From faf5be476037d3baf4da3515789906a176f9b040 Mon Sep 17 00:00:00 2001
From: shuishen <1109946754@qq.com>
Date: Tue, 25 Aug 2026 12:23:59 +0800
Subject: [PATCH] feat(pointcloud): auto-select verified GPU runtime
---
apps/workbench-console/README.md | 17 ++++++++++-------
1 files changed, 10 insertions(+), 7 deletions(-)
diff --git a/apps/workbench-console/README.md b/apps/workbench-console/README.md
index 97bd93d..55ccba1 100644
--- a/apps/workbench-console/README.md
+++ b/apps/workbench-console/README.md
@@ -153,17 +153,20 @@
currently displayed orientation.
An incorrect revision can be removed after confirmation, but the endpoint only
removes that revision directory and cannot remove LAS/PLY sources, semantic
-results, or training outputs. A local CPU training job requires two classes with
-at least 500 confirmed points each, then writes a portable model, metrics, and
-predicted PLY asynchronously. Sources, point indices, class codes, Python
-interpreter and command are all server-fixed; the same model pipeline can later
-use CUDA without data or code changes.
+results, or training outputs. A local training job requires two classes with at
+least 500 confirmed points each, then writes a portable model, metrics, and
+predicted PLY asynchronously. The server probes only the fixed
+`05-3d-pointcloud-gpu` interpreter; when CUDA is available it uses that
+environment, otherwise it uses the retained fixed CPU interpreter. Sources,
+point indices, class codes, Python interpreters and commands are all server-fixed.
+Each job reports the selected device, environment, and PyTorch version.
The supervised area also exposes **Apply trained model**. It discovers only
complete local training directories containing both `model.pt` and `metrics.json`.
Select a discovered model, upload one new RGB PLY/PCD/LAS/LAZ point cloud, and
the server copies its bytes into new `raw` and `processed` inference directories
-before starting a background CPU job. The API accepts neither a browser path nor
+before starting a background job that automatically selects the verified GPU or
+CPU environment. The API accepts neither a browser path nor
an arbitrary model path. Completion shows the classified PLY preview directly in
the workspace and provides downloads for classified LAS, class-count CSV,
prediction JSON, metadata, and model weight. XYZ-only input fails explicitly:
@@ -181,7 +184,7 @@
3. **Point-cloud semantic classification**: classification cases, coloured
point preview, semantic LAS/CSV/GeoJSON and review-only rule outputs.
4. **Annotation, training, and model application**: RGB annotation source,
- annotation revisions, CPU training, training metrics, and applying a trained
+ annotation revisions, automatic GPU/CPU training, training metrics, and applying a trained
model to a new RGB cloud.
Each workspace filters the case library to its own purpose. A point-cloud run
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