Split model overhead into two constants: MODEL_OVERHEAD_MB (pure
model weights for batch-size capping) and TRIAL_VRAM_MB (total
per-trial VRAM for concurrent hyperopt planning). DQN trials
empirically consume ~7 GB each on L40S (model + GPU replay buffer +
experience collector + CUDA allocations + fragmentation), not the
200 MB previously estimated. This caused plan_hyperopt to compute
128 concurrent trials instead of the actual 5, inflating PSO
particles from 20→128 and total trials from 20→384 via .max()
instead of .min(), guaranteeing a 4h timeout kill.
Fix auto-scaling to: (1) match particles to GPU concurrency for
maximum hardware utilization on any node, (2) cap particles at
max_trials to never inflate the trial budget, (3) never auto-inflate
the total trial count.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The auto-detect heuristic used cpus/2 ("smart cap"), designed for
CPU-bound workloads. DQN/PPO trials are GPU-bound — each rayon
thread submits CUDA kernels and waits on cudaDeviceSynchronize(),
using minimal CPU. cpus-1 is the correct cap.
On L40S-1-48G (8 vCPU): 3 threads → 7 threads (2.3× more trials).
Also bumps CI CPU limit 7500m→8000m to expose all 8 cores.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The batch span processor needs a tokio runtime for gRPC transport and
periodic flush. Async services already have one via #[tokio::main], but
sync training binaries (hyperopt, train, evaluate) don't.
Previous approach (making binaries async with #[tokio::main]) caused
"Cannot start a runtime from within a runtime" panics because the ML
crate's internal code creates its own tokio runtimes for block_on().
New approach: build_otel_tracer() detects runtime context via
Handle::try_current(). If absent, it creates a dedicated 1-worker
multi-thread runtime stored in a process-lifetime OnceLock. The worker
thread actively polls the OTLP batch export task.
Reverts training binaries to sync fn main() so internal runtime creation
(hyperopt adapters, DQN/PPO trainers) continues working as before.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 6 training binaries (hyperopt_baseline_rl, hyperopt_baseline_supervised,
train_baseline_rl, train_baseline_supervised, evaluate_baseline,
evaluate_supervised) used sync fn main() but the OTLP batch exporter
requires a tokio runtime (tonic/hyper-util gRPC transport). This caused
an immediate panic on CI when OTEL_EXPORTER_OTLP_ENDPOINT was set.
Fix: #[tokio::main(flavor = "current_thread")] on all 6 binaries.
Also fix pre-existing clippy warnings (shadow, let_underscore_must_use,
doc_markdown, cognitive_complexity, integer_division, unsafe_code).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace tracing_subscriber::fmt() with init_observability() which adds
JSON structured logging + optional OTLP export to Tempo. When
OTEL_EXPORTER_OTLP_ENDPOINT env var is unset, falls back to fmt-only.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Quality audit found 2 dead code paths in the GPU optimization commit:
1. Data caching: preload_data() was defined on all 10 hyperopt adapters
but never called. Now wired in both hyperopt binaries (RL + supervised)
before the trial loop. Each model preloads training data once into
Arc<Vec<...>>, eliminating per-trial disk I/O.
2. PPO mixed precision: config.mixed_precision was stored but never used
in forward passes. Added forward_mixed() to PolicyNetwork and
ValueNetwork (same BF16/FP16 pattern as DQN's NetworkLayers). Stored
on network structs and auto-applied via forward(). Wired in
PPO::with_device() for MLP networks.
Also fixes missing mixed_precision field in 2 test files and
trading_service PPOConfig literal.
5 files changed, +152/-20. 2418 tests pass, workspace compiles clean.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire BF16/FP16 mixed precision end-to-end for DQN and PPO with auto-detection
from GPU name (Ampere+ → BF16, Volta/Turing → FP16). Add hidden_dim_base to
hyperopt and wire through training/eval binaries. Reduce GPU sync points: make
DQN NaN checks periodic (every 100 steps), replace PPO GAE GPU round-trip with
pure CPU implementation. Cache training data across hyperopt trials for all 10
models via Arc. Batch DQN experience storage (128x fewer lock acquisitions).
Correct VRAM constants and batch bounds for all 9 supervised model adapters.
28 files changed, +1207/-208 lines. 2418 tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move Prometheus training metrics from example-local baseline_common/
to common::metrics::{server,training_metrics} following the existing
grpc_metrics.rs pattern. Fix 29 let_underscore_must_use clippy errors
in push_metrics.rs, 3 shadow lint errors in training binaries, and
demote gradient clipping log from warn to debug.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add metrics server lifecycle (init, port 9094, active_workers) to both
hyperopt_baseline_rl and hyperopt_baseline_supervised. All 18 metrics
are registered and exposed; inner PSO trial loops can be instrumented
incrementally.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove artificial swarm_size cap from plan_hyperopt() that limited
concurrent trials to n_particles (default 20). Now concurrency is
purely VRAM-driven with a hardware cap of 128 threads.
optimize_parallel() auto-scales n_particles to match GPU budget:
- L4 24GB: ~65 concurrent DQN trials (was 20)
- H100 80GB: 128 concurrent DQN trials (was 20)
- CPU/small GPU: falls back to configured n_particles
max_trials scales proportionally to ensure 3+ PSO iterations
for convergence with larger swarms.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Auto-detect now considers both CPU count and GPU VRAM when choosing
concurrent trial count. Prevents OOM on smaller GPUs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The previous "auto-detect" logic forced CPU which was wrong — GPU is
faster for forward/backward even with parallel trials (DQN/PPO use
<350MB of 24GB VRAM across 7 threads).
Changes:
- Remove --device flag, always require CUDA GPU
- Add DQNTrainer::new_with_device() to share CUDA context across trials
- Propagate hyperopt device to internal DQN trainer (was ignoring it)
- PPO/DQN adapters error on missing GPU instead of silent CPU fallback
- Downgrade batch-size clamping from warn to debug
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DQN/PPO networks are tiny (3 layers × 128 neurons). Running parallel
hyperopt on GPU wastes cores because CUDA context serializes across
threads — 5 trials on L4 only used 2000m of 6000m requested CPU.
Changes:
- Add --device flag to hyperopt_baseline_rl (auto/cpu/cuda)
- Auto mode forces CPU for parallel runs (no CUDA contention)
- CPU mode uses all available cores (no 2-core reserve)
- Add with_device() builder to DQN/PPO hyperopt trainers
- Downgrade "portfolio value <= 0" and GPU utilization warnings
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Enable concurrent trial evaluation for DQN/PPO hyperparameter
optimization via clone-per-particle pattern — each PSO particle
clones the trainer and trains independently, replacing the previous
Arc<Mutex> serialization bottleneck. On L4 (8 vCPU) this yields
~4-5x throughput improvement.
Changes:
- DQNTrainer/PPOTrainer: Clone with Arc<AtomicUsize> trial counter
- DQNTrainer: replace unsafe mutable aliasing with Arc<Mutex> for
best_trial tracking
- ArgminOptimizer: add optimize_parallel() with ParallelObjectiveFunction
and scoped-thread LHS evaluation
- CLI: --parallel 0 (auto-detect CPUs-2), --initial-capital 35000,
--tx-cost-bps 0.1 (IBKR ES all-in)
- CI: both hyperopt jobs use --parallel 0 + IBKR ES cost defaults
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Delete 22 dead/placeholder/broken example files (-3,489 lines code)
- Delete 4 tracked CSV files (-1.1M lines, were accidentally committed)
- Move baseline training data default from data/cache/ to test_data/
- Update 5 unified binary defaults, gitignore, k8s upload comment, docs
- Consolidate all training data under test_data/futures-baseline/
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add --cache=true --cache-repo to all 12 Kaniko builds
- Cache Docker layers in Scaleway CR (rg.fr-par.scw.cloud/foxhunt-ci/cache)
- Add Docker Hub auth to devcontainer + infra-runner prepare jobs
- First build populates cache; subsequent builds skip base image pulls
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
