# LLM Autotuner - Product Roadmap > **Last Updated**: 2025/12/22 > **Project Status**: Production-Ready with Active Development > **Current Version**: v0.2.0 (Milestone 5 Complete) --- ## Executive Summary The LLM Autotuner is a comprehensive system for automatically optimizing Large Language Model inference parameters. The project has successfully completed five major milestones including tri-mode deployment (Kubernetes/OME, Docker, and Local), full-stack web application, runtime-agnostic configuration, and an AI-powered Agent System. **Key Achievements:** - ✅ 28 tasks executed, 408 experiments run, 312 successful results - ✅ Bayesian optimization achieving 80-87% reduction in experiments vs grid search - ✅ Full-stack web application with React frontend and FastAPI backend - ✅ Runtime-agnostic quantization and parallelism configuration - ✅ GPU-aware optimization with per-GPU efficiency metrics - ✅ YAML import/export for configuration management - ✅ Real-time WebSocket updates and auto-update notifications - ✅ Per-batch SLO filtering with graceful OOM handling - ✅ Documentation refinement (66→15 files, 77% reduction) - ✅ SLO-aware scoring with exponential penalty functions - ✅ **Agent System** with LLM-powered task management, tool execution, and conversational interface - ✅ **Local Deployment Mode** for subprocess-based model execution without containers - ✅ **Remote Dataset Support** via URL fetching with caching --- ## Milestone Overview | | M1: Core Foundation | M2: Web Interface | M3: Runtime-Agnostic | M4: UI/UX Polish | M5: Agent System | M6+: Future | |---|---------------------|-------------------|----------------------|------------------|------------------|-------------| | **Date** | 2025/10/24 | 2025/10/30 | 2025/11/14 | 2025/12/01 | 2025/12/22 | Planned | | **Status** | ✅ Done | ✅ Done | ✅ Done | ✅ Done | ✅ Done | 🔵 Planned | | **Features** | ✅ Grid/Random Search
✅ Docker Mode
✅ OME/K8s Mode
✅ Benchmark Parsing
✅ Scoring Algorithms
✅ CLI Interface | ✅ REST API
✅ React Frontend
✅ Task Queue (ARQ)
✅ Log Streaming
✅ Container Monitor
✅ Preset System | ✅ Bayesian Optimization
✅ Quantization Config
✅ Parallel Config
✅ GPU-Aware Scoring
✅ SLO-Aware Scoring
✅ Per-GPU Metrics | ✅ WebSocket Updates
✅ YAML Import/Export
✅ Auto-Update Notif
✅ Multi-Exp Comparison
✅ Sphinx Docs Site
✅ SLO Filtering | ✅ Agent Chat UI
✅ Tool Framework
✅ Local Deploy Mode
✅ Dataset URL Support
✅ Streaming Markdown
✅ GitHub/HF Tools | 🔵 Distributed Workers
🔵 Multi-User Auth
🔵 Cloud Deployment
🔵 CI/CD Integration
🔵 Advanced Analytics | --- ## Milestone Timeline ``` 2025/10/24 ────► Milestone 1: Core Autotuner Foundation 2025/10/30 ────► Milestone 2: Complete Web Interface & Parameter Preset System 2025/11/14 ────► Milestone 3: Runtime-Agnostic Configuration & GPU-Aware Optimization 2025/12/01 ────► Milestone 4: UI/UX Polish, SLO Filtering & Documentation 2025/12/22 ────► Milestone 5: Agent System & Local Deployment Mode ``` --- ## 🎉 Milestone 1: Core Autotuner Foundation **Date**: 2025/10/24 (tag: `milestone-1`) **Status**: ✅ COMPLETED **Objective**: Establish solid foundation for LLM inference parameter autotuning with complete functionality, proper documentation, and code standards ### Key Accomplishments #### 1.1 Architecture & Implementation ✅ - [x] Multi-tier architecture with clear separation of concerns - [x] OME controller for Kubernetes InferenceService lifecycle - [x] Docker controller for standalone deployment - [x] Benchmark controller (OME BenchmarkJob + Direct CLI modes) - [x] Parameter grid generator and optimizer utilities - [x] Main orchestrator with JSON input **Technical Specs:** - Controllers: `ome_controller.py`, `docker_controller.py`, `benchmark_controller.py`, `direct_benchmark_controller.py` - Utilities: `optimizer.py` (grid search, scoring algorithms) - Templates: Jinja2 for Kubernetes resources #### 1.2 Benchmark Results Parsing & Scoring ✅ - [x] Fixed critical bug in genai-bench result file parsing - [x] Enhanced `DirectBenchmarkController._parse_results()` - [x] Reads correct result files (D*.json pattern) - [x] Handles multiple concurrency levels - [x] Aggregates metrics across all runs - [x] Extracts 15+ performance metrics **Completed `calculate_objective_score()` with 4 objectives:** - `minimize_latency` - E2E latency optimization - `maximize_throughput` - Token throughput optimization - `minimize_ttft` - Time to First Token optimization - `minimize_tpot` - Time Per Output Token optimization **Comprehensive Metrics:** - Latency: mean/min/max/p50/p90/p99 E2E latency - Throughput: output and total token throughput - Request statistics: success rate, error tracking #### 1.3 Code Quality & Standards ✅ - [x] Integrated **black-with-tabs** formatter - [x] Formatted entire codebase (7 Python files, 1957+ lines) - [x] Configuration: 120-char lines, tab indentation - [x] PEP 8 compliance with 2 blank lines between top-level definitions - [x] IDE integration guides (VS Code, PyCharm) #### 1.4 CLI Usability Improvements ✅ - [x] Made `--direct` flag automatic when using `--mode docker` - [x] Simplified command-line interface - [x] Updated help text and usage examples - [x] Better default behaviors for common use cases #### 1.5 Documentation Structure ✅ - [x] Separated 420+ line Troubleshooting into `docs/TROUBLESHOOTING.md` - [x] Created `docs/DEVELOPMENT.md` comprehensive guide - [x] Established documentation conventions - [x] Improved README readability **Documentation Files Created:** - `README.md` - User guide with installation and usage - `CLAUDE.md` - Project overview and development guidelines - `docs/TROUBLESHOOTING.md` - 13 common issues and solutions - `docs/DEVELOPMENT.md` - Code formatting and contribution guide - `docs/DOCKER_MODE.md` - Docker deployment guide - `docs/OME_INSTALLATION.md` - Kubernetes/OME setup #### 1.6 Web Integration Readiness ✅ - [x] Comprehensive codebase analysis: Zero blockers found - [x] Created detailed readiness assessment - [x] Verified all controllers fully implemented (no placeholder functions) - [x] Confirmed orchestrator is programmatically importable - [x] Documented data structures (input/output formats) - [x] Technology stack recommendations (FastAPI, React/Vue) - [x] API endpoint specifications - [x] Implementation roadmap with effort estimates ### Technical Achievements **Code Quality:** - 1,957 lines of production Python code - 100% method implementation (no placeholders in critical paths) - Comprehensive error handling and logging - Clean separation of concerns (controllers, orchestrator, utilities) **Functionality:** - ✅ Full Docker mode support (standalone, no K8s required) - ✅ OME/Kubernetes mode support - ✅ Grid search parameter optimization - ✅ Multi-concurrency benchmark execution - ✅ Comprehensive result aggregation and scoring - ✅ Automatic resource cleanup **Test Results:** - Successfully parsed real benchmark data - Concurrency levels: [1, 4] - Mean E2E Latency: 0.1892s - Mean Throughput: 2,304.82 tokens/s --- ## 🎉 Milestone 2: Complete Web Interface & Parameter Preset System **Date**: 2025/10/30 (tag: `milestone-2`) **Status**: ✅ COMPLETED **Objective**: Build full-stack web application for task management, visualization, and introduce parameter preset system ### Key Accomplishments #### 2.1 Backend API Infrastructure ✅ - [x] FastAPI application with async support - [x] SQLAlchemy ORM with SQLite backend (moved to `~/.local/share/`) - [x] Database models (Task, Experiment) - [x] REST API endpoints (10+ routes) - [x] ARQ background task queue (Redis integration) - [x] Pydantic schemas for validation - [x] Streaming log API endpoints - [x] Health check improvements **API Endpoints:** ``` POST /api/tasks/ - Create task POST /api/tasks/{id}/start - Start task execution GET /api/tasks/ - List tasks GET /api/tasks/{id} - Get task details GET /api/tasks/{id}/logs - Stream logs (SSE) GET /api/experiments/task/{id} - Get experiments GET /api/docker/containers - List containers GET /api/system/health - Health check ``` **Database Migration:** - Moved from local `autotuner.db` to XDG-compliant `~/.local/share/autotuner/` - SQLite WAL mode for concurrent writes - Proper session management with async context #### 2.2 React Frontend Application ✅ - [x] React 18 with TypeScript - [x] Vite build tooling with hot module replacement - [x] React Router for navigation - [x] TanStack Query (React Query) for API state - [x] Tailwind CSS styling - [x] Recharts for metrics visualization - [x] React Hot Toast for notifications **Pages Implemented:** - **Dashboard** - System overview and statistics - **Tasks** - Task list with create/list/monitor/restart - **NewTask** - Task creation wizard with form validation - **Experiments** - Results visualization with charts - **Containers** - Docker container monitoring (Docker mode) **Key Components:** - `TaskResults.tsx` - Results visualization with Recharts - `LogViewer.tsx` - Real-time log streaming viewer - `Layout.tsx` - Main layout with navigation - Form components with validation **UI Features:** - Task creation wizard with parameter presets - Real-time status monitoring (polling-based) - Experiment results table with sorting/filtering - Performance graphs (throughput, latency, TPOT, TTFT) - Container stats (CPU, memory, GPU) - Log streaming with follow mode - URL-based navigation with hash routing - Error notifications with toast messages #### 2.3 ARQ Worker Integration ✅ - [x] Background task processing with Redis queue - [x] Worker configuration (max_jobs=5, timeout=2h) - [x] Log redirection to task-specific files - [x] Graceful shutdown handling - [x] Worker management scripts **Log Management:** - Task logs: `~/.local/share/autotuner/logs/task_.log` - Worker logs: `logs/worker.log` - Python logging library integration - StreamToLogger for real-time capture #### 2.4 Task Management Features ✅ - [x] Task creation UI with form builder - [x] Task restart functionality - [x] Task edit capability - [x] Task status tracking - [x] Real-time log viewing - [x] Environment variable configuration for Docker #### 2.5 Parameter Preset System (Backend) ✅ - [x] Parameter preset API (CRUD operations) - [x] Preset merge functionality - [x] Import/export capabilities - [x] System preset seeding **Note**: Frontend integration for preset system completed in later sprints. ### Bug Fixes & Improvements **Critical Fixes:** - Fixed best experiment selection bug - Fixed model name field linkings - Fixed health check 503 errors - Fixed data display in task view - Refined task restart logic - Enhanced container log viewing **Code Organization:** - Reorganized web backend code structure - Separated orchestrator from web modules - Formatted code with Prettier - Improved error handling and validation ### Technical Stack | Component | Technology | |-----------|-----------| | **Frontend** | React 18, TypeScript, Vite 5 | | **State Management** | TanStack Query 5 | | **Styling** | Tailwind CSS 3 | | **Charts** | Recharts 2 | | **Backend** | FastAPI, Python 3.10+ | | **Database** | SQLite 3 with SQLAlchemy 2 | | **Task Queue** | ARQ 0.26 + Redis 7 | | **API Docs** | Swagger UI (OpenAPI) | ### Statistics - **Commits since Milestone 1**: 40+ - **Frontend Components**: 20+ React components - **API Endpoints**: 15+ routes - **Database Tables**: 2 (tasks, experiments) - **Lines of Code**: ~12,000 total (5,000 backend + 7,000 frontend) --- ## 🎉 Milestone 3: Runtime-Agnostic Configuration Architecture & GPU-Aware Optimization **Date**: 2025/11/14 (tag: `milestone-3`) **Status**: ✅ COMPLETED **Timeline**: 2025/11/10 → 2025/11/14 **Objective**: Unified configuration abstraction for quantization and parallelism across multiple runtimes, plus GPU-aware optimization ### Overview Milestone 3 achieved **two major architectural breakthroughs**: 1. **Runtime-Agnostic Configuration System** - Unified abstraction for quantization and parallel execution across vLLM, SGLang, and TensorRT-LLM 2. **GPU-Aware Optimization** - Per-GPU efficiency metrics enabling fair comparison across different parallelism strategies These foundational changes enable **portable, efficiency-aware autotuning** where users specify high-level intent and the system automatically maps to runtime-specific implementations while optimizing for per-GPU efficiency. ### Part 1: Runtime-Agnostic Configuration System #### 1.1 Quantization Configuration Abstraction ✅ **Problem Solved:** Different inference runtimes use incompatible CLI syntax for quantization. Users had to learn runtime-specific arguments and rewrite configurations when switching engines. **Solution: Three-Layer Abstraction Architecture** **Four-Field Normalized Schema:** ```python { "gemm_dtype": "fp8", # Weight/activation quantization "kvcache_dtype": "fp8_e5m2", # KV cache compression "attention_dtype": "auto", # Attention compute precision "moe_dtype": "auto" # MoE expert quantization } ``` **Modules Created:** 1. **`quantization_mapper.py`** (450 lines) - Runtime-specific CLI argument mapping - 5 production presets: `default`, `kv-cache-fp8`, `dynamic-fp8`, `bf16-stable`, `aggressive-moe` - Validation with dtype compatibility checking - Automatic detection of offline quantization (AWQ, GPTQ, GGUF) 2. **`quantization_integration.py`** (350 lines) - Orchestrator integration layer - Experiment parameter preparation - Conflict resolution between user params and quant config **Runtime Mapping Example:** ``` User Config vLLM Args SGLang Args ──────────────────────────────────────────────────────────────────────────── gemm_dtype: "fp8" → --quantization fp8 --quantization fp8 kvcache_dtype: "fp8_e5m2" → --kv-cache-dtype fp8_e5m2 --kv-cache-dtype fp8_e5m2 attention_dtype: "fp8" → (inferred from gemm) --attention-backend fp8 ``` **Grid Expansion with `__quant__` Prefix:** ```json { "quant_config": { "gemm_dtype": ["auto", "fp8"], "kvcache_dtype": ["auto", "fp8_e5m2"] } } ``` Expands to 4 experiments (2×2): - `__quant__gemm_dtype=auto, __quant__kvcache_dtype=auto` - `__quant__gemm_dtype=auto, __quant__kvcache_dtype=fp8_e5m2` - `__quant__gemm_dtype=fp8, __quant__kvcache_dtype=auto` - `__quant__gemm_dtype=fp8, __quant__kvcache_dtype=fp8_e5m2` **Frontend Integration:** - **`QuantizationConfigForm.tsx`** (612 lines) - Preset mode vs. Custom mode toggle - Real-time preview of generated parameters - Combination count calculation - Validation feedback #### 1.2 Parallel Configuration Abstraction ✅ **Normalized Parameter Schema:** ```python { "tp": 4, # Tensor parallelism "pp": 1, # Pipeline parallelism "dp": 2, # Data parallelism "dcp": 1, # Decode context parallelism (vLLM) "cp": 1, # Context parallelism (TensorRT-LLM) "ep": 1, # Expert parallelism (MoE) "moe_tp": 1, # MoE tensor parallelism "moe_ep": 1 # MoE expert parallelism } ``` **Modules Created:** 1. **`parallel_mapper.py`** (520 lines) - 18 runtime-specific presets (6 per engine) - Constraint validation (e.g., SGLang: `tp % dp == 0`, TensorRT-LLM: no DP support) - world_size calculation: `world_size = tp × pp × dp` 2. **`parallel_integration.py`** (280 lines) - Parameter grid expansion - Orchestrator integration - GPU allocation coordination **Presets Per Engine:** ``` vLLM (6 presets): - single-gpu, high-throughput, large-model-tp, large-model-tp-pp - moe-optimized, long-context (with dcp), balanced SGLang (6 presets): - single-gpu, high-throughput, large-model-tp, large-model-tp-pp - moe-optimized (with moe_dense_tp), balanced - Constraint: tp % dp == 0 TensorRT-LLM (6 presets): - single-gpu, large-model-tp, large-model-tp-pp - moe-optimized (with moe_tp, moe_ep), long-context (with cp) - Constraint: No data parallelism support (dp must be 1) ``` **Runtime Mapping Example:** ``` User Config vLLM Args SGLang Args ───────────────────────────────────────────────────────────────────────────────── tp: 4 → --tensor-parallel-size 4 --tp-size 4 pp: 1 → --pipeline-parallel-size 1 (not supported) dp: 2 → --distributed-executor-backend ray --dp-size 2 --num-gpu-blocks-override ``` **Grid Expansion with `__parallel__` Prefix:** ```json { "parallel_config": { "tp": [2, 4], "pp": 1, "dp": [1, 2] } } ``` Expands to 4 experiments (2×2). **Frontend Integration:** - **`ParallelConfigForm.tsx`** (similar to QuantizationConfigForm) - Preset mode with 18 runtime-specific presets - Custom mode with constraint validation - GPU requirement calculation - Real-time parameter preview ### Part 2: GPU-Aware Optimization #### 2.1 Per-GPU Efficiency Metrics ✅ **Problem Solved:** Traditional throughput metrics favor higher parallelism blindly. A configuration using 8 GPUs with 100 tokens/s looks better than 2 GPUs with 60 tokens/s, but the latter is 2.4× more efficient per GPU. **Solution: Per-GPU Throughput Calculation** **Formula:** ``` per_gpu_throughput = total_throughput / gpu_count ``` **Example Comparison:** ``` Config A: TP=2, throughput=661.36 tokens/s → 330.68 tokens/s/GPU Config B: TP=4, throughput=628.22 tokens/s → 157.06 tokens/s/GPU Winner: Config A (2.1× more efficient) ``` **Implementation:** - GPU info recorded in database: `gpu_info` JSON field - Contains: `model`, `count`, `device_ids`, `world_size` - Automatic calculation during scoring - Frontend displays both total and per-GPU metrics #### 2.2 GPU Information Tracking ✅ **Database Schema:** ```python gpu_info = { "model": "NVIDIA A100", "count": 2, "device_ids": [0, 1], "world_size": 2 } ``` **Recording Logic:** - Captured during experiment setup - Stored in `experiments.gpu_info` column (JSON) - Used for per-GPU metric calculation - Displayed in results table #### 2.3 Enhanced Result Visualization ✅ **Frontend Enhancements:** - Added "GPUs" column to experiment table - Display: `2 (A100)` or `4 (H100)` - Tooltip shows device IDs and world size - Per-GPU throughput column - Color coding for efficiency comparison **Charts:** - Per-GPU efficiency scatter plot - GPU count vs throughput line chart - Pareto frontier with GPU cost consideration ### Technical Achievements **Code Additions:** - **Quantization System**: 800 lines (mapper + integration) - **Parallel System**: 800 lines (mapper + integration) - **GPU Tracking**: 200 lines (backend + frontend) - **Frontend Forms**: 1,200 lines (Quant + Parallel components) - **Documentation**: 3 new docs (QUANTIZATION, PARALLEL, GPU_TRACKING) **Total**: ~3,000 lines of new production code **Functionality:** - ✅ Support for 3 inference runtimes (vLLM, SGLang, TensorRT-LLM) - ✅ 5 quantization presets + custom mode - ✅ 18 parallelism presets (6 per runtime) - ✅ Automatic runtime-specific CLI mapping - ✅ Constraint validation and conflict resolution - ✅ Per-GPU efficiency metrics - ✅ GPU information persistence **Documentation:** - `docs/QUANTIZATION_CONFIGURATION.md` - `docs/PARALLEL_CONFIGURATION.md` - `docs/GPU_TRACKING.md` --- --- ## 🎉 Milestone 4: UI/UX Polish, SLO Filtering & Documentation **Date**: 2025-12-01 (tag: `milestone-4`) **Status**: ✅ COMPLETED **Timeline**: 2025-11-15 → 2025-12-01 **Objective**: Transform from functional prototype to production-ready platform with professional UI, SLO filtering, and comprehensive documentation ### Key Accomplishments #### 4.1 Frontend UI/UX Enhancements ✅ - [x] Real-time WebSocket updates (<100ms latency) - [x] YAML import/export for task configurations - [x] Auto-update notification system (GitHub releases) - [x] Enhanced result visualization with SLO reference lines - [x] Custom logo and branding (SVG icon + favicon) - [x] Protected completed tasks (hidden edit/cleanup buttons) - [x] Clickable task names for details view - [x] UI refinements (width-limited controls, placeholder cleanup) **YAML Import/Export System:** ```typescript // Import: Full-page drag-and-drop zone populateForm(config)} /> // Export: Single-click download ``` **Auto-Update Notifications:** - Automatic version checking against GitHub releases - Notification banner when updates available - Build timestamp tracking: `v1.0.0+20251203T195130Z` #### 4.2 SLO-Aware Benchmarking ✅ - [x] Per-batch SLO filtering (filter non-compliant batches before aggregation) - [x] Graceful OOM handling (partial success support) - [x] Visual SLO indicators (reference lines on performance charts) - [x] Detailed compliance logging per batch **Per-Batch Filtering Example:** ``` [Benchmark] Filtering 4 batches by SLO compliance... [Benchmark] ✗ Batch concurrency=8 violated SLO: {'p90': {'threshold': 5.0, 'actual': 6.2}} [Benchmark] ✓ 3/4 batches passed SLO [Benchmark] Max throughput: 145.2 req/s (from 3 SLO-compliant batches) ``` **Graceful Degradation:** - Experiments succeed if at least one batch completes - Partial results better than no results - OOM at high concurrency doesn't invalidate low-concurrency data #### 4.3 Documentation Refinement ✅ - [x] Aggressive cleanup (66 → 15 files, 77% reduction) - [x] Content merges (GENAI_BENCH_LOGS → TROUBLESHOOTING, etc.) - [x] Reference fixes (zero broken links across all docs) - [x] Focus on long-term maintainability **15 Essential Files Kept:** - **User Guides (4)**: QUICKSTART, DOCKER_MODE, OME_INSTALLATION, TROUBLESHOOTING - **Architecture (3)**: DEPLOYMENT_ARCHITECTURE, GPU_TRACKING, ROADMAP - **Features (4)**: BAYESIAN_OPTIMIZATION, SLO_SCORING, PARALLEL_EXECUTION, WEBSOCKET_IMPLEMENTATION - **Configuration (4)**: UNIFIED_QUANTIZATION_PARAMETERS, PARALLEL_PARAMETERS, PRESET_QUICK_REFERENCE, PVC_STORAGE #### 4.4 Bug Fixes & Infrastructure ✅ - [x] Template parameter fix (OME InferenceService: `params=parameters` instead of `**parameters`) - [x] API proxy configuration (fixed hardcoded URLs in service files) - [x] Pydantic settings fix (added `extra='ignore'` for VITE_* variables) #### 4.5 Documentation Website ✅ - [x] Sphinx documentation with Furo theme - [x] GitHub Actions workflow for automated deployment - [x] MyST Parser for Markdown support - [x] Auto-generated API documentation (autodoc) - [x] Organized directory structure (getting-started, user-guide, features, api) - [x] Dark mode support with custom branding ### Technical Achievements **Code Statistics:** - **Frontend**: ~800 lines (YAML I/O, auto-update, SLO visualization) - **Backend**: ~400 lines (SLO filtering, OOM handling, fixes) - **Total New Code**: ~1,200 lines - **Documentation**: Sphinx site with 15+ pages, GitHub Actions CI/CD **Components Created:** - `TaskYAMLImport.tsx` (180 lines) - Drag-and-drop import with validation - `TaskYAMLExport.tsx` (80 lines) - Single-click YAML export - `UpdateNotification.tsx` (110 lines) - Auto-update banner with GitHub integration - `versionService.ts` (60 lines) - Version checking service - `check_batch_slo_compliance()` (133 lines) - Per-batch SLO validation - `docs/conf.py` - Sphinx configuration with Furo theme - `.github/workflows/docs.yml` - GitHub Pages deployment workflow **Files Modified:** - Frontend: Tasks.tsx, TaskResults.tsx, NewTask.tsx, Logo.tsx (10+ files) - Backend: optimizer.py, direct_benchmark_controller.py, config.py (5 files) - Documentation: README.md, CLAUDE.md, ROADMAP.md (reference fixes) ### Performance Impact | Metric | Before M4 | After M4 | Improvement | |--------|-----------|----------|-------------| | **UI Response Time** | 2-5s polling | <100ms WebSocket | 20-50x faster | | **Config Reusability** | Manual JSON edit | YAML import/export | Instant | | **Update Awareness** | Manual check | Auto-notification | Automatic | | **SLO Visibility** | Numbers only | Visual ref lines | Intuitive | | **OOM Resilience** | Experiment fails | Partial success | Graceful | | **Doc Files** | 66 files | 15 files | 77% reduction | ### Impact Summary **For Users:** - ✅ Faster feedback: WebSocket real-time updates - ✅ Better visualization: SLO reference lines, enhanced charts - ✅ Config management: YAML import/export workflow - ✅ Stay updated: Automatic version checking - ✅ Fewer failures: Graceful OOM handling - ✅ Cleaner UI: Protected actions, clickable names - ✅ Professional branding: Custom logo and favicon **For Operators:** - ✅ Easier troubleshooting: Per-batch SLO logging - ✅ Better resource utilization: Partial success support - ✅ Clearer documentation: 15 essential files vs 66 - ✅ No broken links: All references verified **For Developers:** - ✅ Maintainable docs: Focused, merged content - ✅ Working examples: Templates verified - ✅ Clear architecture: Essential docs only - ✅ Build tracking: Timestamp in version display --- ## 🎉 Milestone 5: Agent System & Local Deployment Mode **Date**: 2025-12-22 (tag: `milestone-5`) **Status**: ✅ COMPLETED **Timeline**: 2025-12-01 → 2025-12-22 **Objective**: Introduce LLM-powered Agent System for conversational task management and add Local Deployment Mode for faster development iteration ### Key Accomplishments #### 5.1 Agent Chat Interface ✅ - [x] Full-featured chat UI with streaming markdown responses (`/agent`) - [x] Session management with persistent conversation history - [x] Editable session titles with auto-generation from first message - [x] IndexedDB-based message storage with backend sync - [x] Server-Sent Events (SSE) for real-time streaming responses **Architecture:** ``` ┌─────────────────────┐ SSE Stream ┌──────────────────┐ │ AgentChat.tsx │◄───────────────────►│ /api/agent/chat │ │ (React Frontend) │ │ (FastAPI) │ └─────────────────────┘ └────────┬─────────┘ │ │ │ Markdown │ OpenAI API ▼ ▼ ┌─────────────────────┐ ┌──────────────────┐ │ StreamingMarkdown │ │ LLM Backend │ │ (react-markdown) │ │ (Configurable) │ └─────────────────────┘ └──────────────────┘ ``` #### 5.2 Tool Execution Framework ✅ - [x] Authorization system with `AuthorizationScope` enum (none, privileged, dangerous) - [x] Privileged tools require user approval before execution - [x] Auto-execute pending tools after authorization granted - [x] Clear visual indicators for tool status in chat UI **Tool Categories Implemented:** | Category | Tools | Authorization | |----------|-------|---------------| | Task Management | `create_task`, `start_task`, `get_task_status`, `get_task_logs` | None | | Worker Control | `restart_arq_worker` | Privileged | | System Utilities | `sleep`, `get_current_time` | None | | GitHub Integration | `search_github_issues`, `create_github_issue`, `comment_github_issue` | None | | HuggingFace CLI | `hf_cache_scan`, `hf_download`, `hf_repo_info` | None | | Experiment Analysis | `get_experiment_logs` | None | #### 5.3 Agent Backend Architecture ✅ - [x] LangChain framework for flexible model support - [x] Support for Claude (Anthropic) and open-source models - [x] Max iterations increased from 10 to 100 for complex tasks - [x] Automatic tool result handling for multi-step operations **API Endpoints:** - `POST /api/agent/chat` - SSE streaming chat endpoint - `GET /api/agent/sessions` - List all sessions - `POST /api/agent/sessions` - Create new session - `PUT /api/agent/sessions/{id}` - Update session title - `DELETE /api/agent/sessions/{id}` - Delete session - `GET /api/agent/sessions/{id}/messages` - Get session messages #### 5.4 Streaming Markdown Component ✅ - [x] Paragraph-aware streaming that preserves atomic elements - [x] GitHub Flavored Markdown support (tables, code blocks, task lists) - [x] Copy buttons for code blocks and tables (copies source, not rendered HTML) - [x] Tailwind typography styling for consistent appearance #### 5.5 Local Deployment Mode ✅ - [x] New `LocalController` for subprocess-based model execution - [x] Direct vLLM/SGLang server launch via `python -m` commands - [x] Automatic port allocation (30000-30100 range) - [x] Process lifecycle management with graceful shutdown - [x] Log capture and streaming to task log files **Runtime Support:** - vLLM local environment: `.venv-vllm/` with CUDA 12 - SGLang local environment: `.venv-sglang/` (SM86 limitation) - Automatic environment detection and activation #### 5.6 Dataset URL Support ✅ - [x] Remote URL dataset loading (CSV, JSONL, compressed archives) - [x] Automatic format detection and conversion - [x] URL-hash based caching in `~/.local/share/autotuner/datasets/` - [x] Deduplication option for prompt datasets - [x] genai-bench submodule updated to fork with URL support #### 5.7 Additional Improvements ✅ - [x] HuggingFace offline mode: Fixed cache path handling for air-gapped environments - [x] GitHub Issue #3 fix: Resolved `analyze_slo_violations()` AttributeError - [x] Foldable experiments list: Collapsible UI for large experiment sets - [x] Comprehensive parameter presets: Runtime-specific vLLM and SGLang presets - [x] Project rebranding: Logo redesign inspired by Novita.ai style ### Technical Achievements **Code Statistics:** - **Agent Frontend**: ~800 lines (AgentChat.tsx, StreamingMarkdown.tsx, AgentMessage.tsx) - **Agent Backend**: ~600 lines (routes/agent.py, schemas/agent.py, services/agent_service.py) - **Tools**: ~500 lines (task_tools.py, worker_tools.py, github_tools.py, hf_tools.py) - **Local Mode**: ~400 lines (local_controller.py, autotuner_worker.py updates) - **Dataset**: ~200 lines (dataset_controller.py) - **Total New Code**: ~2,500 lines **Key Components Created:** - `AgentChat.tsx` (~400 lines) - Main chat interface with message history - `StreamingMarkdown.tsx` (~350 lines) - Paragraph-aware markdown renderer - `LocalController` (~300 lines) - Subprocess-based deployment controller - `DatasetController` (~200 lines) - Remote dataset fetching and caching - 12 agent tools across 6 categories ### Performance Impact | Metric | Before M5 | After M5 | Improvement | |--------|-----------|----------|-------------| | **Task Creation** | Form-only | Conversational + Form | Natural language | | **Deployment Setup** | Docker/K8s required | Local subprocess option | Faster iteration | | **Dataset Loading** | Local files only | Remote URL support | More flexible | | **Agent Iterations** | N/A | Up to 100 steps | Complex workflows | ### Impact Summary **For Users:** - ✅ Natural language task creation and management via Agent - ✅ Faster local development without Docker/Kubernetes overhead - ✅ Remote dataset support for production workload testing - ✅ GitHub integration for issue tracking and collaboration - ✅ HuggingFace integration for model management **For Developers:** - ✅ Local deployment mode for rapid iteration - ✅ Extensible tool framework for custom integrations - ✅ Comprehensive logging and debugging support - ✅ Session-based conversation history ## Current Status: Production-Ready v0.2.0 ✅ ### What Works Today **Core Functionality:** - ✅ Grid search, random search, Bayesian optimization (Optuna TPE) - ✅ Docker mode deployment (recommended) - ✅ Kubernetes/OME mode deployment - ✅ **Local mode deployment** (subprocess-based, no containers) - ✅ Runtime-agnostic quantization configuration (vLLM, SGLang, TensorRT-LLM) - ✅ Runtime-agnostic parallelism configuration (18 presets) - ✅ SLO-aware scoring with exponential penalties - ✅ GPU intelligent scheduling with per-GPU efficiency metrics - ✅ Checkpoint mechanism for fault tolerance - ✅ Multi-objective Pareto optimization - ✅ Model caching optimization - ✅ Full-stack web UI with real-time monitoring - ✅ **Agent System** with LLM-powered conversational interface - ✅ **Remote dataset support** via URL fetching **Performance:** - ✅ 28 tasks executed successfully - ✅ 408 total experiments run - ✅ 312 successful experiments (76.5% success rate) - ✅ Average experiment duration: 303.6 seconds - ✅ Bayesian optimization: 80-87% reduction vs grid search **Infrastructure:** - ✅ FastAPI backend with async support - ✅ React 18 frontend with TypeScript - ✅ WebSocket real-time communication (backend + frontend) - ✅ SQLite database with WAL mode (XDG-compliant location) - ✅ Redis task queue with ARQ worker - ✅ Docker container management - ✅ Kubernetes resource management - ✅ **LangChain-based agent framework** with 12 tools --- ## Future Roadmap ### 🔵 Phase 6: Distributed Architecture & Parallel Execution (Planned) **Priority**: High **Effort**: 3-4 weeks **Value**: ⭐⭐⭐⭐⭐ #### 6.1 Distributed Worker Architecture - [ ] **Central Web Manager**: Single control plane for multiple workers - [ ] **Worker Registration**: Auto-discovery and registration via Redis - [ ] **Heartbeat Monitoring**: Worker health checks and failure detection - [ ] **Work Stealing**: Dynamic task redistribution across workers - [ ] **Worker Pools**: Group workers by capabilities (GPU type, region, etc.) **Architecture Design:** ``` ┌─────────────────────┐ │ Central Web Manager│ │ (FastAPI + Redis) │ └──────────┬──────────┘ │ ┌───────────────────┼───────────────────┐ │ │ │ ┌──────▼──────┐ ┌──────▼──────┐ ┌──────▼──────┐ │ Worker 1 │ │ Worker 2 │ │ Worker 3 │ │ 8×A100 GPUs│ │ 8×H100 GPUs│ │ 4×L40S GPUs│ │ Node: gpu-1│ │ Node: gpu-2│ │ Node: gpu-3│ └─────────────┘ └─────────────┘ └─────────────┘ ``` **Components:** - **Manager**: - Task queue management - Worker registry with capabilities - Experiment distribution algorithm - Result aggregation service - Centralized logging - **Worker**: - Capability advertisement (GPU count, model, memory) - Experiment execution engine - Result reporting via REST API - Local checkpoint storage - Worker-level parallelism (max_parallel per worker) **Benefits:** - **Horizontal Scaling**: Add workers to increase throughput - **Resource Isolation**: Different workers for different GPU types - **Fault Tolerance**: Worker failures don't affect others - **Geographic Distribution**: Workers in different data centers - **Cost Optimization**: Use spot instances for workers **Implementation Plan:** 1. Week 1: Worker registration and discovery 2. Week 2: Task distribution and scheduling 3. Week 3: Result aggregation and monitoring 4. Week 4: Frontend dashboard and testing #### 6.2 Advanced Parallel Execution - [ ] User-configurable max_parallel setting (currently hardcoded at 5) - [ ] Per-worker parallelism configuration - [ ] Dynamic parallelism based on GPU availability - [ ] Experiment dependency graph - [ ] Priority-based scheduling (high/normal/low priority tasks) - [ ] Resource reservation system (reserve GPUs for specific tasks) **Benefits:** - Faster task completion (5-10x speedup with multiple workers) - Better GPU utilization across cluster - Configurable resource allocation per task - Fair scheduling with priority queues #### 6.3 Task Sharding & Load Balancing - [ ] Automatic task splitting across workers - [ ] Load-aware scheduling (balance by GPU count) - [ ] Locality-aware scheduling (prefer same-node experiments) - [ ] Cross-worker result aggregation - [ ] Consistent hashing for worker selection --- ### 🔵 Phase 7: Advanced Optimization & Runtime Features (Planned) **Priority**: Medium **Effort**: 2-4 weeks **Value**: ⭐⭐⭐⭐ #### 7.0 Agent Charting Tool - [ ] Add chart generation tool for Agent to visualize experiment results - [ ] Candidates: Matplotlib (static images), Plotly (interactive HTML) - [ ] Chart types: bar charts, line plots, scatter plots, heatmaps - [ ] Use cases: - Compare throughput/latency across experiments - Visualize parameter sensitivity - Generate Pareto frontier plots - Create SLO compliance charts - [ ] Output: Save charts to files or display inline in chat **Implementation Options:** | Library | Pros | Cons | |---------|------|------| | Matplotlib | Simple, widely used, static images | Not interactive | | Plotly | Interactive, HTML export, beautiful | Larger dependency | | Seaborn | Statistical plots, built on Matplotlib | Limited interactivity | #### 7.1 Runtime-Specific Optimizations **SGLang Radix Cache Management:** - [ ] **Reset radix cache at experiment start**: Clear cache before each experiment - [ ] **Benchmark purity**: Ensure fair comparison without cache pollution - [ ] **Cache warming option**: Optional pre-fill for production scenarios - [ ] **Cache statistics tracking**: Monitor hit rate and memory usage **Implementation:** ```python # Before each experiment def reset_sglang_radix_cache(container_id: str): """Reset SGLang radix cache via HTTP API""" response = requests.post( f"http://localhost:{port}/reset_cache", json={"cache_type": "radix"} ) logger.info(f"Radix cache reset: {response.json()}") ``` **Benefits:** - Fair experiment comparisons (no cached KV states) - Reproducible benchmark results - Accurate TTFT measurements - Option to test both cold-start and warm-cache scenarios **Additional Runtime Features:** - [ ] vLLM prefix caching control - [ ] TensorRT-LLM engine rebuild triggers - [ ] Runtime-specific profiling hooks - [ ] Memory defragmentation between experiments #### 7.2 Multi-Fidelity Optimization - [ ] Progressive benchmark complexity - [ ] Early stopping for poor configurations - [ ] Hyperband algorithm integration - [ ] Adaptive resource allocation - [ ] Quick validation runs (low concurrency, short duration) - [ ] Full benchmark only for promising configs #### 7.3 Transfer Learning - [ ] Model similarity detection (architecture, size, quantization) - [ ] Cross-model parameter transfer - [ ] Historical performance database (SQLite → PostgreSQL) - [ ] Meta-learning for initialization - [ ] Warmstart Bayesian optimization with historical data #### 7.4 Enhanced Multi-Objective Optimization - [ ] NSGA-II algorithm for Pareto frontier - [ ] 3+ objective support (latency, throughput, cost, energy, memory) - [ ] Interactive trade-off exploration - [ ] User preference learning - [ ] Weighted objective combination - [ ] Pareto frontier approximation with surrogate models --- #### 7.5 Enhanced Export & Data Portability - [ ] Export experiment results to CSV - [ ] Export results to JSON for analysis - [ ] Export results to Excel (.xlsx) format - [ ] Batch import multiple task configs - [ ] Template library (export/import task templates) - [ ] Share configurations via file or URL - [ ] YAML parser with schema validation - [ ] Automatic conversion between JSON ↔ YAML - [ ] YAML syntax highlighting in frontend **Benefits:** - Data portability for external analysis tools (Excel, Python, R) - Batch operations for managing multiple tasks - Configuration templates for common use cases - Team collaboration via shared configs - Integration with data science workflows **Export Formats:** - Experiment Results: `.csv`, `.json`, `.xlsx` - Task Configs: `.yaml`, `.json` - Templates: Zip archive with metadata #### 7.6 Custom Dataset Support for GenAI-Bench - [ ] Fetch datasets from user-specified URLs - [ ] Support CSV format parsing - [ ] Support JSONL (JSON Lines) format parsing - [ ] Conversion script to genai-bench format - [ ] Dataset validation and preprocessing - [ ] Automatic schema detection - [ ] Support for custom prompt templates - [ ] Integration with task configuration **Supported Input Formats:** ```csv # CSV format prompt,max_tokens,temperature "Explain quantum computing",100,0.7 "Write a story about AI",200,0.9 ``` ```jsonl # JSONL format {"prompt": "Explain quantum computing", "max_tokens": 100, "temperature": 0.7} {"prompt": "Write a story about AI", "max_tokens": 200, "temperature": 0.9} ``` **Conversion Pipeline:** ```python # Download and convert custom dataset python scripts/prepare_custom_dataset.py \ --url https://example.com/dataset.csv \ --format csv \ --output ./data/custom_benchmark.json # Use in task configuration { "benchmark": { "custom_dataset": "./data/custom_benchmark.json", "task": "text-to-text" } } ``` **Features:** - HTTP/HTTPS URL fetching with authentication support - Automatic format detection (CSV/JSONL) - Field mapping configuration (map CSV columns to genai-bench schema) - Data validation (check required fields, token limits) - Sampling strategies (random, stratified, sequential) - Dataset caching to avoid re-downloading **Benefits:** - Use real production workloads for benchmarking - Test with domain-specific prompts - Reproducible benchmarks with versioned datasets - Support for custom evaluation scenarios - Integration with existing data pipelines **GenAI-Bench Schema Mapping:** ```python # Required fields for genai-bench { "prompt": str, # Input text "output_len": int, # Expected output length "input_len": int, # Input length (auto-calculated if not provided) "temperature": float, # Optional: sampling temperature "top_p": float, # Optional: nucleus sampling "max_tokens": int # Optional: max output tokens } ``` **Implementation Components:** 1. **Dataset Fetcher** (`src/utils/dataset_fetcher.py`) - URL download with retries - Authentication headers support - Local file caching 2. **Format Converters** (`src/utils/dataset_converters/`) - `csv_converter.py`: CSV → genai-bench JSON - `jsonl_converter.py`: JSONL → genai-bench JSON - Base converter interface for extensibility 3. **Validation Module** (`src/utils/dataset_validator.py`) - Schema validation - Token limit checking - Duplicate detection 4. **CLI Tool** (`scripts/prepare_custom_dataset.py`) - Standalone conversion utility - Preview mode (show first N records) - Statistics reporting ### 🔵 Phase 8: Enterprise Features (Planned) **Priority**: Low-Medium **Effort**: 3-5 weeks **Value**: ⭐⭐⭐ #### 8.1 Multi-User Support - [ ] User authentication (OAuth2) - [ ] Role-based access control (RBAC) - [ ] Task ownership and sharing - [ ] Team workspaces #### 8.2 Advanced Monitoring - [ ] Prometheus metrics exporter - [ ] Grafana dashboard templates - [ ] Alert rules for failures - [ ] Performance analytics #### 8.3 CI/CD Integration - [ ] GitHub Actions workflow - [ ] Automated benchmarking on PR - [ ] Performance regression detection - [ ] Automated deployment #### 8.4 Cloud Deployment - [ ] AWS deployment guide (EKS) - [ ] GCP deployment guide (GKE) - [ ] Azure deployment guide (AKS) - [ ] Terraform modules - [ ] Helm charts --- ### 🟢 Phase 9: Research & Innovation (Future) **Priority**: Low **Effort**: Variable **Value**: ⭐⭐⭐ #### 9.1 Auto-Scaling Integration - [ ] Horizontal Pod Autoscaler (HPA) optimization - [ ] Vertical Pod Autoscaler (VPA) tuning - [ ] Knative Serving integration - [ ] Cost-aware scaling #### 9.2 Advanced Benchmarking - [ ] Custom benchmark scenario editor - [ ] Real-world traffic replay - [ ] Synthetic load generation - [ ] Multi-modal benchmarking #### 9.3 Model-Specific Optimization - [ ] Architecture-aware parameter tuning - [ ] Quantization-aware optimization - [ ] Attention mechanism tuning - [ ] Memory layout optimization --- ## Maintenance & Technical Debt ### Recently Fixed (2025/11/25) ✅ **Database Schema Mismatch:** - ❌ Missing columns: `clusterbasemodel_config`, `clusterservingruntime_config`, `created_clusterbasemodel`, `created_clusterservingruntime` - ✅ Fixed: Added ALTER TABLE statements - ✅ Verified: All endpoints working, HTTP 500 errors resolved ### Known Issues 1. **Worker Restart Required** - ⚠️ ARQ worker doesn't hot-reload code changes - Manual restart needed after editing `orchestrator.py`, `controllers/` - **Future**: Add file watcher for auto-restart 2. **Polling-Based UI Updates** - ⚠️ Frontend polls every 2-5 seconds - Inefficient for idle states - **Future**: WebSocket migration (Phase 4) ### Technical Improvements 1. **Testing Coverage** - Current: Manual testing only - Future: Unit tests, integration tests, E2E tests - Target: 80% code coverage 2. **Error Handling** - Current: Basic try-catch blocks - Future: Comprehensive error taxonomy, retry logic, graceful degradation 3. **Database Migration** - Current: Manual SQL commands - Future: Alembic migrations - Version-controlled schema changes --- ## Success Metrics ### Current Performance (Milestone 5) | Metric | Value | Target | |--------|-------|--------| | **Total Tasks** | 28 | - | | **Total Experiments** | 408 | - | | **Success Rate** | 76.5% | >80% | | **Avg Experiment Duration** | 303.6s | <300s | | **Bayesian Efficiency** | 80-87% reduction | >70% | | **UI Response Time** | <200ms | <100ms | | **API Latency (P95)** | <500ms | <200ms | | **Supported Runtimes** | 3 (vLLM, SGLang, TRT-LLM) | - | | **Deployment Modes** | 3 (Docker, OME, Local) | - | | **Agent Tools** | 12 (across 6 categories) | - | ### Future Targets (v2.0) - **Experiment Success Rate**: >90% - **Avg Experiment Duration**: <240s (20% improvement) - **UI Response Time**: <100ms (WebSocket) - **Concurrent Experiments**: >10 parallel - **Cost Reduction**: 50% fewer experiments vs grid search - **Multi-Runtime Support**: Add Triton, others --- **End of Roadmap** | Last Updated: 2025/12/22 | Version: 0.2.0 (Milestone 5 Complete)