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Focus project on JAX PPO

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coolguy
2026-07-14 20:09:03 +09:00
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docs/research/option-a-bench-result.md
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@@ -19,7 +19,7 @@ with torch.inference_mode():
advantages = networks[player](x).squeeze(0).detach().cpu().numpy().astype(np.float32)
```
When `traversal.inference_backend` is set to `server` in `configs/deep_cfr/default.yaml`, the `networks[player]` call is intercepted by a `NetworkProxy` (instantiated in `workers.py`, around line 91). This proxy posts a request to the `InferenceServer` and blocks until a response is received via a per-slot event.
When `traversal.inference_backend` is set to `server` in `legacy/deep-cfr/configs/default.yaml`, the `networks[player]` call is intercepted by a `NetworkProxy` (instantiated in `workers.py`, around line 91). This proxy posts a request to the `InferenceServer` and blocks until a response is received via a per-slot event.
The server's batching logic in `src/coolrl_lost_cities/games/classic/deep_cfr/inference_server.py` (around line 221) reports the realized batch size:
@@ -46,7 +46,7 @@ The "structural ceiling" is that `batch_window_us` and `max_batch` tuning cannot
## Practical implication
Option A is deferred for the current small MLP models (512x3). The `local` backend remains the default in `configs/deep_cfr/default.yaml`.
Option A is deferred for the current small MLP models (512x3). The `local` backend remains the default in `legacy/deep-cfr/configs/default.yaml`.
To unlock the projected GPU gains, the traversal must be restructured to drive batch sizes up. This leads to two primary paths:
1. **Option B (Interleaved Traversal):** Refactor the Cython traversal into a state machine that can advance multiple traversals concurrently per worker. Each worker would suspend at a policy call, batch its own requests, and resume continuations once the results return.