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70 lines
2 KiB
Python
70 lines
2 KiB
Python
#### What this tests ####
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# This tests the router's ability to pick deployment with lowest latency
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import sys, os, asyncio, time, random
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from datetime import datetime
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import traceback
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from dotenv import load_dotenv
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load_dotenv()
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import os, copy
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import pytest
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from litellm import Router
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from litellm.router_strategy.lowest_cost import LowestCostLoggingHandler
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from litellm.caching import DualCache
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### UNIT TESTS FOR LATENCY ROUTING ###
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def test_get_available_deployments():
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test_cache = DualCache()
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model_list = [
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {"model": "gpt-4"},
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"model_info": {"id": "openai-gpt-4"},
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},
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {"model": "groq/llama3-8b-8192"},
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"model_info": {"id": "groq-llama"},
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},
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]
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lowest_cost_logger = LowestCostLoggingHandler(
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router_cache=test_cache, model_list=model_list
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)
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model_group = "gpt-3.5-turbo"
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## CHECK WHAT'S SELECTED ##
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selected_model = lowest_cost_logger.get_available_deployments(
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model_group=model_group, healthy_deployments=model_list
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)
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print("selected model: ", selected_model)
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assert selected_model["model_info"]["id"] == "groq-llama"
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async def _deploy(lowest_latency_logger, deployment_id, tokens_used, duration):
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kwargs = {
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"litellm_params": {
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"metadata": {
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"model_group": "gpt-3.5-turbo",
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"deployment": "azure/chatgpt-v-2",
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},
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"model_info": {"id": deployment_id},
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}
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}
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start_time = time.time()
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response_obj = {"usage": {"total_tokens": tokens_used}}
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time.sleep(duration)
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end_time = time.time()
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lowest_latency_logger.log_success_event(
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response_obj=response_obj,
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kwargs=kwargs,
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start_time=start_time,
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end_time=end_time,
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)
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