Baseten’s Engine Builder enables the deployment of optimized model inference engines. Currently, it supports TensorRT-LLM. Truss Chains allows seamless integration of these engines into structured workflows. This guide provides a quick entry point for Chains users.

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LLama 7B Example

Use the EngineBuilderLLMChainlet baseclass to configure an LLM engine. The additional engine_builder_config field specifies model architecture, repository, and runtime parameters and more, the full options are detailed in the Engine Builder configuration guide. 
import truss_chains as chains
from truss.base import trt_llm_config, truss_config

class Llama7BChainlet(chains.EngineBuilderLLMChainlet):
    remote_config = chains.RemoteConfig(
        compute=chains.Compute(gpu=truss_config.Accelerator.H100),
        assets=chains.Assets(secret_keys=["hf_access_token"]),
    )
    engine_builder_config = truss_config.TRTLLMConfiguration(
        build=trt_llm_config.TrussTRTLLMBuildConfiguration(
            base_model=trt_llm_config.TrussTRTLLMModel.LLAMA,
            checkpoint_repository=trt_llm_config.CheckpointRepository(
                source=trt_llm_config.CheckpointSource.HF,
                repo="meta-llama/Llama-3.1-8B-Instruct",
            ),
            max_batch_size=8,
            max_seq_len=4096,
            tensor_parallel_count=1,
        )
    )

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Differences from Standard Chainlets

  • No run_remote implementation: Unlike regular Chainlets, EngineBuilderLLMChainlet does not require users to implement run_remote(). Instead, it automatically wires into the deployed engine’s API. All LLM Chainlets have the same function signature: chains.EngineBuilderLLMInput as input and a stream (AsyncIterator) of strings as output. Likewise EngineBuilderLLMChainlets can only be used as dependencies, but not have dependencies themselves.
  • No run_local (guide) and watch (guide) Standard Chains support a local debugging mode and watch. However, when using EngineBuilderLLMChainlet, local execution is not available, and testing must be done after deployment. For a faster dev loop of the rest of your chain (everything except the engine builder chainlet) you can substitute those chainlets with stubs like you can do for an already deployed truss model [guide].

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Integrate the Engine Builder Chainlet

After defining an EngineBuilderLLMInput like Llama7BChainlet above, you can use it as a dependency in other conventional chainlets: 
from typing import AsyncIterator
import truss_chains as chains

@chains.mark_entrypoint
class TestController(chains.ChainletBase):
    """Example using the Engine Builder Chainlet in another Chainlet."""

    def __init__(self, llm=chains.depends(Llama7BChainlet)) -> None:
        self._llm = llm

    async def run_remote(self, prompt: str) -> AsyncIterator[str]:
        messages = [{"role": "user", "content": prompt}]
        llm_input = chains.EngineBuilderLLMInput(messages=messages)
        async for chunk in self._llm.run_remote(llm_input):
            yield chunk