From 09c515e3e941ae2b0abd9c8a55600283962c787d Mon Sep 17 00:00:00 2001
From: jgrusewski <jgrusewski@localhost>
Date: Thu, 19 Mar 2026 01:04:09 +0100
Subject: [PATCH] =?UTF-8?q?fix(clippy):=20ZERO=20errors=20across=20entire?=
 =?UTF-8?q?=20workspace=20=E2=80=94=20CI=20ready?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Final 31 ml crate fixes: unsafe_code allows, unused vars prefixed,
boolean simplification, dead code removal, integer suffix, drop cleanup.

cargo fix auto-removed ~30 unused imports from ml crate.

Total clippy cleanup: 278 errors → 0 across all ML crates.
Full workspace: `cargo clippy --workspace --lib -- -D warnings` = 0 errors.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 crates/ml/src/benchmark/gpu_hardware.rs         |  1 -
 crates/ml/src/benchmark/mamba2_benchmark.rs     |  4 ++--
 crates/ml/src/benchmark/stability_validator.rs  |  1 -
 .../cuda_pipeline/gpu_experience_collector.rs   |  1 -
 crates/ml/src/cuda_pipeline/gpu_weights.rs      |  6 +++---
 .../ml/src/data_loaders/dbn_sequence_loader.rs  |  2 +-
 .../ml/src/data_loaders/streaming_dbn_loader.rs |  2 +-
 crates/ml/src/data_loaders/tlob_loader.rs       |  2 +-
 crates/ml/src/dqn/trainable_adapter.rs          |  6 +++---
 crates/ml/src/flash_attention/mod.rs            |  2 +-
 .../ml/src/hyperopt/adapters/continuous_ppo.rs  |  2 +-
 crates/ml/src/hyperopt/adapters/diffusion.rs    |  1 -
 crates/ml/src/hyperopt/adapters/dqn.rs          |  1 -
 crates/ml/src/hyperopt/adapters/kan.rs          |  1 -
 crates/ml/src/hyperopt/adapters/liquid.rs       |  1 -
 crates/ml/src/hyperopt/adapters/ppo.rs          |  3 +--
 crates/ml/src/hyperopt/adapters/tggn.rs         |  1 -
 crates/ml/src/hyperopt/adapters/tlob.rs         |  1 -
 crates/ml/src/hyperopt/adapters/xlstm.rs        |  1 -
 crates/ml/src/kan/trainable.rs                  |  1 -
 crates/ml/src/ppo/trainable_adapter.rs          |  4 +---
 crates/ml/src/tft/training.rs                   |  2 +-
 crates/ml/src/trainers/dqn/config.rs            |  1 +
 crates/ml/src/trainers/dqn/data_loading.rs      |  2 --
 crates/ml/src/trainers/dqn/fused_training.rs    |  3 ++-
 crates/ml/src/trainers/dqn/trainer/metrics.rs   |  2 ++
 crates/ml/src/trainers/dqn/trainer/mod.rs       |  3 ++-
 .../ml/src/trainers/dqn/trainer/train_step.rs   |  4 ++--
 .../src/trainers/dqn/trainer/training_loop.rs   |  4 ++--
 crates/ml/src/trainers/liquid.rs                |  1 -
 crates/ml/src/trainers/tft/trainer.rs           | 17 ++++-------------
 crates/ml/src/trainers/tlob.rs                  |  6 +++---
 crates/ml/src/training_pipeline.rs              |  2 +-
 crates/ml/src/validation/ppo_adapter.rs         |  5 +++--
 crates/ml/src/validation/regime_analysis.rs     |  1 -
 crates/ml/src/xlstm/trainable.rs                |  2 +-
 36 files changed, 39 insertions(+), 60 deletions(-)

diff --git a/crates/ml/src/benchmark/gpu_hardware.rs b/crates/ml/src/benchmark/gpu_hardware.rs
index 139d54482..b93bfb160 100644
--- a/crates/ml/src/benchmark/gpu_hardware.rs
+++ b/crates/ml/src/benchmark/gpu_hardware.rs
@@ -13,7 +13,6 @@
 use ml_core::cuda_autograd::GpuTensor;
 use ml_core::native_types::NativeDevice;
 use ml_core::device::MlDevice;
-use ml_core::MLError as MlCoreError;
 use std::process::Command;
 use std::time::{Duration, Instant};
 use thiserror::Error;
diff --git a/crates/ml/src/benchmark/mamba2_benchmark.rs b/crates/ml/src/benchmark/mamba2_benchmark.rs
index eab36333d..912ad9ada 100644
--- a/crates/ml/src/benchmark/mamba2_benchmark.rs
+++ b/crates/ml/src/benchmark/mamba2_benchmark.rs
@@ -486,7 +486,7 @@ impl Mamba2BenchmarkRunner {
 
         let ml_dev = MlDevice::cuda(0)
             .map_err(|e| anyhow::anyhow!("CUDA device required: {}", e))?;
-        let stream = ml_dev.cuda_stream()
+        let _stream = ml_dev.cuda_stream()
             .map_err(|e| anyhow::anyhow!("No CUDA stream: {}", e))?;
 
         for (input, target) in val_data.iter().take(max_samples) {
@@ -530,7 +530,7 @@ impl Mamba2BenchmarkRunner {
 
         let ml_dev = MlDevice::cuda(0)
             .map_err(|e| anyhow::anyhow!("CUDA device required: {}", e))?;
-        let stream = ml_dev.cuda_stream()
+        let _stream = ml_dev.cuda_stream()
             .map_err(|e| anyhow::anyhow!("No CUDA stream: {}", e))?;
 
         for (input, target) in val_data.iter().take(max_samples) {
diff --git a/crates/ml/src/benchmark/stability_validator.rs b/crates/ml/src/benchmark/stability_validator.rs
index 0a1d44fd1..cb2781663 100644
--- a/crates/ml/src/benchmark/stability_validator.rs
+++ b/crates/ml/src/benchmark/stability_validator.rs
@@ -1,4 +1,3 @@
-use ml_core::MLError;
 
 /// Metrics describing training stability
 #[derive(Debug, Clone, serde::Serialize, serde::Deserialize, Default)]
diff --git a/crates/ml/src/cuda_pipeline/gpu_experience_collector.rs b/crates/ml/src/cuda_pipeline/gpu_experience_collector.rs
index 3a63509e5..fae17054a 100644
--- a/crates/ml/src/cuda_pipeline/gpu_experience_collector.rs
+++ b/crates/ml/src/cuda_pipeline/gpu_experience_collector.rs
@@ -18,7 +18,6 @@ use cudarc::driver::{CudaFunction, CudaSlice, CudaStream, DevicePtr, DevicePtrMu
 use ml_core::cuda_autograd::GpuVarStore;
 use tracing::{debug, info};
 
-use ml_core::nvtx::NvtxRange;
 use crate::MLError;
 use super::gpu_curiosity_trainer::GpuCuriosityTrainer;
 use super::gpu_weights::{
diff --git a/crates/ml/src/cuda_pipeline/gpu_weights.rs b/crates/ml/src/cuda_pipeline/gpu_weights.rs
index 20451a9fb..efbf422c7 100644
--- a/crates/ml/src/cuda_pipeline/gpu_weights.rs
+++ b/crates/ml/src/cuda_pipeline/gpu_weights.rs
@@ -19,7 +19,7 @@
 use std::sync::Arc;
 
 use cudarc::driver::{CudaSlice, CudaStream, DevicePtr, DeviceRepr, PushKernelArg};
-use ml_core::cuda_autograd::{GpuVarStore, GpuParam};
+use ml_core::cuda_autograd::GpuVarStore;
 use tracing::info;
 
 use crate::MLError;
@@ -1189,7 +1189,7 @@ pub fn extract_rmsnorm_weights(
 ) -> Result<Option<RmsNormWeightSet>, MLError> {
 
     // Check if the GpuVarStore contains RMSNorm keys (distributional dueling)
-    if !vars.get(RMSNORM_WEIGHT_NAMES[0]).is_some() {
+    if vars.get(RMSNORM_WEIGHT_NAMES[0]).is_none() {
         return Ok(None);
     }
 
@@ -1219,7 +1219,7 @@ pub fn sync_rmsnorm_weights(
 ) -> Result<(), MLError> {
 
     // Only sync if the GpuVarStore has RMSNorm keys
-    if !vars.get(RMSNORM_WEIGHT_NAMES[0]).is_some() {
+    if vars.get(RMSNORM_WEIGHT_NAMES[0]).is_none() {
         return Ok(());
     }
 
diff --git a/crates/ml/src/data_loaders/dbn_sequence_loader.rs b/crates/ml/src/data_loaders/dbn_sequence_loader.rs
index 4ee90c3ea..caf17302e 100644
--- a/crates/ml/src/data_loaders/dbn_sequence_loader.rs
+++ b/crates/ml/src/data_loaders/dbn_sequence_loader.rs
@@ -29,7 +29,7 @@
 //! ```
 
 use anyhow::{Context, Result};
-use ml_core::native_types::{NativeDevice, NativeDType, NativeTensor};
+use ml_core::native_types::NativeDevice;
 use ml_core::cuda_autograd::GpuTensor;
 use data::providers::databento::dbn_parser::{DbnParser, ProcessedMessage};
 use dbn::decode::{DbnDecoder, DbnMetadata};
diff --git a/crates/ml/src/data_loaders/streaming_dbn_loader.rs b/crates/ml/src/data_loaders/streaming_dbn_loader.rs
index 11f9fcc01..eff880680 100644
--- a/crates/ml/src/data_loaders/streaming_dbn_loader.rs
+++ b/crates/ml/src/data_loaders/streaming_dbn_loader.rs
@@ -39,7 +39,7 @@
 //! | Streaming| <512MB       | ~95%  |
 
 use anyhow::{Context, Result};
-use ml_core::native_types::{NativeDevice, NativeDType, NativeTensor};
+use ml_core::native_types::NativeDevice;
 use ml_core::cuda_autograd::GpuTensor;
 use data::providers::databento::dbn_parser::{DbnParser, ProcessedMessage};
 use dbn::decode::{DbnDecoder, DbnMetadata, DecodeRecordRef};
diff --git a/crates/ml/src/data_loaders/tlob_loader.rs b/crates/ml/src/data_loaders/tlob_loader.rs
index 06e708d60..51087dc57 100644
--- a/crates/ml/src/data_loaders/tlob_loader.rs
+++ b/crates/ml/src/data_loaders/tlob_loader.rs
@@ -29,7 +29,7 @@
 //! ```
 
 use anyhow::{Context, Result};
-use ml_core::native_types::{NativeDevice, NativeDType, NativeTensor};
+use ml_core::native_types::NativeDevice;
 use ml_core::cuda_autograd::GpuTensor;
 use dbn::decode::{DbnDecoder, DbnMetadata, DecodeRecordRef};
 use dbn::RecordRefEnum;
diff --git a/crates/ml/src/dqn/trainable_adapter.rs b/crates/ml/src/dqn/trainable_adapter.rs
index 5a1d214cd..5134f6eb9 100644
--- a/crates/ml/src/dqn/trainable_adapter.rs
+++ b/crates/ml/src/dqn/trainable_adapter.rs
@@ -1,9 +1,9 @@
+#![allow(unsafe_code)] // Required for safetensors f32-to-u8 slice reinterpretation
 //! UnifiedTrainable trait implementation for DQN model
 //!
 //! This adapter wraps the DQN implementation to provide a unified
 //! training interface compatible with the ML training orchestration system.
 
-use ml_core::device::MlDevice;
 use ml_core::native_types::NativeDevice;
 use std::collections::HashMap as StdHashMap;
 
@@ -297,13 +297,13 @@ impl UnifiedTrainable for DQNTrainableAdapter {
                     raw_bytes.len() / std::mem::size_of::<f32>(),
                 )
             }.to_vec(); // cpu-side bytes→f32 clone
-            host_data.insert(name.to_string(), (shape, f32_data));
+            host_data.insert(name.clone(), (shape, f32_data));
         }
 
         // Import checkpoint data into the DQN's VarStore via direct CudaSlice writes.
         let vars = self.dqn.get_q_network_vars();
         let stream = self.dqn.cuda_stream();
-        for (name, (shape, f32_data)) in &host_data {
+        for (name, (_shape, f32_data)) in &host_data {
             if let Some(param) = vars.get(name) {
                 if param.data.len() == f32_data.len() {
                     // Direct HtoD memcpy into the existing CudaSlice
diff --git a/crates/ml/src/flash_attention/mod.rs b/crates/ml/src/flash_attention/mod.rs
index 1e599c83f..e59bd839d 100644
--- a/crates/ml/src/flash_attention/mod.rs
+++ b/crates/ml/src/flash_attention/mod.rs
@@ -44,7 +44,7 @@ use std::sync::Arc;
 use cudarc::cublas::CudaBlas;
 use cudarc::driver::CudaStream;
 
-use ml_core::native_types::{NativeDevice, NativeDType, NativeTensor};
+use ml_core::native_types::NativeDevice;
 use ml_core::cuda_autograd::GpuTensor;
 use serde::{Deserialize, Serialize};
 
diff --git a/crates/ml/src/hyperopt/adapters/continuous_ppo.rs b/crates/ml/src/hyperopt/adapters/continuous_ppo.rs
index f37165193..6d9ee564a 100644
--- a/crates/ml/src/hyperopt/adapters/continuous_ppo.rs
+++ b/crates/ml/src/hyperopt/adapters/continuous_ppo.rs
@@ -425,7 +425,7 @@ impl HyperparameterOptimizable for ContinuousPPOTrainer {
         };
 
         // Create continuous PPO agent
-        let mut ppo_agent = ContinuousPPO::new(ppo_config)
+        let ppo_agent = ContinuousPPO::new(ppo_config)
             .map_err(|e| MLError::TrainingError(format!("Failed to create PPO agent: {}", e)))?;
 
         // Training loop (simplified for hyperopt)
diff --git a/crates/ml/src/hyperopt/adapters/diffusion.rs b/crates/ml/src/hyperopt/adapters/diffusion.rs
index 7b6e45802..ee2557ac2 100644
--- a/crates/ml/src/hyperopt/adapters/diffusion.rs
+++ b/crates/ml/src/hyperopt/adapters/diffusion.rs
@@ -5,7 +5,6 @@
 //! optimization of the Diffusion model via the unified framework.
 
 use std::sync::Arc;
-use cudarc::driver::CudaStream;
 use ml_core::device::MlDevice;
 use std::path::PathBuf;
 use tracing::{info, warn};
diff --git a/crates/ml/src/hyperopt/adapters/dqn.rs b/crates/ml/src/hyperopt/adapters/dqn.rs
index 80a3ecb90..5aa0793c2 100644
--- a/crates/ml/src/hyperopt/adapters/dqn.rs
+++ b/crates/ml/src/hyperopt/adapters/dqn.rs
@@ -1885,7 +1885,6 @@ impl DQNTrainer {
                 &vars, stream,
             )?
         };
-        drop(vars);
 
         info!(
             is_branching,
diff --git a/crates/ml/src/hyperopt/adapters/kan.rs b/crates/ml/src/hyperopt/adapters/kan.rs
index f9133d479..d2bdb4d3b 100644
--- a/crates/ml/src/hyperopt/adapters/kan.rs
+++ b/crates/ml/src/hyperopt/adapters/kan.rs
@@ -5,7 +5,6 @@
 //! hyperparameter optimization of the KAN model via the unified framework.
 
 use std::sync::Arc;
-use cudarc::driver::CudaStream;
 use ml_core::device::MlDevice;
 use serde::{Deserialize, Serialize};
 use std::path::PathBuf;
diff --git a/crates/ml/src/hyperopt/adapters/liquid.rs b/crates/ml/src/hyperopt/adapters/liquid.rs
index f9e2f32b7..49a438edd 100644
--- a/crates/ml/src/hyperopt/adapters/liquid.rs
+++ b/crates/ml/src/hyperopt/adapters/liquid.rs
@@ -17,7 +17,6 @@
 //! | 7         | batch_size      | linear | [8, 512]           |
 
 use std::sync::Arc;
-use cudarc::driver::CudaStream;
 use ml_core::device::MlDevice;
 use serde::{Deserialize, Serialize};
 use std::path::PathBuf;
diff --git a/crates/ml/src/hyperopt/adapters/ppo.rs b/crates/ml/src/hyperopt/adapters/ppo.rs
index 1893c6416..a7fd78ef0 100644
--- a/crates/ml/src/hyperopt/adapters/ppo.rs
+++ b/crates/ml/src/hyperopt/adapters/ppo.rs
@@ -52,7 +52,6 @@ use crate::ppo::trajectory_replay::TrajectoryReplayBuffer;
 use crate::MLError;
 
 use crate::cuda_pipeline::gpu_backtest_evaluator::{GpuBacktestConfig, GpuBacktestEvaluator};
-use ml_core::cuda_autograd::GpuTensor;
 use cudarc::driver::CudaSlice;
 
 /// Pure model VRAM in MB (actor + critic + optimizers + gradients).
@@ -1600,7 +1599,7 @@ impl PPOTrainer {
         composite_reward: &mut Option<CompositeReward>,
         gae_gamma: f32,
         gae_lambda: f32,
-        device: &MlDevice,
+        _device: &MlDevice,
     ) -> anyhow::Result<TrajectoryBatch> {
         use crate::ppo::trajectories::{Trajectory, TrajectoryStep};
         use rand::Rng;
diff --git a/crates/ml/src/hyperopt/adapters/tggn.rs b/crates/ml/src/hyperopt/adapters/tggn.rs
index 4cd13f466..ed85da4ab 100644
--- a/crates/ml/src/hyperopt/adapters/tggn.rs
+++ b/crates/ml/src/hyperopt/adapters/tggn.rs
@@ -5,7 +5,6 @@
 //! hyperparameter optimization of the TGGN model via the unified framework.
 
 use std::sync::Arc;
-use cudarc::driver::CudaStream;
 use ml_core::device::MlDevice;
 use serde::{Deserialize, Serialize};
 use std::path::PathBuf;
diff --git a/crates/ml/src/hyperopt/adapters/tlob.rs b/crates/ml/src/hyperopt/adapters/tlob.rs
index 6c789f530..a7437d6f7 100644
--- a/crates/ml/src/hyperopt/adapters/tlob.rs
+++ b/crates/ml/src/hyperopt/adapters/tlob.rs
@@ -5,7 +5,6 @@
 //! hyperparameter optimization of the TLOB model via the unified framework.
 
 use std::sync::Arc;
-use cudarc::driver::CudaStream;
 use ml_core::device::MlDevice;
 use serde::{Deserialize, Serialize};
 use std::path::PathBuf;
diff --git a/crates/ml/src/hyperopt/adapters/xlstm.rs b/crates/ml/src/hyperopt/adapters/xlstm.rs
index 5b266bce4..4dcf2c4de 100644
--- a/crates/ml/src/hyperopt/adapters/xlstm.rs
+++ b/crates/ml/src/hyperopt/adapters/xlstm.rs
@@ -5,7 +5,6 @@
 //! hyperparameter optimization of the xLSTM model via the unified framework.
 
 use std::sync::Arc;
-use cudarc::driver::CudaStream;
 use ml_core::device::MlDevice;
 use std::path::PathBuf;
 use tracing::{info, warn};
diff --git a/crates/ml/src/kan/trainable.rs b/crates/ml/src/kan/trainable.rs
index 41bdf9f07..928c727b1 100644
--- a/crates/ml/src/kan/trainable.rs
+++ b/crates/ml/src/kan/trainable.rs
@@ -8,7 +8,6 @@
 //! The GpuVarStore holds all trainable parameters and the GpuAdamW optimizer
 //! runs the update step entirely on GPU.
 
-use std::collections::BTreeMap;
 use std::collections::HashMap;
 use std::sync::Arc;
 
diff --git a/crates/ml/src/ppo/trainable_adapter.rs b/crates/ml/src/ppo/trainable_adapter.rs
index 4f5a14064..f4aac0b4b 100644
--- a/crates/ml/src/ppo/trainable_adapter.rs
+++ b/crates/ml/src/ppo/trainable_adapter.rs
@@ -10,10 +10,8 @@ use std::collections::HashMap;
 use std::path::Path;
 use tracing::info;
 
-use super::gae::compute_gae_single_trajectory;
 use super::ppo::{PPOConfig, PPO};
-use super::trajectories::{Trajectory, TrajectoryBatch, TrajectoryStep};
-use crate::common::action::FactoredAction;
+use super::trajectories::TrajectoryBatch;
 use crate::training::unified_trainer::{CheckpointMetadata, TrainingMetrics, UnifiedTrainable};
 use crate::MLError;
 
diff --git a/crates/ml/src/tft/training.rs b/crates/ml/src/tft/training.rs
index 063aff909..5028a8254 100644
--- a/crates/ml/src/tft/training.rs
+++ b/crates/ml/src/tft/training.rs
@@ -745,7 +745,7 @@ impl TFTTrainer {
         };
 
         let mut abs_diff_sum = 0.0_f64;
-        let mut count = 0usize;
+        let mut count = 0_usize;
         for (i, &t) in target_host.iter().enumerate() {
             // Map target index to prediction index (extract median quantile)
             let pred_idx = i * stride + median_idx;
diff --git a/crates/ml/src/trainers/dqn/config.rs b/crates/ml/src/trainers/dqn/config.rs
index 09c27b27e..87127813f 100644
--- a/crates/ml/src/trainers/dqn/config.rs
+++ b/crates/ml/src/trainers/dqn/config.rs
@@ -1,3 +1,4 @@
+#![allow(unsafe_code)] // Required for f32<->u32 CudaSlice reinterpret casts and safetensors serialization
 //! DQN Configuration and Hyperparameters
 //!
 //! Contains all training configuration including learning rates,
diff --git a/crates/ml/src/trainers/dqn/data_loading.rs b/crates/ml/src/trainers/dqn/data_loading.rs
index 3417e213c..870e99f00 100644
--- a/crates/ml/src/trainers/dqn/data_loading.rs
+++ b/crates/ml/src/trainers/dqn/data_loading.rs
@@ -7,8 +7,6 @@ use std::path::Path;
 use std::sync::Arc;
 
 use anyhow::{Context, Result};
-use ml_core::device::MlDevice;
-use ml_core::cuda_autograd::GpuTensor;
 use tracing::{debug, info, warn};
 
 use crate::features::extraction::OHLCVBar;
diff --git a/crates/ml/src/trainers/dqn/fused_training.rs b/crates/ml/src/trainers/dqn/fused_training.rs
index ed8904287..099a7604f 100644
--- a/crates/ml/src/trainers/dqn/fused_training.rs
+++ b/crates/ml/src/trainers/dqn/fused_training.rs
@@ -1,3 +1,4 @@
+#![allow(unsafe_code)] // Required for f32->u32 CudaSlice reinterpret cast in PER index path
 //! Fused CUDA Training Module
 //!
 //! High-performance H100-optimized training path that replaces 2,100+ Candle kernel
@@ -311,7 +312,7 @@ impl FusedTrainingCtx {
         &mut self,
         batch: &BatchSample,
         agent: &mut DQNAgentType,
-        device: &MlDevice,
+        _device: &MlDevice,
     ) -> Result<GpuTrainResult> {
         let gpu_batch = batch.gpu_batch.as_ref()
             .ok_or_else(|| anyhow::anyhow!("Fused training requires gpu_batch (GPU PER)"))?;
diff --git a/crates/ml/src/trainers/dqn/trainer/metrics.rs b/crates/ml/src/trainers/dqn/trainer/metrics.rs
index e6e768479..ab2c63aed 100644
--- a/crates/ml/src/trainers/dqn/trainer/metrics.rs
+++ b/crates/ml/src/trainers/dqn/trainer/metrics.rs
@@ -435,6 +435,8 @@ impl DQNTrainer {
                     best_val = v;
                 } else if v > second_best {
                     second_best = v;
+                } else {
+                    // v <= second_best: no update needed
                 }
             }
             gaps.push(best_val - second_best);
diff --git a/crates/ml/src/trainers/dqn/trainer/mod.rs b/crates/ml/src/trainers/dqn/trainer/mod.rs
index 0a5bf02cf..30725ab37 100644
--- a/crates/ml/src/trainers/dqn/trainer/mod.rs
+++ b/crates/ml/src/trainers/dqn/trainer/mod.rs
@@ -1,3 +1,4 @@
+#![allow(unsafe_code)] // Required for safetensors f32-to-u8 slice serialization
 //! DQN Trainer Implementation
 //!
 //! Main training loop and execution logic for Deep Q-Network.
@@ -778,7 +779,7 @@ impl DQNTrainer {
                     })?;
                     let vars = head.get_q_network_vars();
                     let vars_data = vars.data();
-                    let ser_stream = vars.stream();
+                    let _ser_stream = vars.stream();
                     for (name, param) in vars_data.iter() {
                         if let Ok(tensor) = GpuTensor::new(param.data.clone(), param.shape.clone()) {
                             all_tensors.insert(
diff --git a/crates/ml/src/trainers/dqn/trainer/train_step.rs b/crates/ml/src/trainers/dqn/trainer/train_step.rs
index a4cbac655..e6045bbd6 100644
--- a/crates/ml/src/trainers/dqn/trainer/train_step.rs
+++ b/crates/ml/src/trainers/dqn/trainer/train_step.rs
@@ -359,7 +359,7 @@ impl DQNTrainer {
             let r_gn_gpu = result.grad_norm_gpu;
 
             // Get vars for accumulation. Var is an Arc wrapper so cloning is cheap.
-            let vars: Vec<GpuTensor> = agent
+            let _vars: Vec<GpuTensor> = agent
                 .optimizer_vars()
                 .map_err(|e| anyhow::anyhow!("Failed to get optimizer vars: {}", e))?;
 
@@ -443,7 +443,7 @@ impl DQNTrainer {
 
         // === Phase 2: Average and apply gradients (single optimizer step) ===
         if let Some(ref mut grads) = accumulated_grads {
-            let vars: Vec<GpuTensor> = agent
+            let _vars: Vec<GpuTensor> = agent
                 .optimizer_vars()
                 .map_err(|e| anyhow::anyhow!("Failed to get optimizer vars: {}", e))?;
 
diff --git a/crates/ml/src/trainers/dqn/trainer/training_loop.rs b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
index 6ba19d8a9..5ad363026 100644
--- a/crates/ml/src/trainers/dqn/trainer/training_loop.rs
+++ b/crates/ml/src/trainers/dqn/trainer/training_loop.rs
@@ -1014,7 +1014,7 @@ impl DQNTrainer {
                             let r_idx_gpu = result.indices_gpu;
                             let r_grads = result.grads;
 
-                            let vars = agent.optimizer_vars()
+                            let _vars = agent.optimizer_vars()
                                 .map_err(|e| anyhow::anyhow!("optimizer vars: {e}"))?;
                             // Accumulate gradients elementwise
                             match &mut accumulated_grads {
@@ -1075,7 +1075,7 @@ impl DQNTrainer {
 
                         // Scale and apply (single optimizer step)
                         if let Some(ref mut grads) = accumulated_grads {
-                            let vars = agent.optimizer_vars()
+                            let _vars = agent.optimizer_vars()
                                 .map_err(|e| anyhow::anyhow!("optimizer vars: {e}"))?;
                             // Scale accumulated gradients by 1/N
                             let stream_ref = self.cuda_stream.as_ref().ok_or_else(|| anyhow::anyhow!("CUDA stream needed"))?;
diff --git a/crates/ml/src/trainers/liquid.rs b/crates/ml/src/trainers/liquid.rs
index 6dbe009a6..8eaa71e29 100644
--- a/crates/ml/src/trainers/liquid.rs
+++ b/crates/ml/src/trainers/liquid.rs
@@ -14,7 +14,6 @@ use tracing::{debug, info, warn};
 
 use crate::liquid::adapter::LiquidTrainableAdapter;
 use crate::liquid::candle_cfc::{CfCTrainConfig, DeviceConfig};
-use crate::training::unified_trainer::UnifiedTrainable;
 use crate::MLError;
 
 /// Liquid CfC training hyperparameters (maps to gRPC LiquidParams)
diff --git a/crates/ml/src/trainers/tft/trainer.rs b/crates/ml/src/trainers/tft/trainer.rs
index 0f544f53b..e6c685aaa 100644
--- a/crates/ml/src/trainers/tft/trainer.rs
+++ b/crates/ml/src/trainers/tft/trainer.rs
@@ -10,7 +10,6 @@ use std::time::{Duration, Instant, SystemTime};
 
 use ml_core::device::MlDevice;
 use ml_core::cuda_autograd::GpuTensor;
-use ml_core::cuda_autograd::GpuVarStore;
 use ml_core::cuda_autograd::stream_ops::StreamTensor;
 
 /// Convert `GpuTensor` -> `StreamTensor` by moving the internal CudaSlice.
@@ -23,7 +22,6 @@ fn gpu_to_stream(t: GpuTensor, stream: &std::sync::Arc<cudarc::driver::CudaStrea
 fn stream_to_gpu(t: StreamTensor) -> Result<GpuTensor, crate::MLError> {
     GpuTensor::new(t.data, t.shape)
 }
-use ndarray::Dimension;
 use tokio::sync::mpsc;
 use tracing::{debug, error, info, instrument, warn};
 
@@ -256,16 +254,9 @@ impl TFTTrainer {
         // The old candle_optimisers::adam::ParamsAdam has been eliminated.
         // For now, return an error indicating the optimizer needs migration.
         let _lr = self.training_config.learning_rate;
-        return Err(MLError::ModelError(
+        Err(MLError::ModelError(
             "TFT optimizer not yet migrated from candle_optimisers to GpuAdamW".to_string(),
-        ));
-
-        info!(
-            "Initialized AdamW optimizer with lr={:.2e}",
-            self.training_config.learning_rate
-        );
-
-        Ok(())
+        ))
     }
 
     /// Check if an error is an OOM (Out of Memory) error
@@ -552,7 +543,7 @@ impl TFTTrainer {
                 }
 
                 // Actually drop optimizer to free 1100MB GPU memory
-                drop(self.optimizer.take());
+                self.optimizer.take();
                 if self.device.is_cuda() {
                     Self::sync_cuda_device(&self.device).ok();
                 }
@@ -970,7 +961,7 @@ impl TFTTrainer {
     /// Implements pinball loss across multiple quantiles:
     /// L(y, q_tau) = sum_i max(tau * (y_i - q_tau), (tau - 1) * (y_i - q_tau))
     fn compute_quantile_loss(&self, predictions: &GpuTensor, targets: &GpuTensor) -> MLResult<f64> {
-        use ml_core::cuda_autograd::GpuTensor;
+        
 
         let stream = self.device.cuda_stream().map_err(|e| {
             MLError::DeviceError(format!("quantile_loss requires CUDA: {e}"))
diff --git a/crates/ml/src/trainers/tlob.rs b/crates/ml/src/trainers/tlob.rs
index 47fbee26e..e4a2bb018 100644
--- a/crates/ml/src/trainers/tlob.rs
+++ b/crates/ml/src/trainers/tlob.rs
@@ -409,7 +409,7 @@ impl TLOBTrainer {
     /// `to_scalar()` synchronisation. A single scalar extraction happens
     /// after the loop completes, with a NaN guard every 100 batches.
     async fn train_epoch(&mut self, sequences: &[OrderBookSequence]) -> Result<f64> {
-        let stream = self.device.cuda_stream().map_err(|e| {
+        let stream = self.device.cuda_stream().map_err(|_e| {
             anyhow::anyhow!("TLOB train_epoch requires CUDA stream")
         })?;
         let stream = Arc::clone(stream);
@@ -451,7 +451,7 @@ impl TLOBTrainer {
     /// Loss is accumulated on-device to avoid per-batch `to_scalar()`.
     /// MAE already returns `f64` (CPU scalar) so it stays as f64 sum.
     async fn validate_epoch(&self, sequences: &[OrderBookSequence]) -> Result<(f64, f64)> {
-        let stream = self.device.cuda_stream().map_err(|e| {
+        let stream = self.device.cuda_stream().map_err(|_e| {
             anyhow::anyhow!("TLOB validate_epoch requires CUDA stream")
         })?;
         let stream = Arc::clone(stream);
@@ -510,7 +510,7 @@ impl TLOBTrainer {
     /// Calculate gradient norm by downloading parameters (monitoring only).
     fn calculate_gradient_norm(&self) -> Result<f64> {
         // Parameter norm (not gradient norm) — monitors for weight explosion
-        let stream = self.device.cuda_stream().map_err(|e| {
+        let stream = self.device.cuda_stream().map_err(|_e| {
             anyhow::anyhow!("TLOB requires CUDA stream")
         })?;
         let mut total_sq = 0.0_f64;
diff --git a/crates/ml/src/training_pipeline.rs b/crates/ml/src/training_pipeline.rs
index 1cf86f2fc..3b04706b4 100644
--- a/crates/ml/src/training_pipeline.rs
+++ b/crates/ml/src/training_pipeline.rs
@@ -13,7 +13,7 @@ use std::collections::HashMap;
 use std::sync::Arc;
 use std::time::Instant;
 
-use ml_core::native_types::{NativeDevice, NativeDType, NativeTensor};
+use ml_core::native_types::NativeDevice;
 use ml_core::cuda_autograd::GpuTensor;
 use ml_core::cuda_autograd::GpuAdamW;
 use serde::{Deserialize, Serialize};
diff --git a/crates/ml/src/validation/ppo_adapter.rs b/crates/ml/src/validation/ppo_adapter.rs
index 3ea5c4528..8e891c050 100644
--- a/crates/ml/src/validation/ppo_adapter.rs
+++ b/crates/ml/src/validation/ppo_adapter.rs
@@ -350,8 +350,9 @@ impl ValidatableStrategy for PpoLstmStrategy {
 fn pad_or_truncate(features: &[f32], target_dim: usize) -> Vec<f32> {
     let mut result = vec![0.0_f32; target_dim];
     let copy_len = features.len().min(target_dim);
-    result.get_mut(..copy_len)
-        .map(|dst| dst.copy_from_slice(features.get(..copy_len).unwrap_or_default()));
+    if let Some(dst) = result.get_mut(..copy_len) {
+        dst.copy_from_slice(features.get(..copy_len).unwrap_or_default());
+    }
     result
 }
 
diff --git a/crates/ml/src/validation/regime_analysis.rs b/crates/ml/src/validation/regime_analysis.rs
index a69b6661b..217b6b8c8 100644
--- a/crates/ml/src/validation/regime_analysis.rs
+++ b/crates/ml/src/validation/regime_analysis.rs
@@ -8,7 +8,6 @@
 //! device, reading back only 3 mask vectors for the final grouping.
 
 use std::collections::HashMap;
-use std::sync::Arc;
 
 use ml_core::cuda_autograd::GpuTensor;
 use ml_core::device::MlDevice;
diff --git a/crates/ml/src/xlstm/trainable.rs b/crates/ml/src/xlstm/trainable.rs
index 67b2653a6..785eff4bb 100644
--- a/crates/ml/src/xlstm/trainable.rs
+++ b/crates/ml/src/xlstm/trainable.rs
@@ -10,7 +10,7 @@ use std::sync::Arc;
 use cudarc::cublas::CudaBlas;
 use cudarc::driver::CudaStream;
 
-use ml_core::cuda_autograd::{AdamWConfig, GpuAdamW, GpuLinear, GpuTensor, GpuVarStore, LossKernels};
+use ml_core::cuda_autograd::{AdamWConfig, GpuAdamW, GpuTensor, GpuVarStore, LossKernels};
 use ml_core::cuda_autograd::stream_ops::StreamTensor;
 
 use super::config::XLSTMConfig;