bioptimus.models.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_spec: ModelSpec,
                    transport: Client,
                    input_gene_names: list[str] | None = None,
                    output_gene_names: list[str] | None = None)

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

@log_failures
def predict(request: ModelRequest) -> ModelResponse

Sends a prediction request to the endpoint.

Tile request payload to send.

Returns: Parsed model response with predictions.

embed

@log_failures
def embed(request: ModelRequest) -> ModelResponse

Sends an embedding request to the endpoint.

Tile request payload to send.

Returns: Parsed model response with embeddings.

predict_with_embedding

@log_failures
def predict_with_embedding(request: ModelRequest) -> ModelResponse

Send a combined prediction+embedding request to the endpoint. Returns both the gene prediction (output) and the tile embedding (embedding) from a single forward pass, so callers needing both avoid a second inference.

Tile request payload to send.

Returns: Parsed model response with embedding populated alongside output.

predict_async

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

Sends a prediction request asynchronously.

Tile request payload to send.

An aiohttp.ClientSession for connection pooling.

Returns: Parsed model response with predictions.

embed_async

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

Sends an embedding request asynchronously.

Tile request payload to send.

An aiohttp.ClientSession for connection pooling.

Returns: Parsed model response with embeddings.

predict_with_embedding_async

@log_failures
async def predict_with_embedding_async(request: ModelRequest,
                                       session: Any = None) -> ModelResponse

Send a combined prediction+embedding request asynchronously.

Tile request payload to send.

An aiohttp.ClientSession for connection pooling.

Returns: Parsed model response with embedding populated alongside output.

call

def __call__(request: ModelRequest) -> ModelResponse

Alias for predict.

Overview

Get Started

Preprocessing

Workflows

Troubleshooting

Reference

EndpointModel

model_spec

input_gene_names

output_gene_names

predict

embed

predict_with_embedding

predict_async

embed_async

predict_with_embedding_async

call

​EndpointModel

​model_spec

​input_gene_names

​output_gene_names

​predict

​embed

​predict_with_embedding

​predict_async

​embed_async

​predict_with_embedding_async

​__call__

EndpointModel

model_spec

input_gene_names

output_gene_names

predict

embed

predict_with_embedding

predict_async

embed_async

predict_with_embedding_async

call