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+# Deep Sequence Models (deep_ssm)
+
+Always reference these instructions first and fallback to search or bash commands only when you encounter unexpected information that does not match the info here.
+
+## Working Effectively
+
+### Environment Setup
+**CRITICAL**: Installation can take 45+ minutes due to large dependencies and potential build compilation. NEVER CANCEL build operations.
+
+#### Method 1: Complete Installation (Recommended)
+```bash
+# Create conda environment with Python 3.9
+conda create -n deep_ssm python=3.9
+conda activate deep_ssm
+
+# Install PyTorch with CUDA support - NEVER CANCEL: Takes 10+ minutes
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
+
+# Install core dependencies - NEVER CANCEL: Takes 15+ minutes  
+pip install lightning==2.3.3 hydra-core==1.2.0 omegaconf==2.2.3 wandb tqdm einops datasets==2.4.0 transformers==4.42.4 pandas scikit-learn==1.5.1
+
+# Install mamba dependencies (requires CUDA dev tools) - NEVER CANCEL: Takes 20+ minutes
+pip install mamba-ssm[causal-conv1d] causal-conv1d triton==2.2.0
+
+# Install package in editable mode
+pip install -e src/
+```
+
+#### Method 2: Minimal Installation (When full install fails)
+```bash
+# Use system Python or existing environment
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
+pip install lightning hydra-core omegaconf wandb tqdm einops
+pip install -e src/
+```
+
+**When to use**: Network timeouts, missing CUDA dev tools, or CI environments with restrictions.
+
+#### Method 4: Emergency Minimal Setup (If most packages fail)
+```bash
+# Only install absolutely essential packages
+pip install torch --index-url https://download.pytorch.org/whl/cpu  # CPU-only PyTorch
+pip install -e src/
+```
+
+**When to use**: Severe network restrictions or build environment issues. Only basic imports will work.
+
+**Note**: If installation fails due to network timeouts or missing CUDA tools, use Method 2. Some advanced models (Mamba) will not work, but basic functionality and S5 models will work.
+
+**Common Installation Failures**:
+- `ReadTimeoutError: HTTPSConnectionPool... Read timed out`: Network issues, retry or use Method 2
+- `NameError: name 'bare_metal_version' is not defined`: CUDA dev tools missing for causal-conv1d
+- `subprocess-exited-with-error`: Missing build dependencies, skip problematic packages
+
+#### Method 3: Development Environment (For Sherlock cluster)
+```bash
+# Load required modules first
+ml python/3.9.0 && ml gcc/10.1.0 && ml cudnn/8.9.0.131 && ml load cuda/12.4.1
+./setup_env.sh
+```
+
+### Data Setup
+```bash
+# For BCI experiments, set data environment variable
+export DEEP_SSM_DATA=/path/to/data
+
+# Download BCI data (if needed)
+gsutil cp gs://cfan/interspeech24/brain2text_competition_data.pkl .
+
+# On Sherlock cluster, data is pre-available:
+export DEEP_SSM_DATA=/scratch/groups/swl1
+```
+
+### Running Training
+
+#### S5 Model on Sequential CIFAR10
+```bash
+# Basic sequential CIFAR10 training - NEVER CANCEL: Takes 30+ minutes per epoch
+python -m example
+
+# Grayscale version (faster convergence)
+python -m example --grayscale
+
+# With wandb logging
+python -m example --grayscale --wandb
+
+# MNIST variant
+python -m example --dataset mnist --d_model 256 --weight_decay 0.0
+```
+
+#### BCI Models with Hydra Configuration
+```bash
+# Debug run (quick test) - Takes 2-3 minutes
+python run.py --config-name="baseline_gru" trainer_cfg.fast_dev_run=1
+
+# Full GRU training - NEVER CANCEL: Takes 60+ minutes
+python run.py --config-name="baseline_gru"
+
+# Mamba model training (requires full installation) - NEVER CANCEL: Takes 90+ minutes  
+python run.py --config-name="baseline_mamba"
+```
+
+#### Safari Models (Advanced)
+```bash
+# Safari training - NEVER CANCEL: Takes 45+ minutes
+python scripts/train_safari.py
+```
+
+## Validation
+
+### Always Run These Tests After Making Changes
+```bash
+# Test basic imports (should complete in <30 seconds)
+python -c "import deep_ssm; print('Package installed correctly')"
+
+# Test PyTorch CUDA availability
+python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
+
+# Test configuration loading (only if omegaconf installed)
+python -c "
+try:
+    from omegaconf import OmegaConf
+    print('Hydra configs working')
+except ImportError:
+    print('omegaconf not available - use Method 1 installation')
+"
+
+# Check core dependencies availability
+python -c "
+try:
+    import lightning
+    print('Lightning available')
+except ImportError:
+    print('Lightning NOT available - some training scripts will fail')
+"
+
+# Run basic mixer tests (only if all dependencies available)
+# NOTE: This will fail if einops, absl, or other test dependencies are missing
+python tests/mixers/test_mixers.py
+```
+
+### Manual Validation Scenarios
+**CRITICAL**: Always test one complete training scenario after making code changes:
+
+1. **Quick S5 Test** (5 minutes):
+   ```bash
+   python -m example --dataset mnist --epochs 1 --batch_size 50
+   ```
+
+2. **BCI Debug Test** (3 minutes):
+   ```bash
+   python run.py --config-name="baseline_gru" trainer_cfg.fast_dev_run=1
+   ```
+
+3. **Full Training Validation** (30+ minutes - run occasionally):
+   ```bash
+   python -m example --grayscale --epochs 5
+   ```
+
+## Time Expectations and Build Commands
+
+### Timing Guide (NEVER CANCEL these operations)
+- **Environment setup**: 15-45 minutes depending on method and network speed
+- **PyTorch installation**: 5-15 minutes (2GB+ download)
+- **Complete dependency installation**: 20-40 minutes total
+- **S5 CIFAR training**: 30 minutes per epoch (250 epochs = ~125 hours total)
+- **BCI model training**: 60-90 minutes for full run  
+- **Debug runs**: 2-5 minutes
+- **Tests**: 30 seconds to 5 minutes
+- **Package installation**: 2-3 minutes
+- **File operations**: <1 second for basic commands
+
+### Build Timeout Recommendations
+- Set timeout to 60+ minutes for full installations
+- Set timeout to 30+ minutes for PyTorch installation
+- Set timeout to 20+ minutes for individual large packages
+- Use 2+ minute timeouts for basic operations
+
+### Common Validation Commands
+```bash
+# Always run before committing - Takes <2 minutes
+python -c "import deep_ssm.mixers.s5_fjax.ssm; print('S5 imports work')"
+python scripts/run.py --config-name="baseline_gru" trainer_cfg.fast_dev_run=1
+
+# Check configuration files
+find configs/ -name "*.yaml" | head -5
+```
+
+## Repository Structure
+
+### Key Entry Points
+- `example.py`: S5 model training on CIFAR10/MNIST
+- `scripts/run.py`: BCI model training with Hydra configs  
+- `scripts/train_safari.py`: Advanced Safari model training
+
+### Important Directories
+- `src/deep_ssm/`: Core package code
+- `src/safari/`: Safari models and utilities submodule
+- `configs/bci/`: BCI model configurations
+- `configs/configs_safari/`: Safari model configurations
+- `tests/mixers/`: Unit tests for mixer layers
+
+### Configuration Files
+- `configs/bci/baseline_gru.yaml`: GRU model config
+- `configs/bci/baseline_mamba.yaml`: Mamba model config
+- `requirements.txt`: Python dependencies
+
+## Known Issues and Workarounds
+
+### Installation Problems
+- **Network timeouts**: Use Method 2 installation if pip hangs (common in CI environments)
+- **CUDA compilation fails**: Skip mamba-ssm installation, use S5 models only - error: "nvcc was not found" or "bare_metal_version not defined"
+- **Conda activation errors**: Use `eval "$(conda shell.bash hook)"` before `conda activate`
+- **PyTorch version conflicts**: Install PyTorch first, then other dependencies to avoid conflicts
+- **torchtext version issues**: May need to skip specific versions or install without version constraints
+
+### Runtime Issues  
+- **CUDA out of memory**: Reduce batch_size in configs
+- **Hydra output directory**: Outputs go to `./outputs/YY-MM-DD/HH-MM-SS/`
+- **Missing DEEP_SSM_DATA**: BCI models require this environment variable
+
+### Model-Specific Notes
+- **S5 models**: Work with minimal installation
+- **Mamba models**: Require full installation with CUDA dev tools
+- **Safari models**: Use separate configuration system
+
+## Performance Notes
+- **Expected CIFAR10 accuracy**: 88%+ in 250 epochs
+- **BCI dataset**: Large neural time series data
+- **GPU recommended**: All models benefit significantly from CUDA
+- **Memory usage**: 8GB+ GPU memory for larger models
+
+## Development Workflow
+1. Always test installation with: `python -c "import deep_ssm"`
+2. Run debug mode first: `trainer_cfg.fast_dev_run=1` 
+3. Validate with short runs before full training
+4. Monitor GPU memory usage during training
+5. Check `./outputs/` directory for results and logs
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+.git
+*.pyc
+__pycache__/
+*.egg-info/
+data/
+outputs/
+checkpoint/
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