Detailed step-by-step plan covering: S3 bucket creation, CI builder
updates, 3 base image Dockerfiles, CI pipeline rewiring, 8 cache PVCs,
all service deployment YAML updates, training job template, and cleanup.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace per-service Docker images with generic base images + S3-based binary
distribution. CI compiles and uploads stripped binaries to S3. Pods fetch
binaries via initContainer with PVC cache fallback for trading resilience.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The GPU experience collector only supported DQNAgentType::Standard, but
enable_regime_qnetwork defaults to true, creating RegimeConditional
agents. This silently fell back to CPU with a debug! message invisible
at INFO log level.
- Add primary_head() accessor to RegimeConditionalDQN (returns trending head)
- Extract weights from primary head for both collector init and weight sync
- Upgrade debug! to warn! for missing GPU collection prerequisites
- Add warn! to PPO for silent GPU skip when raw market data not uploaded
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The GPU experience collection kernels (dqn_experience_kernel.cu,
ppo_experience_kernel.cu) use cudarc::nvrtc::compile_ptx() at runtime.
Without libnvrtc.so the kernel compile fails silently and falls back to
CPU experience collection. Adding cuda-nvrtc-12-4 (~30MB) enables full
GPU-accelerated experience collection.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Both images now use identical cuda:12.4.1-cudnn-runtime base, so maintaining
two separate Dockerfiles and build jobs was wasteful. Single image contains
all 7 training binaries, halving registry storage and Kaniko build time.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
candle's CUDA backend links against libcudnn.so.9 for kernel operations.
Switch training-rl base from cuda:12.4.1-runtime to cuda:12.4.1-cudnn-runtime.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
These files were part of the Phase 2 cuda_pipeline but were untracked
and not included in previous commits. Required for compilation with
--features ml/cuda.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Phase 3 of the CUDA pipeline: both trainers now take a GPU-first path
for experience collection (128×500 = 64K experiences per kernel launch,
zero CPU-GPU roundtrips per timestep) with automatic CPU fallback.
- DQN: upload features alongside targets, GPU collection branch before
CPU loop, gpu_batch_to_experiences() conversion into replay buffer
- PPO: set_raw_market_data() for CudaSlice upload, GPU collection
branch bypasses collect_rollouts + prepare_training_batch entirely,
gpu_batch_to_trajectory_batch() conversion with in-kernel GAE
- Configurable GPU batch sizes (gpu_n_episodes, gpu_timesteps_per_episode)
and trading params (initial_capital, avg_spread) via hyperparameters
- SAFETY training diagnostics downgraded from warn! to debug!
- 4 new batch index-math validation tests in cuda_pipeline
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 10 shared device functions (gpu_random, leaky_relu, matvec_leaky_relu,
action_to_exposure, action_to_tx_cost, barrier_init/check/reset,
diversity_entropy, curiosity_inference) and shared constants from
dqn_experience_kernel.cu into a new common_device_functions.cuh header.
The DQN kernel now expects the common header to be prepended via NVRTC
source concatenation at compile time. DQN-specific functions
(q_forward_dueling, argmax_q) remain in the kernel file. This enables
the upcoming PPO kernel to reuse the same shared functions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Single monolithic kernel with in-kernel GAE, full portfolio simulation,
shared device functions header, 5-layer critic matching trainer exactly.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds GpuExperienceCollector field to DQNTrainer, initializes it when
dueling networks and curiosity module are present on CUDA device, and
syncs GPU weight copies after each training epoch.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three fixes from spec review:
1. diversity_entropy returns -0.1 penalty (threshold < 1.0) instead of raw entropy
2. barrier_done included in episode termination (barrier hit ends episode)
3. step_in_episode counter resets properly on mid-loop episode reset
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Full design for monolithic CUDA kernel that runs the entire DQN
experience collection pipeline on GPU: Dueling Q-network inference
(online + target), epsilon-greedy action selection, portfolio
simulation, barrier tracking, diversity entropy, curiosity inference,
and reward combination. 128 parallel episodes × 500 timesteps =
64,000 experiences per kernel launch with zero CPU roundtrips.
Key decisions:
- Approach A (monolithic kernel) for speed + accuracy
- Parallel episode architecture (N threads × L timesteps)
- Q-network + curiosity inference in raw CUDA (matrix-vector multiply)
- Curiosity training stays on Candle (periodic batch updates)
- TD error pre-computed for priority replay
- Weight sync after gradient updates (~1.2MB upload)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
RL models (DQN/PPO) only need basic CUDA runtime (~2GB), while supervised
models need cuDNN (~4GB). Splitting saves ~2GB pull time per RL job.
Rename the L4 GPU pool from ci-compile to ci-rl to reflect its actual use.
Add DaemonSet image pre-puller to cache training images on GPU nodes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace raw f64 price/quantity in SmallBatchOptimizer with typed
financial values. Conversion to f64 happens at the SIMD boundary
(_mm256_loadu_pd) only. Delete 3 orphaned dead-code files:
financial_safe.rs, simd_optimizations.rs, tests/financial_tests.rs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Aliased re-exports avoid collision with existing types::Price/Quantity/Money
during migration. Will rename to canonical names in Phase 6 cleanup.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Ratio arithmetic (Add, Sub, Mul, Div) now clamps overflow to
f64::MAX/f64::MIN instead of producing Inf from finite inputs
- Cross-type Price*Quantity→Money and Money/Quantity→Price use
saturating i128→i64 conversion via clamp instead of silent truncation
- Added clamp_finite() and saturating_i128_to_i64() helper functions
- Added 9 edge-case tests: 89/89 passing, clippy clean
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
compile-training now needs compile-services. Both request 28 vCPUs but
the ci-compile-cpu pool (POP2-32C-128G) has only 32 vCPUs total.
Build-services jobs start as soon as compile-services finishes while
compile-training runs; no wall-clock penalty.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
compile-training was stuck on GPU node autoscale (L4 pool) but only
needs CUDA stubs for candle-kernels PTX generation, not a real GPU.
Switch to .rust-base-cpu (ci-compile-cpu pool) with ci-builder image
override for CUDA stubs. Unify pool labels — all compilation on CPU.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add Serialize/Deserialize to all four financial types
- Ratio::ln returns 0.0 for non-positive inputs (prevents NaN leak)
- Ratio::exp clamps to f64::MAX on overflow (prevents Inf leak)
- Ratio::powi clamps to f64::MAX/MIN on overflow
- Add 4 invariant-preservation tests (80 total)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Phase 1 of GPU pipeline: eliminates per-bar/per-step heap allocations
by pre-uploading training data as GPU tensors at epoch start.
- New cuda_pipeline module with DqnGpuData + PpoGpuData structs
- DQN: bulk upload [N,51] features + [N,4] targets, narrow() slices
- PPO: bulk upload [N,state_dim] states, zero-copy per-step access
- VRAM safety guards (2GB limit) and estimation utilities
- 8 new tests, 2411 total ML tests passing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
