torsh-cli 0.1.2

# ToRSh CLI Implementation Roadmap

## Current Status: v0.1.2 - Foundation Complete, Active Implementation Phase

### Overview
The torsh-cli crate has an **excellent architectural foundation** with comprehensive command structure and CLI framework. We are actively implementing real functionality to replace stubs and mock implementations.

### Recent Progress (v0.1.1)
- ✅ Basic model analysis with format detection (ToRSh, PyTorch, ONNX, TensorFlow, TFLite)
- ✅ Enhanced system information and device detection
- ✅ Real hardware detection for CUDA, Metal, ROCm, Vulkan, OpenCL
- ✅ SciRS2 POLICY compliant implementations with unified access patterns
- 🚧 Model validation with realistic inference simulation using SciRS2
- 🚧 Enhanced model types and structures

### Architecture ✅ (Complete)
- [x] Comprehensive CLI structure with clap 4.x
- [x] Modular command organization
- [x] Configuration management system
- [x] Progress indicators and user feedback
- [x] Multiple output formats (JSON, YAML, table)
- [x] Async runtime with tokio
- [x] Logging and tracing infrastructure
- [x] Shell completion support
- [x] Feature-gated dependencies for optional components

## Phase 1: Core Command Implementation 🚧 (High Priority)

### Model Operations (Priority: **CRITICAL**)

#### Model Conversion ⚠️ (Stub Implementation)
- [ ] **Real PyTorch → ToRSh conversion**
  - [ ] Load PyTorch models using torchvision-style loaders
  - [ ] Map PyTorch tensor operations to torsh-tensor
  - [ ] Convert PyTorch nn.Module to torsh-nn Module
  - [ ] Preserve model metadata and state
  - [ ] Handle different PyTorch versions (1.x → 2.x)

- [ ] **ONNX Import/Export**
  - [ ] ONNX → ToRSh conversion using torsh-fx graph transformation
  - [ ] ToRSh → ONNX export for interoperability
  - [ ] Operator mapping and validation
  - [ ] Support for custom operators

- [ ] **TensorFlow/TFLite Support**
  - [ ] TensorFlow SavedModel → ToRSh conversion
  - [ ] TFLite model support for edge deployment
  - [ ] Graph optimization during conversion

#### Model Optimization ⚠️ (Mock Implementation)
- [ ] **JIT Compilation Integration**
  - [ ] Connect to torsh-jit for kernel fusion
  - [ ] Graph optimization passes
  - [ ] Target-specific optimizations (CPU SIMD, CUDA kernels)
  - [ ] Performance benchmarking integration

- [ ] **Mixed Precision Optimization**
  - [ ] Automatic mixed precision (AMP) setup
  - [ ] FP16/BF16 conversion strategies
  - [ ] Accuracy validation during precision reduction

#### Model Quantization ⚠️ (Stub Implementation)
- [ ] **torsh-quantization Integration**
  - [ ] Dynamic quantization implementation
  - [ ] Static quantization with calibration dataset
  - [ ] Quantization-aware training (QAT) setup
  - [ ] INT8/INT4 precision support
  - [ ] Per-channel vs per-tensor quantization

- [ ] **Quantization Validation**
  - [ ] Accuracy degradation measurement
  - [ ] Performance improvement validation
  - [ ] Model size reduction reporting

#### Model Inspection ⚠️ (Basic Implementation)
- [ ] **Real Model Analysis**
  - [ ] Connect to torsh-tensor for actual model loading
  - [ ] Real parameter counting and memory analysis
  - [ ] Computational complexity analysis using torsh-profiler
  - [ ] Model architecture visualization
  - [ ] Layer-wise statistics and profiling

#### Model Validation ⚠️ (Mock Implementation)
- [ ] **Real Validation Pipeline**
  - [ ] Load actual validation datasets using torsh-data
  - [ ] Run inference on real models
  - [ ] Accuracy metric computation
  - [ ] Performance benchmarking
  - [ ] Regression testing against reference models

### Training Commands 📋 (Medium Priority)

#### Training Management ⚠️ (Stub Implementation)
- [ ] **torsh-optim Integration**
  - [ ] Real optimizer setup (Adam, AdamW, SGD, etc.)
  - [ ] Learning rate scheduling
  - [ ] Gradient clipping and accumulation
  - [ ] Mixed precision training

- [ ] **torsh-data Integration**
  - [ ] DataLoader configuration and management
  - [ ] Dataset preprocessing pipelines
  - [ ] Parallel data loading with worker management
  - [ ] Data augmentation integration

- [ ] **Distributed Training Setup**
  - [ ] Connect to torsh-distributed for DDP setup
  - [ ] Multi-GPU training coordination
  - [ ] Process group initialization
  - [ ] Gradient synchronization

#### Checkpoint Management 📋
- [ ] **State Management**
  - [ ] Model state saving and loading
  - [ ] Optimizer state persistence
  - [ ] Training metrics and curves storage
  - [ ] Resume training from checkpoints

### Dataset Operations 📋 (Medium Priority)

#### Dataset Management ⚠️ (Stub Implementation)
- [ ] **torsh-data Integration**
  - [ ] Dataset downloading and caching
  - [ ] Common dataset support (ImageNet, CIFAR, etc.)
  - [ ] Custom dataset validation and preprocessing
  - [ ] Dataset statistics and analysis

- [ ] **Data Pipeline Validation**
  - [ ] Data integrity checks
  - [ ] Preprocessing pipeline testing
  - [ ] Performance profiling of data loading

### Hub Integration 📋 (Low Priority)

#### Model Hub Operations ⚠️ (Stub Implementation)
- [ ] **torsh-hub Integration**
  - [ ] Model uploading and downloading
  - [ ] Version management and metadata
  - [ ] Authentication and authorization
  - [ ] Model discovery and search

## Phase 2: Advanced Features 📋 (Medium Priority)

### Benchmarking and Profiling 🚧

#### Performance Analysis ⚠️ (Mock Implementation)
- [ ] **torsh-profiler Integration**
  - [ ] Real performance profiling with CPU/GPU metrics
  - [ ] Memory usage analysis
  - [ ] Bottleneck identification
  - [ ] Comparative benchmarking

- [ ] **torsh-benches Integration**
  - [ ] Standard benchmark suite execution
  - [ ] Custom benchmark creation
  - [ ] Performance regression testing

### Development Tools 📋

#### Debugging and Analysis ⚠️ (Basic Implementation)
- [ ] **Code Generation Tools**
  - [ ] Model architecture code generation
  - [ ] Training script templates
  - [ ] Custom operator scaffolding

- [ ] **Testing Utilities**
  - [ ] Model unit test generation
  - [ ] Gradient checking utilities
  - [ ] Numerical stability analysis

### System Information ✅ (Partially Complete)

#### Enhanced Diagnostics 🚧
- [ ] **Real Hardware Detection**
  - [ ] CUDA device enumeration and capabilities
  - [ ] Metal device support detection
  - [ ] CPU SIMD capabilities analysis
  - [ ] Memory availability and optimization suggestions

## Phase 3: Advanced Integration 📋 (Low Priority)

### External Tool Integration 📋
- [ ] **TensorBoard Integration**
  - [ ] Training metrics logging
  - [ ] Model graph visualization
  - [ ] Hyperparameter tracking

- [ ] **MLflow Integration**
  - [ ] Experiment tracking and management
  - [ ] Model registry integration
  - [ ] Automated ML pipeline integration

### Deployment Features 📋
- [ ] **Model Serving**
  - [ ] REST API server generation
  - [ ] gRPC service setup
  - [ ] WebAssembly compilation for browser deployment

- [ ] **Edge Deployment**
  - [ ] Mobile optimization (iOS/Android)
  - [ ] Embedded system optimization
  - [ ] WASM target compilation

## Technical Implementation Notes

### SciRS2 Policy Compliance ✅
- **CRITICAL**: All implementations MUST use SciRS2 ecosystem:
  - `scirs2_autograd::ndarray` for array operations (never direct ndarray)
  - `scirs2_core::random` for random number generation (never direct rand)
  - Full utilization of SciRS2's advanced features (SIMD, GPU, memory-efficient ops)

### Current Architecture Strengths ✅
- Modern async/await patterns with tokio
- Excellent error handling with anyhow and thiserror
- Comprehensive CLI argument parsing with clap 4.x
- Good separation of concerns with modular command structure
- Feature-gated dependencies for optional functionality
- Progress reporting and user feedback systems

### Implementation Strategy
1. **Start with Model Operations**: These are most critical for user adoption
2. **Focus on torsh-tensor Integration**: Most commands need real tensor operations
3. **Connect to Real Backends**: Replace mock implementations with actual functionality
4. **Maintain CLI Contract**: Keep current argument structure and behavior
5. **Add Comprehensive Testing**: Unit and integration tests for all commands

## Success Metrics

### Completion Targets
- **Phase 1**: 80% of core commands functional (model, basic training)
- **Phase 2**: 95% of advanced features implemented
- **Phase 3**: 100% production-ready with external integrations

### Quality Standards
- All commands must handle real ToRSh models and tensors
- Error handling should provide actionable user guidance
- Performance should be comparable to native PyTorch tools
- Memory usage should be optimized for large model operations

### Testing Requirements
- Unit tests for all command argument parsing
- Integration tests with actual models and datasets
- Performance regression tests for critical operations
- CLI behavior tests for user experience validation

## Current Blockers and Dependencies

### High Priority Dependencies
1. **torsh-tensor stability**: Core tensor operations must be reliable
2. **torsh-autograd maturity**: Training commands depend on autograd functionality
3. **torsh-quantization completion**: Quantization commands need real implementation
4. **torsh-hub API**: Hub integration depends on service availability

### Technical Debt
- Replace all `tokio::time::sleep` mock implementations
- Implement real file I/O for model loading/saving
- Add proper error handling for malformed models
- Create comprehensive test coverage for all commands

This roadmap prioritizes **real functionality over mock implementations** while maintaining the excellent CLI architecture already established.

## Immediate Action Items (Next Sprint)

### 🔥 Critical Priority (Week 1-2)

#### 1. Model Serialization & Loading
- [ ] Implement real ToRSh model serialization format (`.torsh` files)
  - [ ] Design binary format with versioning support
  - [ ] Implement model saving with full metadata
  - [ ] Implement model loading with validation
  - [ ] Add backward compatibility handling
- [ ] PyTorch model loading integration
  - [ ] Use existing PyTorch format parsers
  - [ ] Map PyTorch tensors to torsh-tensor
  - [ ] Preserve model architecture information
- [ ] Add model format validation and conversion

#### 2. Enhanced Model Analysis
- [ ] Real parameter counting (not just file size estimation)
- [ ] Layer-by-layer analysis with memory profiling
- [ ] FLOPS calculation for common operations
- [ ] Activation shape inference through model graph
- [ ] Add visual model architecture representation (ASCII art)

#### 3. Core Tensor Integration
- [ ] Replace SciRS2 placeholder tensors with real torsh-tensor operations
- [ ] Implement real forward pass for model validation
- [ ] Add gradient checking utilities
- [ ] Memory-efficient large model handling

### ⚡ High Priority (Week 3-4)

#### 4. Training Command Implementation
- [ ] Basic training loop with torsh-optim
- [ ] Checkpoint saving and loading
- [ ] TensorBoard integration for metrics
- [ ] Learning rate scheduling
- [ ] Mixed precision training support

#### 5. Dataset Operations
- [ ] torsh-data DataLoader integration
- [ ] Common dataset support (CIFAR-10, MNIST, ImageNet)
- [ ] Custom dataset validation
- [ ] Data augmentation pipeline

#### 6. Hub Integration Foundation
- [ ] Model registry design
- [ ] Upload/download protocol
- [ ] Model versioning system
- [ ] Authentication framework

### 📋 Medium Priority (Week 5-8)

#### 7. Advanced Model Operations
- [ ] Real quantization using torsh-quantization
  - [ ] Dynamic quantization
  - [ ] Static quantization with calibration
  - [ ] QAT (Quantization-Aware Training)
- [ ] Model pruning implementation
  - [ ] Magnitude-based pruning
  - [ ] Structured pruning
  - [ ] Iterative pruning with fine-tuning

#### 8. Benchmarking & Profiling
- [ ] Real performance benchmarking with hardware metrics
- [ ] Memory profiling integration
- [ ] Bottleneck identification
- [ ] Multi-device comparison

#### 9. Development Tools
- [ ] Code generation for model architectures
- [ ] Training script templates
- [ ] Model debugging utilities
- [ ] Numerical stability checks

### 🔧 Technical Improvements (Ongoing)

#### Code Quality
- [ ] Remove all `tokio::time::sleep` stubs
- [ ] Add comprehensive error types
- [ ] Improve error messages with actionable suggestions
- [ ] Add progress bars for all long operations

#### Testing
- [ ] Unit tests for all model operations
- [ ] Integration tests with real models
- [ ] CLI argument parsing tests
- [ ] Performance regression tests

#### Documentation
- [ ] Add inline documentation for all public APIs
- [ ] Create usage examples for each command
- [ ] Add troubleshooting guide
- [ ] Create video tutorials

## Success Metrics

### v0.1.2 Goals (Current)
- [ ] 50% of model commands functional with real operations
- [ ] Basic training workflow operational
- [ ] Model inspection working with all supported formats
- [ ] Zero SCIRS2 POLICY violations

### v0.1.1 Goals (Next Release)
- [ ] 80% of core commands functional
- [ ] Distributed training support
- [ ] Complete quantization pipeline
- [ ] Hub integration operational

### v0.1.1 Goals (Production-Ready)
- [ ] 100% core functionality implemented
- [ ] Comprehensive test coverage (>80%)
- [ ] Performance comparable to PyTorch CLI tools
- [ ] Production deployment examples