- Add training_worker.py: long-lived subprocess that handles GPU training
work, owns HF model wrapper (views into vLLM GPU memory), Apollo
optimizer, and checkpoint sync
- train_router.py: now forwards /train requests via async ZMQ instead of
running training in-process. Adds /checkpoint and /train/status endpoints
- export_hook.py: store model_path in __metadata__ so training worker can
find it without cross-process communication
- This fixes two bugs:
1. Process boundary issue - model_path was set in worker process but
needed in API server process
2. Blocking event loop - training blocked vLLM's async event loop
Architecture: vLLM API server <-> ZMQ <-> training subprocess
The subprocess loads IPC handles once, creates views into vLLM's GPU
memory, and handles training requests without blocking inference.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- DEFAULT_RANK = 64 in train_router.py
- All references use the constant, not magic numbers
- ~2.5GB optimizer state instead of ~10GB
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Optimizer state (momentum, variance estimates) now persists between
training sessions:
- Saved to /tmp/apollo_optimizer_state.pt during checkpoint sync
- Restored on next /train call if available
- Preserves training continuity for incremental learning
Previously each /train call started with fresh optimizer state,
losing accumulated gradient history.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
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>
Core components for online fine-tuning of Qwen3.5-27B with CUDA IPC
shared weight memory between vLLM and the training process:
- apollo_mini.py: rank-1 optimizer (SGD memory, AdamW quality)
- apollo_worker.py: HTTP daemon coordinating training with vLLM
- weight_mapping.py: vLLM merged → HF separate layout (zero-copy views)
- training_example.py: tokenization with chat template
- export_weights.py: CUDA IPC handle export from vLLM
- train.py: standalone training script (alternative to daemon)
- DESIGN.md: architecture and protocol documentation
Validated: CUDA IPC autograd works on real Qwen3.5 weights (B200).
Apollo-Mini rank-1 projection + scaling + in-place update confirmed.
Co-Authored-By: Kent Overstreet <kent.overstreet@gmail.com>
