admin/Seg_Data_Server_Net
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add real workspace acceptance

Browse Source
This commit is contained in:
admin
2026-06-30 17:33:15 +08:00
parent 4eb9452760
commit 53b81dd04d
11 changed files with 418 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

76
README.md
View File

@@ -69,15 +69,39 @@ SEG_SOURCE_ROOT=../Seg
SEG_DATA_SERVER_ROOT=.
SEG_BACKEND_DB=var/seg_data_server.sqlite3
SEG_BACKEND_LOG_DIR=var/job_logs
SEG_BACKEND_HOST=0.0.0.0
SEG_BACKEND_PORT=8010
SEG_BACKEND_RELOAD=1
SEG_TASK_CONDA_ENV=seg_smp
SEG_MMSEG_CONDA_ENV=seg_mmcv
SEG_BACKEND_CONDA_ENV=seg_smp
SEG_WEIGHT_MODE=copy
SEG_ENABLE_SHELL_TASKS=1
SEG_VALIDATE_DEEP=1
SEG_FRONTEND_HOST=0.0.0.0
SEG_FRONTEND_PORT=5173
VITE_API_BASE=http://localhost:8010
```
Environment variables used during deployment:
| Variable | Purpose |
| --- | --- |
| `SEG_SOURCE_ROOT` | Path to the original `Seg/` algorithm workspace. |
| `SEG_DATA_SERVER_ROOT` | Runtime root for this web project. Keep `.` for normal deployments. |
| `SEG_BACKEND_DB` | SQLite database used for datasets, jobs, and profiles. |
| `SEG_BACKEND_LOG_DIR` | Directory for job logs streamed through SSE. |
| `SEG_BACKEND_HOST` / `SEG_BACKEND_PORT` | FastAPI listen address. |
| `SEG_BACKEND_RELOAD` | Set `1` for development reload, `0` for long-running production workers. |
| `SEG_BACKEND_CONDA_ENV` | Conda env used to run FastAPI. |
| `SEG_TASK_CONDA_ENV` | Default env for dataset, SegModel, YOLO, visual, and analysis jobs. |
| `SEG_MMSEG_CONDA_ENV` | Dedicated env for full MMSeg/mmcv jobs. |
| `SEG_WEIGHT_MODE` | `copy` or `reflink` when syncing weights. |
| `SEG_ENABLE_SHELL_TASKS` | Enables execution of the wrapped legacy shell/Python tasks. |
| `SEG_VALIDATE_DEEP` | Enables deep acceptance by default for local agent runs. |
| `SEG_FRONTEND_HOST` / `SEG_FRONTEND_PORT` | Vite development server listen address. |
| `VITE_API_BASE` | Backend URL embedded into the frontend build or used by the dev server. |
Install system prerequisites first: Git, Conda or Miniconda, Node.js/npm, a
working NVIDIA driver and `nvidia-smi` for GPU discovery, and enough free disk
space for the copied weights. Full weight sync currently needs about 35 GB
@@ -129,11 +153,38 @@ scripts/run_backend.sh
scripts/run_frontend.sh
```
Defaults are `0.0.0.0:8010` for the backend and Vite's dev port for the
frontend. Override backend binding with `SEG_BACKEND_HOST` and
`SEG_BACKEND_PORT`. For a production process manager such as systemd,
supervisor, or Docker Compose, call the same two scripts so `.env` and the
configured conda environments are used consistently.
Defaults are `0.0.0.0:8010` for the backend and `0.0.0.0:5173` for the
frontend development server. Override backend binding with `SEG_BACKEND_HOST`
and `SEG_BACKEND_PORT`; override the Vite dev server with `SEG_FRONTEND_HOST`
and `SEG_FRONTEND_PORT`.
For long-running production service management, disable backend reload and
start the same script from systemd, supervisor, or Docker Compose so `.env`
and the configured conda environments are used consistently:
```bash
SEG_BACKEND_RELOAD=0 scripts/run_backend.sh
```
For a static frontend deployment, set `VITE_API_BASE` to the public backend
URL before building, then serve `frontend/dist/` with Nginx or another static
file server. A quick local preview is:
```bash
cd frontend
npm run build
npm run preview -- --host 0.0.0.0 --port 4173
```
If systemd is used, a minimal backend unit can call the script directly:
```ini
[Service]
WorkingDirectory=/home/wkmgc/Desktop/Data_Disk_1/Seg/Seg_Data_Server_Net
Environment=SEG_BACKEND_RELOAD=0
ExecStart=/home/wkmgc/Desktop/Data_Disk_1/Seg/Seg_Data_Server_Net/scripts/run_backend.sh
Restart=always
```
Validate a deployment before handing it to operators:
@@ -208,6 +259,16 @@ discovery. MMSeg full-model readiness is validated in
`SEG_MMSEG_CONDA_ENV` by importing `mmcv._ext` and building a local MMSeg
`EncoderDecoder` from the existing config tree.
`POST /api/acceptance/real` runs the same operator-facing path on existing
non-synthetic workspace files. It discovers a real `DataSet_Own/*_Ori` image
with a matching mask, discovers a real YOLO image/txt pair from
`Seg_All_In_One_YoloModel/Yolo数据集构建/Data`, uploads those files through the
dataset API, validates YOLO and mask readiness, generates `dataset.yaml`, runs
YOLO prediction and heatmap jobs, and runs the legacy stack job on the uploaded
real image/mask pair. The latest report is available from
`GET /api/acceptance/real/latest` and is shown in the coverage panel as
`真实数据`.
For stronger runtime proof, `POST /api/acceptance/deep` runs minimal training
loops for the three model families: one SegModel optimizer step, one YOLO
segmentation epoch on a synthetic 64x64 dataset, one YOLO GradCAM heatmap
@@ -308,5 +369,6 @@ non-training validation pass is needed.
The web dashboard calls validation in light mode by default:
`/api/agents/validate?run_build=false&run_acceptance=false&run_deep=false`.
Pass `run_acceptance=true` or `run_deep=true` only when you explicitly want the
agent to launch the heavier runtime acceptance checks from the browser/API.
Pass `run_acceptance=true`, `run_real=true`, or `run_deep=true` only when you
explicitly want the agent to launch the heavier runtime acceptance checks from
the browser/API.