Sometimes you want to write one “main” implementation of a complicated inference task, but then re-use it for similar variations. For example:
  • Deploy it on different hardware and with different concurrency.
  • Replace a dependency (e.g. silence detection in audio files) with a different implementation of that step - while keeping all other processing the same.
  • Deploy the same inference flow, but exchange the model weights used. E.g. for a large and small version of an LLM or different model weights fine-tuned to
    domains.
  • Add an adapter to convert between a different input/output schema.
In all of those cases, you can create lightweight subclasses of your main chainlet. Below are some example code snippets - they can all be combined with each other!

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Example base class

import asyncio
import truss_chains as chains


class Preprocess2x(chains.ChainletBase):
    async def run_remote(self, number: int) -> int:
        return 2 * number


class MyBaseChainlet(chains.ChainletBase):
    remote_config = chains.RemoteConfig(
        compute=chains.Compute(cpu_count=1, memory="100Mi"),
        options=chains.ChainletOptions(enable_b10_tracing=True),
    )

    def __init__(self, preprocess=chains.depends(Preprocess2x)):
        self._preprocess = preprocess

    async def run_remote(self, number: int) -> float:
        return 1.0 / await self._preprocess.run_remote(number)


# Assert base behavior.
with chains.run_local():
    chainlet = MyBaseChainlet()
    result = asyncio.get_event_loop().run_until_complete(chainlet.run_remote(4))
    assert result == 1 / (4 * 2)

​
Adapter for different I/O

The base class MyBaseChainlet works with integer inputs and returns floats. If you want to reuse the computation, but provide an alternative interface (e.g. for a different client with different request/response schema), you can create a subclass which does the I/O conversion. The actual computation is delegated to the base classes above. 
class ChainletStringIO(MyBaseChainlet):
    async def run_remote(self, number: str) -> str:
        return str(await super().run_remote(int(number)))


# Assert new behavior.
with chains.run_local():
    chainlet_string_io = ChainletStringIO()
    result = asyncio.get_event_loop().run_until_complete(
        chainlet_string_io.run_remote("4"))
    assert result == "0.125"

​
Chain with substituted dependency

The base class MyBaseChainlet uses preprocessing that doubles the input. If you want to use a different variant of preprocessing - while keeping MyBaseChainlet.run_remote and everything else as is - you can define a shallow subclass of MyBaseChainlet where you use a different dependency Preprocess8x, which multiplies by 8 instead of 2. 
class Preprocess8x(chains.ChainletBase):
    async def run_remote(self, number: int) -> int:
        return 8 * number


class Chainlet8xPreprocess(MyBaseChainlet):
    def __init__(self, preprocess=chains.depends(Preprocess8x)):
        super().__init__(preprocess=preprocess)


# Assert new behavior.
with chains.run_local():
    chainlet_8x_preprocess = Chainlet8xPreprocess()
    result = asyncio.get_event_loop().run_until_complete(
        chainlet_8x_preprocess.run_remote(4))
    assert result == 1 / (4 * 8)

​
Override remote config.

If you want to re-deploy a chain, but change some deployment options, e.g. run on different hardware, you can create a subclass and override remote_config. 
class Chainlet16Core(MyBaseChainlet):
    remote_config = chains.RemoteConfig(
        compute=chains.Compute(cpu_count=16, memory="100Mi"),
        options=chains.ChainletOptions(enable_b10_tracing=True),
    )
Be aware that remote_config is a class variable. In the example above we created a completely new RemoteConfig value, because changing fields inplace would also affect the base class.If you want to share config between the base class and subclasses, you can define them in additional variables e.g. for the image:

DOCKER_IMAGE = chains.DockerImage(pip_requirements=[...], ...)


class MyBaseChainlet(chains.ChainletBase):
    remote_config = chains.RemoteConfig(docker_image=DOCKER_IMAGE, ...)


class Chainlet16Core(MyBaseChainlet):
    remote_config = chains.RemoteConfig(docker_image=DOCKER_IMAGE, ...)