Upgrade PyTorch documentation to latest versions (#16970)

## Summary

Point to PyTorch 2.9, Python 3.14, CUDA 12.8, etc.

Author: charliermarsh

docs/guides/integration/pytorch.md

@@ -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]
@@ -473,15 +467,15 @@ 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
 $ # 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.