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Enable real classifiers in local dev by default #952

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Enable real classifiers in local dev by default #952

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5f17e1a
feat: enable real classifiers in local dev by default
mihow Sep 17, 2025
0959796
Merge branch 'main' into feat/default-processing-service
mihow Nov 18, 2025
fff22be
Merge main into the real-classifiers-in-local-dev branch
mihow Oct 13, 2026
77a5793
feat(dev): keep the example service's downloaded models across rebuilds
mihow Oct 13, 2026
6806786
docs(processing-services): state the size of the first model download…
mihow Oct 13, 2026
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7 changes: 5 additions & 2 deletions docker-compose.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@ volumes:
minio_data:
driver: local
node_modules:
ml_backend_model_cache:

services:
django: &django
Expand Down Expand Up @@ -187,9 +188,11 @@ services:

ml_backend:
build:
context: ./processing_services/minimal
context: ./processing_services/example
volumes:
- ./processing_services/minimal/:/app
- ./processing_services/example/:/app
Comment on lines 191 to +193

Copilot AI Nov 18, 2025

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The example service uses ML models (transformers, PyTorch) that download models on first use. Consider adding cache volumes to avoid re-downloading models on container restarts:

volumes:
  - ./processing_services/example/:/app
  - ./processing_services/example/huggingface_cache:/root/.cache/huggingface
  - ./processing_services/example/pytorch_cache:/root/.cache/torch

This is similar to the configuration in processing_services/example/docker-compose.yml and would improve startup performance after the initial model download.

Copilot uses AI. Check for mistakes.
# Hugging Face and torch downloads survive container rebuilds; the first start downloads the models.
- ml_backend_model_cache:/root/.cache
ports:
- "2005:2000"
networks:
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4 changes: 2 additions & 2 deletions processing_services/README.md
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Expand Up @@ -11,8 +11,8 @@ In this directory, we define locally-run processing services as FastAPI apps. A
- `/process`: receives source images and existing detections via a `PipelineRequest` and returns a `PipelineResponse` containing detections

`processing_services` contains 2 apps:
- `example`: demos how to add custom pipelines/algorithms.
- `minimal`: a simple ML backend for basic testing of the processing service API. This minimal app also runs within the main Antenna docker compose stack.
- `example`: demos how to add custom pipelines/algorithms with real models. It runs as `ml_backend` in the main Antenna docker compose stack for local development, so jobs there produce real detections and classifications. The first job downloads the models (about 3.6 GB for the zero-shot pipelines; about a minute on a fast connection) into the `ml_backend_model_cache` volume, which keeps them across rebuilds; `docker volume rm` it to download again.
- `minimal`: a simple ML backend with no models and fixed outputs, for testing the processing service API. The CI stack (`docker-compose.ci.yml`) uses it, and it can be started next to the main stack with `docker compose -f processing_services/docker-compose.yml up -d ml_backend_minimal`.

If your goal is to run an ML backend locally, simply copy the `example` app and follow the steps below.

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