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Upgrade PyTorch documentation to latest versions (#16970) 

## Summary

Point to PyTorch 2.9, Python 3.14, CUDA 12.8, etc.
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Charlie Marsh 2025-12-03 07:01:49 -08:00 committed by GitHub
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docs/guides/integration/pytorch.md
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@ -34,28 +34,22 @@ As such, the necessary packaging configuration will vary depending on both the p
support and the accelerators you want to enable.
To start, consider the following (default) configuration, which would be generated by running
`uv init --python 3.12` followed by `uv add torch torchvision`.
`uv init --python 3.14` followed by `uv add torch torchvision`.
In this case, PyTorch would be installed from PyPI, which hosts CPU-only wheels for Windows and
macOS, and GPU-accelerated wheels on Linux (targeting CUDA 12.6):
macOS, and GPU-accelerated wheels on Linux (targeting CUDA 12.8, as of PyTorch 2.9.1):
```toml
[project]
name = "project"
version = "0.1.0"
requires-python = ">=3.12"
requires-python = ">=3.14"
dependencies = [
"torch>=2.7.0",
"torchvision>=0.22.0",
"torch>=2.9.1",
"torchvision>=0.24.1",
]
```
!!! tip "Supported Python versions"
At time of writing, PyTorch does not yet publish wheels for Python 3.14; as such projects with
`requires-python = ">=3.14"` may fail to resolve. See the
[compatibility matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix).
This is a valid configuration for projects that want to use CPU builds on Windows and macOS, and
CUDA-enabled builds on Linux. However, if you need to support different platforms or accelerators,
you'll need to configure the project accordingly.
@ -117,7 +111,7 @@ In such cases, the first step is to add the relevant PyTorch index to your `pypr
```toml
[[tool.uv.index]]
name = "pytorch-rocm"
url = "https://download.pytorch.org/whl/rocm6.3"
url = "https://download.pytorch.org/whl/rocm6.4"
explicit = true
```
@ -254,10 +248,10 @@ As a complete example, the following project would use PyTorch's CPU-only builds
[project]
name = "project"
version = "0.1.0"
requires-python = ">=3.12.0"
requires-python = ">=3.14.0"
dependencies = [
"torch>=2.7.0",
"torchvision>=0.22.0",
"torch>=2.9.1",
"torchvision>=0.24.1",
]
[tool.uv.sources]
@ -287,10 +281,10 @@ and CPU-only builds on all other platforms (e.g., macOS and Windows):
[project]
name = "project"
version = "0.1.0"
requires-python = ">=3.12.0"
requires-python = ">=3.14.0"
dependencies = [
"torch>=2.7.0",
"torchvision>=0.22.0",
"torch>=2.9.1",
"torchvision>=0.24.1",
]
[tool.uv.sources]
@ -321,11 +315,11 @@ builds on Windows and macOS (by way of falling back to PyPI):
[project]
name = "project"
version = "0.1.0"
requires-python = ">=3.12.0"
requires-python = ">=3.14.0"
dependencies = [
"torch>=2.7.0",
"torchvision>=0.22.0",
"pytorch-triton-rocm>=3.3.0 ; sys_platform == 'linux'",
"torch>=2.9.1",
"torchvision>=0.24.1",
"pytorch-triton-rocm>=3.5.1 ; sys_platform == 'linux'",
]
[tool.uv.sources]
@ -341,7 +335,7 @@ pytorch-triton-rocm = [
[[tool.uv.index]]
name = "pytorch-rocm"
url = "https://download.pytorch.org/whl/rocm6.3"
url = "https://download.pytorch.org/whl/rocm6.4"
explicit = true
```
@ -351,11 +345,11 @@ Or, for Intel GPU builds:
[project]
name = "project"
version = "0.1.0"
requires-python = ">=3.12.0"
requires-python = ">=3.14.0"
dependencies = [
"torch>=2.7.0",
"torchvision>=0.22.0",
"pytorch-triton-xpu>=3.3.0 ; sys_platform == 'win32' or sys_platform == 'linux'",
"torch>=2.9.1",
"torchvision>=0.24.1",
"pytorch-triton-xpu>=3.5.0 ; sys_platform == 'win32' or sys_platform == 'linux'",
]
[tool.uv.sources]
@ -389,17 +383,17 @@ extra. For example, the following configuration would use PyTorch's CPU-only for
[project]
name = "project"
version = "0.1.0"
requires-python = ">=3.12.0"
requires-python = ">=3.14.0"
dependencies = []
[project.optional-dependencies]
cpu = [
"torch>=2.7.0",
"torchvision>=0.22.0",
"torch>=2.9.1",
"torchvision>=0.24.1",
]
cu128 = [
"torch>=2.7.0",
"torchvision>=0.22.0",
"torch>=2.9.1",
"torchvision>=0.24.1",
]
[tool.uv]
@ -473,7 +467,7 @@ then use the most-compatible PyTorch index for all relevant packages (e.g., `tor
etc.). If no such GPU is found, uv will fall back to the CPU-only index. uv will continue to respect
existing index configuration for any packages outside the PyTorch ecosystem.
You can also select a specific backend (e.g., CUDA 12.6) with `--torch-backend=cu126` (or
You can also select a specific backend (e.g., CUDA 12.8) with `--torch-backend=cu126` (or
`UV_TORCH_BACKEND=cu126`):
```shell
@ -481,7 +475,7 @@ $ # With a command-line argument.
$ uv pip install torch torchvision --torch-backend=cu126
$ # With an environment variable.
$ UV_TORCH_BACKEND=cu126 uv pip install torch torchvision
$ UV_TORCH_BACKEND=cu128 uv pip install torch torchvision
```
At present, `--torch-backend` is only available in the `uv pip` interface.