max batch size.
         max_requests_per_work = self.max_batch_size
-        pending_requests_per_running_or_pending_work = metrics["pending_requests"] / (
-            replicas + metrics["pending_works"]
-        )
-        if pending_requests_per_running_or_pending_work >= max_requests_per_work:
+        pending_requests_per_work = metrics["pending_requests"] / (replicas + metrics["pending_works"])
+        if pending_requests_per_work >= max_requests_per_work:
             return replicas + 1
 
         # scale in if the number of pending requests is below 25% of max_requests_per_work
         min_requests_per_work = max_requests_per_work * 0.25
-        pending_requests_per_running_work = metrics["pending_requests"] / replicas
-        if pending_requests_per_running_work < min_requests_per_work:
+        pending_requests_per_work = metrics["pending_requests"] / replicas
+        if pending_requests_per_work < min_requests_per_work:
             return replicas - 1
 
         return replicas
 
 
 app = L.LightningApp(
     MyAutoScaler(
+        # work class and args
         PyTorchServer,
-        min_replicas=2,
+        cloud_compute=L.CloudCompute("gpu"),
+        # autoscaler specific args
+        min_replicas=1,
         max_replicas=4,
         autoscale_interval=10,
         endpoint="predict",
         input_type=RequestModel,
         output_type=Any,
         timeout_batching=1,
+        max_batch_size=8,
     )
 )

src/lightning_app/CHANGELOG.md

@@ -13,7 +13,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
--
+- Changed the default port of `PythonServer` from `7777` to a free port at runtime ([#15966](https://github.com/Lightning-AI/lightning/pull/15966))
 
 - Remove the `AutoScaler` dependency `aiohttp` from the base requirements ([#15971](https://github.com/Lightning-AI/lightning/pull/15971))
 
@@ -30,7 +30,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
--
+- Fixed `AutoScaler` failing due to port collision across works ([#15966](https://github.com/Lightning-AI/lightning/pull/15966))
 
 
 ## [1.8.4] - 2022-12-08

src/lightning_app/components/auto_scaler.py

@@ -450,7 +450,8 @@ def workers(self) -> List[LightningWork]:
     def create_work(self) -> LightningWork:
         """Replicates a LightningWork instance with args and kwargs provided via ``__init__``."""
         # TODO: Remove `start_with_flow=False` for faster initialization on the cloud
-        return self._work_cls(*self._work_args, **self._work_kwargs, start_with_flow=False)
+        self._work_kwargs.update(dict(start_with_flow=False))
+        return self._work_cls(*self._work_args, **self._work_kwargs)
 
     def add_work(self, work) -> str:
         """Adds a new LightningWork instance.

src/lightning_app/components/serve/python_server.py

@@ -75,17 +75,13 @@ class PythonServer(LightningWork, abc.ABC):
     @requires(["torch", "lightning_api_access"])
     def __init__(  # type: ignore
         self,
-        host: str = "127.0.0.1",
-        port: int = 7777,
         input_type: type = _DefaultInputData,
         output_type: type = _DefaultOutputData,
         **kwargs,
     ):
         """The PythonServer Class enables to easily get your machine learning server up and running.
 
         Arguments:
-            host: Address to be used for running the server.
-            port: Port to be used to running the server.
             input_type: Optional `input_type` to be provided. This needs to be a pydantic BaseModel class.
                 The default data type is good enough for the basic usecases and it expects the data
                 to be a json object that has one key called `payload`
@@ -129,7 +125,7 @@ def predict(self, request):
             ...
             >>> app = LightningApp(SimpleServer())
         """
-        super().__init__(parallel=True, host=host, port=port, **kwargs)
+        super().__init__(parallel=True, **kwargs)
         if not issubclass(input_type, BaseModel):
             raise TypeError("input_type must be a pydantic BaseModel class")
         if not issubclass(output_type, BaseModel):