# PyTorch Python 3.10 Installation Guide

## 🚀 Quick Start

### Option 1: Automated Setup (Recommended)

```bash
# 1. Create conda environment
conda create -n pytorch_env python=3.10
conda activate pytorch_env

# 2. Download repository
git clone https://huggingface.co/RDHub/pytorch_python_310
cd pytorch_python_310

# 3. Install all packages
pip install -r lib_wheel/requirements.txt --find-links lib_wheel --no-index

# 4. Set up CUDA libraries
bash setup_cuda_libs.sh

# 5. Test installation
python -c "import torch; print(f'PyTorch {torch.__version__} - CUDA: {torch.cuda.is_available()}')"
```

### Option 2: Manual Setup

```bash
# 1. Install packages
pip install -r lib_wheel/requirements.txt --find-links lib_wheel --no-index

# 2. Create CUDA library activation script
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
cat > $CONDA_PREFIX/etc/conda/activate.d/pytorch_cuda_libs.sh << 'EOF'
#!/bin/bash
NVIDIA_LIB_PATH=$(find $CONDA_PREFIX -path "*/nvidia/*/lib" -type d 2>/dev/null | tr '\n' ':')
CUSPARSELT_LIB_PATH=$(find $CONDA_PREFIX -path "*/cusparselt/lib" -type d 2>/dev/null | tr '\n' ':')
export LD_LIBRARY_PATH="${NVIDIA_LIB_PATH}${CUSPARSELT_LIB_PATH}${LD_LIBRARY_PATH}"
EOF
chmod +x $CONDA_PREFIX/etc/conda/activate.d/pytorch_cuda_libs.sh

# 3. Reactivate environment
conda deactivate && conda activate your_env_name
```

## ✅ What's Included

- **PyTorch 2.7.1** with CUDA 12.6 support
- **Transformers 4.52.3** for HuggingFace models
- **NumPy 2.0.2** (compatible with OpenCV)
- **OpenCV 4.10.0** for computer vision
- **80+ compatible packages** tested together
- **NVIDIA CUDA libraries** (12.6.x series)

## 🧪 Testing Your Installation

```python
import torch
import transformers
import numpy as np
import cv2

# Test PyTorch CUDA
print(f"PyTorch: {torch.__version__}")
print(f"CUDA Available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")

# Test basic operations
x = torch.randn(100, 100).cuda()
y = torch.matmul(x, x.T)
print("✅ GPU tensor operations working!")

# Test transformers
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
print("✅ Transformers working!")

print(f"NumPy: {np.__version__}")
print(f"OpenCV: {cv2.__version__}")
```

## 🔧 Troubleshooting

### Issue: "libcufile.so.0: cannot open shared object file"

**Solution:** Run the CUDA setup script:
```bash
bash setup_cuda_libs.sh
conda deactivate && conda activate your_env_name
```

### Issue: "libcusparseLt.so.0: cannot open shared object file"

**Solution:** Ensure all NVIDIA packages are installed:
```bash
pip install --force-reinstall lib_wheel/nvidia_cusparselt_cu12-*.whl
pip install --force-reinstall lib_wheel/nvidia_cufile_cu12-*.whl
```

### Issue: OpenCV + NumPy compatibility errors

**Solution:** Use the exact versions provided:
```bash
pip install --force-reinstall lib_wheel/numpy-2.0.2-*.whl
pip install --force-reinstall lib_wheel/opencv_python-4.10.0.84-*.whl
```

## 📋 System Requirements

- **OS:** Linux x86_64 (Ubuntu 22.04+ recommended)
- **Python:** 3.10
- **CUDA:** Compatible with 12.2+ (12.6 optimal)
- **Conda:** Required for library path management
- **Storage:** ~2GB for all wheels

## 🎯 Verified Configurations

✅ **Ubuntu 22.04** + Python 3.10 + CUDA 12.2  
✅ **Ubuntu 22.04** + Python 3.10 + CUDA 12.6  
✅ **RTX 4090** + CUDA 12.6  
✅ **Conda environments**  

## 🔗 Resources

- **Repository:** https://huggingface.co/RDHub/pytorch_python_310
- **PyTorch Docs:** https://pytorch.org/docs/
- **CUDA Toolkit:** https://developer.nvidia.com/cuda-toolkit