forked from phoenix/litellm-mirror
66 lines
No EOL
2.4 KiB
Markdown
66 lines
No EOL
2.4 KiB
Markdown
# Multiple Instances
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Load balance multiple instances of the same model
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The proxy will handle routing requests (using LiteLLM's Router). **Set `rpm` in the config if you want maximize throughput**
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:::info
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For more details on routing strategies / params, see [Routing](../routing.md)
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:::
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## Load Balancing using multiple litellm instances (Kubernetes, Auto Scaling)
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LiteLLM Proxy supports sharing rpm/tpm shared across multiple litellm instances, pass `redis_host`, `redis_password` and `redis_port` to enable this. (LiteLLM will use Redis to track rpm/tpm usage )
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Example config
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```yaml
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model_list:
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- model_name: gpt-3.5-turbo
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litellm_params:
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model: azure/<your-deployment-name>
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api_base: <your-azure-endpoint>
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api_key: <your-azure-api-key>
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rpm: 6 # Rate limit for this deployment: in requests per minute (rpm)
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- model_name: gpt-3.5-turbo
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litellm_params:
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model: azure/gpt-turbo-small-ca
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api_base: https://my-endpoint-canada-berri992.openai.azure.com/
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api_key: <your-azure-api-key>
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rpm: 6
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router_settings:
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redis_host: <your redis host>
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redis_password: <your redis password>
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redis_port: 1992
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```
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## Router settings on config - routing_strategy, model_group_alias
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litellm.Router() settings can be set under `router_settings`. You can set `model_group_alias`, `routing_strategy`, `num_retries`,`timeout` . See all Router supported params [here](https://github.com/BerriAI/litellm/blob/1b942568897a48f014fa44618ec3ce54d7570a46/litellm/router.py#L64)
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Example config with `router_settings`
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```yaml
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model_list:
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- model_name: gpt-3.5-turbo
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litellm_params:
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model: azure/<your-deployment-name>
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api_base: <your-azure-endpoint>
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api_key: <your-azure-api-key>
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rpm: 6 # Rate limit for this deployment: in requests per minute (rpm)
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- model_name: gpt-3.5-turbo
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litellm_params:
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model: azure/gpt-turbo-small-ca
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api_base: https://my-endpoint-canada-berri992.openai.azure.com/
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api_key: <your-azure-api-key>
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rpm: 6
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router_settings:
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model_group_alias: {"gpt-4": "gpt-3.5-turbo"} # all requests with `gpt-4` will be routed to models with `gpt-3.5-turbo`
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routing_strategy: least-busy # Literal["simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing"]
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num_retries: 2
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timeout: 30 # 30 seconds
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redis_host: <your redis host>
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redis_password: <your redis password>
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redis_port: 1992
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``` |