These can be either an integer or a range.
Range options are necessary for `FREE_LIMIT` to take effect when used in
conjunction with `TIMELINE_LIMIT_*`.
It is currently tied to `services.avahi.enable` which might not be
desirable.
With this change it is possible to disable the service with
`services.printing.browsed.enable = false`
Factor out part of the provisioning script into a
wait-until-service-is-ready script, and put it unconditionally in
front of ExecStartPost=, so that services that depend on influxdb2 are
not started until influxdb2 responds to requests.
Fixes https://github.com/NixOS/nixpkgs/issues/317017 ("Scrutiny tries to start before influxdb has started")
We currently package all CUDA versions from 10.0 onwards. In
some cases, CUDA is the only thing preventing us from removing old
versions of GCC. Since we currently don’t deprecate or remove CUDA
versions, this will be an increasing drag on compiler maintenance in
Nixpkgs going forward unless we establish a sensible policy. After
discussing this with @SomeoneSerge in the context of old versions
of GCC, I learned that there was already a desire to remove at least
versions prior to 11.3, as those versions were only packaged in the
old “runfile” format, but that it was blocked on someone doing
the work to warn about the upcoming deprecation for a release cycle.
This change adds a release note and warnings indicating that CUDA 10.x
and 11.x will be removed in Nixpkgs 25.05, about 8 months from now.
I chose this version cut‐off because these versions of CUDA require
GCC < 12. GCC releases a major version every year, and seems to
support about four releases at a time, releasing the last update to
the oldest version and marking it as unsupported on their site around
the time of the release of the next major version. Therefore, by the
time of the 25.05 release, we should expect GCC 15 to be released
and GCC 11 to become unsupported. Adding a warning and communicating
the policy of only shipping CUDA versions that work with supported
compilers in the release notes means that we should be able to
clean up old versions as required without any issue or extensive
deprecation period in future, without obligating us to do so if there
is a strongly compelling reason to be more lenient. That should help
solve both shipping an indefinitely‐growing list of CUDA versions
and an indefinitely‐growing list of GCC and LLVM versions.
As I’m not a user of CUDA myself, I can’t be sure of how sensible
this version support policy is, but I think it’s fair to say that
it’s reasonable for Nixpkgs to choose not to maintain compiler
versions that are unsupported upstream just for the sake of versions
of CUDA that are also unmaintained. CUDA 11.x has not received an
update for two years already, and would only become unsupported in
Nixpkgs in over half a year’s time.
CUDA 10.x is currently unused in‐tree except for the unmaintained
Caffe and NVIDIA DCGM, which depends on multiple CUDA versions solely
so that it can provide plugins for those versions. The latest DCGM
version has already removed support for CUDA 10.x and is just awaiting
an update in Nixpkgs. They maintain a list of supported versions to
build plugins for in their CMake build system, so it should be simple
enough for us to only build support for the versions of CUDA that we
support in Nixpkgs.
From what I can tell, CUDA 11.x is currently used by the following
packages other than DCGM:
* `catboost`, because of
<https://github.com/catboost/catboost/issues/2540>. It looks like
upstream has since redesigned this part of their build system, so
perhaps the problem is no longer present, or would be easier to fix.
* `magma_2_6_2`, an old version from before upstream added CUDA
12 support. This seems okay to break to me; that version is not
maintained and will never be updated for new CUDA versions, and
the CUDA support is optional.
* `paddlepaddle`, which, uh, also requires OpenSSL 1.1 of all
things. <https://github.com/PaddlePaddle/Paddle/issues/67571>
states that PaddlePaddle supports up to 12.3.
* `python3Packages.cupy`, which is listed as “possibly incompatible
with cutensor 2.0 that comes with `cudaPackages_12`”. I’m
not sure what the “possibly” means here, but according to
<https://github.com/cupy/cupy/tree/v13.3.0?tab=readme-ov-file#installation>
they ship binary wheels using CUDA 12.x so I think this should
be fine.
* `python3Packages.tensorrt`, which supports CUDA 12.x going by
<https://github.com/NVIDIA/TensorRT/blob/release/10.4/CMakeLists.txt#L111>.
* TensorFlow, which has a link to
<https://www.tensorflow.org/install/source#gpu> above the
`python3Packages.tensorflow-bin` definition, but that page lists
the versions we package as supporting CUDA 12.x.
Given the years since CUDA 11.x received any update upstream, and the
seemingly very limited set of packages that truly require it, I think
the policy of being able to drop versions that require unsupported
compilers starting from the next Nixpkgs release is a reasonable
one, but of course I’m open to feedback from the CUDA maintainers
about this.
When `diskImage = null`, the root fs is a tmpfs instead of
`/dev/vda`. Thus, it doesn't have to wait for virtio modules to load
before being mounted. The root fs is a dependency of shared
directories by nature of being their parent directory. Without
depending on `/dev/vda`, these shared directories may attempt to mount
without virtio modules being loaded.
The package has been updated to 0.4 which will result in an auto-migration of the config. This updates our config to match the new expected format. Assertions have been added to warn users that they need to migrate their configuration.
