---
title: PyTorch Python 3.10 Wheel Collection
library_name: pytorch
license: mit
tags:
- pytorch
- wheels
- python3.10
- cuda
- transformers
- machine-learning
- deep-learning
- dependency-management
language:
- en
pipeline_tag: other
---

# PyTorch Python 3.10 Wheel Collection

Complete PyTorch ML stack with all dependencies - no conflicts, easy installation.

## 📋 What's Included

- **Python:** 3.10 compatible
- **PyTorch:** 2.7.1 + CUDA 12.6
- **Transformers:** 4.52.3
- **NumPy:** 2.0.2 (compatible version)
- **SciPy:** 1.15.2
- **All Dependencies:** 80+ wheels, fully tested together

## 🚀 Installation (Super Easy!)

**One command installation from HuggingFace:**

```bash
# Download and install everything
from huggingface_hub import snapshot_download
import subprocess
import os

# Download all wheels
repo_path = snapshot_download(repo_id="RDHub/pytorch_python_310")
wheel_path = os.path.join(repo_path, "lib_wheel")

# Install all wheels
subprocess.run(["pip", "install"] + [f"{wheel_path}/*.whl"], shell=True)
```

**Or manually:**

```bash
# 1. Download repository
git clone https://huggingface.co/RDHub/pytorch_python_310

# 2. Install everything with requirements file for correct versions
cd pytorch_python_310
pip install -r lib_wheel/requirements.txt --find-links lib_wheel --no-index

# 3. Set up CUDA libraries (for conda environments)
# Create activation script for automatic library path setup
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
cat > $CONDA_PREFIX/etc/conda/activate.d/pytorch_cuda_libs.sh << 'EOF'
#!/bin/bash
# Set up NVIDIA CUDA library paths for PyTorch
NVIDIA_LIB_PATH=$(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

# 4. Reactivate environment and test
conda deactivate && conda activate your_env_name
python -c "import torch; print(f'PyTorch {torch.__version__} - CUDA: {torch.cuda.is_available()}')"
```

## ✅ Key Versions

| Package | Version | Python |
|---------|---------|---------|
| PyTorch | 2.7.1 | 3.10 |
| Transformers | 4.52.3 | 3.10 |
| NumPy | 2.0.2 | 3.10 |
| CUDA | 12.6 | - |

## 🎯 Use Cases

Perfect for:
- Machine Learning projects
- Large Language Model training
- Computer Vision
- Audio processing
- Research environments

## 📝 Notes

- **No dependency conflicts** - all versions tested together
- **Offline ready** - no internet needed after download
- **CUDA included** - ready for GPU training with library path setup
- **Linux x86_64** compatible
- **Requires conda environment** - for automatic CUDA library path management

---

**Repository Size:** ~2GB  
**Total Packages:** 80+ wheels  
**Tested:** Ubuntu 22.04, Python 3.10