Benoit Chesneau 8559854b4f feat(dirty): add per-app worker allocation for memory optimization ...

Allow dirty apps to specify how many workers should load them, enabling
significant memory savings for heavy applications like ML models.

- Add `workers` class attribute to DirtyApp (None = all workers)
- Add `parse_dirty_app_spec()` to parse "module:Class:N" format
- Add `DirtyNoWorkersAvailableError` for app-specific error handling
- Update DirtyArbiter with per-app worker tracking and routing
- Maintain backward compatibility when no dirty_apps configured

Example: 8 workers x 10GB model = 80GB RAM needed
With workers=2: 2 x 10GB = 20GB RAM (75% savings)

Configuration formats:
- Class attribute: `workers = 2` on DirtyApp subclass
- Config format: `module:class:N` (e.g., `myapp.ml:HugeModel:2`)

2026-02-01 02:40:09 +01:00

..

__init__.py

feat(dirty): add streaming support and async client benchmarks

2026-01-25 10:23:25 +01:00

test_arbiter_streaming.py

feat(dirty): add streaming support and async client benchmarks

2026-01-25 10:23:25 +01:00

test_client_streaming_async.py

feat(dirty): add streaming support and async client benchmarks

2026-01-25 10:23:25 +01:00

test_client_streaming.py

feat(dirty): add streaming support and async client benchmarks

2026-01-25 10:23:25 +01:00

test_multi_app_routing.py

test(dirty): add multi-app routing tests

2026-01-25 10:23:25 +01:00

test_per_app_worker_allocation.py

feat(dirty): add per-app worker allocation for memory optimization

2026-02-01 02:40:09 +01:00

test_streaming_integration.py

feat(dirty): add streaming support and async client benchmarks

2026-01-25 10:23:25 +01:00

test_worker_streaming.py

feat(dirty): add streaming support and async client benchmarks

2026-01-25 10:23:25 +01:00