Using CUDA platform selection

Also,
* Replaced lazy with eager artifacts.

Author: stemann

O/ONNXRuntime/build_tarballs.jl

@@ -2,14 +2,15 @@
 # `julia build_tarballs.jl --help` to see a usage message.
 using BinaryBuilder, Pkg
 
+include(joinpath(@__DIR__, "..", "..", "platforms", "cuda.jl"))
+
 name = "ONNXRuntime"
 version = v"1.10.0"
 
 # Cf. https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements
 # Cf. https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html#requirements
 cuda_versions = [
-    # No CUDA 10.2, since pre-built x86_64 CUDA binaries are built for CUDA 11
-    v"11.3", # Using 11.3, and not 11.4, to be compatible with TensorRT (JLL) v8.0.1 (which includes aarch64 support)
+    v"11.3.1", # Using 11.3, and not 11.4, to be compatible with TensorRT (JLL) v8.0.1 (which includes aarch64 support)
 ]
 cuda_aarch64_tag = "10.2"
 cudnn_version = v"8.2.4"
@@ -134,12 +135,12 @@ dependencies = [
         compat = tensorrt_compat,
         platforms = cuda_platforms),
     Dependency("Zlib_jll"; platforms = cuda_platforms),
-    BuildDependency(PackageSpec("CUDA_full_jll", v"11.3.1"); platforms = cuda_platforms),
+    BuildDependency(PackageSpec("CUDA_full_jll", first(cuda_versions)); platforms = cuda_platforms),
     HostBuildDependency(PackageSpec("protoc_jll", v"3.16.1"))
 ]
 
 # Build the tarballs, and possibly a `build.jl` as well.
 build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies;
-    lazy_artifacts = true,
+    augment_platform_block = CUDA.augment,
     julia_compat = "1.6",
     preferred_gcc_version = v"8")