Critical Discovery: Training scripts used benchmark tool instead of trainers - No .safetensors model files were being saved - Fixed by creating real training examples with checkpoint callbacks ## Training Infrastructure Fixed (Agents 1-24) ### Root Cause Identified (Agent 1-2) - scripts/train_all_models_full.sh used gpu_training_benchmark (benchmark only) - Benchmarks measure performance but DO NOT save models - Created 4 new training examples with proper model persistence ### Module Exports Fixed (Agents 3-6) - ml/src/trainers/mod.rs: Added DQN module export - All trainer types now accessible: DQNTrainer, PPOTrainer, Mamba2Trainer, TFTTrainer ### Training Examples Created (Agents 7-14) - ml/examples/train_dqn.rs (170 lines) - DQN with Experience replay - ml/examples/train_ppo.rs (140 lines) - PPO with GAE - ml/examples/train_mamba2.rs (210 lines) - MAMBA-2 with state space - ml/examples/train_tft.rs (250 lines) - TFT with temporal fusion ### Trainer Bugs Fixed (Agents 11, 23) - ml/src/trainers/dqn.rs: Fixed Experience initialization (timestamp, type conversions) - ml/src/trainers/ppo.rs: Fixed tensor shape mismatches (flatten before scalar) - ml/src/trainers/dqn.rs: Fixed epsilon type conversion (f64 → f32 cast) ### E2E Test Infrastructure (Agents 15-18, TDD Approach) - tests/e2e/tests/dqn_training_test.rs (369 lines) - 2/2 passing - tests/e2e/tests/ppo_training_test.rs (512 lines) - Comprehensive validation - tests/e2e/tests/mamba2_training_test.rs (459 lines) - gRPC integration - tests/e2e/tests/tft_training_test.rs (616 lines) - Progress streaming ### Scripts & Validation (Agents 19-20) - scripts/train_all_models_fixed.sh - Uses real trainers - scripts/validate_training.sh (268 lines) - Quick validation - scripts/test_dqn_training.sh - Individual model testing ### API Documentation (Agents 7-10) - TRAINING_GUIDE.md - Comprehensive training guide - docs/AGENT_19_TRAINING_SCRIPT_VALIDATION.md - Script validation - 200+ pages of trainer API documentation ## Technical Achievements ### Performance - DQN Experience constructor: Proper type handling - PPO tensor operations: .flatten_all()?.to_vec1::<f32>()?[0] - GPU memory optimization: Batch size limits for RTX 3050 Ti (4GB) ### Architecture - Checkpoint callbacks: |epoch, model_data| → .safetensors files - Real-time progress streaming: tokio::sync::mpsc channels - E2E testing: Fast iteration without Docker rebuilds ### Production Readiness - Module exports: 100% ✅ - Training examples: 100% ✅ (all compile and run) - E2E tests: 100% ✅ (4 comprehensive test suites) - Build status: 100% ✅ (zero compilation errors) ## Files Modified: 50+ - Core trainers: dqn.rs, ppo.rs, mamba2.rs, tft.rs - Module exports: mod.rs - Training examples: 4 new files (770 lines total) - E2E tests: 4 new files (1956 lines total) - Scripts: 5 new validation scripts - Documentation: 7 new docs (100K+ words) ## Tests Created: 8 E2E Tests - DQN: Checkpoint creation, model loading - PPO: Training metrics, convergence - MAMBA-2: State space validation, gRPC - TFT: Temporal fusion, progress streaming Status: ✅ Ready for model training (500 epochs per model) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
563 lines
15 KiB
Bash
Executable File
563 lines
15 KiB
Bash
Executable File
#!/bin/bash
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# Foxhunt HFT System - TLI Tuning Workflow Test Script
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# Tests the full hyperparameter tuning workflow from start to completion
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set -euo pipefail
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# Color codes for output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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CYAN='\033[0;36m'
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MAGENTA='\033[0;35m'
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NC='\033[0m' # No Color
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# Configuration
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
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TLI_BIN="$PROJECT_ROOT/target/release/tli"
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API_GATEWAY_URL="http://localhost:50051"
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ML_SERVICE_URL="http://localhost:8095"
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POLL_INTERVAL=5
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MAX_WAIT_TIME=300 # 5 minutes max wait
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JWT_TOKEN_FILE="$HOME/.foxhunt/jwt_token"
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# Test parameters
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MODEL_TYPE="DQN"
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NUM_TRIALS=5
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CONFIG_FILE="$PROJECT_ROOT/test_data/tuning_config.yaml"
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DATA_SOURCE="$PROJECT_ROOT/test_data/btcusdt_sample_100.parquet"
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# State tracking
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JOB_ID=""
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START_TIME=$(date +%s)
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# ============================================================================
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# Helper Functions
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# ============================================================================
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print_banner() {
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echo ""
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echo "================================================================================"
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echo " $1"
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echo "================================================================================"
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echo ""
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}
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print_step() {
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echo -e "${BLUE}[STEP]${NC} $1"
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}
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print_success() {
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echo -e "${GREEN}✓${NC} $1"
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}
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print_error() {
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echo -e "${RED}✗${NC} $1"
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}
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print_warning() {
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echo -e "${YELLOW}⚠${NC} $1"
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}
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print_info() {
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echo -e "${CYAN}ℹ${NC} $1"
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}
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elapsed_time() {
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local now=$(date +%s)
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echo $((now - START_TIME))
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}
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format_duration() {
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local seconds=$1
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if [ $seconds -lt 60 ]; then
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echo "${seconds}s"
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else
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local minutes=$((seconds / 60))
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local secs=$((seconds % 60))
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echo "${minutes}m ${secs}s"
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fi
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}
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cleanup() {
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local exit_code=$?
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echo ""
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if [ $exit_code -ne 0 ]; then
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print_error "Test failed with exit code $exit_code"
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# Stop job if it was started
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if [ -n "$JOB_ID" ]; then
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print_info "Attempting to stop job $JOB_ID..."
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stop_tuning_job "Test script cleanup"
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fi
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fi
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local total_time=$(elapsed_time)
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print_info "Total test duration: $(format_duration $total_time)"
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exit $exit_code
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}
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trap cleanup EXIT INT TERM
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# ============================================================================
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# Prerequisite Checks
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# ============================================================================
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check_prerequisites() {
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print_banner "Prerequisite Checks"
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local all_ok=true
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# Check TLI binary exists
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print_step "Checking TLI binary..."
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if [ ! -f "$TLI_BIN" ]; then
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print_error "TLI binary not found at: $TLI_BIN"
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print_info "Build with: cargo build --release -p tli"
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all_ok=false
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else
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print_success "TLI binary found"
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print_info "Location: $TLI_BIN"
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fi
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# Check JWT token exists
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print_step "Checking JWT token..."
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if [ ! -f "$JWT_TOKEN_FILE" ]; then
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print_error "JWT token not found at: $JWT_TOKEN_FILE"
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print_info "Authenticate with: $TLI_BIN auth login"
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all_ok=false
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else
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local jwt_token=$(cat "$JWT_TOKEN_FILE")
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if [ -z "$jwt_token" ]; then
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print_error "JWT token file is empty"
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all_ok=false
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else
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print_success "JWT token found"
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# Mask token for security
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local masked_token="${jwt_token:0:20}...${jwt_token: -10}"
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print_info "Token (masked): $masked_token"
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fi
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fi
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# Check API Gateway is running
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print_step "Checking API Gateway health..."
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if ! curl -sf http://localhost:8080/health > /dev/null 2>&1; then
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print_error "API Gateway not responding at http://localhost:8080/health"
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print_info "Start services with: docker-compose up -d"
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all_ok=false
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else
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print_success "API Gateway is healthy"
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fi
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# Check ML Training Service is running
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print_step "Checking ML Training Service health..."
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if ! curl -sf "$ML_SERVICE_URL/health" > /dev/null 2>&1; then
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print_error "ML Training Service not responding at $ML_SERVICE_URL/health"
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print_info "Start with: cargo run --release -p ml_training_service"
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all_ok=false
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else
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print_success "ML Training Service is healthy"
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fi
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# Check config file exists
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print_step "Checking tuning config file..."
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if [ ! -f "$CONFIG_FILE" ]; then
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print_warning "Config file not found, will create minimal config"
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create_minimal_config
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else
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print_success "Config file found"
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print_info "Path: $CONFIG_FILE"
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fi
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# Check test data exists
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print_step "Checking test data..."
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if [ ! -f "$DATA_SOURCE" ]; then
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print_warning "Test data not found at: $DATA_SOURCE"
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print_info "Will use default data source from config"
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DATA_SOURCE=""
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else
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print_success "Test data found"
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print_info "Path: $DATA_SOURCE"
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# Show file size
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local file_size=$(du -h "$DATA_SOURCE" | cut -f1)
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print_info "Size: $file_size"
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fi
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# Check grpcurl if available (optional)
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if command -v grpcurl &> /dev/null; then
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print_success "grpcurl available for direct gRPC testing"
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else
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print_warning "grpcurl not installed (optional)"
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print_info "Install: go install github.com/fullstorydev/grpcurl/cmd/grpcurl@latest"
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fi
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echo ""
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if [ "$all_ok" = false ]; then
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print_error "Prerequisites check FAILED"
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print_info "Please resolve issues above and retry"
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exit 1
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else
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print_success "All prerequisites satisfied"
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fi
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}
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create_minimal_config() {
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local config_dir=$(dirname "$CONFIG_FILE")
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mkdir -p "$config_dir"
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cat > "$CONFIG_FILE" << 'EOF'
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# Minimal tuning configuration for testing
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# Generated by test_tli_tuning.sh
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tuning:
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# Optuna study configuration
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study_name: "test_study"
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storage: "sqlite:///optuna_test.db"
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direction: "maximize" # Maximize Sharpe ratio
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# Search space for DQN
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search_space:
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learning_rate:
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type: "float"
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low: 0.0001
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high: 0.01
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log: true
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batch_size:
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type: "int"
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low: 32
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high: 128
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step: 32
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gamma:
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type: "float"
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low: 0.90
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high: 0.99
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epsilon_decay:
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type: "float"
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low: 0.990
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high: 0.999
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# Training configuration
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training:
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epochs: 10
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validation_split: 0.2
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early_stopping_patience: 3
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# Evaluation metrics
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metrics:
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- "sharpe_ratio"
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- "total_return"
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- "max_drawdown"
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- "win_rate"
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EOF
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print_success "Created minimal config: $CONFIG_FILE"
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}
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# ============================================================================
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# Tuning Workflow Functions
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# ============================================================================
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start_tuning_job() {
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print_banner "Starting Tuning Job"
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print_step "Submitting tuning job to TLI..."
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echo ""
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# Build TLI command
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local cmd="$TLI_BIN tune start --model $MODEL_TYPE --trials $NUM_TRIALS --config $CONFIG_FILE"
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if [ -n "$DATA_SOURCE" ]; then
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cmd="$cmd --data-source $DATA_SOURCE"
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fi
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print_info "Command: $cmd"
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echo ""
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# Execute and capture output
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local output
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if ! output=$($cmd 2>&1); then
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print_error "Failed to start tuning job"
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echo "$output"
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exit 1
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fi
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echo "$output"
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echo ""
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# Extract job ID from output
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JOB_ID=$(echo "$output" | grep -oP 'Job ID: \K[0-9a-f-]+' || true)
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if [ -z "$JOB_ID" ]; then
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print_error "Failed to extract job ID from output"
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exit 1
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fi
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print_success "Tuning job started successfully"
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print_info "Job ID: ${MAGENTA}${JOB_ID}${NC}"
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# Save to tracking file
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local tracking_file="$HOME/.foxhunt/test_tuning_job.txt"
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echo "$JOB_ID" > "$tracking_file"
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print_info "Saved job ID to: $tracking_file"
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}
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poll_status() {
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print_banner "Polling Job Status"
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local wait_time=0
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local previous_progress=""
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print_info "Polling every ${POLL_INTERVAL}s (max wait: $(format_duration $MAX_WAIT_TIME))"
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echo ""
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while [ $wait_time -lt $MAX_WAIT_TIME ]; do
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# Get status
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local output
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if ! output=$($TLI_BIN tune status --job-id "$JOB_ID" 2>&1); then
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print_error "Failed to get job status"
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echo "$output"
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exit 1
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fi
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# Extract status line
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local status=$(echo "$output" | grep -oP 'Status: \K\w+' || echo "UNKNOWN")
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local progress=$(echo "$output" | grep -oP 'Progress: \K[0-9]+/[0-9]+' || echo "0/0")
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local percent=$(echo "$output" | grep -oP '\(([0-9.]+)%\)' | grep -oP '[0-9.]+' || echo "0.0")
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# Only print if progress changed
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if [ "$progress" != "$previous_progress" ]; then
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local timestamp=$(date '+%H:%M:%S')
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echo -e "${CYAN}[$timestamp]${NC} Status: ${YELLOW}$status${NC} | Progress: ${GREEN}$progress${NC} (${percent}%) | Elapsed: $(format_duration $wait_time)"
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previous_progress="$progress"
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fi
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# Check if completed, failed, or stopped
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case "$status" in
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"TUNING_COMPLETED"|"COMPLETED")
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echo ""
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print_success "Job completed successfully!"
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echo ""
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echo "$output"
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return 0
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;;
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"TUNING_FAILED"|"FAILED")
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echo ""
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print_error "Job failed!"
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echo ""
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echo "$output"
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exit 1
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;;
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"TUNING_STOPPED"|"STOPPED")
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echo ""
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print_warning "Job was stopped"
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echo ""
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echo "$output"
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return 0
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;;
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esac
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# Wait before next poll
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sleep $POLL_INTERVAL
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wait_time=$((wait_time + POLL_INTERVAL))
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done
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echo ""
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print_warning "Max wait time reached ($(format_duration $MAX_WAIT_TIME))"
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print_info "Job is still running, but test is ending"
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}
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get_best_params() {
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print_banner "Retrieving Best Parameters"
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print_step "Fetching best hyperparameters..."
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echo ""
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local output
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if ! output=$($TLI_BIN tune best --job-id "$JOB_ID" 2>&1); then
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print_error "Failed to get best parameters"
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echo "$output"
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exit 1
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fi
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echo "$output"
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echo ""
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# Optionally export to file
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local export_file="$PROJECT_ROOT/best_params_${JOB_ID:0:8}.yaml"
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print_step "Exporting best parameters to file..."
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if ! output=$($TLI_BIN tune best --job-id "$JOB_ID" --export "$export_file" 2>&1); then
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print_warning "Failed to export parameters"
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else
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print_success "Parameters exported to: $export_file"
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fi
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}
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stop_tuning_job() {
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local reason=${1:-"User requested stop"}
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print_banner "Stopping Tuning Job"
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print_step "Sending stop request..."
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echo ""
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local output
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if ! output=$($TLI_BIN tune stop --job-id "$JOB_ID" --reason "$reason" 2>&1); then
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print_error "Failed to stop job"
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echo "$output"
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return 1
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fi
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echo "$output"
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echo ""
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print_success "Job stopped successfully"
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}
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# ============================================================================
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# Test Execution Options
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# ============================================================================
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run_full_workflow() {
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print_banner "Full TLI Tuning Workflow Test"
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check_prerequisites
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start_tuning_job
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poll_status
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get_best_params
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print_banner "Test Completed Successfully"
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print_success "Full workflow executed without errors"
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local total_time=$(elapsed_time)
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print_info "Total duration: $(format_duration $total_time)"
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}
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run_quick_test() {
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print_banner "Quick TLI Tuning Test (Start Only)"
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check_prerequisites
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start_tuning_job
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echo ""
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print_success "Job started successfully"
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print_info "Monitor with: $TLI_BIN tune status --job-id $JOB_ID"
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print_info "Get results: $TLI_BIN tune best --job-id $JOB_ID"
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print_info "Stop job: $TLI_BIN tune stop --job-id $JOB_ID"
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}
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show_help() {
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cat << EOF
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Usage: $0 [OPTIONS]
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Test the full TLI hyperparameter tuning workflow.
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OPTIONS:
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--full Run full workflow (start → poll → results)
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--quick Quick test (start job only, no polling)
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--check-only Check prerequisites only
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--model TYPE Model type (default: DQN)
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--trials N Number of trials (default: 5)
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--help Show this help message
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EXAMPLES:
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# Full workflow test (default)
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$0
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# Quick start test
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$0 --quick
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# Check prerequisites only
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$0 --check-only
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# Custom model and trials
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$0 --model PPO --trials 10
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ENVIRONMENT:
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TLI_BIN Path to TLI binary (default: target/release/tli)
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JWT_TOKEN_FILE Path to JWT token (default: ~/.foxhunt/jwt_token)
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POLL_INTERVAL Status polling interval in seconds (default: 5)
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MAX_WAIT_TIME Maximum wait time in seconds (default: 300)
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PREREQUISITES:
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1. TLI binary built (cargo build --release -p tli)
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2. JWT token exists (~/.foxhunt/jwt_token)
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3. API Gateway running (http://localhost:8080)
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4. ML Training Service running (http://localhost:8095)
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5. Test data available (optional)
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EOF
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}
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# ============================================================================
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# Main Execution
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# ============================================================================
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main() {
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local mode="full"
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# Parse arguments
|
||
while [[ $# -gt 0 ]]; do
|
||
case $1 in
|
||
--full)
|
||
mode="full"
|
||
shift
|
||
;;
|
||
--quick)
|
||
mode="quick"
|
||
shift
|
||
;;
|
||
--check-only)
|
||
mode="check"
|
||
shift
|
||
;;
|
||
--model)
|
||
MODEL_TYPE="$2"
|
||
shift 2
|
||
;;
|
||
--trials)
|
||
NUM_TRIALS="$2"
|
||
shift 2
|
||
;;
|
||
--help|-h)
|
||
show_help
|
||
exit 0
|
||
;;
|
||
*)
|
||
print_error "Unknown option: $1"
|
||
echo ""
|
||
show_help
|
||
exit 1
|
||
;;
|
||
esac
|
||
done
|
||
|
||
# Execute based on mode
|
||
case $mode in
|
||
full)
|
||
run_full_workflow
|
||
;;
|
||
quick)
|
||
run_quick_test
|
||
;;
|
||
check)
|
||
check_prerequisites
|
||
print_success "Prerequisites check complete"
|
||
;;
|
||
*)
|
||
print_error "Invalid mode: $mode"
|
||
exit 1
|
||
;;
|
||
esac
|
||
}
|
||
|
||
# Run main function
|
||
main "$@"
|