## Summary
If the user explicitly authenticated to pyx, then we attempt to use the
pyx PyTorch URLs; otherwise, we stick to `download.pytorch.org` as the
default.
## Summary
This implements the iOS part of
https://github.com/astral-sh/uv/issues/8029
FYI: @freakboy3742
<!-- What's the purpose of the change? What does it do, and why? -->
## Test Plan
Create a venv with uv and run `cargo run pip install --python-platform
arm64-apple-ios pillow`. Then the iOS binary of pillow should be
installed inside the venv.
## Summary
Packages like `triton` should come from the PyTorch index, but they
don't actually vary across (e.g.) the `cu128` or `cu129` indexes.
Closes https://github.com/astral-sh/uv/issues/15446.
## Test Plan
Validate that the following pins to `cu128`, rather than `cpu`:
```
echo "vllm\ntorch==2.7.1+cu128" | cargo run pip compile --torch-backend=auto --extra-index-url https://wheels.vllm.ai/b2f6c247a9b84556a8ea0e75bb4a2db765ff3315 - --python-platform linux --python-version 3.13 -v
```
## Summary
Add torch cuda 12.9 backend
<!-- What's the purpose of the change? What does it do, and why? -->
## Test Plan
<!-- How was it tested? -->
---------
Signed-off-by: youkaichao <youkaichao@gmail.com>
Co-authored-by: Charlie Marsh <charlie.r.marsh@gmail.com>
## Summary
The PyTorch team publishes ARM Linux wheels for `triton` to the PyTorch
index, which aren't available on PyPI.
## Test Plan
```
echo "torch" | cargo run pip compile - --torch-backend=cu128 --python-platform aarch64-unknown-linux-gnu --python-version 3.13
```
Previously failed because it couldn't find a compatible `triton` wheel.
## Summary
Make the use of `Self` consistent. Mostly done by running `cargo clippy
--fix -- -A clippy::all -W clippy::use_self`.
## Test Plan
<!-- How was it tested? -->
No need.
## Summary
This PR intends to enable `--torch-backend=auto` to detect Intel GPUs
automatically:
- On Linux, detection is performed using the `lspci` command via
`Display controller` id.
- On Windows, ~~detection is done via a `powershell` query to
`Win32_VideoController`~~. Skip support for now—revisit once a better
solution is available.
Currently, Intel GPUs (XPU) do not rely on specific driver or toolkit
versions to distribute different PyTorch wheels.
## Test Plan
<!-- How was it tested? -->
On Linux:
~~On Windows:
~~
---------
Co-authored-by: Charlie Marsh <charlie.r.marsh@gmail.com>
## Summary
Allows `--torch-backend=auto` to detect AMD GPUs. The approach is fairly
well-documented inline, but I opted for `rocm_agent_enumerator` over
(e.g.) `rocminfo` since it seems to be the recommended approach for
scripting:
https://rocm.docs.amd.com/projects/rocminfo/en/latest/how-to/use-rocm-agent-enumerator.html.
Closes https://github.com/astral-sh/uv/issues/14086.
## Test Plan
```
root@rocm-jupyter-gpu-mi300x1-192gb-devcloud-atl1:~# ./uv-linux-libc-11fb582c5c046bae09766ceddd276dcc5bb41218/uv pip install torch --torch-backend=auto
Resolved 11 packages in 251ms
Prepared 2 packages in 6ms
Installed 11 packages in 257ms
+ filelock==3.18.0
+ fsspec==2025.5.1
+ jinja2==3.1.6
+ markupsafe==3.0.2
+ mpmath==1.3.0
+ networkx==3.5
+ pytorch-triton-rocm==3.3.1
+ setuptools==80.9.0
+ sympy==1.14.0
+ torch==2.7.1+rocm6.3
+ typing-extensions==4.14.0
```
---------
Co-authored-by: Zanie Blue <contact@zanie.dev>
[Two benchmark
jobs](4433771099)
were failing with `error: cannot find attribute clap in this scope`
based on #14120. This updates the recently added `#[clap(name = rocm...`
lines to use `cfg_attr(feature = "clap",`.
This includes some initial work on adding Pyodide support (issue
#12729). It is enough to get
```
uv pip compile -p /path/to/pyodide --extra-index-url file:/path/to/simple-index
```
to work which should already be quite useful.
## Test Plan
* added a unit test for `pyodide_platform`
* integration tested manually with:
```
cargo run pip install \
-p /home/rchatham/Documents/programming/tmp/pyodide-venv-test/.pyodide-xbuildenv-0.29.3/0.27.4/xbuildenv/pyodide-root/dist/python \
--extra-index-url file:/home/rchatham/Documents/programming/tmp/pyodide-venv-test/.pyodide-xbuildenv-0.29.3/0.27.4/xbuildenv/pyodide-root/package_index \
--index-strategy unsafe-best-match --target blah --no-build \
numpy pydantic
```
---------
Co-authored-by: konsti <konstin@mailbox.org>
Co-authored-by: Zanie Blue <contact@zanie.dev>
## Summary
If you use `--torch-backend=auto`, we want to avoid selecting (e.g.) a
`+cu124` build of `torch` alongside a `+cu126` build of `torchvision`.
## Summary
It's possible that the PyTorch version the user depends on isn't in the
latest index. These indexes are equally trusted, so we should override
the policy.
Closes#12357.
## Summary
This is a prototype that I'm considering shipping under `--preview`,
based on [`light-the-torch`](https://github.com/pmeier/light-the-torch).
`light-the-torch` patches pip to pull PyTorch packages from the PyTorch
indexes automatically. And, in particular, `light-the-torch` will query
the installed CUDA drivers to determine which indexes are compatible
with your system.
This PR implements equivalent behavior under `--torch-backend auto`,
though you can also set `--torch-backend cpu`, etc. for convenience.
When enabled, the registry client will fetch from the appropriate
PyTorch index when it sees a package from the PyTorch ecosystem (and
ignore any other configured indexes, _unless_ the package is explicitly
pinned to a different index).
Right now, this is only implemented in the `uv pip` CLI, since it
doesn't quite fit into the lockfile APIs given that it relies on feature
detection on the currently-running machine.
## Test Plan
On macOS, you can test this with (e.g.):
```shell
UV_TORCH_BACKEND=auto UV_CUDA_DRIVER_VERSION=450.80.2 cargo run \
pip install torch --python-platform linux --python-version 3.12
```
On a GPU-enabled EC2 machine:
```shell
ubuntu@ip-172-31-47-149:~/uv$ UV_TORCH_BACKEND=auto cargo run pip install torch -v
Finished `dev` profile [unoptimized + debuginfo] target(s) in 0.31s
Running `target/debug/uv pip install torch -v`
DEBUG uv 0.6.6 (e95ca063b 2025-03-14)
DEBUG Searching for default Python interpreter in virtual environments
DEBUG Found `cpython-3.13.0-linux-x86_64-gnu` at `/home/ubuntu/uv/.venv/bin/python3` (virtual environment)
DEBUG Using Python 3.13.0 environment at: .venv
DEBUG Acquired lock for `.venv`
DEBUG At least one requirement is not satisfied: torch
warning: The `--torch-backend` setting is experimental and may change without warning. Pass `--preview` to disable this warning.
DEBUG Detected CUDA driver version from `/sys/module/nvidia/version`: 550.144.3
...
```
