| ---
|
| 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 |
