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PyTorch 2.14.0 GA (2 Sep 2026): binaries on PyPI + download.pytorch.org; TorchVision 0.29.0. After-work upgrade checklist — check CUDA/Python wheels on get-started. Not investment advice.
PyTorch 2.14.0 reached general availability on 2 September 2026. Per the GA announcement, binaries are on PyPI and download.pytorch.org; install from the familiar get-started / locally page. Companion notes: PyTorch v2.14.0, TorchVision v0.29.0, and the PyTorch 2.14 Release Blog. This is an after-work upgrade checklist for Python / data / ML notebooks — not investment advice.
The only course CTA is Introduction to Python Programming.
| Item | Detail |
|---|---|
| PyTorch | 2.14.0 GA — binaries on PyPI + download.pytorch.org |
| TorchVision | 0.29.0 (ABI stability w.r.t. torch 2.14; see release notes) |
| Install matrix | get-started locally — pick OS / package / Python / CUDA (or CPU / ROCm / XPU) |
| Narrative blog | pytorch.org/blog/pytorch-2-14-release-blog |
Blog highlights worth skimming once: official wheels for Python 3.15 (including free-threaded 3.15t on supported platforms), and TorchVision ABI stability so 0.29 can ride future torch minors without a forced vision reinstall.
1. Snapshot what you have now
python -c "import torch; print(torch.__version__, torch.version.cuda, torch.cuda.is_available())"
python -c "import torchvision; print(torchvision.__version__)" # if installed
2. Pick the matching wheel on get-started — do not guess the CUDA tag from memory. Open pytorch.org/get-started/locally, choose your OS, pip/conda, Python, and CUDA (or CPU), then copy the exact install line.
3. Upgrade in a fresh venv (safer than in-place on a shared laptop env):
python -m venv .venv-pt214 && source .venv-pt214/bin/activate
# paste the get-started pip line here, then:
python -c "import torch; print(torch.__version__); print('cuda?', torch.cuda.is_available())"
4. Re-check TorchVision if your notebook imports it — pin the GA pair (torch 2.14.0 + torchvision 0.29.0) unless the get-started matrix says otherwise.
5. One smoke test, then stop — import your usual model class or run a tiny torch.randn matmul on the device you care about. Do not rebuild a training stack tonight.
Confirm the get-started wheel for your machine, then if Python packaging itself is the blocker, Introduction to Python Programming.