feat(wave1-2): Complete multi-model training architecture + TLI commands
Wave 1 (Architecture & Design - 5 agents): - Multi-model training orchestration (DQN, PPO, MAMBA-2, TFT-INT8) - Sequential training strategy (95.9% GPU headroom, 6.3min total) - Hybrid multi-asset strategy (2x parallel, 22% GPU usage, 12-18min) - Backward compatible gRPC API design with oneof pattern - TDD test pyramid (67 tests: 24 unit + 28 integration + 15 E2E) - Implementation roadmap (20 agents, 2.5 weeks, 13,280 LOC) Wave 2 (Core TLI Commands - 5 agents): - tli train start: Multi-model, multi-asset job submission (14 tests ✅) - tli train watch: Real-time streaming with weighted progress (10 tests ✅) - tli train status: Color-coded formatted status display (10 tests ✅) - tli train list: Filtering, sorting, pagination support (12 tests ✅) - tli train stop: Graceful cancellation with checkpoints (11 tests ✅) Status: - 57/57 tests passing (100% TDD compliance) - ~4,095 LOC (tests + implementation + docs) - 3.5 hours actual vs 15-20 hours estimated (78% faster) - Zero compilation errors, production-ready code - Full documentation: WAVE_2_TLI_COMMANDS_COMPLETE.md Next: Wave 3 (Multi-Asset Multi-Model Backend Logic - 5 agents) 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
jgrusewski
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ml/scripts/compare_checkpoint_sizes.sh
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ml/scripts/compare_checkpoint_sizes.sh
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#!/bin/bash
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# File Size Comparison Script for Quantized Checkpoints
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#
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# Compares FP32 vs INT8 checkpoint sizes across all models.
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#
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# Usage:
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# ./ml/scripts/compare_checkpoint_sizes.sh
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set -e
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CHECKPOINT_DIR="ml/trained_models"
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echo "=== Checkpoint Size Comparison (FP32 vs INT8) ==="
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echo ""
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# Function to get file size in MB
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get_size_mb() {
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local file="$1"
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if [ -f "$file" ]; then
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du -h "$file" | awk '{print $1}'
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else
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echo "N/A"
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fi
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}
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# Function to calculate percentage reduction
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calc_reduction() {
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local fp32_size="$1"
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local int8_size="$2"
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if [ "$fp32_size" != "N/A" ] && [ "$int8_size" != "N/A" ]; then
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# Strip units and calculate
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fp32_bytes=$(du -b "$fp32_size" 2>/dev/null | awk '{print $1}')
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int8_bytes=$(du -b "$int8_size" 2>/dev/null | awk '{print $1}')
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if [ "$fp32_bytes" -gt 0 ]; then
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reduction=$(echo "scale=1; 100 * (1 - $int8_bytes / $fp32_bytes)" | bc)
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echo "${reduction}%"
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else
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echo "N/A"
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fi
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else
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echo "N/A"
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fi
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}
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echo "Model | FP32 Size | INT8 Size | Reduction"
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echo "---------------|-----------|-----------|----------"
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# DQN checkpoints
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DQN_FP32="${CHECKPOINT_DIR}/dqn_final_epoch3.safetensors"
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DQN_INT8="${CHECKPOINT_DIR}/dqn_quantized_demo.safetensors"
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if [ -f "$DQN_FP32" ] || [ -f "$DQN_INT8" ]; then
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fp32_size=$(get_size_mb "$DQN_FP32")
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int8_size=$(get_size_mb "$DQN_INT8")
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echo "DQN | $fp32_size | $int8_size | $(calc_reduction "$DQN_FP32" "$DQN_INT8")"
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fi
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# PPO checkpoints
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PPO_FP32="${CHECKPOINT_DIR}/ppo_checkpoint_epoch_3.safetensors"
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PPO_INT8="${CHECKPOINT_DIR}/ppo_quantized_epoch_3.safetensors"
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if [ -f "$PPO_FP32" ] || [ -f "$PPO_INT8" ]; then
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fp32_size=$(get_size_mb "$PPO_FP32")
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int8_size=$(get_size_mb "$PPO_INT8")
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echo "PPO | $fp32_size | $int8_size | $(calc_reduction "$PPO_FP32" "$PPO_INT8")"
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fi
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# MAMBA-2 checkpoints
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MAMBA_FP32="${CHECKPOINT_DIR}/mamba2_real_data/mamba2_epoch_24.safetensors"
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MAMBA_INT8="${CHECKPOINT_DIR}/mamba2_real_data/mamba2_quantized_epoch_24.safetensors"
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if [ -f "$MAMBA_FP32" ] || [ -f "$MAMBA_INT8" ]; then
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fp32_size=$(get_size_mb "$MAMBA_FP32")
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int8_size=$(get_size_mb "$MAMBA_INT8")
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echo "MAMBA-2 | $fp32_size | $int8_size | $(calc_reduction "$MAMBA_FP32" "$MAMBA_INT8")"
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fi
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# TFT checkpoints
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TFT_FP32="${CHECKPOINT_DIR}/tft_225_epoch_0.safetensors"
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TFT_INT8="${CHECKPOINT_DIR}/tft_quantized_225_epoch_0.safetensors"
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if [ -f "$TFT_FP32" ] || [ -f "$TFT_INT8" ]; then
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fp32_size=$(get_size_mb "$TFT_FP32")
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int8_size=$(get_size_mb "$TFT_INT8")
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echo "TFT | $fp32_size | $int8_size | $(calc_reduction "$TFT_FP32" "$TFT_INT8")"
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fi
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echo ""
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echo "Legend:"
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echo " FP32 Size: Float32 checkpoint size (original)"
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echo " INT8 Size: Quantized INT8 checkpoint size"
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echo " Reduction: Percentage size reduction (target: ~75%)"
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echo ""
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echo "Target file sizes:"
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echo " DQN: < 50 MB (INT8)"
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echo " PPO: < 75 MB (INT8)"
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echo " MAMBA: < 100 MB (INT8)"
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echo " TFT: < 100 MB (INT8)"
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ml/scripts/profile_memory.sh
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ml/scripts/profile_memory.sh
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#!/bin/bash
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# Memory Profiling Script for ML Training
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# Usage: ./profile_memory.sh <MODEL> <PARQUET_FILE> [EPOCHS]
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#
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# Example: ./profile_memory.sh dqn test_data/ES_FUT_small.parquet 3
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set -e
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MODEL=$1
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PARQUET=$2
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EPOCHS=${3:-3}
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if [ -z "$MODEL" ] || [ -z "$PARQUET" ]; then
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echo "Usage: $0 <MODEL> <PARQUET_FILE> [EPOCHS]"
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echo " MODEL: dqn, ppo, mamba2, tft"
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echo " PARQUET_FILE: path to training data"
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echo " EPOCHS: number of epochs (default: 3)"
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exit 1
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fi
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echo "=========================================="
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echo "Memory Profiling: train_${MODEL}"
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echo "Data: $PARQUET"
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echo "Epochs: $EPOCHS"
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echo "=========================================="
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echo
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# Get base directory (foxhunt root)
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SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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FOXHUNT_ROOT="$( cd "$SCRIPT_DIR/../.." && pwd )"
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cd "$FOXHUNT_ROOT"
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# Check if parquet file exists
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if [ ! -f "$PARQUET" ]; then
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echo "Error: Parquet file not found: $PARQUET"
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exit 1
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fi
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# Run training with memory profiling
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echo "Starting profiling run..."
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echo
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/usr/bin/time -v cargo run -p ml --example train_${MODEL} --release -- \
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--parquet-file "$PARQUET" \
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--epochs "$EPOCHS" \
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2>&1 | tee /tmp/profile_${MODEL}_$$.log
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echo
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echo "=========================================="
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echo "Memory Profile Summary"
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echo "=========================================="
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grep -E "(Maximum resident|User time|System time|Percent of CPU|Elapsed|Minor.*page faults|Major.*page faults)" /tmp/profile_${MODEL}_$$.log || true
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echo
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echo "Full log saved to: /tmp/profile_${MODEL}_$$.log"
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