| | ---
|
| | title: PyTorch Python 3.10 Wheel Collection
|
| | library_name: pytorch
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| | license: mit
|
| | tags:
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| | - 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
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| | # Download and install everything
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| | from huggingface_hub import snapshot_download
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| | 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
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| | git clone https://huggingface.co/RDHub/pytorch_python_310
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| |
|
| | # 2. Install everything with requirements file for correct versions
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| | cd pytorch_python_310
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| | pip install -r lib_wheel/requirements.txt --find-links lib_wheel --no-index
|
| |
|
| | # 3. Set up CUDA libraries (for conda environments)
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| | # Create activation script for automatic library path setup
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| | mkdir -p $CONDA_PREFIX/etc/conda/activate.d
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| | 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
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| | chmod +x $CONDA_PREFIX/etc/conda/activate.d/pytorch_cuda_libs.sh
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| |
|
| | # 4. Reactivate environment and test
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| | conda deactivate && conda activate your_env_name
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| | 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
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| |
|
| | Perfect for:
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| | - Machine Learning projects
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| | - Large Language Model training
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| | - Computer Vision
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| | - Audio processing
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| | - Research environments
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| |
|
| | ## 📝 Notes
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| |
|
| | - **No dependency conflicts** - all versions tested together
|
| | - **Offline ready** - no internet needed after download
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| | - **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 |
