Validation & acceptance testing - Bioptimus Documentation

Use these checks after deploying (on-premise or SageMaker) to confirm the service is healthy and producing expected results.

1. Service health

curl -s http://localhost:8080/ping | python3 -m json.tool
# {"status": "ok", "models": ["h1", "tissue-seg"]}

curl -s http://localhost:8080/ping | python3 -m json.tool
# {"status": "ok", "models": ["m-optimus", "tissue-seg"]}

2. Model availability (SDK)

available_backbones() lists the models the Bioptimus SDK can construct (read from its bundled configs) — it does not query the server. The /ping response in step 1 is the source of truth for what the deployed endpoint actually serves.

from bioptimus.models.backbones import Backbone
print(Backbone.available_backbones())
# ['h1', 'm-optimus', 'tissue-seg']

3. Synthetic-tile verification (recommended)

One command runs steps 1–4 together: it sends a synthetic tile through the endpoint and checks that the request succeeds (auth and routing), that the output matches the model’s declared contract (length, no NaN/infinity, correct tile echoed back), and reports an error code plus a fix for anything that fails.

bioptimus verify --model h1 --backend remote --base-url http://localhost:8080
# h1 on remote: ok (4 checks)

bioptimus verify --model m-optimus --backend aws \
    --endpoint-name my-endpoint --region-name eu-north-1

from bioptimus.observability.healthcheck import verify_endpoint

report = verify_endpoint("h1", "remote", base_url="http://localhost:8080")
print(report.summary())
for check in report.failures:
    print(check.name, check.error_code, check.remediation)

The command exits non-zero when any check fails; add --json for machine-readable output in a deployment pipeline. It issues one real inference request.

4. Output shape checks

Model	Expected output length
H-Optimus (`/api/embed/h1`)	1536
M-Optimus embedding	1536
M-Optimus prediction	number of output genes (see `/api/metadata/m-optimus`)
Tissue segmentation	H×W (262,144 for a 512×512 tile)