🔥 PyTorch High Sierra CUDA Revival

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🔥 PyTorch High Sierra CUDA Revival

The Final Fully GPU-Accelerated Deep Learning Stack for macOS

This project restores full CUDA compute support on macOS High Sierra using NVIDIA CUDA 10.2, cuDNN 7.6.5, and a custom‑built PyTorch 1.7.0.
A completely discontinued ecosystem — resurrected and preserved.

📚 Quick Docs Menu

Installation: docs/installation.md
Troubleshooting: docs/troubleshooting.md
Compatibility Matrix: docs/compatibility_matrix.md
Architecture Overview: docs/architecture_overview.md
Rebuild From Source: docs/rebuild_from_source.md
Performance Benchmarks: docs/performance_benchmarks.md
Historical Notes: docs/historical_notes.md
Environment Specs: docs/environment_specs.md
Examples: examples/

📌 Background & Motivation

CUDA on macOS is dead.
Apple killed NVIDIA support.
NVIDIA discontinued macOS drivers.
PyTorch removed CUDA for macOS entirely.

But High Sierra (10.13.6) is the last remaining macOS that can still run:

CUDA 10.2
cuDNN 7.6.5
NVIDIA WebDrivers
Pascal GPUs at full power

This repository preserves the only functioning deep learning GPU environment for macOS.

This is not just a wheel —
It is a technical museum piece that still works.

🖥️ System Compatibility Matrix

Component	Version	Status
macOS	10.13.6 High Sierra	✔ Supported
NVIDIA Drivers	WebDriver 387/387+	✔ Required
CUDA Toolkit	10.2	✔ Fully Compatible
cuDNN	7.6.5	✔ Final Version for macOS
GPU Architecture	Pascal (SM_61)	✔ Optimal
PyTorch	1.7.0a0 (Custom CUDA Build)	✔ Working
Python	3.8.x	✔ Compatible

🧩 Supported GPUs (All WebDriver-Compatible)

Pascal (Full CUDA Support)

GeForce GTX 1050 / 1050 Ti
GeForce GTX 1060
GeForce GTX 1070
GeForce GTX 1080
GeForce GTX 1080 Ti
Titan X / Titan Xp

Kepler (Works but slower)

GT 710 / GT 730
GTX 770 / 780 / 780 Ti

Official WebDriver Mac GPUs

GeForce 600 Series

GTX 680

GeForce 200 Series

GTX 285

GeForce 100 Series

GT 120

GeForce 8 Series

8800 GT

Quadro Series

Quadro K5000 (Mac)
Quadro 4000 (Mac)

Quadro FX Series

FX 4800
FX 5600

If WebDriver works → CUDA PyTorch works.

📚 Documentation & Preservation Index

Architecture overview: docs/architecture_overview.md
Troubleshooting (WebDriver, CUDA, cuDNN, sm_61, nvcc, SIP): docs/troubleshooting.md
Performance benchmarks (matmul, transformer, kernels): docs/performance_benchmarks.md
Compatibility matrix (GPU list & tiers): docs/compatibility_matrix.md
Rebuild from source (wheel reproduction): docs/rebuild_from_source.md
Historical notes (macOS CUDA deprecation timeline): docs/historical_notes.md

Preservation artifacts: archive/ (build logs, NVCC flags, CMake cache, wheel hashes, CUDA lib hashes, test outputs)

Environment reproducibility: environment/ (pip JSON, YAML, full CUDA/cuDNN dumps, system specs)

Schemas for CI validation: schemas/

🚀 Installation

1️⃣ Download & Install (Release Asset)

Direct wheel download:

https://github.com/careunix/PyTorch-HighSierra-CUDA-Revival/releases/download/v0.1.0/torch-1.7.0a0-cp38-cp38-macosx_10_13_x86_64.whl

Option A — install directly from URL:

pip install "https://github.com/careunix/PyTorch-HighSierra-CUDA-Revival/releases/download/v0.1.0/torch-1.7.0a0-cp38-cp38-macosx_10_13_x86_64.whl"

Option B — download then install:

curl -L -o torch-1.7.0a0-cp38-cp38-macosx_10_13_x86_64.whl \
  https://github.com/careunix/PyTorch-HighSierra-CUDA-Revival/releases/download/v0.1.0/torch-1.7.0a0-cp38-cp38-macosx_10_13_x86_64.whl
pip install ./torch-1.7.0a0-cp38-cp38-macosx_10_13_x86_64.whl

2️⃣ Integrity & Signature Verification

SHA256 should be:

38da4acfe780a041b1f73f67c66efcdb37e9773615446f6a02ed2586f3cff9c7

Check locally:

shasum -a 256 torch-1.7.0a0-cp38-cp38-macosx_10_13_x86_64.whl | awk '{print $1}'

Verify GPG signature (public key is in repo):

# Import public key (from repo)
gpg --import archive/keys/careunix.pub.asc

# Or fetch raw key without cloning
curl -L -o careunix.pub.asc \
  https://raw.githubusercontent.com/careunix/PyTorch-HighSierra-CUDA-Revival/main/archive/keys/careunix.pub.asc
gpg --import careunix.pub.asc

# Verify detached signature (from repo)
curl -L -o SIGNATURE.asc \
  https://raw.githubusercontent.com/careunix/PyTorch-HighSierra-CUDA-Revival/main/wheel/SIGNATURE.asc
gpg --verify SIGNATURE.asc torch-1.7.0a0-cp38-cp38-macosx_10_13_x86_64.whl

3️⃣ Verify CUDA functionality

import torch
print("CUDA:", torch.cuda.is_available())
print("GPU:", torch.cuda.get_device_name(0))

Expected:

CUDA: True
GPU: GeForce GTX 1060

🔧 Verified Test Environment

$ nvcc --version
Cuda compilation tools, release 10.2, V10.2.89

$ python --version
Python 3.8.20

$ conda --version
conda 23.5.2

$ python -m pip --version
pip 24.2 (Python 3.8)

This wheel was built & validated inside the environment above. Any system matching these versions (or close variants) will work.

📦 Python Package Environment — Verified Working Set

PyTorch CUDA 10.2 Build on macOS High Sierra (careunix)

This section documents the exact Python package list used during the successful compilation of:

PyTorch 1.7.0a0 (custom CUDA build)
CUDA 10.2
cuDNN 7.6.5
Python 3.8.20
Conda 23.5.2

Environment name: gpt2env
Primary developer: careunix

These versions are confirmed 100% compatible and form a fully reproducible build environment.

📌 Export Your Own List

You can generate the same format with:

python -m pip list --format=json > pip_environment.json

✔ Verified Working Package Set

(Direct JSON output from careunix’s functional build environment)

[
  {"name": "accelerate", "version": "1.0.1"},
  {"name": "autocommand", "version": "2.2.2"},
  {"name": "backports.tarfile", "version": "1.2.0"},
  {"name": "Brotli", "version": "1.0.9"},
  {"name": "certifi", "version": "2024.8.30"},
  {"name": "cffi", "version": "1.17.1"},
  {"name": "charset-normalizer", "version": "3.3.2"},
  {"name": "click", "version": "8.1.8"},
  {"name": "filelock", "version": "3.16.1"},
  {"name": "future", "version": "0.18.3"},
  {"name": "huggingface-hub", "version": "0.0.12"},
  {"name": "idna", "version": "3.7"},
  {"name": "importlib_metadata", "version": "8.0.0"},
  {"name": "importlib_resources", "version": "6.4.0"},
  {"name": "inflect", "version": "7.3.1"},
  {"name": "jaraco.collections", "version": "5.1.0"},
  {"name": "jaraco.context", "version": "5.3.0"},
  {"name": "jaraco.functools", "version": "4.0.1"},
  {"name": "jaraco.text", "version": "3.12.1"},
  {"name": "joblib", "version": "1.4.2"},
  {"name": "mkl-fft", "version": "1.3.8"},
  {"name": "mkl-random", "version": "1.2.4"},
  {"name": "mkl-service", "version": "2.4.0"},
  {"name": "more-itertools", "version": "10.3.0"},
  {"name": "numpy", "version": "1.23.5"},
  {"name": "packaging", "version": "25.0"},
  {"name": "pip", "version": "24.2"},
  {"name": "platformdirs", "version": "4.2.2"},
  {"name": "pycparser", "version": "2.21"},
  {"name": "PySocks", "version": "1.7.1"},
  {"name": "PyYAML", "version": "6.0.2"},
  {"name": "regex", "version": "2024.11.6"},
  {"name": "requests", "version": "2.32.3"},
  {"name": "sacremoses", "version": "0.1.1"},
  {"name": "setuptools", "version": "75.1.0"},
  {"name": "six", "version": "1.16.0"},
  {"name": "tokenizers", "version": "0.10.3"},
  {"name": "tomli", "version": "2.0.1"},
  {
    "name": "torch",
    "version": "1.7.0a0",
    "editable_project_location": "/Users/careunix/pytorch"
  },
  {"name": "tqdm", "version": "4.67.1"},
  {"name": "transformers", "version": "4.8.2"},
  {"name": "typeguard", "version": "4.3.0"},
  {"name": "typing_extensions", "version": "4.11.0"},
  {"name": "urllib3", "version": "2.2.3"},
  {"name": "wheel", "version": "0.44.0"},
  {"name": "zipp", "version": "3.19.2"}
]

🏁 Notes

This list ensures your build can be identically recreated.
This project is part of the CUDA Revival effort led by careunix.
If you fork the main repo, keep this section for scientific reproducibility.

🧪 Tests & Validation

Run all tests and capture outputs: bash scripts/run_all_tests.sh
Export environment to JSON + YAML: bash scripts/export_env.sh
System probe (GPU, driver, CUDA, cuDNN → JSON): python scripts/system_probe.py --out environment/system_specs.json
Validate wheel integrity and tags: python scripts/validate_whl.py

CI pipelines: link checks, style (black + flake8), CPU fallback tests, JSON schema validation.

⚡ GPU Stress Test (cuBLAS)

import torch
a = torch.randn(10000, 10000, device="cuda")
b = torch.randn(10000, 10000, device="cuda")
c = torch.mm(a, b)
torch.cuda.synchronize()
print("OK:", c.shape)

Confirms:

GPU kernels
Full BLAS acceleration
CUDA stability

🤖 GPT‑2 Medium Inference Example

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

device = "cuda"
tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium")
model = GPT2LMHeadModel.from_pretrained("gpt2-medium").to(device)

inp = tokenizer.encode("Once upon a time", return_tensors="pt").to(device)
out = model.generate(inp, max_length=60)
print(tokenizer.decode(out[0]))

🧠 CUDA Libraries Included

Core GPU Libraries

libcudart
libcublas
libcufft
libcurand
libcusolver
libcusparse
nvrtc
nvToolsExt

Deep Learning

cuDNN 7.6.5
MKLDNN
QNNPACK
NNPACK

🔧 Build Configuration (Verified)

CUDA version: 10.2
cuDNN version: 7.6.5
GPU arch: sm_61
USE_CUDA: ON
USE_CUDNN: ON
USE_MKLDNN: ON
USE_NNPACK: ON
USE_QNNPACK: ON
Build Type: Release

🧩 CPU‑Only Mode

If CUDA is not available:

The wheel still imports normally
PyTorch works fully (CPU backend)
No configuration needed

This makes the package safe for any High Sierra installation.

🧬 Project Purpose

Archive & preserve the last NVIDIA macOS CUDA stack
Keep High Sierra CUDA systems alive
Support researchers using Pascal GPUs
Provide a fully reproducible environment
Document a disappeared ecosystem

This repository is digital preservation.

🤝 Contributing

You may contribute via:

GPU compatibility reports
Benchmarks
Documentation improvements
Mirrors / backups / alternative builds

📜 License

MIT License

Preserving the final NVIDIA‑powered AI stack ever possible on macOS.
_{Pascal lives. care unix;)}

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
.github/workflows		.github/workflows
archive		archive
assets		assets
docs		docs
environment		environment
examples		examples
schemas		schemas
scripts		scripts
tests		tests
wheel		wheel
.DS_Store		.DS_Store
.editorconfig		.editorconfig
.gitattributes		.gitattributes
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
README.md		README.md
RELEASE_NOTES.md		RELEASE_NOTES.md
SECURITY.md		SECURITY.md
pyproject.toml		pyproject.toml
requirements-dev.txt		requirements-dev.txt

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🔥 PyTorch High Sierra CUDA Revival

The Final Fully GPU-Accelerated Deep Learning Stack for macOS

📚 Quick Docs Menu

📌 Background & Motivation

🖥️ System Compatibility Matrix

🧩 Supported GPUs (All WebDriver-Compatible)

Pascal (Full CUDA Support)

Kepler (Works but slower)

Official WebDriver Mac GPUs

GeForce 600 Series

GeForce 200 Series

GeForce 100 Series

GeForce 8 Series

Quadro Series

Quadro FX Series

📚 Documentation & Preservation Index

🚀 Installation

1️⃣ Download & Install (Release Asset)

2️⃣ Integrity & Signature Verification

3️⃣ Verify CUDA functionality

🔧 Verified Test Environment

📦 Python Package Environment — Verified Working Set

PyTorch CUDA 10.2 Build on macOS High Sierra (careunix)

📌 Export Your Own List

✔ Verified Working Package Set

🏁 Notes

🧪 Tests & Validation

⚡ GPU Stress Test (cuBLAS)

🤖 GPT‑2 Medium Inference Example

🧠 CUDA Libraries Included

Core GPU Libraries

Deep Learning

🔧 Build Configuration (Verified)

🧩 CPU‑Only Mode

🧬 Project Purpose

🤝 Contributing

📜 License

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