diff --git a/Dockerfile b/Dockerfile
index 364345d6..3db86adf 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -4,8 +4,21 @@
 #################### BASE BUILD IMAGE ####################
 FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
 
+# Set the DEBIAN_FRONTEND variable to noninteractive to avoid interactive prompts
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Preconfigure tzdata for US Central Time (build running in us-central-1 but this really doesn't matter.)
+RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
+    && echo 'tzdata tzdata/Zones/America select Chicago' | debconf-set-selections
+
+# We install an older version of python here for testing to make sure vllm works with older versions of Python.
+# For the actual openai compatible server, we will use the latest version of Python.
 RUN apt-get update -y \
-    && apt-get install -y python3-pip git
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa -y \
+    && apt-get update -y \
+    && apt-get install -y python3.8 python3.8-dev python3.8-venv python3-pip git \
+    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
 
 # Workaround for https://github.com/openai/triton/issues/2507 and
 # https://github.com/pytorch/pytorch/issues/107960 -- hopefully
diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py
index 8c9a7ad3..191142d2 100644
--- a/vllm/entrypoints/openai/serving_completion.py
+++ b/vllm/entrypoints/openai/serving_completion.py
@@ -1,7 +1,7 @@
 import asyncio
 import time
 from fastapi import Request
-from typing import AsyncGenerator, AsyncIterator, Callable, List, Optional
+from typing import AsyncGenerator, AsyncIterator, Callable, List, Optional, Dict, Tuple
 from vllm.logger import init_logger
 from vllm.utils import random_uuid
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -19,8 +19,8 @@ from vllm.entrypoints.openai.serving_engine import OpenAIServing
 
 logger = init_logger(__name__)
 
-TypeTokenIDs = list[int]
-TypeTopLogProbs = List[Optional[dict[int, float]]]
+TypeTokenIDs = List[int]
+TypeTopLogProbs = List[Optional[Dict[int, float]]]
 TypeCreateLogProbsFn = Callable[
     [TypeTokenIDs, TypeTopLogProbs, Optional[int], int], LogProbs]
 
@@ -29,7 +29,7 @@ async def completion_stream_generator(
     request: CompletionRequest,
     raw_request: Request,
     on_abort,
-    result_generator: AsyncIterator[tuple[int, RequestOutput]],
+    result_generator: AsyncIterator[Tuple[int, RequestOutput]],
     create_logprobs_fn: TypeCreateLogProbsFn,
     request_id: str,
     created_time: int,
@@ -126,7 +126,7 @@ async def completion_stream_generator(
     yield "data: [DONE]\n\n"
 
 
-def parse_prompt_format(prompt) -> tuple[bool, list]:
+def parse_prompt_format(prompt) -> Tuple[bool, list]:
     # get the prompt, openai supports the following
     # "a string, array of strings, array of tokens, or array of token arrays."
     prompt_is_tokens = False
@@ -151,7 +151,7 @@ def parse_prompt_format(prompt) -> tuple[bool, list]:
 
 
 def request_output_to_completion_response(
-    final_res_batch: list[RequestOutput],
+    final_res_batch: List[RequestOutput],
     request: CompletionRequest,
     create_logprobs_fn: TypeCreateLogProbsFn,
     request_id: str,
@@ -302,7 +302,7 @@ class OpenAIServingCompletion(OpenAIServing):
         except ValueError as e:
             return self.create_error_response(str(e))
 
-        result_generator: AsyncIterator[tuple[
+        result_generator: AsyncIterator[Tuple[
             int, RequestOutput]] = merge_async_iterators(*generators)
 
         # Similar to the OpenAI API, when n != best_of, we do not stream the