Chainlet classes

APIs for creating user-defined Chainlets.

class `truss_chains.ChainletBase`

Base class for all chainlets. Inheriting from this class adds validations to make sure subclasses adhere to the chainlet pattern and facilitates remote chainlet deployment. Refer to the docs and this example chainlet for more guidance on how to create subclasses.

class `truss_chains.ModelBase`

Base class for all standalone models. Inheriting from this class adds validations to make sure subclasses adhere to the truss model pattern.

class `truss_chains.EngineBuilderLLMChainlet`

method final async run_remote(llm_input)

Parameters:

Name	Type	Description
`llm_input`	EngineBuilderLLMInput	OpenAI compatible request.

Returns: AsyncIterator[str]

function `truss_chains.depends`

Sets a “symbolic marker” to indicate to the framework that a chainlet is a dependency of another chainlet. The return value of depends is intended to be used as a default argument in a chainlet’s __init__-method. When deploying a chain remotely, a corresponding stub to the remote is injected in its place. In run_local mode an instance of a local chainlet is injected. Refer to the docs and this example chainlet for more guidance on how make one chainlet depend on another chainlet.

Despite the type annotation, this does not immediately provide a chainlet instance. Only when deploying remotely or using run_local a chainlet instance is provided.

Parameters:

Name	Type	Default	Description
`chainlet_cls`	Type[ChainletBase]		The chainlet class of the dependency.
`retries`	int	`1`	The number of times to retry the remote chainlet in case of failures (e.g. due to transient network issues). For streaming, retries are only made if the request fails before streaming any results back. Failures mid-stream not retried.
`timeout_sec`	float	`600.0`	Timeout for the HTTP request to this chainlet.
`use_binary`	bool	`False`	Whether to send data in binary format. This can give a parsing speedup and message size reduction (~25%) for numpy arrays. Use `NumpyArrayField` as a field type on pydantic models for integration and set this option to `True`. For simple text data, there is no significant benefit.
`concurrency_limit`	int	`300`	The maximum number of concurrent requests to send to the remote chainlet. Excessive requests will be queued and a warning will be shown. Try to design your algorithm in a way that spreads requests evenly over time so that this the default value can be used.

Returns: A “symbolic marker” to be used as a default argument in a chainlet’s initializer.

function `truss_chains.depends_context`

Sets a “symbolic marker” for injecting a context object at runtime. Refer to the docs and this example chainlet for more guidance on the __init__-signature of chainlets.

Despite the type annotation, this does not immediately provide a context instance. Only when deploying remotely or using run_local a context instance is provided.

Returns: A “symbolic marker” to be used as a default argument in a chainlet’s initializer.

class `truss_chains.DeploymentContext`

Bases: pydantic.BaseModel Bundles config values and resources needed to instantiate Chainlets. The context can optionally be added as a trailing argument in a Chainlet’s __init__ method and then used to set up the chainlet (e.g. using a secret as an access token for downloading model weights). Parameters:

Name	Type	Default	Description
`chainlet_to_service`	Mapping[str,DeployedServiceDescriptor]		A mapping from chainlet names to service descriptors. This is used to create RPC sessions to dependency chainlets. It contains only the chainlet services that are dependencies of the current chainlet.
`secrets`	Mapping[str,str]		A mapping from secret names to secret values. It contains only the secrets that are listed in `remote_config.assets.secret_keys` of the current chainlet.
`data_dir`	Path\|None	`None`	The directory where the chainlet can store and access data, e.g. for downloading model weights.
`environment`	Environment\|None	`None`	The environment that the chainlet is deployed in. None if the chainlet is not associated with an environment.

method get_baseten_api_key()

Returns: str

method get_service_descriptor(chainlet_name)

Parameters:

Name	Type	Description
`chainlet_name`	str	The name of the chainlet.

Returns: DeployedServiceDescriptor

class `truss_chains.Environment`

Bases: pydantic.BaseModel The environment the chainlet is deployed in.

Parameters: name (str) – The name of the environment.

class `truss_chains.ChainletOptions`

Bases: pydantic.BaseModel Parameters:

Name	Type	Default	Description
`enable_b10_tracing`	bool	`False`	enables baseten-internal trace data collection. This helps baseten engineers better analyze chain performance in case of issues. It is independent of a potentially user-configured tracing instrumentation. Turning this on, could add performance overhead.
`enable_debug_logs`	bool	`False`	Sets log level to debug in deployed server.
`env_variables`	Mapping[str,str]	`{}`	static environment variables available to the deployed chainlet.
`health_checks`	HealthChecks	`truss.base.truss_config.HealthChecks()`	Configures health checks for the chainlet. See guide.
`metadata`	JsonValue\|None	`None`	Arbitrary JSON object to describe chainlet.
`streaming_read_timeout`	int	`60`	Amount of time (in seconds) between each streamed chunk before a timeout is triggered.
`transport`	Union[HTTPOptions\|WebsocketOptions\|GRPCOptions]‘	`None`	Allows to customize certain transport protocols, e.g. websocket pings.

class `truss_chains.RPCOptions`

Bases: pydantic.BaseModel Options to customize RPCs to dependency chainlets. Parameters:

Name	Type	Default	Description
`retries`	int	`1`	The number of times to retry the remote chainlet in case of failures (e.g. due to transient network issues). For streaming, retries are only made if the request fails before streaming any results back. Failures mid-stream not retried.
`timeout_sec`	float	`600.0`	Timeout for the HTTP request to this chainlet.
`use_binary`	bool	`False`	Whether to send data in binary format. This can give a parsing speedup and message size reduction (~25%) for numpy arrays. Use `NumpyArrayField` as a field type on pydantic models for integration and set this option to `True`. For simple text data, there is no significant benefit.
`concurrency_limit`	int	`300`	The maximum number of concurrent requests to send to the remote chainlet. Excessive requests will be queued and a warning will be shown. Try to design your algorithm in a way that spreads requests evenly over time so that this the default value can be used.

function `truss_chains.mark_entrypoint`

Decorator to mark a chainlet as the entrypoint of a chain. This decorator can be applied to one chainlet in a source file and then the CLI push command simplifies: only the file, not the class within, must be specified. Optionally a display name for the Chain (not the Chainlet) can be set (effectively giving a custom default value for the name arg of the CLI push command). Example usage:

import truss_chains as chains

@chains.mark_entrypoint
class MyChainlet(ChainletBase):
    ...

# OR with custom Chain name.
@chains.mark_entrypoint("My Chain Name")
class MyChainlet(ChainletBase):
    ...

Remote Configuration

These data structures specify for each chainlet how it gets deployed remotely, e.g. dependencies and compute resources.

class `truss_chains.RemoteConfig`

Bases: pydantic.BaseModel Bundles config values needed to deploy a chainlet remotely. This is specified as a class variable for each chainlet class, e.g.:

import truss_chains as chains


class MyChainlet(chains.ChainletBase):
    remote_config = chains.RemoteConfig(
        docker_image=chains.DockerImage(
            pip_requirements=["torch==2.0.1", ...]
        ),
        compute=chains.Compute(cpu_count=2, gpu="A10G", ...),
        assets=chains.Assets(secret_keys=["hf_access_token"], ...),
    )

Parameters:

Name	Type	Default
`docker_image`	DockerImage	`truss_chains.DockerImage()`
`compute`	Compute	`truss_chains.Compute()`
`assets`	Assets	`truss_chains.Assets()`
`name`	str\|None	`None`
`options`	ChainletOptions	`truss_chains.ChainletOptions()`

class `truss_chains.DockerImage`

Bases: pydantic.BaseModel Configures the docker image in which a remoted chainlet is deployed.

Any paths are relative to the source file where DockerImage is defined and must be created with the helper function [make_abs_path_here] (#function-truss-chains-make-abs-path-here). This allows you for example organize chainlets in different (potentially nested) modules and keep their requirement files right next their python source files.

Parameters:

Name	Type	Default	Description
`base_image`	BasetenImage\|CustomImage	`truss_chains.BasetenImage()`	The base image used by the chainlet. Other dependencies and assets are included as additional layers on top of that image. You can choose a Baseten default image for a supported python version (e.g. `BasetenImage.PY311`), this will also include GPU drivers if needed, or provide a custom image (e.g. `CustomImage(image="python:3.11-slim")`).
`pip_requirements_file`	AbsPath\|None	`None`	Path to a file containing pip requirements. The file content is naively concatenated with `pip_requirements`.
`pip_requirements`	list[str]	`[]`	A list of pip requirements to install. The items are naively concatenated with the content of the `pip_requirements_file`.
`apt_requirements`	list[str]	`[]`	A list of apt requirements to install.
`data_dir`	AbsPath\|None	`None`	Data from this directory is copied into the docker image and accessible to the remote chainlet at runtime.
`external_package_dirs`	list[AbsPath]\|None	`None`	A list of directories containing additional python packages outside the chain’s workspace dir, e.g. a shared library. This code is copied into the docker image and importable at runtime.
`truss_server_version_override`	str\|None	`None`	By default, deployed Chainlets use the truss server implementation corresponding to the truss version of the user’s CLI. To use a specific version, e.g. pinning it for exact reproducibility, the version can be overridden here. Valid versions correspond to truss releases on PyPi: https://pypi.org/project/truss/#history, e.g. “0.9.80”.

class `truss_chains.BasetenImage`

Bases: Enum Default images, curated by baseten, for different python versions. If a Chainlet uses GPUs, drivers will be included in the image.

Enum Member	Value
`PY39`	py39
`PY310`	py310
`PY311`	py311

class `truss_chains.CustomImage`

Bases: pydantic.BaseModel Configures the usage of a custom image hosted on dockerhub. Parameters:

Name	Type	Default	Description
`image`	str		Reference to image on dockerhub.
`python_executable_path`	str\|None	`None`	Absolute path to python executable (if default `python` is ambiguous).
`docker_auth`	DockerAuthSettings\|None	`None`	See corresponding truss config.

class `truss_chains.Compute`

Specifies which compute resources a chainlet has in the remote deployment.

Not all combinations can be exactly satisfied by available hardware, in some cases more powerful machine types are chosen to make sure requirements are met or over-provisioned. Refer to the baseten instance reference.

Parameters:

Name	Type	Default	Description
`cpu_count`	int	`1`	Minimum number of CPUs to allocate.
`memory`	str	`'2Gi'`	Minimum memory to allocate, e.g. “2Gi” (2 gibibytes).
`gpu`	str\|Accelerator\|None	`None`	GPU accelerator type, e.g. “A10G”, “A100”, refer to the truss config for more choices.
`gpu_count`	int	`1`	Number of GPUs to allocate.
`predict_concurrency`	int\|Literal[‘cpu_count’]	`1`	Number of concurrent requests a single replica of a deployed chainlet handles.

Concurrency concepts are explained in this guide. It is important to understand the difference between predict_concurrency and the concurrency target (used for autoscaling, i.e. adding or removing replicas). Furthermore, the predict_concurrency of a single instance is implemented in two ways:

Via python’s asyncio, if run_remote is an async def. This requires that run_remote yields to the event loop.
With a threadpool if it’s a synchronous function. This requires that the threads don’t have significant CPU load (due to the GIL).

class `truss_chains.Assets`

Specifies which assets a chainlet can access in the remote deployment. For example, model weight caching can be used like this:

import truss_chains as chains
from truss.base import truss_config

mistral_cache = truss_config.ModelRepo(
    repo_id="mistralai/Mistral-7B-Instruct-v0.2",
    allow_patterns=["*.json", "*.safetensors", ".model"]
)
chains.Assets(cached=[mistral_cache], ...)

Parameters:

Name	Type	Default	Description
`cached`	Iterable[ModelRepo]	`()`	One or more `truss_config.ModelRepo` objects.
`secret_keys`	Iterable[str]	`()`	Names of secrets stored on baseten, that the chainlet should have access to. You can manage secrets on baseten here.
`external_data`	Iterable[ExternalDataItem]	`()`	Data to be downloaded from public URLs and made available in the deployment (via `context.data_dir`).

Core

General framework and helper functions.

function `truss_chains.push`

Deploys a chain remotely (with all dependent chainlets). Parameters:

Name	Type	Default	Description
`entrypoint`	Type[ChainletT]		The chainlet class that serves as the entrypoint to the chain.
`chain_name`	str		The name of the chain.
`publish`	bool	`True`	Whether to publish the chain as a published deployment (it is a draft deployment otherwise)
`promote`	bool	`True`	Whether to promote the chain to be the production deployment (this implies publishing as well).
`only_generate_trusses`	bool	`False`	Used for debugging purposes. If set to True, only the the underlying truss models for the chainlets are generated in `/tmp/.chains_generated`.
`remote`	str	`'baseten'`	name of a remote config in .trussrc. If not provided, it will be inquired.
`environment`	str\|None	`None`	The name of an environment to promote deployment into.
`progress_bar`	Type[progress.Progress]\|None	`None`	Optional rich.progress.Progress if output is desired.
`include_git_info`	bool	`False`	Whether to attach git versioning info (sha, branch, tag) to deployments made from within a git repo. If set to True in .trussrc, it will always be attached.

Returns: ChainService: A chain service handle to the deployed chain.

class `truss_chains.deployment.deployment_client.ChainService`

Handle for a deployed chain. A ChainService is created and returned when using push. It bundles the individual services for each chainlet in the chain, and provides utilities to query their status, invoke the entrypoint etc.

method get_info()

Queries the statuses of all chainlets in the chain.

Returns: List of DeployedChainlet, (name, is_entrypoint, status, logs_url) for each chainlet.

property name : str

method run_remote(json)

Invokes the entrypoint with JSON data. Parameters:

Name	Type	Description
`json`	JSON dict	Input data to the entrypoint

Returns: The JSON response.

property run_remote_url : str

URL to invoke the entrypoint.

property status_page_url : str

Link to status page on Baseten.

function `truss_chains.make_abs_path_here`

Helper to specify file paths relative to the immediately calling module. E.g. in you have a project structure like this:

root/
    chain.py
    common_requirements.text
    sub_package/
        chainlet.py
        chainlet_requirements.txt

You can now in root/sub_package/chainlet.py point to the requirements file like this:

shared = make_abs_path_here("../common_requirements.text")
specific = make_abs_path_here("chainlet_requirements.text")

This helper uses the directory of the immediately calling module as an absolute reference point for resolving the file location. Therefore, you MUST NOT wrap the instantiation of make_abs_path_here into a function (e.g. applying decorators) or use dynamic code execution.Ok:

def foo(path: AbsPath):
    abs_path = path.abs_path


foo(make_abs_path_here("./somewhere"))

Not Ok:

def foo(path: str):
    dangerous_value = make_abs_path_here(path).abs_path


foo("./somewhere")

Parameters:

Name	Type	Description
`file_path`	str	Absolute or relative path.

Returns: AbsPath

function `truss_chains.run_local`

Context manager local debug execution of a chain. The arguments only need to be provided if the chainlets explicitly access any the corresponding fields of DeploymentContext. Parameters:

Name	Type	Default	Description
`secrets`	Mapping[str,str]\|None	`None`	A dict of secrets keys and values to provide to the chainlets.
`data_dir`	Path\|str\|None	`None`	Path to a directory with data files.
`chainlet_to_service`	Mapping[str,DeployedServiceDescriptor]	`None`	A dict of chainlet names to service descriptors.

Example usage (as trailing main section in a chain file):

import os
import truss_chains as chains


class HelloWorld(chains.ChainletBase):
    ...


if __name__ == "__main__":
    with chains.run_local(
        secrets={"some_token": os.environ["SOME_TOKEN"]},
        chainlet_to_service={
            "SomeChainlet": chains.DeployedServiceDescriptor(
                name="SomeChainlet",
                display_name="SomeChainlet",
                predict_url="https://...",
                options=chains.RPCOptions(),
            )
        },
    ):
        hello_world_chain = HelloWorld()
        result = hello_world_chain.run_remote(max_value=5)

    print(result)

Refer to the local debugging guide for more details.

class `truss_chains.DeployedServiceDescriptor`

Bases: pydantic.BaseModel Bundles values to establish an RPC session to a dependency chainlet, specifically with StubBase. Parameters:

Name	Type	Default
`name`	str
`display_name`	str
`options`	RPCOptions
`predict_url`	str\|None	`None`
`internal_url`	InternalURL	`None`

class `truss_chains.StubBase`

Bases: BasetenSession, ABC Base class for stubs that invoke remote chainlets. Extends BasetenSession with methods for data serialization, de-serialization and invoking other endpoints. It is used internally for RPCs to dependency chainlets, but it can also be used in user-code for wrapping a deployed truss model into the Chains framework. It flexibly supports JSON and pydantic inputs and output. Example usage:

import pydantic
import truss_chains as chains


class WhisperOutput(pydantic.BaseModel):
    ...


class DeployedWhisper(chains.StubBase):
    # Input JSON, output JSON.
    async def run_remote(self, audio_b64: str) -> Any:
        return await self.predict_async(
            inputs={"audio": audio_b64})
        # resp == {"text": ..., "language": ...}

    # OR Input JSON, output pydantic model.
    async def run_remote(self, audio_b64: str) -> WhisperOutput:
        return await self.predict_async(
            inputs={"audio": audio_b64}, output_model=WhisperOutput)

    # OR Input and output are pydantic models.
    async def run_remote(self, data: WhisperInput) -> WhisperOutput:
        return await self.predict_async(data, output_model=WhisperOutput)


class MyChainlet(chains.ChainletBase):

    def __init__(self, ..., context=chains.depends_context()):
        ...
        self._whisper = DeployedWhisper.from_url(
            WHISPER_URL,
            context,
            options=chains.RPCOptions(retries=3),
        )

    async def run_remote(self, ...):
       await self._whisper.run_remote(...)

Parameters:

Name	Type	Description
`service_descriptor`	DeployedServiceDescriptor]	Contains the URL and other configuration.
`api_key`	str	A baseten API key to authorize requests.

classmethod from_url(predict_url, context_or_api_key, options=None)

Factory method, convenient to be used in chainlet’s __init__-method. Parameters:

Name	Type	Description
`predict_url`	str	URL to predict endpoint of another chain / truss model.
`context_or_api_key`	DeploymentContext	Deployment context object, obtained in the chainlet’s `__init__` or Baseten API key.
`options`	RPCOptions	RPC options, e.g. retries.

Invocation Methods

async predict_async(inputs: PydanticModel, output_model: Type[PydanticModel]) → PydanticModel
async predict_async(inputs: JSON, output_model: Type[PydanticModel]) → PydanticModel
async predict_async(inputs: JSON) → JSON
async predict_async_stream(inputs: PydanticModel | JSON) -> AsyncIterator[bytes]

Deprecated synchronous methods:

predict_sync(inputs: PydanticModel, output_model: Type[PydanticModel]) → PydanticModel
predict_sync(inputs: JSON, output_model: Type[PydanticModel]) → PydanticModel
predict_sync(inputs: JSON) → JSON

class `truss_chains.RemoteErrorDetail`

Bases: pydantic.BaseModel When a remote chainlet raises an exception, this pydantic model contains information about the error and stack trace and is included in JSON form in the error response. Parameters:

Name	Type
`exception_cls_name`	str
`exception_module_name`	str\|None
`exception_message`	str
`user_stack_trace`	list[StackFrame]

method format()

Format the error for printing, similar to how Python formats exceptions with stack traces.

Returns: str

class `truss_chains.GenericRemoteException`

Bases: Exception Raised when calling a remote chainlet results in an error and it is not possible to re-raise the same exception that was raise remotely in the caller.

Reference

Inference API

Management API

CLI reference

SDK reference

​Chainlet classes

​class truss_chains.ChainletBase

​class truss_chains.ModelBase

​class truss_chains.EngineBuilderLLMChainlet

​method final async run_remote(llm_input)

​function truss_chains.depends

​function truss_chains.depends_context

​class truss_chains.DeploymentContext

​method get_baseten_api_key()

​method get_service_descriptor(chainlet_name)

​class truss_chains.Environment

​class truss_chains.ChainletOptions

​class truss_chains.RPCOptions

​function truss_chains.mark_entrypoint

​Remote Configuration

​class truss_chains.RemoteConfig

​class truss_chains.DockerImage

​class truss_chains.BasetenImage

​class truss_chains.CustomImage

​class truss_chains.Compute

​class truss_chains.Assets

​Core

​function truss_chains.push

​class truss_chains.deployment.deployment_client.ChainService

​method get_info()

​property name : str

​method run_remote(json)

​property run_remote_url : str

​property status_page_url : str

​function truss_chains.make_abs_path_here

​function truss_chains.run_local

​class truss_chains.DeployedServiceDescriptor

​class truss_chains.StubBase

​classmethod from_url(predict_url, context_or_api_key, options=None)

​Invocation Methods

​class truss_chains.RemoteErrorDetail

​method format()

​class truss_chains.GenericRemoteException

Chainlet classes

class `truss_chains.ChainletBase`

class `truss_chains.ModelBase`

class `truss_chains.EngineBuilderLLMChainlet`

method final async run_remote(llm_input)

function `truss_chains.depends`

function `truss_chains.depends_context`

class `truss_chains.DeploymentContext`

method get_baseten_api_key()

method get_service_descriptor(chainlet_name)

class `truss_chains.Environment`

class `truss_chains.ChainletOptions`

class `truss_chains.RPCOptions`

function `truss_chains.mark_entrypoint`

Remote Configuration

class `truss_chains.RemoteConfig`

class `truss_chains.DockerImage`

class `truss_chains.BasetenImage`

class `truss_chains.CustomImage`

class `truss_chains.Compute`

class `truss_chains.Assets`

Core

function `truss_chains.push`

class `truss_chains.deployment.deployment_client.ChainService`

method get_info()

property name : str

method run_remote(json)

property run_remote_url : str

property status_page_url : str

function `truss_chains.make_abs_path_here`

function `truss_chains.run_local`

class `truss_chains.DeployedServiceDescriptor`

class `truss_chains.StubBase`

classmethod from_url(predict_url, context_or_api_key, options=None)

Invocation Methods

class `truss_chains.RemoteErrorDetail`

method format()

class `truss_chains.GenericRemoteException`