endpoint_model

Client-agnostic endpoint model. Provides a single EndpointModel that pairs a ModelSpec with any Client implementation (HTTP, AWS SageMaker, GCP, Azure, …). This eliminates the need for per-backend model subclasses.

EndpointModel

class EndpointModel()

Model endpoint backed by a pluggable client. Combines a ModelSpec (what the model expects and produces) with a Client (how to reach it). Supports both synchronous (predict / embed) and asynchronous (predict_async / embed_async) dispatch.

Model specification describing input requirements and output shape.

A configured Client instance (e.g. HTTPClient, AWSClient).

Optional ordered Ensembl IDs expected as bulk RNA input (M-Optimus ).

Optional ordered Ensembl IDs of predicted output genes (M-Optimus).

Example:

from bioptimus.models.clients.http import HTTPClient

client = HTTPClient(
    "http://localhost:8080",
    endpoints={"embedding": "/api/embed/h1"},
)
model = EndpointModel(H1_SPEC, client)
response = model(request)

model_spec

@property
def model_spec() -> ModelSpec

Model specification including tile and output config.

input_gene_names

@property
def input_gene_names() -> list[str] | None

Ordered Ensembl IDs expected as bulk RNA input.

output_gene_names

@property
def output_gene_names() -> list[str] | None

Ordered Ensembl IDs of predicted output genes.

predict

def predict(request: ModelRequest) -> ModelResponse

Send a prediction request to the endpoint.

ModelRequest

required

Tile request payload to send.

ModelResponse

Parsed model response with predictions.

embed

def embed(request: ModelRequest) -> ModelResponse

Send an embedding request to the endpoint.

ModelRequest

required

Tile request payload to send.

ModelResponse

Parsed model response with embeddings.

predict_async

async def predict_async(request: ModelRequest,
                        session: Any = None) -> ModelResponse

Send a prediction request asynchronously.

ModelRequest

required

Tile request payload to send.

Any

An ClientSession for connection pooling.

ModelResponse

Parsed model response with predictions.

embed_async

async def embed_async(request: ModelRequest,
                      session: Any = None) -> ModelResponse

Send an embedding request asynchronously.

ModelRequest

required

Tile request payload to send.

Any

An ClientSession for connection pooling.

ModelResponse

Parsed model response with embeddings.

Overview

Get Started

Preprocessing

Workflows

Reference

EndpointModel

model_spec

input_gene_names

output_gene_names

predict

embed

predict_async

embed_async

​EndpointModel

​model_spec

​input_gene_names

​output_gene_names

​predict

​embed

​predict_async

​embed_async

EndpointModel

model_spec

input_gene_names

output_gene_names

predict

embed

predict_async

embed_async