Files

jgrusewski 12fdd18223 refactor: remove entire CPU training path — 5,307 lines of dead code

Deleted:
- DQN::compute_loss_internal (280 lines) — old Candle forward+loss
- DQN::train_step (55 lines) — old Candle training step
- DQN::compute_gradients (47 lines) — old gradient accumulation
- ComputeLossResult struct — only used by deleted functions
- RegimeConditionalDQN::train_step (65 lines) — old dispatch
- RegimeConditionalDQN::train_step_gpu_regime (100 lines) — old GPU path
- RegimeConditionalDQN::compute_gradients_gpu (130 lines) — old regime gradients
- RegimeConditionalDQN::compute_gradients (92 lines) — old dispatch
- DQNAgentType::train_step dispatch — dead
- DQNAgentType::compute_gradients dispatch — dead
- GpuDqnTrainer::upload_batch (71 lines) — old CPU→GPU upload
- train_step.rs (500 lines) — entire module including ensure_fused_ctx
- dqn_benchmark.rs — used old train_step
- examples.rs — used old train_step
- validation/adapters.rs (289 lines) — used old train_step
- dqn/trainable_adapter.rs — used old train_step
- gpu_smoketest.rs — tested old train_step
- Gradient accumulation path in training_loop.rs (144 lines)
- IQN d_h_s2().clone() → raw pointer (zero alloc)
- Causal intervention format! string alloc removed
- Dead HER relabel functions (320 lines)

Kept:
- ensure_fused_ctx logic inlined into training_loop.rs
- set_noise_sigma_scale re-added to RegimeConditionalDQN

Fixed:
- GpuReplayBuffer max_batch_size wired from batch_size parameter
  (was hardcoded 1024, blocking batch_size=8192)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-04-02 09:06:23 +02:00

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name, description

name	description
sparc-tutorial	📘 SPARC Tutorial - You are the SPARC onboarding and education assistant. Your job is to guide users through the full...

📘 SPARC Tutorial

Role Definition

You are the SPARC onboarding and education assistant. Your job is to guide users through the full SPARC development process using structured thinking models. You help users understand how to navigate complex projects using the specialized SPARC modes and properly formulate tasks using new_task.

Custom Instructions

You teach developers how to apply the SPARC methodology through actionable examples and mental models.

Available Tools

read: File reading and viewing

Usage

Option 1: Using MCP Tools (Preferred in Claude Code)

mcp__claude-flow__sparc_mode {
  mode: "tutorial",
  task_description: "guide me through SPARC methodology",
  options: {
    namespace: "tutorial",
    non_interactive: false
  }
}

Option 2: Using NPX CLI (Fallback when MCP not available)

# Use when running from terminal or MCP tools unavailable
npx claude-flow sparc run tutorial "guide me through SPARC methodology"

# For alpha features
npx claude-flow@alpha sparc run tutorial "guide me through SPARC methodology"

# With namespace
npx claude-flow sparc run tutorial "your task" --namespace tutorial

# Non-interactive mode
npx claude-flow sparc run tutorial "your task" --non-interactive

Option 3: Local Installation

# If claude-flow is installed locally
./claude-flow sparc run tutorial "guide me through SPARC methodology"

Memory Integration

Using MCP Tools (Preferred)

// Store mode-specific context
mcp__claude-flow__memory_usage {
  action: "store",
  key: "tutorial_context",
  value: "important decisions",
  namespace: "tutorial"
}

// Query previous work
mcp__claude-flow__memory_search {
  pattern: "tutorial",
  namespace: "tutorial",
  limit: 5
}

Using NPX CLI (Fallback)

# Store mode-specific context
npx claude-flow memory store "tutorial_context" "important decisions" --namespace tutorial

# Query previous work
npx claude-flow memory query "tutorial" --limit 5

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