|
|
---
|
|
|
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 |
