Add conda environment selector

README.md

@@ -71,6 +71,10 @@ The task builder also reads `GET /api/system/gpus` and lets an operator choose
 CPU or one or more GPUs before launch. Selected GPUs are passed to the backend
 as `gpus`, exported as `CUDA_VISIBLE_DEVICES`, and reflected into YOLO/visual
 `device` parameters and MMSeg config-generation `gpu_count/gpu_ids`.
+The same job launcher reads `GET /api/system/envs` and provides an Auto/manual
+conda environment selector. Auto keeps the backend defaults (`seg_smp` for
+general SegModel/YOLO/dataset tasks and `seg_mmcv` for MMSeg); manual mode
+sends `conda_env` with the job request for custom algorithm environments.
 
 The coverage panel calls `GET /api/coverage` and verifies that the user-facing
 scripts from the existing `Seg/` workspace are mapped to web jobs. MMSeg

backend/app/agents/evaluation_agent.py

@@ -68,6 +68,11 @@ def evaluate_project() -> dict:
         and "gpu_count" in frontend_text
         and "gpus" in frontend_text
         and "CUDA_VISIBLE_DEVICES" in jobs_text,
+        "conda_env_selection_ui": "/api/system/envs" in frontend_text
+        and "selectedCondaEnv" in frontend_text
+        and "runtimeSelector" in frontend_text
+        and "conda_env" in frontend_text
+        and "request.conda_env" in jobs_text,
         "live_log_stream_ui": "EventSource" in frontend_text
         and "eventSourceRef" in frontend_text
         and "log_size" in frontend_text

backend/tests/test_jobs.py

@@ -22,6 +22,19 @@ def test_build_task_sets_cuda_visible_devices(tmp_path, monkeypatch):
     assert spec.env["CUDA_VISIBLE_DEVICES"] == "2,0"
 
 
+def test_build_task_uses_requested_conda_env(tmp_path, monkeypatch):
+    captured = {}
+
+    def fake_build_module_task(job_type, params, conda_env):
+        captured["conda_env"] = conda_env
+        return CommandSpec(["python", "-c", "print('ok')"], tmp_path, "fake task")
+
+    monkeypatch.setattr(jobs, "build_module_task", fake_build_module_task)
+    jobs._build_task(JobCreate(type="segmodel.train", params={}, conda_env="seg_custom"))
+
+    assert captured["conda_env"] == "seg_custom"
+
+
 def test_job_progress_reports_log_size(tmp_path):
     log_path = tmp_path / "job.log"
     log_path.write_text("line one\nline two\n", encoding="utf-8")

frontend/src/main.tsx

@@ -167,6 +167,13 @@ type GpuPayload = {
   }>;
 };
 
+type CondaEnvPayload = {
+  available: boolean;
+  envs: Array<{ name: string; path: string; active?: boolean }>;
+  task_default?: string;
+  mmseg_default?: string;
+};
+
 type RuntimeCheck = {
   module: string;
   package?: string;
@@ -327,6 +334,7 @@ function formatBytes(value?: number) {
 function useData() {
   const [catalog, setCatalog] = useState<Catalog | null>(null);
   const [gpus, setGpus] = useState<GpuPayload | null>(null);
+  const [condaEnvs, setCondaEnvs] = useState<CondaEnvPayload | null>(null);
   const [jobs, setJobs] = useState<Job[]>([]);
   const [results, setResults] = useState<ResultItem[]>([]);
   const [curves, setCurves] = useState<TrainingCurve[]>([]);
@@ -342,9 +350,10 @@ function useData() {
 
   async function refresh() {
     try {
-      const [catalogNext, gpusNext, readinessNext, capabilitiesNext, jobsNext, resultsNext, curvesNext, datasetsNext, coverageNext, acceptanceNext, deepAcceptanceNext, agentEvaluationNext] = await Promise.all([
+      const [catalogNext, gpusNext, envsNext, readinessNext, capabilitiesNext, jobsNext, resultsNext, curvesNext, datasetsNext, coverageNext, acceptanceNext, deepAcceptanceNext, agentEvaluationNext] = await Promise.all([
         api<Catalog>("/api/catalog"),
         api<GpuPayload>("/api/system/gpus"),
+        api<CondaEnvPayload>("/api/system/envs"),
         api<RuntimeReadinessPayload>("/api/system/readiness"),
         api<CapabilityPayload>("/api/capabilities"),
         api<Job[]>("/api/jobs"),
@@ -358,6 +367,7 @@ function useData() {
       ]);
       setCatalog(catalogNext);
       setGpus(gpusNext);
+      setCondaEnvs(envsNext);
       setRuntimeReadiness(readinessNext);
       setCapabilities(capabilitiesNext);
       setJobs(jobsNext);
@@ -392,7 +402,7 @@ function useData() {
     return () => window.clearInterval(timer);
   }, []);
 
-  return { catalog, gpus, runtimeReadiness, capabilities, agentEvaluation, jobs, results, curves, datasets, datasetValidations, coverage, acceptance, deepAcceptance, error, refresh };
+  return { catalog, gpus, condaEnvs, runtimeReadiness, capabilities, agentEvaluation, jobs, results, curves, datasets, datasetValidations, coverage, acceptance, deepAcceptance, error, refresh };
 }
 
 function StatusPill({ status }: { status: string }) {
@@ -415,7 +425,7 @@ function JobProgressBar({ progress }: { progress?: JobProgress }) {
 }
 
 function App() {
-  const { catalog, gpus, runtimeReadiness, capabilities, agentEvaluation, jobs, results, curves, datasets, datasetValidations, coverage, acceptance, deepAcceptance, error, refresh } = useData();
+  const { catalog, gpus, condaEnvs, runtimeReadiness, capabilities, agentEvaluation, jobs, results, curves, datasets, datasetValidations, coverage, acceptance, deepAcceptance, error, refresh } = useData();
   const [taskType, setTaskType] = useState("mock.echo");
   const [params, setParams] = useState(JSON.stringify(defaultParams["mock.echo"], null, 2));
   const [selectedJob, setSelectedJob] = useState<Job | null>(null);
@@ -428,6 +438,7 @@ function App() {
   const [resultFamilyFilter, setResultFamilyFilter] = useState("all");
   const [resultRoleFilter, setResultRoleFilter] = useState("all");
   const [selectedGpuIds, setSelectedGpuIds] = useState<number[]>([]);
+  const [selectedCondaEnv, setSelectedCondaEnv] = useState("auto");
   const [uploadKind, setUploadKind] = useState<"images" | "labels" | "masks">("images");
   const [uploadFiles, setUploadFiles] = useState<FileList | null>(null);
   const [agentValidation, setAgentValidation] = useState<ValidationAgentPayload | null>(null);
@@ -482,6 +493,7 @@ function App() {
   }, [curves, results, selectedDataset]);
   const selectedYoloWeightReady = Boolean(selectedYoloOutputs.bestWeight);
   const availableGpus = gpus?.gpus ?? [];
+  const condaEnvOptions = useMemo(() => ["auto", ...Array.from(new Set((condaEnvs?.envs ?? []).map((item) => item.name))).sort()], [condaEnvs]);
   const selectedGpuDevice = selectedGpuIds.length ? selectedGpuIds.map((_, index) => index).join(",") : "cpu";
   const resultFamilyOptions = useMemo(() => ["all", ...Array.from(new Set(results.map((item) => item.family ?? "artifact"))).sort()], [results]);
   const resultRoleOptions = useMemo(() => ["all", ...Array.from(new Set(results.map((item) => item.role ?? "artifact"))).sort()], [results]);
@@ -522,11 +534,12 @@ function App() {
   }
 
   function jobPayload(type: string, rawParams: Record<string, unknown>) {
-    const payload: { type: string; params: Record<string, unknown>; gpus?: number[] } = {
+    const payload: { type: string; params: Record<string, unknown>; gpus?: number[]; conda_env?: string } = {
       type,
       params: paramsWithGpuSelection(type, rawParams)
     };
     if (selectedGpuIds.length) payload.gpus = selectedGpuIds;
+    if (selectedCondaEnv !== "auto") payload.conda_env = selectedCondaEnv;
     return payload;
   }
 
@@ -889,6 +902,18 @@ function App() {
                 ))}
               </div>
             </div>
+            <div className="runtimeSelector">
+              <label>
+                <span>Conda 环境</span>
+                <select value={selectedCondaEnv} onChange={(event) => setSelectedCondaEnv(event.target.value)} aria-label="conda environment">
+                  {condaEnvOptions.map((name) => (
+                    <option key={name} value={name}>
+                      {name === "auto" ? `Auto (${taskType.startsWith("mmseg.") ? condaEnvs?.mmseg_default ?? "seg_mmcv" : condaEnvs?.task_default ?? "seg_smp"})` : name}
+                    </option>
+                  ))}
+                </select>
+              </label>
+            </div>
             <label className="field">
               <span>参数 JSON</span>
               <textarea value={params} onChange={(event) => setParams(event.target.value)} />

frontend/src/styles.css

@@ -473,6 +473,34 @@ h2 {
   font-size: 10px;
 }
 
+.runtimeSelector {
+  margin-top: 10px;
+  padding: 10px;
+  border: 1px solid var(--line);
+  border-radius: 7px;
+  background: #101310;
+}
+
+.runtimeSelector label {
+  display: grid;
+  gap: 7px;
+}
+
+.runtimeSelector span {
+  color: var(--muted);
+  font-size: 12px;
+}
+
+.runtimeSelector select {
+  width: 100%;
+  height: 38px;
+  padding: 0 10px;
+  border-radius: 6px;
+  border: 1px solid var(--line);
+  background: var(--field);
+  color: var(--ink);
+}
+
 .field {
   display: grid;
   gap: 8px;