Model card synced with fastforks 7204e33f

README.md

@@ -65,6 +65,12 @@ Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `selective-igev-eth3d-finetune
 | --- | --- | --- | --- | --- | --- | --- |
 | `selective-igev-eth3d-finetune` | 0.36 (375x1242) | 0.50 | 0.14† | 2.25 (375x1242) | 1.02† | 0.35† |
 
+### torch.compile and TensorRT
+
+| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `selective-igev-eth3d-finetune` | 994x1482 | 998.4 | 690.7 | 125.8 | 460.8 | 256 | 348.76 | 1238 / 1488 | 919 / 1088 | 1148 |
+
 ### Fixed-size speed and peak memory, all models
 
 | model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
@@ -84,6 +90,12 @@ Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `selective-igev-eth3d-finetune
 | `selective-igev-sceneflow` | portable | 190.6 ms / 282 / 362 | 575.4 ms / 769 / 1036 | 1420.3 ms / 1690 / 2030 | 8917.6 ms (n=3) / 10549 / 14320 |
 | `selective-igev-sceneflow` | kernels | 82.5 ms / 260 / 368 | 213.5 ms / 693 / 1010 | 473.3 ms / 1502 / 2046 | 2808.4 ms (n=3) / 6928 / 8800 |
 
+### Apple silicon (MPS)
+
+| model | `portable` | `kernels` | speed-up |
+| --- | --- | --- | --- |
+| `selective-igev-sceneflow` | 2292 ms / 1727 MiB (float16) | 1878 ms / 1704 MiB | 1.22x |
+
 Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
 
 - `selective-igev-eth3d-train`, `selective-igev-kitti12`, `selective-igev-kitti15`, `selective-igev-middlebury-finetune`, `selective-igev-middlebury-train`, `selective-igev-sceneflow`: same network and settings as `selective-igev-eth3d-finetune`, measured once (rows not repeated).
@@ -128,7 +140,7 @@ Every variant is checked against golden outputs of the original code at its pinn
 ```python
 from fastforks import stereo
 
-model = stereo.load("selective-igev-eth3d-finetune")  # portable PyTorch on the GPU if there is one, else the CPU
+model = stereo.load("selective-igev-eth3d-finetune")  # mode="auto": the preferred backend from recorded measurements
 result = model("left.png", "right.png")
 result.disparity  # [H, W] float32 tensor, input resolution
 result.backend  # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
@@ -139,3 +151,5 @@ Install with `pip install fastforks` (see <https://github.com/kleinicke/fastfork
 ## Kernels backend
 
 `stereo.load("selective-igev-eth3d-finetune", mode="kernels")` runs our CUDA kernels (`igev`, `cutlass_conv`, `cudnn_hacc`). It needs a prebuilt kernel for your GPU architecture (downloaded from <https://huggingface.co/fastforks/kernels> on first use) and a recent NVIDIA driver; if there is none it raises `BackendUnavailableError` with the reason and never falls back silently. `mode="auto"` picks `kernels` when it can run and logs the choice.
+
+<!-- staged from fastforks 7204e33f (2026-11-05) -->