Without this, it's impossible to override the lockFile as the default
overrideAttrs is applied after the composition in buildNimPackage has
read the lock file and generated the nim flags from it.
this makes it a lot easier to create a modified stdenv with a
different set of defaultHardeningFlags and as a bonus allows us
to inject the correct defaultHardeningFlags into toolchain wrapper
scripts, reducing repetition.
while most hardening flags are arguably more of a compiler thing,
it works better to put them in bintools-wrapper because cc-wrapper
can easily refer to bintools but not vice-versa.
mkDerivation can still easily refer to either when it is constructed.
this also switches fortran-hook.sh to use the same defaults for
NIX_HARDENING_ENABLE as for C. previously NIX_HARDENING_ENABLE
defaults were apparently used to avoid passing problematic flags
to a fortran compiler, but this falls apart as soon as mkDerivation
sets its own NIX_HARDENING_ENABLE - cc.hardeningUnsupportedFlags
is a more appropriate mechanism for this as it actively filters
out flags from being used by the wrapper, so switch to using that
instead.
this is still an imperfect mechanism because it doesn't handle a
compiler which has both langFortran *and* langC very well - applying
the superset of the two's hardeningUnsupportedFlags to either
compiler's invocation. however this is nothing new - cc-wrapper
already poorly handles a langFortran+langC compiler, applying two
setup hooks that have contradictory options.
cudaPackages.cuda_compat: ignore missing libs provided at runtime
cudaPackages.gpus: Jetson should never build by default
cudaPackages.flags: don't build Jetson capabilities by default
cudaPackages: re-introduce filter for pre-existing CUDA redist packages in overrides
cudaPackages: only recurseIntoAttrs for the latest of each major version
cudaPackages.nvccCompatabilities: use GCC 10 through CUDA 11.5 to avoid a GLIBC incompatability
cudaPackages.cutensor: acquire libcublas through cudatoolkit prior to 11.4
cudaPackages.cuda_compat: mark as broken on aarch64-linux if not targeting Jetson
cudaPackages.cutensor_1_4: fix build
cudaPackages: adjust use of autoPatchelfIgnoreMissingDeps
cudaPackages.cuda_nvprof: remove unecessary override to add addOpenGLRunpath
cudaPackages: use getExe' to avoid patchelf warning about missing meta.mainProgram
cudaPackages: fix evaluation with Nix 2.3
cudaPackages: fix platform detection for Jetson/non-Jetson aarch64-linux
python3Packages.tensorrt: mark as broken if required packages are missing
Note: evaluating the name of the derivation will fail if tensorrt is not present,
which is why we wrap the value in `lib.optionalString`.
cudaPackages.flags.getNixSystem: add guard based on jetsonTargets
cudaPackages.cudnn: use explicit path to patchelf
cudaPackages.tensorrt: use explicit path to patchelf
This is useful for the cuda variants of packages like opencv and pytorch,
whose xxxxConfig.cmake files do find_package(CUDAToolkit REQUIRED)
regardless of whether they actually use it. With the propagated hook,
we no longer have to manually add cuda dependencies into torch/opencvs
reverse dependencies
cudaPackages.cuda_nvcc: fix setupCudaHook propagation
