Fork a dedicated CudaStream once in DQNTrainer constructor and share it
via Arc::clone with all GPU components (experience collector, fused
trainer, portfolio sim, monitoring). This eliminates:
1. Candle's default stream (stream 0) from the training hot path
2. Dual-stream cudarc event tracking conflicts during CUDA Graph capture
3. Implicit serialization points between default and forked streams
The fused training context no longer forks its own stream — it receives
the trainer's unified stream. With all GPU work on a single stream,
cudarc event tracking is safely disabled in GpuDqnTrainer::new(),
removing the cuStreamWaitEvent/cuEventRecord overhead that broke CUDA
Graph capture.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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