consciousness/training/pyproject.toml
Kent Overstreet 7e7e9a4b69 training: integrate /train into vLLM process (no separate daemon)
Remove standalone worker.py daemon. Training now runs inside vLLM:

- train_router.py: FastAPI router patched into vLLM's build_app()
- /train served on same port as /completions, /score
- Lazy-loads HF model with vLLM weight views on first request
- HOGWILD training: no pause, weights updated in-place

The previous architecture had a separate daemon on port 8080 that
communicated with vLLM via pause/resume endpoints. This was wrong -
training should run in-process, sharing GPU memory directly.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>
2026-04-16 02:04:26 -04:00

27 lines
563 B
TOML

[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"
[project]
name = "apollo-plugin"
version = "0.1.0"
description = "Apollo training plugin for vLLM"
requires-python = ">=3.10"
dependencies = [
"torch",
"aiohttp",
"safetensors",
]
[project.optional-dependencies]
dev = ["pytest"]
[project.entry-points."vllm.general_plugins"]
apollo = "apollo_plugin:register"
[project.scripts]
apollo-checkpoint = "apollo_plugin.checkpoint_sync:main"
[tool.setuptools.packages.find]
where = ["."]
include = ["apollo_plugin*"]