fix(proxy_server.py): allow worker config to just be the config filepath

litellm/proxy/proxy_server.py

@@ -598,22 +598,22 @@ def save_worker_config(**data):
     os.environ["WORKER_CONFIG"] = json.dumps(data)
 
 def initialize(
-    model,
-    alias,
-    api_base,
-    api_version,
-    debug,
-    temperature,
-    max_tokens,
-    request_timeout,
-    max_budget,
-    telemetry,
-    drop_params,
-    add_function_to_prompt,
-    headers,
-    save,
-    config, 
-    use_queue
+    model=None,
+    alias=None,
+    api_base=None,
+    api_version=None,
+    debug=False,
+    temperature=None,
+    max_tokens=None,
+    request_timeout=600,
+    max_budget=None,
+    telemetry=False,
+    drop_params=True,
+    add_function_to_prompt=True,
+    headers=None,
+    save=False,
+    use_queue=False,
+    config=None, 
 ):
     global user_model, user_api_base, user_debug, user_max_tokens, user_request_timeout, user_temperature, user_telemetry, user_headers, experimental, llm_model_list, llm_router, general_settings, master_key, user_custom_auth
     generate_feedback_box()
@@ -737,8 +737,16 @@ def litellm_completion(*args, **kwargs):
 async def startup_event():
     global prisma_client, master_key
     import json
-    worker_config = json.loads(os.getenv("WORKER_CONFIG"))
+
+    worker_config = litellm.get_secret("WORKER_CONFIG")
+    print(f"worker_config: {worker_config}")
     print_verbose(f"worker_config: {worker_config}")
+    # check if it's a valid file path
+    if os.path.isfile(worker_config):
+        initialize(config=worker_config)
+    else:
+        # if not, assume it's a json string
+        worker_config = json.loads(os.getenv("WORKER_CONFIG"))
         initialize(**worker_config)
     print_verbose(f"prisma client - {prisma_client}")
     if prisma_client: 

litellm/tests/test_proxy_gunicorn.py

@@ -0,0 +1,61 @@
+# #### What this tests ####
+# #  Allow the user to easily run the local proxy server with Gunicorn
+## LOCAL TESTING ONLY 
+# import sys, os, subprocess
+# import traceback
+# from dotenv import load_dotenv
+
+# load_dotenv()
+# import os, io
+
+# # this file is to test litellm/proxy
+
+# sys.path.insert(
+#     0, os.path.abspath("../..")
+# )  # Adds the parent directory to the system path 
+# import pytest
+# import litellm 
+
+# ### LOCAL Proxy Server INIT ###
+# from litellm.proxy.proxy_server import save_worker_config  # Replace with the actual module where your FastAPI router is defined
+# filepath = os.path.dirname(os.path.abspath(__file__))
+# config_fp = f"{filepath}/test_configs/test_config_custom_auth.yaml"
+# def get_openai_info(): 
+#     return {
+#         "api_key": os.getenv("AZURE_API_KEY"),
+#         "api_base": os.getenv("AZURE_API_BASE"),
+#     }
+
+# def run_server(host="0.0.0.0",port=8008,num_workers=None):
+#     if num_workers is None:
+#         # Set it to min(8,cpu_count())
+#         import multiprocessing
+#         num_workers = min(4,multiprocessing.cpu_count())
+
+#     ### LOAD KEYS ###
+
+#     # Load the Azure keys. For now get them from openai-usage
+#     azure_info = get_openai_info()
+#     print(f"Azure info:{azure_info}")
+#     os.environ["AZURE_API_KEY"] = azure_info['api_key']
+#     os.environ["AZURE_API_BASE"] = azure_info['api_base']
+#     os.environ["AZURE_API_VERSION"] = "2023-09-01-preview"
+
+#     ### SAVE CONFIG ### 
+
+#     os.environ["WORKER_CONFIG"] = config_fp
+
+#     # In order for the app to behave well with signals, run it with gunicorn
+#     # The first argument must be the "name of the command run"
+#     cmd = f"gunicorn litellm.proxy.proxy_server:app --workers {num_workers} --worker-class uvicorn.workers.UvicornWorker --bind {host}:{port}"
+#     cmd = cmd.split()
+#     print(f"Running command: {cmd}")
+#     import sys
+#     sys.stdout.flush()
+#     sys.stderr.flush()
+
+#     # Make sure to propage env variables
+#     subprocess.run(cmd)  # This line actually starts Gunicorn
+
+# if __name__ == "__main__":
+#     run_server()