Meta-crates (Phase 2):
- rtx-core / rtx-training / rtx-inference-stack gain cuda and metal
features threading into their sub-crates; GPU was previously
unreachable through the user-facing bundles.
- rtx-training restores rtx-distributed (the hpc-channels blocker is
gone) so the advertised DistributedTransformerTrainer resolves; drops
the unused rtx-runtime dep.
- rtx-transformers drops unused rtx-backend/rtx-backend-cpu deps
(stale comment referenced a teacher that never used them).
Never-compiled CUDA paths fixed (surfaced by the new feature wiring,
verified on RTX 5060 Ti / CUDA 13.1):
- rtx-compress build.rs: missing Path/Command/fs imports.
- rtx-flash-attention flash_decode_forward: reborrow &mut kernel args.
- rtx-transformers: rope kernel include path, cudarc 0.18 Arc<CudaModule>,
PushKernelArg imports in jepa_gpu, edition-2024 ref patterns.
- rtx-memory: full cudarc 0.18 port (CudaContext, stream-based alloc,
DevicePtr accessors, error enum formatting) across gpu_pinning,
gpu_transfer, gpu_real, gpu_allocator/arena, gpu_tests.
JEPA (Phase 3):
- JepaRunConfig::apply_cluster_plan consumes ClusterTrainingPlan
(batch size, TP/DP, world size, total steps) so jepa_cluster is no
longer standalone dead config; ViTSizeStr::approx_params_m feeds
JepaParallelConfig::for_model_and_cluster.
- WebDatasetShard::load reads real .tar shards from disk via the
existing parser (gzip rejected explicitly); to_in_memory documented
as synthetic/test-only.
- New image-decode feature actually defines the dep for the previously
unreachable cfg(feature = "image-decode") JPEG/PNG decode path.
Co-Authored-By: Claude Fable 5 <[email protected]>
