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feat: 新增 compute_gateway、compute_poller、agent 模块，重构前端 dist

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- 新增 backend/app/modules/compute_gateway（client/sync）计算网关模块
- 新增 backend/app/workers/compute_poller 计算轮询 worker
- 新增 compute/agent/process_manager 进程管理器
- 新增 scripts/ 脚本目录
- 更新 Docker 部署配置（app/compute/nginx）
- 更新后端平台 API、数据库 SQL、core 配置
- 更新前端多个视图组件及 API 模块
- 重构 frontend/dist 构建产物（新 hash）
- 更新多项文档

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
wuyongtao
2026-07-22 17:32:59 +08:00
parent 1e438164c1
commit 836343b29e
180 changed files with 3352 additions and 263 deletions
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2
.gitignore vendored
View File

@@ -187,3 +187,5 @@ cython_debug/
# PyPI configuration file
.pypirc
docker/llamafactory-latest.tar.gz
docker/compute/data/

658
backend/app/api/v1/endpoints/platform.py
View File

@@ -1,12 +1,17 @@
from __future__ import annotations
from typing import Any
import uuid
import json
from pathlib import Path
from typing import Any
from fastapi import APIRouter, Body, File, HTTPException, Query, UploadFile
from fastapi.responses import PlainTextResponse
from app.core.config import get_settings
from app.db.platform_store import get_platform_store
from app.modules.compute_gateway.client import ComputeNodeClient
from app.modules.compute_gateway.sync import poll_compute_jobs_once
router = APIRouter()
@@ -19,6 +24,77 @@ def fail(status_code: int, message: str) -> HTTPException:
return HTTPException(status_code=status_code, detail={"code": status_code, "message": message, "data": None})
def _node_for_task(task: dict[str, Any]) -> dict[str, Any] | None:
return next((node for node in get_platform_store().compute_nodes() if node["id"] == task.get("compute_node_id")), None)
def _task_for_compute_job(job_id: str) -> dict[str, Any] | None:
return next((task for task in get_platform_store().tasks() if task.get("compute_job_id") == job_id), None)
async def _submit_fine_tune_task(store: Any, payload: dict[str, Any]) -> dict[str, Any]:
task_id = str(payload.get("task_id") or payload.get("id") or "")
if task_id and get_settings().compute_mode != "simulator":
try:
preflight = await _fine_tune_preflight(store, task_id, payload, validate=True)
except Exception as exc: # noqa: BLE001 - task has not entered running state yet
raise RuntimeError(f"preflight failed: {exc}") from exc
if not preflight["valid"]:
errors = "; ".join(preflight.get("errors") or ["preflight failed"])
raise RuntimeError(f"preflight failed: {errors}")
task = store.start_task(payload)
if get_settings().compute_mode == "simulator":
return task
node, job_payload = store.build_compute_job_payload(task["id"])
job = await ComputeNodeClient(node["api_base_url"]).create_job(job_payload)
return store.apply_compute_job(task["id"], job)
async def _fine_tune_preflight(
store: Any,
task_id: str,
payload: dict[str, Any] | None = None,
validate: bool = True,
) -> dict[str, Any]:
node, job_payload = store.prepare_compute_job_payload(task_id, payload or {})
if get_settings().compute_mode == "simulator":
preview = {
"valid": True,
"errors": [],
"warnings": ["compute_mode=simulator skips remote compute validation"],
"engine": job_payload.get("engine") or job_payload.get("training_engine") or "llama_factory",
"command": [],
"command_text": "",
"work_dir": "",
"env": {},
"path_checks": [],
}
else:
client = ComputeNodeClient(node["api_base_url"])
preview = await (client.validate_job(job_payload) if validate else client.preview_job(job_payload))
errors = list(preview.get("errors") or [])
warnings = list(preview.get("warnings") or [])
if not node.get("enabled"):
errors.append(f"compute node disabled: {node.get('code')}")
if node.get("scheduler_status") not in {"online", "draining"}:
errors.append(f"compute node not schedulable: {node.get('code')} status={node.get('scheduler_status')}")
return {
"valid": bool(preview.get("valid", not errors)) and not errors,
"errors": errors,
"warnings": warnings,
"node": {
"id": node.get("id"),
"code": node.get("code"),
"name": node.get("name"),
"api_base_url": node.get("api_base_url"),
"scheduler_status": node.get("scheduler_status"),
"gpu_count": node.get("gpu_count"),
},
"job_payload": job_payload,
"preview": preview,
}
@router.post("/login")
async def login(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
user = get_platform_store().login(payload.get("username", ""), payload.get("password", ""))
@@ -84,7 +160,29 @@ async def delete_user(user_id: str, current_username: str | None = Query(default
@router.get("/model-manage/local-models")
async def local_models() -> dict[str, Any]:
models = [{"path": item.get("path") or "", "name": item["name"]} for item in get_platform_store().models()]
store = get_platform_store()
models = [{"path": item.get("path") or "", "name": item["name"], "source": "registered"} for item in store.models()]
seen = {item["path"] for item in models if item.get("path")}
if get_settings().compute_mode != "simulator":
for node in store.compute_nodes():
if not node.get("enabled"):
continue
try:
result = await ComputeNodeClient(node["api_base_url"]).list_files(root="models", directories_only=True)
except Exception:
continue
for item in result.get("items") or []:
path = str(item.get("path") or "")
if not path or path in seen:
continue
seen.add(path)
models.append(
{
"path": path,
"name": item.get("name") or path.rsplit("/", 1)[-1],
"source": f"compute:{node.get('code')}",
}
)
return ok({"models": models})
@@ -95,6 +193,7 @@ async def trained_models() -> dict[str, Any]:
@router.delete("/model-manage/trained-models/{model_id}")
async def delete_trained_model(model_id: str, type: str = Query(default="merged")) -> dict[str, Any]:
get_platform_store().delete_trained_model(model_id)
return ok({"deleted": model_id, "type": type})
@@ -113,7 +212,14 @@ async def model_list() -> dict[str, Any]:
@router.post("/model-manage")
async def create_model(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
return ok(get_platform_store().create_model(payload))
try:
return ok(get_platform_store().create_model(payload))
except KeyError as exc:
raise fail(400, f"missing field: {exc}")
except ValueError as exc:
raise fail(400, str(exc))
except Exception as exc: # noqa: BLE001 - keep API errors visible to deployment smoke checks
raise fail(500, f"create model failed: {exc}")
@router.get("/model-manage/{model_id}")
@@ -199,12 +305,73 @@ async def activate_dataset_version(file_id: str, payload: dict[str, Any] = Body(
@router.delete("/dataset-manage/versions/{file_id}/{version_id}")
async def delete_dataset_version(file_id: str, version_id: str) -> dict[str, Any]:
return ok(get_platform_store().file_versions(file_id))
try:
return ok(get_platform_store().delete_file_version(file_id, version_id))
except KeyError:
raise fail(404, "dataset version not found")
except ValueError as exc:
raise fail(400, str(exc))
async def _sync_dataset_file_to_compute_nodes(
store: Any,
dataset_id: str,
file_id: str,
filename: str,
content: bytes,
) -> list[dict[str, Any]]:
results: list[dict[str, Any]] = []
if get_settings().compute_mode == "simulator":
return results
target_name = Path(filename or f"{file_id}.jsonl").name
target_relative_path = f"datasets/{dataset_id}/{target_name}"
for node in store.compute_nodes():
if not node.get("enabled"):
continue
try:
result = await ComputeNodeClient(node["api_base_url"]).upload_file(
target_name,
content,
target_relative_path,
resource_type="dataset",
resource_id=dataset_id,
)
store.upsert_resource_replica(
node["id"],
"dataset",
dataset_id,
str(result.get("local_path") or ""),
)
results.append(
{
"node_id": node["id"],
"node_code": node.get("code"),
"success": True,
"local_path": result.get("local_path"),
"byte_size": result.get("byte_size"),
"checksum_sha256": result.get("checksum_sha256"),
}
)
except Exception as exc: # noqa: BLE001 - keep upload usable while exposing sync failures
results.append(
{
"node_id": node["id"],
"node_code": node.get("code"),
"success": False,
"error": str(exc),
}
)
return results
@router.post("/dataset-manage/upload/{dataset_id}")
async def upload_dataset_files(dataset_id: str, files: list[UploadFile] = File(default=[])) -> dict[str, Any]:
async def upload_dataset_files(
dataset_id: str,
files: list[UploadFile] = File(default=[]),
sync_to_compute: bool = Query(default=True),
) -> dict[str, Any]:
created: list[dict[str, Any]] = []
compute_sync: list[dict[str, Any]] = []
store = get_platform_store()
try:
store.dataset(dataset_id)
@@ -214,8 +381,19 @@ async def upload_dataset_files(dataset_id: str, files: list[UploadFile] = File(d
for file in files:
raw = await file.read()
content = raw.decode("utf-8", errors="replace")
created.append(store.add_dataset_file(conn, dataset_id, file.filename or "upload.jsonl", content))
return ok({"files": created})
created_file = store.add_dataset_file(conn, dataset_id, file.filename or "upload.jsonl", content)
created.append(created_file)
if sync_to_compute:
compute_sync.extend(
await _sync_dataset_file_to_compute_nodes(
store,
dataset_id,
created_file["id"],
created_file["name"],
raw,
)
)
return ok({"files": created, "compute_sync": compute_sync})
@router.get("/dataset-manage/download/{dataset_id}")
@@ -299,12 +477,45 @@ async def create_fine_tune(payload: dict[str, Any] = Body(...)) -> dict[str, Any
@router.post("/fine-tune/start")
async def start_fine_tune(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
store = get_platform_store()
try:
return ok(get_platform_store().start_task(payload))
return ok(await _submit_fine_tune_task(store, payload))
except KeyError:
raise fail(404, "fine tune task not found")
except RuntimeError as exc:
task_id = str(payload.get("task_id") or payload.get("id") or "")
if task_id:
store.mark_task_failed(task_id, str(exc))
raise fail(409, str(exc))
except Exception as exc: # noqa: BLE001 - mark task failed when remote submit fails
task_id = str(payload.get("task_id") or payload.get("id") or "")
if task_id:
store.mark_task_failed(task_id, str(exc))
raise fail(502, f"submit compute job failed: {exc}")
@router.post("/fine-tune/{task_id}/preflight")
async def fine_tune_preflight(task_id: str, payload: dict[str, Any] | None = Body(default=None)) -> dict[str, Any]:
try:
return ok(await _fine_tune_preflight(get_platform_store(), task_id, payload or {}, validate=True))
except KeyError:
raise fail(404, "fine tune task not found")
except RuntimeError as exc:
raise fail(409, str(exc))
except Exception as exc: # noqa: BLE001 - expose compute validation errors to training create page
raise fail(502, f"compute preflight failed: {exc}")
@router.post("/fine-tune/{task_id}/command-preview")
async def fine_tune_command_preview(task_id: str, payload: dict[str, Any] | None = Body(default=None)) -> dict[str, Any]:
try:
return ok(await _fine_tune_preflight(get_platform_store(), task_id, payload or {}, validate=False))
except KeyError:
raise fail(404, "fine tune task not found")
except RuntimeError as exc:
raise fail(409, str(exc))
except Exception as exc: # noqa: BLE001
raise fail(502, f"compute command preview failed: {exc}")
@router.get("/fine-tune/{task_id}")
@@ -315,6 +526,37 @@ async def fine_tune_detail(task_id: str) -> dict[str, Any]:
raise fail(404, "fine tune task not found")
@router.get("/fine-tune/{task_id}/logs")
async def fine_tune_logs(
task_id: str,
tail_lines: int | None = Query(default=500, ge=1, le=5000),
offset: int | None = Query(default=None, ge=0),
limit: int | None = Query(default=None, ge=1, le=5000),
) -> dict[str, Any]:
store = get_platform_store()
try:
task = store.task(task_id)
except KeyError:
raise fail(404, "fine tune task not found")
if task.get("compute_job_id"):
node = _node_for_task(task)
if node:
try:
logs = await ComputeNodeClient(node["api_base_url"]).job_logs(task["compute_job_id"], tail_lines, offset, limit)
if task.get("status") in {"queued", "running", "failed", "stopped", "completed"}:
try:
job = await ComputeNodeClient(node["api_base_url"]).get_job(task["compute_job_id"])
store.apply_compute_job(task_id, job)
except Exception:
pass
return ok({"source": "compute", **logs})
except Exception as exc: # noqa: BLE001 - keep failure reason visible even when log fetch fails
content = task.get("failure_reason") or f"fetch compute log failed: {exc}"
return ok({"job_id": task.get("compute_job_id"), "source": "task", "file": task.get("log_file") or "", "content": content, "size": f"{len(content.encode('utf-8'))} B"})
content = task.get("failure_reason") or ""
return ok({"job_id": task.get("compute_job_id") or "", "source": "task", "file": task.get("log_file") or "", "content": content, "size": f"{len(content.encode('utf-8'))} B"})
@router.put("/fine-tune/{task_id}")
async def update_fine_tune(task_id: str, payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
try:
@@ -325,8 +567,14 @@ async def update_fine_tune(task_id: str, payload: dict[str, Any] = Body(...)) ->
@router.post("/fine-tune/stop/{task_id}")
async def stop_fine_tune(task_id: str) -> dict[str, Any]:
store = get_platform_store()
try:
return ok(get_platform_store().stop_task(task_id))
task = store.task(task_id)
node = _node_for_task(task)
if task.get("compute_job_id") and node and get_settings().compute_mode != "simulator":
job = await ComputeNodeClient(node["api_base_url"]).stop_job(task["compute_job_id"])
return ok(store.apply_compute_job(task_id, job))
return ok(store.stop_task(task_id))
except KeyError:
raise fail(404, "fine tune task not found")
@@ -336,6 +584,27 @@ async def stop_fine_tune_alt(task_id: str) -> dict[str, Any]:
return await stop_fine_tune(task_id)
@router.post("/fine-tune/{task_id}/retry")
async def retry_fine_tune(task_id: str, payload: dict[str, Any] | None = Body(default=None)) -> dict[str, Any]:
store = get_platform_store()
payload = payload or {}
try:
task = store.task(task_id)
except KeyError:
raise fail(404, "fine tune task not found")
if task["status"] not in {"failed", "stopped"} and not payload.get("force"):
raise fail(409, "only failed or stopped tasks can be retried without force=true")
retry_payload = {**task, **payload, "task_id": task_id, "id": task_id}
store.reset_task_for_retry(task_id, retry_payload)
try:
return ok(await _submit_fine_tune_task(store, retry_payload))
except RuntimeError as exc:
raise fail(409, str(exc))
except Exception as exc: # noqa: BLE001 - mark retry failed when remote submit fails
store.mark_task_failed(task_id, str(exc))
raise fail(502, f"retry fine tune task failed: {exc}")
@router.delete("/fine-tune/{task_id}")
async def delete_fine_tune(task_id: str) -> dict[str, Any]:
get_platform_store().delete_task(task_id)
@@ -358,17 +627,217 @@ async def fine_tune_checkpoints(task_id: str) -> dict[str, Any]:
return ok(checkpoints)
@router.get("/model-eval")
async def model_eval_list() -> dict[str, Any]:
return ok(get_platform_store().eval_tasks())
@router.get("/model-eval/{task_id}")
async def model_eval_detail(task_id: str) -> dict[str, Any]:
try:
return ok(get_platform_store().eval_task(task_id))
except KeyError:
raise fail(404, "eval task not found")
@router.post("/model-eval/start")
async def model_eval_start(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
task = get_platform_store().create_eval_task(payload)
return ok({"task_id": task["id"], **task})
@router.delete("/model-eval/{task_id}")
async def model_eval_delete(task_id: str) -> dict[str, Any]:
get_platform_store().delete_eval_task(task_id)
return ok({"deleted": task_id})
@router.get("/dimension")
async def dimension_list() -> dict[str, Any]:
return ok(get_platform_store().dimensions())
@router.post("/dimension")
async def dimension_create(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
return ok(get_platform_store().create_dimension(payload))
@router.get("/dimension/{dimension_id}")
async def dimension_detail(dimension_id: str) -> dict[str, Any]:
try:
return ok(get_platform_store().dimension(dimension_id))
except KeyError:
raise fail(404, "dimension not found")
@router.put("/dimension/{dimension_id}")
async def dimension_update(dimension_id: str, payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
try:
return ok(get_platform_store().update_dimension(dimension_id, payload))
except KeyError:
raise fail(404, "dimension not found")
@router.delete("/dimension/{dimension_id}")
async def dimension_delete(dimension_id: str) -> dict[str, Any]:
get_platform_store().delete_dimension(dimension_id)
return ok({"deleted": dimension_id})
@router.get("/model-compare")
async def model_compare_list() -> dict[str, Any]:
return ok(get_platform_store().compare_tasks())
@router.post("/model-compare")
async def model_compare_create(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
task = get_platform_store().create_compare_task(payload)
return ok({"id": task["id"]})
@router.post("/model-compare/all/stop-all")
async def model_compare_stop_all() -> dict[str, Any]:
return ok({"stopped": True})
@router.post("/model-compare/stop-by-pid")
async def model_compare_stop_by_pid(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
return ok({"stopped": True, "pid": payload.get("pid")})
@router.get("/model-compare/{task_id}")
async def model_compare_detail(task_id: str) -> dict[str, Any]:
try:
return ok(get_platform_store().compare_task(task_id))
except KeyError:
raise fail(404, "compare task not found")
@router.delete("/model-compare/{task_id}")
async def model_compare_delete(task_id: str) -> dict[str, Any]:
get_platform_store().delete_compare_task(task_id)
return ok({"deleted": task_id})
@router.get("/model-compare/{task_id}/load-status")
async def model_compare_load_status(task_id: str) -> dict[str, Any]:
try:
task = get_platform_store().compare_task(task_id)
except KeyError:
raise fail(404, "compare task not found")
load_status = task.get("load_status") or {"loaded_models": []}
if isinstance(load_status, str):
try:
load_status = json.loads(load_status)
except json.JSONDecodeError:
load_status = {"loaded_models": []}
return ok({"all_ready": all(item.get("status") in {"ready", "running"} for item in load_status.get("loaded_models", [])), **load_status})
@router.post("/model-compare/{task_id}/load-status")
async def model_compare_update_load_status(task_id: str, payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
try:
return ok(get_platform_store().update_compare_task(task_id, {"load_status": payload.get("load_status") or {"loaded_models": []}}))
except KeyError:
raise fail(404, "compare task not found")
@router.post("/model-compare/{task_id}/load")
async def model_compare_load(task_id: str) -> dict[str, Any]:
try:
task = get_platform_store().compare_task(task_id)
models = task.get("models") or []
if isinstance(models, str):
try:
models = json.loads(models)
except json.JSONDecodeError:
models = []
loaded_models = [
{
"model_id": item.get("model_id"),
"model_name": item.get("model_name"),
"status": "ready",
"pid": 45000 + index,
"port": item.get("port") or 18000 + index,
}
for index, item in enumerate(models)
if isinstance(item, dict)
]
return ok(get_platform_store().update_compare_task(task_id, {"status": "loaded", "load_status": {"loaded_models": loaded_models}}))
except KeyError:
raise fail(404, "compare task not found")
@router.post("/model-compare/{task_id}/unload")
async def model_compare_unload(task_id: str) -> dict[str, Any]:
try:
return ok(get_platform_store().update_compare_task(task_id, {"status": "pending", "load_status": {"loaded_models": []}}))
except KeyError:
raise fail(404, "compare task not found")
@router.post("/model-compare/{task_id}/start-model")
async def model_compare_start_model(task_id: str, payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
return ok({"pid": 45001, "port": payload.get("port") or 18001, "task_id": task_id})
@router.post("/model-compare/chat-with-port")
async def model_compare_chat_with_port(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
question = ""
for message in payload.get("messages") or []:
if message.get("role") == "user":
question = str(message.get("content") or "")
content = f"当前后端已收到推理请求：{question[:120]}"
return ok({"response": content, "content": content})
@router.post("/model-compare/stream-chat")
async def model_compare_stream_chat(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
question = payload.get("user_question") or payload.get("question") or ""
return ok({"response": f"当前后端已收到流式推理请求：{str(question)[:120]}"})
@router.post("/model-chat/batch")
async def model_chat_batch(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
return ok({"responses": [], "request": payload})
@router.post("/model-chat/local/chat")
async def model_chat_local(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
return ok({"response": "local chat adapter is not connected yet", "request": payload})
@router.post("/model-chat/local/preload")
async def model_chat_local_preload(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
return ok({"loaded": True, "request": payload})
@router.post("/model-chat/trained/preload")
async def model_chat_trained_preload(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
return ok({"loaded": True, "request": payload})
@router.get("/compute/nodes")
async def compute_nodes() -> dict[str, Any]:
return ok(get_platform_store().compute_nodes())
@router.get("/compute/nodes/{node_id}")
async def compute_node_detail(node_id: str) -> dict[str, Any]:
node = next((item for item in get_platform_store().compute_nodes() if item["id"] == node_id), None)
if not node:
raise fail(404, "compute node not found")
return ok(node)
@router.post("/compute/nodes")
async def create_compute_node(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
try:
return ok(get_platform_store().create_compute_node(payload))
except KeyError as exc:
raise fail(400, f"missing field: {exc}")
except ValueError as exc:
raise fail(400, str(exc))
@router.put("/compute/nodes/{node_id}")
@@ -377,11 +846,47 @@ async def update_compute_node(node_id: str, payload: dict[str, Any] = Body(...))
return ok(get_platform_store().update_compute_node(node_id, payload))
except KeyError:
raise fail(404, "compute node not found")
except ValueError as exc:
raise fail(400, str(exc))
@router.post("/compute/nodes/{node_id}/test-connection")
async def test_compute_node(node_id: str) -> dict[str, Any]:
return ok({"node_id": node_id, "success": True, "latency_ms": 12})
store = get_platform_store()
node = next((item for item in store.compute_nodes() if item["id"] == node_id), None)
if not node:
raise fail(404, "compute node not found")
client = ComputeNodeClient(node["api_base_url"])
try:
result = await client.test_connection()
store.replace_node_gpus(node_id, result["gpus"])
updated = store.update_compute_node_health(node_id, result["health"], True)
return ok(
{
"node_id": node_id,
"success": True,
"latency_ms": result["latency_ms"],
"gpu_count": len(result["gpus"]),
"health": updated["health_detail"],
}
)
except Exception as exc: # noqa: BLE001 - return the connection error for node maintenance
updated = store.update_compute_node_health(node_id, {}, False, str(exc))
return ok(
{
"node_id": node_id,
"success": False,
"latency_ms": 0,
"gpu_count": updated.get("gpu_count", 0),
"error": str(exc),
"health": updated["health_detail"],
}
)
@router.post("/compute/nodes/{node_id}/health-check")
async def health_check_compute_node(node_id: str) -> dict[str, Any]:
return await test_compute_node(node_id)
@router.post("/compute/nodes/{node_id}/enable")
@@ -404,6 +909,38 @@ async def compute_node_replicas(node_id: str) -> dict[str, Any]:
return ok(get_platform_store().replicas(node_id))
@router.get("/compute/nodes/{node_id}/engines")
async def compute_node_engines(node_id: str) -> dict[str, Any]:
node = next((item for item in get_platform_store().compute_nodes() if item["id"] == node_id), None)
if not node:
raise fail(404, "compute node not found")
health = node.get("health_detail") or {}
live_error = ""
try:
health = await ComputeNodeClient(node["api_base_url"]).health()
except Exception as exc: # noqa: BLE001 - stored health is enough for offline node detail
live_error = str(exc)
capabilities = health.get("capabilities") or node.get("capabilities") or []
return ok(
{
"node_id": node_id,
"items": [
{
"engine": "llama_factory",
"display_name": "LLaMA-Factory",
"status": "available" if "llama_factory" in capabilities else "unknown",
"version": health.get("llama_factory_version") or "",
"home": health.get("llama_factory_home") or "",
"home_exists": bool(health.get("llama_factory_home_exists")),
"capabilities": capabilities,
"execution_mode": health.get("execution_mode") or "",
"last_error": live_error,
}
],
}
)
@router.get("/compute/gpus")
async def compute_gpus() -> dict[str, Any]:
return ok(get_platform_store().gpus())
@@ -414,10 +951,107 @@ async def compute_queue() -> dict[str, Any]:
return ok(get_platform_store().queue())
@router.get("/compute/jobs/{job_id}")
async def compute_job_detail(job_id: str) -> dict[str, Any]:
task = _task_for_compute_job(job_id)
if not task:
raise fail(404, "compute job not found")
node = _node_for_task(task)
if not node:
raise fail(404, "compute node not found")
return ok(await ComputeNodeClient(node["api_base_url"]).get_job(job_id))
@router.post("/compute/jobs/{job_id}/stop")
async def compute_job_stop(job_id: str) -> dict[str, Any]:
task = _task_for_compute_job(job_id)
if not task:
raise fail(404, "compute job not found")
node = _node_for_task(task)
if not node:
raise fail(404, "compute node not found")
job = await ComputeNodeClient(node["api_base_url"]).stop_job(job_id)
get_platform_store().apply_compute_job(task["id"], job)
return ok(job)
@router.get("/compute/jobs/{job_id}/logs")
async def compute_job_logs(
job_id: str,
tail_lines: int | None = Query(default=200, ge=1, le=5000),
offset: int | None = Query(default=None, ge=0),
limit: int | None = Query(default=None, ge=1, le=5000),
) -> dict[str, Any]:
task = _task_for_compute_job(job_id)
if not task:
raise fail(404, "compute job not found")
node = _node_for_task(task)
if not node:
raise fail(404, "compute node not found")
return ok(await ComputeNodeClient(node["api_base_url"]).job_logs(job_id, tail_lines, offset, limit))
@router.post("/compute/jobs/{job_id}/retry")
async def compute_job_retry(job_id: str, payload: dict[str, Any] | None = Body(default=None)) -> dict[str, Any]:
store = get_platform_store()
payload = payload or {}
task = _task_for_compute_job(job_id)
if not task:
raise fail(404, "compute job not found")
return await retry_fine_tune(task["id"], payload)
@router.post("/compute/jobs/{job_id}/priority")
async def compute_job_priority(job_id: str, payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
task = _task_for_compute_job(job_id)
if not task:
raise fail(404, "compute job not found")
priority = str(payload.get("priority") or "normal")
return ok(get_platform_store().update_task_priority(task["id"], priority))
@router.post("/internal/compute-sync/jobs/poll")
async def poll_compute_jobs() -> dict[str, Any]:
return ok(await poll_compute_jobs_once())
@router.post("/internal/compute-sync/resources")
async def create_compute_sync(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
sync_id = get_platform_store().create_sync_job(payload.get("target_node_id", "node_01"), payload)
return ok(get_platform_store().sync_job(sync_id))
store = get_platform_store()
node_id = payload.get("target_node_id") or payload.get("target_compute_node_id")
if not node_id:
raise fail(400, "target_node_id is required")
node = next((item for item in store.compute_nodes() if item["id"] == node_id), None)
if not node:
raise fail(404, "compute node not found")
sync_id = store.create_sync_job(node_id, payload)
replicas = []
failures = []
resources = payload.get("resources") or []
for resource in resources:
if not resource.get("source_path"):
continue
try:
result = await ComputeNodeClient(node["api_base_url"]).import_local_file(
{
"source_path": resource["source_path"],
"target_relative_path": resource.get("target_relative_path"),
"resource_type": resource.get("resource_type"),
"resource_id": resource.get("resource_id"),
}
)
replicas.append(
store.upsert_resource_replica(
node_id,
str(resource.get("resource_type") or "file"),
str(resource.get("resource_id") or result["id"]),
result["local_path"],
)
)
except Exception as exc: # noqa: BLE001 - collect per-resource failures
failures.append({"resource_id": str(resource.get("resource_id")), "error": str(exc)})
store.update_sync_job(sync_id, "failed" if failures else "completed", 100 if not failures else 99, completed=True)
return ok({"sync": store.sync_job(sync_id), "replicas": replicas, "failed": failures})
@router.get("/internal/compute-sync/resources/{sync_id}")

2
backend/app/core/config.py
View File

@@ -28,6 +28,8 @@ class Settings:
compute_mode: str = os.getenv("COMPUTE_MODE", "real")
compute_status_sync_mode: str = os.getenv("COMPUTE_STATUS_SYNC_MODE", "polling")
compute_poll_interval_seconds: int = _int_env("COMPUTE_POLL_INTERVAL_SECONDS", 3)
compute_request_timeout_seconds: int = _int_env("COMPUTE_REQUEST_TIMEOUT_SECONDS", 5)
compute_service_token: str = os.getenv("COMPUTE_SERVICE_TOKEN", "")
log_level: str = os.getenv("LOG_LEVEL", "INFO")
log_dir: str = os.getenv("LOG_DIR", "./logs")
log_file_prefix: str = os.getenv("LOG_FILE_PREFIX", "backend")

589
backend/app/db/platform_store.py
View File

@@ -53,6 +53,13 @@ def json_dumps(value: Any) -> str:
return json.dumps(value, ensure_ascii=False, separators=(",", ":"))
def safe_float(value: Any, default: float = 0) -> float:
try:
return float(str(value).replace("[N/A]", "").strip() or default)
except (TypeError, ValueError):
return default
def new_id(prefix: str) -> str:
return f"{prefix}_{uuid.uuid4().hex[:12]}"
@@ -178,12 +185,33 @@ class PlatformStore:
schema_path = Path(__file__).with_name("sql") / "001_platform_runtime.sql"
with self.connect() as conn:
conn.executescript(schema_path.read_text(encoding="utf-8"))
columns = conn.execute(
"SELECT column_name FROM information_schema.columns WHERE table_name='users'"
).fetchall()
column_names = {row["column_name"] for row in columns}
if "password" in column_names and "password_hash" not in column_names:
user_columns = self._column_names(conn, "users")
if "password" in user_columns and "password_hash" not in user_columns:
conn.execute("ALTER TABLE users RENAME COLUMN password TO password_hash")
self._ensure_columns(
conn,
"compute_nodes",
{
"api_version": "TEXT NOT NULL DEFAULT 'v1'",
"capabilities": "TEXT NOT NULL DEFAULT '[]'",
"description": "TEXT",
},
)
self._ensure_columns(conn, "gpus", {"last_seen_at": "TEXT"})
self._ensure_columns(conn, "fine_tune_tasks", {"compute_job_id": "TEXT"})
def _column_names(self, conn: PgConnection, table_name: str) -> set[str]:
columns = conn.execute(
"SELECT column_name FROM information_schema.columns WHERE table_name=?",
(table_name,),
).fetchall()
return {row["column_name"] for row in columns}
def _ensure_columns(self, conn: PgConnection, table_name: str, columns: dict[str, str]) -> None:
existing = self._column_names(conn, table_name)
for column, definition in columns.items():
if column not in existing:
conn.execute(f"ALTER TABLE {table_name} ADD COLUMN {column} {definition}")
def ensure_seed_data(self) -> None:
with self.connect() as conn:
@@ -436,7 +464,7 @@ class PlatformStore:
utcnow(),
),
)
return self.model(model_id)
return dict(conn.execute("SELECT * FROM models WHERE id=?", (model_id,)).fetchone())
def update_model(self, model_id: str, payload: dict[str, Any]) -> dict[str, Any]:
current = self.model(model_id)
@@ -461,7 +489,7 @@ class PlatformStore:
model_id,
),
)
return self.model(model_id)
return dict(conn.execute("SELECT * FROM models WHERE id=?", (model_id,)).fetchone())
def delete_model(self, model_id: str) -> None:
with self.connect() as conn:
@@ -481,6 +509,10 @@ class PlatformStore:
for row in rows
]
def delete_trained_model(self, model_id: str) -> None:
with self.connect() as conn:
conn.execute("DELETE FROM trained_models WHERE id=? OR name=?", (model_id, model_id))
def datasets(self) -> list[dict[str, Any]]:
with self.connect() as conn:
rows = conn.execute("SELECT * FROM datasets ORDER BY create_time DESC").fetchall()
@@ -641,6 +673,26 @@ class PlatformStore:
conn.execute("UPDATE dataset_files SET active_version_id=? WHERE id=?", (version_id, file_id))
return {"version": version, "content": row["content"]}
def delete_file_version(self, file_id: str, version_id: str) -> dict[str, Any]:
with self.connect() as conn:
row = conn.execute("SELECT * FROM dataset_files WHERE id=?", (file_id,)).fetchone()
if not row:
raise KeyError(file_id)
versions = json_loads(row["versions"], [])
if row["active_version_id"] == version_id:
raise ValueError("active dataset version cannot be deleted")
if len(versions) <= 1:
raise ValueError("last dataset version cannot be deleted")
next_versions = [item for item in versions if item["id"] != version_id]
if len(next_versions) == len(versions):
raise KeyError(version_id)
conn.execute("UPDATE dataset_files SET versions=? WHERE id=?", (json_dumps(next_versions), file_id))
return {
"versions": next_versions,
"active_version_id": row["active_version_id"],
"next_version_number": max(item.get("version", 0) for item in next_versions) + 1,
}
def tasks(self) -> list[dict[str, Any]]:
self.refresh_runtime_state()
with self.connect() as conn:
@@ -668,6 +720,8 @@ class PlatformStore:
"train_duration": self._duration(row["start_time"], row["completed_at"]) if row["start_time"] else "",
"compute_node_id": row["compute_node_id"],
"sync_job_id": row["sync_job_id"],
"compute_job_id": row.get("compute_job_id"),
"completed_at": row.get("completed_at"),
}
)
return payload
@@ -689,6 +743,7 @@ class PlatformStore:
"status": "pending",
"train_type": payload.get("train_type", "SFT"),
"train_method": payload.get("train_method", "lora"),
"engine": payload.get("engine", payload.get("training_engine", "llama_factory")),
"template": payload.get("template", "qwen"),
"base_model": base_model,
"train_dataset_id": train_dataset_id,
@@ -751,7 +806,7 @@ class PlatformStore:
"""
UPDATE fine_tune_tasks
SET payload=?, status='syncing', progress=8, process_id=?, start_time=?,
compute_node_id=?, gpus=?, sync_job_id=?
compute_node_id=?, gpus=?, sync_job_id=?, compute_job_id=NULL
WHERE id=?
""",
(
@@ -766,9 +821,140 @@ class PlatformStore:
)
return self.task(task_id)
def stop_task(self, task_id: str) -> dict[str, Any]:
def reset_task_for_retry(self, task_id: str, payload: dict[str, Any] | None = None) -> dict[str, Any]:
current = self.task(task_id)
override = payload or {}
merged = {
**current,
**override,
"id": task_id,
"status": "pending",
"progress": 0,
"process_id": None,
"compute_job_id": None,
}
for runtime_key in ["failure_reason", "log_file", "artifacts"]:
merged.pop(runtime_key, None)
with self.connect() as conn:
conn.execute(
"""
UPDATE fine_tune_tasks
SET payload=?, status='pending', progress=0, process_id=NULL, start_time=NULL,
completed_at=NULL, compute_node_id=NULL, gpus=?, sync_job_id=NULL, compute_job_id=NULL
WHERE id=?
""",
(json_dumps(merged), json_dumps(merged.get("gpus", [])), task_id),
)
return self.task(task_id)
def update_task_priority(self, task_id: str, priority: str) -> dict[str, Any]:
current = self.task(task_id)
priority = priority if priority in {"low", "normal", "high", "urgent"} else "normal"
merged = {**current, "priority": priority}
with self.connect() as conn:
conn.execute("UPDATE fine_tune_tasks SET payload=? WHERE id=?", (json_dumps(merged), task_id))
return self.task(task_id)
def prepare_compute_job_payload(self, task_id: str, payload: dict[str, Any] | None = None) -> tuple[dict[str, Any], dict[str, Any]]:
task = self.task(task_id)
task.update({"status": "failed", "progress": min(task.get("progress", 0), 99)})
merged = {**task, **(payload or {}), "id": task_id}
node = self.schedule_node(merged)
selected_gpus = merged.get("gpus") or [0]
return node, self._compute_job_payload_from_task_node(merged, node, selected_gpus)
def _compute_job_payload_from_task_node(
self,
task: dict[str, Any],
node: dict[str, Any],
selected_gpus: list[int] | list[Any] | None = None,
) -> dict[str, Any]:
with self.connect() as conn:
model = conn.execute("SELECT * FROM models WHERE id=?", (task.get("base_model"),)).fetchone()
dataset = conn.execute("SELECT * FROM datasets WHERE id=?", (task.get("train_dataset_id"),)).fetchone()
model_path = (model and model.get("path")) or task.get("base_model")
dataset_name = task.get("dataset") or (dataset and dataset.get("name")) or task.get("train_dataset_id")
health_detail = node.get("health_detail") or {}
dataset_root = str(health_detail.get("dataset_root") or f"{node['data_root'].rstrip('/')}/datasets")
output_root = str(health_detail.get("output_root") or f"{node['data_root'].rstrip('/')}/outputs")
output_dir = task.get("output_dir") or f"{output_root.rstrip('/')}/{task['name']}"
return {
**task,
"id": task["id"],
"name": task["name"],
"base_model": model_path,
"model_name_or_path": model_path,
"dataset": dataset_name,
"dataset_dir": dataset_root,
"output_dir": output_dir,
"gpus": selected_gpus or task.get("gpus") or [0],
"compute_node_id": node["id"],
"compute_node_code": node["code"],
}
def build_compute_job_payload(self, task_id: str) -> tuple[dict[str, Any], dict[str, Any]]:
task = self.task(task_id)
node = next((item for item in self.compute_nodes() if item["id"] == task.get("compute_node_id")), None)
if not node:
raise RuntimeError("compute node not found")
return node, self._compute_job_payload_from_task_node(task, node, task.get("gpus") or [0])
def apply_compute_job(self, task_id: str, job: dict[str, Any]) -> dict[str, Any]:
status_map = {
"queued": "queued",
"running": "running",
"completed": "completed",
"failed": "failed",
"stopped": "stopped",
}
current = self.task(task_id)
status = status_map.get(str(job.get("status")), str(job.get("status") or current["status"]))
progress = int(job.get("progress", current.get("progress", 0)) or 0)
payload = {
**current,
"status": status,
"progress": progress,
"process_id": job.get("pid") or current.get("process_id"),
"compute_job_id": job.get("id") or current.get("compute_job_id"),
"output_dir": job.get("output_dir") or current.get("output_dir"),
"log_file": job.get("log_file") or current.get("log_file"),
"artifacts": job.get("artifacts") or current.get("artifacts") or [],
}
if status == "failed":
payload["failure_reason"] = job.get("error") or job.get("message") or current.get("failure_reason") or "compute job failed"
elif status in {"queued", "running", "completed"}:
payload.pop("failure_reason", None)
completed_at = utcnow() if status in {"completed", "failed", "stopped"} and not current.get("completed_at") else None
with self.connect() as conn:
conn.execute(
"""
UPDATE fine_tune_tasks
SET payload=?, status=?, progress=?, process_id=?, compute_job_id=?, completed_at=COALESCE(?, completed_at)
WHERE id=?
""",
(
json_dumps(payload),
status,
progress,
payload.get("process_id"),
payload.get("compute_job_id"),
completed_at,
task_id,
),
)
if status == "completed":
self._ensure_trained_model(conn, payload)
return self.task(task_id)
def running_compute_tasks(self) -> list[dict[str, Any]]:
return [
task
for task in self.tasks()
if task.get("compute_job_id") and task.get("compute_node_id") and task["status"] in {"syncing", "queued", "running"}
]
def mark_task_failed(self, task_id: str, reason: str) -> dict[str, Any]:
task = self.task(task_id)
task.update({"status": "failed", "progress": min(task.get("progress", 0), 99), "failure_reason": reason})
with self.connect() as conn:
conn.execute(
"UPDATE fine_tune_tasks SET status='failed', payload=?, completed_at=? WHERE id=?",
@@ -776,10 +962,179 @@ class PlatformStore:
)
return self.task(task_id)
def stop_task(self, task_id: str, status: str = "stopped") -> dict[str, Any]:
task = self.task(task_id)
task.update({"status": status, "progress": min(task.get("progress", 0), 99)})
with self.connect() as conn:
conn.execute(
"UPDATE fine_tune_tasks SET status=?, payload=?, completed_at=? WHERE id=?",
(status, json_dumps(task), utcnow(), task_id),
)
return self.task(task_id)
def delete_task(self, task_id: str) -> None:
with self.connect() as conn:
conn.execute("DELETE FROM fine_tune_tasks WHERE id=?", (task_id,))
def _json_payload_row(self, row: PgRow) -> dict[str, Any]:
payload = json_loads(row["payload"], {})
payload.update({"id": row["id"], "status": row.get("status"), "create_time": row["create_time"]})
return payload
def eval_tasks(self) -> list[dict[str, Any]]:
with self.connect() as conn:
rows = conn.execute("SELECT * FROM eval_tasks ORDER BY create_time DESC").fetchall()
return [self._json_payload_row(row) for row in rows]
def eval_task(self, task_id: str) -> dict[str, Any]:
with self.connect() as conn:
row = conn.execute("SELECT * FROM eval_tasks WHERE id=?", (task_id,)).fetchone()
if not row:
raise KeyError(task_id)
payload = self._json_payload_row(row)
payload.setdefault("sample_count", 0)
payload.setdefault("completed_count", 0)
payload.setdefault("passed_count", 0)
payload.setdefault("overall_score", payload.get("score") or 0)
payload.setdefault("overall_score_max", 100)
payload.setdefault("overall_evaluation", "")
payload.setdefault("improvement_suggestions", [])
payload.setdefault("dimension_summary", [])
payload.setdefault("samples", [])
return payload
def create_eval_task(self, payload: dict[str, Any]) -> dict[str, Any]:
task_id = str(payload.get("id") or payload.get("task_id") or new_id("eval"))
name = str(payload.get("eval_task_name") or payload.get("name") or f"eval-{task_id[-6:]}")
status = str(payload.get("status") or "pending")
now = payload.get("create_time") or utcnow()
data = {
**payload,
"id": task_id,
"eval_task_name": name,
"status": status,
"create_time": now,
"metric": payload.get("metric") or "custom",
}
with self.connect() as conn:
model = conn.execute("SELECT name FROM models WHERE id=?", (str(payload.get("model_id")),)).fetchone()
dataset = conn.execute("SELECT name FROM datasets WHERE id=?", (str(payload.get("dataset_id")),)).fetchone()
if model:
data.setdefault("model_name", model["name"])
if dataset:
data.setdefault("dataset", dataset["name"])
conn.execute(
"INSERT INTO eval_tasks (id, name, payload, status, create_time) VALUES (?, ?, ?, ?, ?)",
(task_id, name, json_dumps(data), status, now),
)
return self.eval_task(task_id)
def delete_eval_task(self, task_id: str) -> None:
with self.connect() as conn:
conn.execute("DELETE FROM eval_tasks WHERE id=?", (task_id,))
def dimensions(self) -> list[dict[str, Any]]:
with self.connect() as conn:
rows = conn.execute("SELECT * FROM eval_dimensions ORDER BY create_time DESC").fetchall()
return [
{
**json_loads(row["payload"], {}),
"id": row["id"],
"name": row["name"],
"is_active": bool(row["is_active"]),
"is_default": bool(row["is_default"]),
"create_time": row["create_time"],
}
for row in rows
]
def dimension(self, dimension_id: str) -> dict[str, Any]:
with self.connect() as conn:
row = conn.execute("SELECT * FROM eval_dimensions WHERE id=?", (dimension_id,)).fetchone()
if not row:
raise KeyError(dimension_id)
return {
**json_loads(row["payload"], {}),
"id": row["id"],
"name": row["name"],
"is_active": bool(row["is_active"]),
"is_default": bool(row["is_default"]),
"create_time": row["create_time"],
}
def create_dimension(self, payload: dict[str, Any]) -> dict[str, Any]:
dimension_id = str(payload.get("id") or new_id("dim"))
name = str(payload.get("name") or f"dimension-{dimension_id[-6:]}")
now = payload.get("create_time") or utcnow()
data = {**payload, "id": dimension_id, "name": name, "create_time": now}
with self.connect() as conn:
conn.execute(
"INSERT INTO eval_dimensions (id, name, payload, is_active, is_default, create_time) VALUES (?, ?, ?, ?, ?, ?)",
(dimension_id, name, json_dumps(data), 1 if data.get("is_active", True) else 0, 1 if data.get("is_default") else 0, now),
)
return self.dimension(dimension_id)
def update_dimension(self, dimension_id: str, payload: dict[str, Any]) -> dict[str, Any]:
current = self.dimension(dimension_id)
merged = {**current, **payload, "id": dimension_id}
with self.connect() as conn:
conn.execute(
"UPDATE eval_dimensions SET name=?, payload=?, is_active=?, is_default=? WHERE id=?",
(
merged["name"],
json_dumps(merged),
1 if merged.get("is_active", True) else 0,
1 if merged.get("is_default") else 0,
dimension_id,
),
)
return self.dimension(dimension_id)
def delete_dimension(self, dimension_id: str) -> None:
with self.connect() as conn:
conn.execute("DELETE FROM eval_dimensions WHERE id=?", (dimension_id,))
def compare_tasks(self) -> list[dict[str, Any]]:
with self.connect() as conn:
rows = conn.execute("SELECT * FROM compare_tasks ORDER BY create_time DESC").fetchall()
return [self._json_payload_row(row) for row in rows]
def compare_task(self, task_id: str) -> dict[str, Any]:
with self.connect() as conn:
row = conn.execute("SELECT * FROM compare_tasks WHERE id=?", (task_id,)).fetchone()
if not row:
raise KeyError(task_id)
return self._json_payload_row(row)
def create_compare_task(self, payload: dict[str, Any]) -> dict[str, Any]:
task_id = str(payload.get("id") or new_id("cmp"))
name = str(payload.get("name") or payload.get("model_name") or f"compare-{task_id[-6:]}")
status = str(payload.get("status") or "pending")
now = payload.get("create_time") or utcnow()
data = {**payload, "id": task_id, "name": name, "model_name": payload.get("model_name") or name, "status": status, "create_time": now}
data.setdefault("load_status", json_dumps({"loaded_models": []}))
with self.connect() as conn:
conn.execute(
"INSERT INTO compare_tasks (id, name, payload, status, create_time) VALUES (?, ?, ?, ?, ?)",
(task_id, name, json_dumps(data), status, now),
)
return self.compare_task(task_id)
def update_compare_task(self, task_id: str, payload: dict[str, Any]) -> dict[str, Any]:
current = self.compare_task(task_id)
merged = {**current, **payload, "id": task_id}
status = str(merged.get("status") or current.get("status") or "pending")
with self.connect() as conn:
conn.execute(
"UPDATE compare_tasks SET name=?, payload=?, status=? WHERE id=?",
(merged.get("name") or merged.get("model_name") or task_id, json_dumps(merged), status, task_id),
)
return self.compare_task(task_id)
def delete_compare_task(self, task_id: str) -> None:
with self.connect() as conn:
conn.execute("DELETE FROM compare_tasks WHERE id=?", (task_id,))
def schedule_node(self, payload: dict[str, Any]) -> dict[str, Any]:
requested = payload.get("requested_node_id") or payload.get("compute_node_id")
nodes = self.compute_nodes()
@@ -793,7 +1148,20 @@ class PlatformStore:
if selected:
return selected
if not candidates:
raise RuntimeError("no available compute node")
if not nodes:
raise RuntimeError("no available compute node: no compute node configured")
reasons = []
for node in nodes:
if not node["enabled"]:
reason = "disabled"
elif node["scheduler_status"] != "online":
reason = f"status={node['scheduler_status']}"
elif node["current_running_jobs"] >= node["max_parallel_jobs"]:
reason = f"capacity full {node['current_running_jobs']}/{node['max_parallel_jobs']}"
else:
reason = "not selected"
reasons.append(f"{node['code']}({reason})")
raise RuntimeError(f"no available compute node: {', '.join(reasons)}")
return sorted(candidates, key=lambda n: (-n["scheduler_weight"], n["current_running_jobs"], n["code"]))[0]
def create_sync_job(self, node_id: str, task: dict[str, Any]) -> str:
@@ -809,7 +1177,8 @@ class PlatformStore:
sync_id,
node_id,
json_dumps(
[
task.get("resources")
or [
{"resource_type": "model", "resource_id": task.get("base_model")},
{"resource_type": "dataset", "resource_id": task.get("train_dataset_id")},
]
@@ -819,6 +1188,46 @@ class PlatformStore:
)
return sync_id
def update_sync_job(self, sync_id: str, status: str, progress: int, completed: bool = False) -> dict[str, Any]:
with self.connect() as conn:
conn.execute(
"UPDATE resource_sync_jobs SET status=?, progress=?, completed_at=COALESCE(?, completed_at) WHERE id=?",
(status, progress, utcnow() if completed else None, sync_id),
)
return self.sync_job(sync_id)
def upsert_resource_replica(
self,
node_id: str,
resource_type: str,
resource_id: str,
local_path: str,
status: str = "available",
sync_status: str = "synced",
) -> dict[str, Any]:
with self.connect() as conn:
row = conn.execute(
"SELECT * FROM resource_replicas WHERE node_id=? AND resource_type=? AND resource_id=?",
(node_id, resource_type, resource_id),
).fetchone()
if row:
conn.execute(
"UPDATE resource_replicas SET local_path=?, status=?, sync_status=? WHERE id=?",
(local_path, status, sync_status, row["id"]),
)
replica_id = row["id"]
else:
replica_id = new_id("replica")
conn.execute(
"""
INSERT INTO resource_replicas
(id, node_id, resource_type, resource_id, local_path, status, sync_status, create_time)
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
""",
(replica_id, node_id, resource_type, resource_id, local_path, status, sync_status, utcnow()),
)
return dict(conn.execute("SELECT * FROM resource_replicas WHERE id=?", (replica_id,)).fetchone())
def progress(self, task_id: str) -> dict[str, Any]:
task = self.task(task_id)
status = task.get("status", "pending")
@@ -829,6 +1238,7 @@ class PlatformStore:
"running": "training with LLaMA-Factory",
"completed": "training completed",
"failed": "training stopped",
"stopped": "training stopped",
}
progress = int(task.get("progress", 0) or 0)
eta = "--" if status in {"completed", "failed"} else f"{max(1, math.ceil((100 - progress) / 10))} min"
@@ -853,23 +1263,62 @@ class PlatformStore:
**dict(row),
"enabled": bool(row["enabled"]),
"tags": json_loads(row["tags"], []),
"capabilities": json_loads(row.get("capabilities"), []),
"health_detail": json_loads(row["health_detail"], {}),
"current_running_jobs": running_map.get(row["id"], 0),
}
for row in rows
]
def _normalize_tags(self, value: Any) -> list[str]:
if isinstance(value, str):
parts = value.replace("，", ",").split(",")
return [item.strip() for item in parts if item.strip()]
if isinstance(value, list):
return [str(item).strip() for item in value if str(item).strip()]
return []
def _normalize_compute_node_payload(self, payload: dict[str, Any], current: dict[str, Any] | None = None) -> dict[str, Any]:
merged = {**(current or {}), **payload}
api_base_url = str(merged.get("api_base_url") or "").rstrip("/")
if not api_base_url:
raise ValueError("api_base_url is required")
file_gateway_url = str(merged.get("file_gateway_url") or api_base_url).rstrip("/")
weight = max(0, min(1000, int(merged.get("scheduler_weight", 100))))
max_jobs = max(1, int(merged.get("max_parallel_jobs", 1)))
return {
**merged,
"code": str(merged.get("code") or "").strip(),
"name": str(merged.get("name") or merged.get("code") or "").strip(),
"api_base_url": api_base_url,
"file_gateway_url": file_gateway_url,
"enabled": bool(merged.get("enabled", True)),
"scheduler_status": str(merged.get("scheduler_status") or "offline"),
"scheduler_weight": weight,
"tags": self._normalize_tags(merged.get("tags")),
"gpu_count": max(0, int(merged.get("gpu_count", 0) or 0)),
"max_parallel_jobs": max_jobs,
"data_root": str(merged.get("data_root") or "/data/yg-ft"),
"model_root": str(merged.get("model_root") or "/data/yg-ft/models"),
"log_root": str(merged.get("log_root") or "/opt/yg-ft/logs/training"),
"api_version": str(merged.get("api_version") or "v1"),
"capabilities": merged.get("capabilities") or [],
"description": merged.get("description") or "",
"health_detail": merged.get("health_detail") or {"status": "registered"},
}
def update_compute_node(self, node_id: str, payload: dict[str, Any]) -> dict[str, Any]:
current = next((n for n in self.compute_nodes() if n["id"] == node_id), None)
if not current:
raise KeyError(node_id)
merged = {**current, **payload}
merged = self._normalize_compute_node_payload(payload, current)
with self.connect() as conn:
conn.execute(
"""
UPDATE compute_nodes
SET name=?, api_base_url=?, file_gateway_url=?, enabled=?, scheduler_status=?,
scheduler_weight=?, tags=?, max_parallel_jobs=?, last_health_check_at=?
scheduler_weight=?, tags=?, max_parallel_jobs=?, data_root=?, model_root=?, log_root=?,
api_version=?, capabilities=?, description=?, last_health_check_at=?, health_detail=?
WHERE id=?
""",
(
@@ -881,46 +1330,116 @@ class PlatformStore:
merged["scheduler_weight"],
json_dumps(merged["tags"]),
merged["max_parallel_jobs"],
utcnow(),
merged["data_root"],
merged["model_root"],
merged["log_root"],
merged["api_version"],
json_dumps(merged["capabilities"]),
merged["description"],
payload.get("last_health_check_at") or current.get("last_health_check_at"),
json_dumps(merged["health_detail"]),
node_id,
),
)
return next(n for n in self.compute_nodes() if n["id"] == node_id)
def create_compute_node(self, payload: dict[str, Any]) -> dict[str, Any]:
payload = self._normalize_compute_node_payload(payload)
if not payload["code"]:
raise ValueError("code is required")
node_id = payload.get("id") or new_id("node")
now = utcnow()
tags = payload.get("tags") or []
with self.connect() as conn:
conn.execute(
"""
INSERT INTO compute_nodes
(id, code, name, api_base_url, file_gateway_url, enabled, scheduler_status,
scheduler_weight, tags, gpu_count, current_running_jobs, max_parallel_jobs,
data_root, model_root, log_root, last_health_check_at, health_detail)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 0, ?, ?, ?, ?, ?, ?)
data_root, model_root, log_root, api_version, capabilities, description,
last_health_check_at, health_detail)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 0, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
(
node_id,
payload["code"],
payload.get("name") or payload["code"],
payload["name"] or payload["code"],
payload["api_base_url"],
payload.get("file_gateway_url") or payload["api_base_url"],
1 if payload.get("enabled", True) else 0,
payload.get("scheduler_status", "offline"),
int(payload.get("scheduler_weight", 100)),
json_dumps(tags),
int(payload.get("gpu_count", 0)),
int(payload.get("max_parallel_jobs", 1)),
payload.get("data_root", "/data/yg-ft"),
payload.get("model_root", "/models"),
payload.get("log_root", "/data/yg-ft/training-logs"),
payload["file_gateway_url"],
1 if payload["enabled"] else 0,
payload["scheduler_status"],
payload["scheduler_weight"],
json_dumps(payload["tags"]),
payload["gpu_count"],
payload["max_parallel_jobs"],
payload["data_root"],
payload["model_root"],
payload["log_root"],
payload["api_version"],
json_dumps(payload["capabilities"]),
payload["description"],
now,
json_dumps(payload.get("health_detail") or {"status": "registered"}),
json_dumps(payload["health_detail"]),
),
)
return next(node for node in self.compute_nodes() if node["id"] == node_id)
def update_compute_node_health(self, node_id: str, health: dict[str, Any], success: bool, error: str | None = None) -> dict[str, Any]:
current = next((n for n in self.compute_nodes() if n["id"] == node_id), None)
if not current:
raise KeyError(node_id)
status = "online" if success and current.get("enabled") else "offline"
if current.get("scheduler_status") == "draining" and success:
status = "draining"
detail = {
**(current.get("health_detail") or {}),
**health,
"status": "ok" if success else "failed",
"last_error": error or "",
"checked_at": utcnow(),
}
return self.update_compute_node(
node_id,
{
"scheduler_status": status,
"last_health_check_at": detail["checked_at"],
"health_detail": detail,
"data_root": health.get("data_root") or current.get("data_root"),
"api_version": str(health.get("api_version") or current.get("api_version") or "v1"),
"capabilities": health.get("capabilities") or current.get("capabilities") or [],
},
)
def replace_node_gpus(self, node_id: str, gpus: list[dict[str, Any]]) -> None:
now = utcnow()
with self.connect() as conn:
conn.execute("DELETE FROM gpus WHERE node_id=?", (node_id,))
for index, item in enumerate(gpus):
gpu_index = int(item.get("gpu_index", item.get("id", index)) or 0)
memory_total = safe_float(item.get("memory_total_gb") or item.get("memory_total"))
if not memory_total and item.get("memory_total_mb") is not None:
memory_total = round(safe_float(item.get("memory_total_mb")) / 1024, 2)
power_limit = safe_float(item.get("power_limit_w") or item.get("power_limit"))
temperature = int(safe_float(item.get("temperature") or item.get("base_temperature"), 35))
conn.execute(
"""
INSERT INTO gpus
(id, node_id, gpu_index, uuid, name, memory_total_gb, power_limit_w, base_temperature, last_seen_at)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
(
f"{node_id}_gpu_{gpu_index}",
node_id,
gpu_index,
str(item.get("uuid") or f"{node_id}-GPU-{gpu_index}"),
str(item.get("name") or "Unknown GPU"),
memory_total or 0,
power_limit or 0,
temperature,
now,
),
)
conn.execute("UPDATE compute_nodes SET gpu_count=? WHERE id=?", (len(gpus), node_id))
def gpus(self) -> list[dict[str, Any]]:
self.refresh_runtime_state()
with self.connect() as conn:
@@ -951,6 +1470,7 @@ class PlatformStore:
reserved = task is not None and task.get("status") in {"syncing", "queued"}
memory_used = round(row["memory_total_gb"] * (0.72 if busy else 0.18 if reserved else 0.04), 1)
gpu_percent = 86 if busy else 22 if reserved else 3
memory_total = float(row["memory_total_gb"] or 0)
items.append(
{
"id": row["gpu_index"],
@@ -961,8 +1481,8 @@ class PlatformStore:
"uuid": row["uuid"],
"gpu_percent": gpu_percent,
"memory_used_gb": memory_used,
"memory_total_gb": row["memory_total_gb"],
"memory_percent": round(memory_used / row["memory_total_gb"] * 100, 1),
"memory_total_gb": memory_total,
"memory_percent": round(memory_used / memory_total * 100, 1) if memory_total else 0,
"temperature": row["base_temperature"] + (21 if busy else 6 if reserved else 0),
"power_w": round(row["power_limit_w"] * (0.7 if busy else 0.25 if reserved else 0.08), 1),
"power_limit_w": row["power_limit_w"],
@@ -1034,12 +1554,14 @@ class PlatformStore:
}
def queue(self) -> list[dict[str, Any]]:
return [
priority_score = {"urgent": 3, "high": 2, "normal": 1, "low": 0}
items = [
{
"id": task["id"],
"name": task["name"],
"status": task["status"],
"progress": task.get("progress", 0),
"priority": task.get("priority", "normal"),
"compute_node_id": task.get("compute_node_id"),
"gpus": task.get("gpus", []),
"create_time": task.get("create_time"),
@@ -1047,6 +1569,7 @@ class PlatformStore:
for task in self.tasks()
if task["status"] in {"pending", "syncing", "queued", "running"}
]
return sorted(items, key=lambda item: (-priority_score.get(item["priority"], 1), item["create_time"]), reverse=False)
def replicas(self, node_id: str) -> list[dict[str, Any]]:
with self.connect() as conn:

42
backend/app/db/sql/001_platform_runtime.sql
View File

@@ -76,6 +76,9 @@ CREATE TABLE IF NOT EXISTS compute_nodes (
data_root TEXT NOT NULL,
model_root TEXT NOT NULL,
log_root TEXT NOT NULL,
api_version TEXT NOT NULL DEFAULT 'v1',
capabilities TEXT NOT NULL DEFAULT '[]',
description TEXT,
last_health_check_at TEXT,
health_detail TEXT NOT NULL
);
@@ -88,7 +91,8 @@ CREATE TABLE IF NOT EXISTS gpus (
name TEXT NOT NULL,
memory_total_gb DOUBLE PRECISION NOT NULL,
power_limit_w DOUBLE PRECISION NOT NULL,
base_temperature INTEGER NOT NULL
base_temperature INTEGER NOT NULL,
last_seen_at TEXT
);
CREATE TABLE IF NOT EXISTS fine_tune_tasks (
@@ -103,7 +107,8 @@ CREATE TABLE IF NOT EXISTS fine_tune_tasks (
completed_at TEXT,
compute_node_id TEXT REFERENCES compute_nodes(id) ON DELETE SET NULL,
gpus TEXT NOT NULL,
sync_job_id TEXT
sync_job_id TEXT,
compute_job_id TEXT
);
CREATE TABLE IF NOT EXISTS resource_replicas (
@@ -127,7 +132,40 @@ CREATE TABLE IF NOT EXISTS resource_sync_jobs (
completed_at TEXT
);
CREATE TABLE IF NOT EXISTS eval_tasks (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
payload TEXT NOT NULL,
status TEXT NOT NULL,
create_time TEXT NOT NULL
);
CREATE TABLE IF NOT EXISTS eval_dimensions (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
payload TEXT NOT NULL,
is_active INTEGER NOT NULL DEFAULT 1,
is_default INTEGER NOT NULL DEFAULT 0,
create_time TEXT NOT NULL
);
CREATE TABLE IF NOT EXISTS compare_tasks (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
payload TEXT NOT NULL,
status TEXT NOT NULL,
create_time TEXT NOT NULL
);
CREATE INDEX IF NOT EXISTS idx_fine_tune_status ON fine_tune_tasks(status);
CREATE INDEX IF NOT EXISTS idx_fine_tune_compute_job ON fine_tune_tasks(compute_job_id);
CREATE INDEX IF NOT EXISTS idx_fine_tune_compute_node_status ON fine_tune_tasks(compute_node_id, status);
CREATE INDEX IF NOT EXISTS idx_dataset_files_dataset ON dataset_files(dataset_id);
CREATE INDEX IF NOT EXISTS idx_gpus_node ON gpus(node_id);
CREATE UNIQUE INDEX IF NOT EXISTS uq_gpus_node_index ON gpus(node_id, gpu_index);
CREATE INDEX IF NOT EXISTS idx_replicas_resource ON resource_replicas(resource_type, resource_id);
CREATE UNIQUE INDEX IF NOT EXISTS uq_replicas_node_resource ON resource_replicas(node_id, resource_type, resource_id);
CREATE INDEX IF NOT EXISTS idx_sync_jobs_node_status ON resource_sync_jobs(target_node_id, status);
CREATE INDEX IF NOT EXISTS idx_eval_tasks_status ON eval_tasks(status);
CREATE INDEX IF NOT EXISTS idx_eval_dimensions_active ON eval_dimensions(is_active);
CREATE INDEX IF NOT EXISTS idx_compare_tasks_status ON compare_tasks(status);

17
backend/app/main.py
View File

@@ -1,9 +1,13 @@
import asyncio
from contextlib import suppress
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from app.api.v1.router import api_router
from app.core.config import get_settings
from app.core.logging import configure_logging, setup_request_logging
from app.workers.compute_poller import run_compute_poller
def create_app() -> FastAPI:
@@ -20,6 +24,19 @@ def create_app() -> FastAPI:
)
setup_request_logging(app)
app.include_router(api_router, prefix=settings.route_prefix)
@app.on_event("startup")
async def start_workers() -> None:
app.state.compute_poller_task = asyncio.create_task(run_compute_poller())
@app.on_event("shutdown")
async def stop_workers() -> None:
task = getattr(app.state, "compute_poller_task", None)
if task:
task.cancel()
with suppress(asyncio.CancelledError):
await task
return app

206
backend/app/modules/compute_gateway/client.py Normal file
View File

@@ -0,0 +1,206 @@
from __future__ import annotations
import time
from typing import Any
from urllib.parse import urljoin
import httpx
from app.core.config import get_settings
def _join_url(base_url: str, path: str) -> str:
return urljoin(base_url.rstrip("/") + "/", path.lstrip("/"))
def _unwrap_items(payload: Any) -> list[dict[str, Any]]:
if isinstance(payload, list):
return [item for item in payload if isinstance(item, dict)]
if isinstance(payload, dict):
data = payload.get("data")
if isinstance(data, dict) and isinstance(data.get("items"), list):
return [item for item in data["items"] if isinstance(item, dict)]
if isinstance(payload.get("items"), list):
return [item for item in payload["items"] if isinstance(item, dict)]
if isinstance(data, list):
return [item for item in data if isinstance(item, dict)]
return []
def _unwrap_dict(payload: Any) -> dict[str, Any]:
if isinstance(payload, dict) and isinstance(payload.get("data"), dict):
return payload["data"]
return payload if isinstance(payload, dict) else {}
class ComputeNodeClient:
"""Application-side client for one compute node.
The client accepts both current YG Compute API responses and common
wrapper shapes such as `{code,message,data}` to make future engine/node
adapters less brittle.
"""
def __init__(self, api_base_url: str, token: str | None = None, timeout: float | None = None) -> None:
settings = get_settings()
self.api_base_url = api_base_url.rstrip("/")
self.token = token or settings.compute_service_token
self.timeout = timeout or settings.compute_request_timeout_seconds
self.route_prefix = settings.route_prefix.rstrip("/") or "/modelTF"
def headers(self) -> dict[str, str]:
if not self.token:
return {}
return {"X-Compute-Token": self.token}
async def test_connection(self) -> dict[str, Any]:
started = time.perf_counter()
health = await self.health()
gpus = await self.gpus()
return {
"success": True,
"latency_ms": int((time.perf_counter() - started) * 1000),
"health": health,
"gpus": gpus,
}
async def health(self) -> dict[str, Any]:
paths = [f"{self.route_prefix}/v1/compute/health", f"{self.route_prefix}/health", "/health"]
last_error = ""
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
for path in paths:
try:
response = await client.get(_join_url(self.api_base_url, path))
response.raise_for_status()
return _unwrap_dict(response.json())
except Exception as exc: # noqa: BLE001 - keep endpoint compatibility fallback broad
last_error = str(exc)
raise RuntimeError(last_error or "compute health check failed")
async def gpus(self) -> list[dict[str, Any]]:
paths = [
f"{self.route_prefix}/compute/resources/gpus",
f"{self.route_prefix}/v1/compute/resources/gpus",
"/compute/resources/gpus",
]
last_error = ""
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
for path in paths:
try:
response = await client.get(_join_url(self.api_base_url, path))
response.raise_for_status()
return _unwrap_items(response.json())
except Exception as exc: # noqa: BLE001
last_error = str(exc)
raise RuntimeError(last_error or "compute gpu discovery failed")
async def create_job(self, payload: dict[str, Any]) -> dict[str, Any]:
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
response = await client.post(_join_url(self.api_base_url, f"{self.route_prefix}/compute/jobs"), json=payload)
response.raise_for_status()
return _unwrap_dict(response.json())
async def preview_job(self, payload: dict[str, Any]) -> dict[str, Any]:
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
response = await client.post(
_join_url(self.api_base_url, f"{self.route_prefix}/compute/jobs/preview"),
json=payload,
)
response.raise_for_status()
return _unwrap_dict(response.json())
async def validate_job(self, payload: dict[str, Any]) -> dict[str, Any]:
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
response = await client.post(
_join_url(self.api_base_url, f"{self.route_prefix}/compute/jobs/validate"),
json=payload,
)
response.raise_for_status()
return _unwrap_dict(response.json())
async def check_paths(self, paths: list[dict[str, Any]]) -> dict[str, Any]:
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
response = await client.post(
_join_url(self.api_base_url, f"{self.route_prefix}/compute/files/check-paths"),
json={"paths": paths},
)
response.raise_for_status()
return _unwrap_dict(response.json())
async def list_files(
self,
root: str = "data",
relative_path: str = "",
directories_only: bool = False,
) -> dict[str, Any]:
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
response = await client.get(
_join_url(self.api_base_url, f"{self.route_prefix}/compute/files/list"),
params={"root": root, "relative_path": relative_path, "directories_only": directories_only},
)
response.raise_for_status()
return _unwrap_dict(response.json())
async def get_job(self, job_id: str) -> dict[str, Any]:
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
response = await client.get(_join_url(self.api_base_url, f"{self.route_prefix}/compute/jobs/{job_id}"))
response.raise_for_status()
return _unwrap_dict(response.json())
async def stop_job(self, job_id: str) -> dict[str, Any]:
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
response = await client.post(_join_url(self.api_base_url, f"{self.route_prefix}/compute/jobs/{job_id}/stop"))
response.raise_for_status()
return _unwrap_dict(response.json())
async def job_logs(
self,
job_id: str,
tail_lines: int | None = None,
offset: int | None = None,
limit: int | None = None,
) -> dict[str, Any]:
params = {
key: value
for key, value in {"tail_lines": tail_lines, "offset": offset, "limit": limit}.items()
if value is not None
}
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
response = await client.get(
_join_url(self.api_base_url, f"{self.route_prefix}/compute/jobs/{job_id}/logs"),
params=params,
)
response.raise_for_status()
return _unwrap_dict(response.json())
async def import_local_file(self, payload: dict[str, Any]) -> dict[str, Any]:
async with httpx.AsyncClient(timeout=self.timeout, headers=self.headers()) as client:
response = await client.post(
_join_url(self.api_base_url, f"{self.route_prefix}/compute/files/import-local"),
json=payload,
)
response.raise_for_status()
return _unwrap_dict(response.json())
async def upload_file(
self,
filename: str,
content: bytes,
target_relative_path: str,
resource_type: str | None = None,
resource_id: str | None = None,
) -> dict[str, Any]:
data = {
"target_relative_path": target_relative_path,
"resource_type": resource_type or "",
"resource_id": resource_id or "",
}
files = {"file": (filename, content)}
async with httpx.AsyncClient(timeout=max(self.timeout, 60), headers=self.headers()) as client:
response = await client.post(
_join_url(self.api_base_url, f"{self.route_prefix}/compute/files/upload"),
data=data,
files=files,
)
response.raise_for_status()
return _unwrap_dict(response.json())

27
backend/app/modules/compute_gateway/sync.py Normal file
View File

@@ -0,0 +1,27 @@
from __future__ import annotations
from typing import Any
from app.db.platform_store import get_platform_store
from app.modules.compute_gateway.client import ComputeNodeClient
def _node_for_task(task: dict[str, Any]) -> dict[str, Any] | None:
return next((node for node in get_platform_store().compute_nodes() if node["id"] == task.get("compute_node_id")), None)
async def poll_compute_jobs_once() -> dict[str, Any]:
store = get_platform_store()
synced: list[dict[str, Any]] = []
failed: list[dict[str, str]] = []
for task in store.running_compute_tasks():
node = _node_for_task(task)
if not node:
failed.append({"task_id": task["id"], "error": "compute node not found"})
continue
try:
job = await ComputeNodeClient(node["api_base_url"]).get_job(task["compute_job_id"])
synced.append(store.apply_compute_job(task["id"], job))
except Exception as exc: # noqa: BLE001 - keep polling other jobs
failed.append({"task_id": task["id"], "error": str(exc)})
return {"synced": len(synced), "failed": failed, "items": synced}

31
backend/app/workers/compute_poller.py Normal file
View File

@@ -0,0 +1,31 @@
from __future__ import annotations
import asyncio
from app.core.config import get_settings
from app.core.logging import get_logger
from app.modules.compute_gateway.sync import poll_compute_jobs_once
logger = get_logger(__name__)
async def run_compute_poller() -> None:
settings = get_settings()
if settings.compute_mode == "simulator" or settings.compute_status_sync_mode != "polling":
logger.info("compute poller disabled", extra={"compute_mode": settings.compute_mode})
return
interval = max(3, settings.compute_poll_interval_seconds)
logger.info("compute poller started", extra={"interval_seconds": interval})
while True:
try:
result = await poll_compute_jobs_once()
if result["synced"] or result["failed"]:
logger.info("compute jobs polled", extra={"result": result})
except asyncio.CancelledError:
logger.info("compute poller stopped")
raise
except Exception as exc: # noqa: BLE001 - keep background polling alive
logger.exception("compute poller failed", extra={"error": str(exc)})
await asyncio.sleep(interval)

54
compute/README.md
View File

@@ -25,5 +25,57 @@ compute/
## 运行模式
- 默认 `COMPUTE_EXECUTION_MODE=real`，Compute API 只暴露健康检查和接口契约；真实训练执行器完成前，创建作业会返回未实现错误。
- 默认 `COMPUTE_EXECUTION_MODE=real`，Compute API 会通过 `compute.agent.process_manager.ProcessManager` 启动真实 `llamafactory-cli train` 子进程，并将日志写入 `TRAINING_LOG_ROOT`。
- 真实模式下 GPU 发现优先使用宿主机 `nvidia-smi`。如果部署环境暂时无法调用 `nvidia-smi`，可通过 `COMPUTE_GPU_COUNT`、`COMPUTE_GPU_NAME`、`COMPUTE_GPU_MEMORY_GB`、`COMPUTE_GPU_POWER_LIMIT_W` 声明兼容 GPU 清单，便于应用侧先完成节点登记和联调。
- 仅隔离联调时可设置 `COMPUTE_EXECUTION_MODE=simulator`，启用内存状态机和合成 GPU/日志数据。该模式不得作为生产运行路径。
- 服务间鉴权默认开启：设置 `COMPUTE_AUTH_ENABLED=true` 和一致的 `COMPUTE_SERVICE_TOKEN`，应用侧会通过 `X-Compute-Token` 调用 Compute API。
- 真实训练作业会登记到 `TRAINING_LOG_ROOT/compute-jobs.json`。Compute API 重启后会恢复作业索引，继续提供状态、停止和日志查询。
- 同一算力节点内按 GPU ID 做轻量锁定；已有运行中作业占用的 GPU 不允许再次提交，避免同机多 GPU 场景下误复用。
真实执行前提：
- 镜像或宿主机环境中 `llamafactory-cli` 可执行。
- `LLAMA_FACTORY_HOME` 指向 LLaMA-Factory 工作目录。
- 基座模型路径和数据集名称/目录已经在算力服务器本地可访问。
- 应用侧训练任务中的 GPU、模型、数据集配置能映射到当前节点本地路径。
## 应用侧接入
应用平台通过“算力节点”页面维护每台 GPU 服务器的 `Compute API` 和 `File Gateway` 地址。点击连接测试时，Backend API 会主动调用：
```text
GET /modelTF/v1/compute/health
GET /modelTF/compute/resources/gpus
```
连接成功后，应用侧会同步节点健康信息、能力标签和 GPU 清单到 PostgreSQL。多节点阶段仍按“每台算力服务器 = 单机多 GPU 节点”管理，每台服务器都部署 Compute API、Agent、File Gateway 契约和 LLaMA-Factory。
训练闭环：
```text
Frontend 创建/启动训练
-> Backend API 选择 compute_nodes 节点
-> Backend API POST /modelTF/compute/jobs 到目标 Compute API
-> Compute API 启动 llamafactory-cli 子进程
-> Backend Worker 定时 GET /modelTF/compute/jobs/{id}
-> Backend API 同步 fine_tune_tasks 状态、进度、PID、日志路径和产物索引
```
## 当前接口能力
日志接口：
```text
GET /modelTF/compute/jobs/{job_id}/logs?tail_lines=200
GET /modelTF/compute/jobs/{job_id}/logs?offset=0&limit=500
```
返回 `content`、`metrics`、`total_lines`、`offset`、`limit`、`has_more`、`next_offset`，用于前端增量刷新和日志平台采集。
文件导入：
```text
POST /modelTF/compute/files/import-local
```
该接口用于应用侧调度前把算力服务器本地可访问的模型/数据集路径导入到 `YG_FT_DATA_ROOT` 内部。目标路径会校验不能逃逸出 `YG_FT_DATA_ROOT`，源路径必须已存在于算力服务器本地或挂载目录。

246
compute/agent/process_manager.py Normal file
View File

@@ -0,0 +1,246 @@
from __future__ import annotations
import os
import json
import contextlib
import signal
import subprocess
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
TERMINAL_STATUSES = {"completed", "failed", "stopped"}
@dataclass
class ManagedProcess:
id: str
name: str
command: list[str]
work_dir: str
log_path: Path
output_dir: str
gpus: list[int]
process: subprocess.Popen[Any] | None
created_at: float
pid: int | None = None
status: str = "running"
progress: int = 5
artifacts: list[dict[str, Any]] = field(default_factory=list)
class ProcessManager:
def __init__(self, log_root: str) -> None:
self.log_root = Path(log_root)
self.log_root.mkdir(parents=True, exist_ok=True)
self.registry_path = self.log_root / "compute-jobs.json"
self.jobs: dict[str, ManagedProcess] = {}
self._load_registry()
def create_job(self, payload: dict[str, Any], command: list[str], work_dir: str) -> dict[str, Any]:
job_id = str(payload.get("id") or f"job_{int(time.time() * 1000)}")
if job_id in self.jobs and self.jobs[job_id].status not in TERMINAL_STATUSES:
raise ValueError(f"job {job_id} is already running")
output_dir = str(payload.get("output_dir") or f"/data/yg-ft/outputs/{payload.get('name', job_id)}")
Path(output_dir).mkdir(parents=True, exist_ok=True)
log_path = self.log_root / f"{job_id}.log"
env = os.environ.copy()
gpus = [int(item) for item in payload.get("gpus") or []]
locked = self.locked_gpus()
conflict = sorted(set(gpus).intersection(locked))
if conflict:
raise ValueError(f"gpu already locked: {conflict}")
if gpus:
env["CUDA_VISIBLE_DEVICES"] = ",".join(str(item) for item in gpus)
env.update({str(k): str(v) for k, v in payload.get("env", {}).items()})
cwd = work_dir if Path(work_dir).exists() else None
with log_path.open("ab") as log_file:
log_file.write(f"[INFO] starting job_id={job_id} command={' '.join(command)}\n".encode("utf-8"))
process = subprocess.Popen(
command,
cwd=cwd,
env=env,
stdout=log_file,
stderr=subprocess.STDOUT,
)
managed = ManagedProcess(
id=job_id,
name=str(payload.get("name") or job_id),
command=command,
work_dir=work_dir,
log_path=log_path,
output_dir=output_dir,
gpus=gpus,
process=process,
created_at=time.time(),
pid=process.pid,
progress=10,
)
self.jobs[job_id] = managed
data = self.serialize(managed)
self._save_registry()
return data
def get_job(self, job_id: str) -> dict[str, Any] | None:
job = self.jobs.get(job_id)
if not job:
return None
return self.serialize(job)
def list_jobs(self) -> list[dict[str, Any]]:
return [self.serialize(job) for job in self.jobs.values()]
def stop_job(self, job_id: str) -> dict[str, Any] | None:
job = self.jobs.get(job_id)
if not job:
return None
if job.status not in TERMINAL_STATUSES:
try:
if job.process is not None and os.name == "nt":
job.process.terminate()
elif job.pid is not None:
os.kill(job.pid, signal.SIGTERM)
if job.process is not None:
job.process.wait(timeout=10)
except Exception:
if job.process is not None:
job.process.kill()
elif job.pid is not None:
with contextlib.suppress(Exception):
os.kill(job.pid, signal.SIGKILL)
job.status = "stopped"
job.progress = min(job.progress, 99)
data = self.serialize(job)
self._save_registry()
return data
def logs(self, job_id: str) -> str:
job = self.jobs.get(job_id)
if not job or not job.log_path.exists():
return ""
return job.log_path.read_text(encoding="utf-8", errors="replace")
def serialize(self, job: ManagedProcess) -> dict[str, Any]:
code = job.process.poll() if job.process is not None else None
if job.status not in TERMINAL_STATUSES:
if job.process is None and job.pid is not None and not self._pid_alive(job.pid):
job.status = "failed"
job.progress = min(job.progress, 99)
code = -1
elif code is None:
job.status = "running"
elapsed = max(0, int(time.time() - job.created_at))
job.progress = min(95, max(job.progress, 10 + elapsed // 6))
elif code == 0:
job.status = "completed"
job.progress = 100
job.artifacts = self._collect_artifacts(job.output_dir)
else:
job.status = "failed"
job.progress = min(job.progress, 99)
self._save_registry()
return {
"id": job.id,
"name": job.name,
"status": job.status,
"progress": job.progress,
"pid": job.pid,
"gpus": job.gpus,
"created_at": job.created_at,
"command": job.command,
"work_dir": job.work_dir,
"output_dir": job.output_dir,
"log_file": str(job.log_path),
"artifacts": job.artifacts,
"return_code": code,
}
def locked_gpus(self) -> set[int]:
locked: set[int] = set()
for job in self.jobs.values():
status = self.serialize(job)["status"]
if status in {"queued", "running"}:
locked.update(job.gpus)
return locked
def _collect_artifacts(self, output_dir: str) -> list[dict[str, Any]]:
root = Path(output_dir)
if not root.exists():
return []
artifacts: list[dict[str, Any]] = []
for path in root.rglob("*"):
if path.is_file():
artifacts.append(
{
"path": str(path),
"name": path.name,
"size": path.stat().st_size,
}
)
return artifacts[:200]
def _save_registry(self) -> None:
items = []
for job in self.jobs.values():
items.append(
{
"id": job.id,
"name": job.name,
"command": job.command,
"work_dir": job.work_dir,
"log_path": str(job.log_path),
"output_dir": job.output_dir,
"gpus": job.gpus,
"pid": job.pid,
"created_at": job.created_at,
"status": job.status,
"progress": job.progress,
"artifacts": job.artifacts,
}
)
self.registry_path.write_text(json.dumps(items, ensure_ascii=False, indent=2), encoding="utf-8")
def _load_registry(self) -> None:
if not self.registry_path.exists():
return
try:
items = json.loads(self.registry_path.read_text(encoding="utf-8"))
except json.JSONDecodeError:
return
for item in items if isinstance(items, list) else []:
if not isinstance(item, dict):
continue
pid = item.get("pid")
status = item.get("status", "failed")
if status not in TERMINAL_STATUSES and pid and not self._pid_alive(int(pid)):
status = "failed"
job = ManagedProcess(
id=str(item["id"]),
name=str(item.get("name") or item["id"]),
command=[str(part) for part in item.get("command") or []],
work_dir=str(item.get("work_dir") or ""),
log_path=Path(item.get("log_path") or self.log_root / f"{item['id']}.log"),
output_dir=str(item.get("output_dir") or ""),
gpus=[int(gpu) for gpu in item.get("gpus") or []],
process=None,
pid=int(pid) if pid else None,
created_at=float(item.get("created_at") or time.time()),
status=status,
progress=int(item.get("progress") or 0),
artifacts=item.get("artifacts") or [],
)
self.jobs[job.id] = job
def _pid_alive(self, pid: int) -> bool:
if pid <= 0:
return False
try:
os.kill(pid, 0)
return True
except OSError:
return False

462
compute/api/main.py
View File

@@ -2,12 +2,17 @@ from __future__ import annotations
import os
import math
import hashlib
import shutil
import subprocess
import time
from pathlib import Path
from typing import Any
from fastapi import FastAPI, HTTPException
from fastapi import FastAPI, File, Form, HTTPException, Query, Request, UploadFile
from fastapi.responses import FileResponse, JSONResponse
from compute.agent.process_manager import ProcessManager
from compute.engines.llama_factory.adapter import build_command, parse_log_line
@@ -15,6 +20,20 @@ def create_app() -> FastAPI:
app = FastAPI(title="YG Fine-Tune Compute API")
jobs: dict[str, dict[str, Any]] = {}
route_prefix = os.getenv("MODELTF_ROUTE_PREFIX", "/modelTF").rstrip("/") or "/modelTF"
process_manager = ProcessManager(os.getenv("TRAINING_LOG_ROOT", "/opt/yg-ft/logs/training"))
@app.middleware("http")
async def compute_token_auth(request: Request, call_next):
token = os.getenv("COMPUTE_SERVICE_TOKEN", "")
auth_enabled = os.getenv("COMPUTE_AUTH_ENABLED", "true").lower() == "true"
public_paths = {f"{route_prefix}/health", "/health"}
if auth_enabled and token and request.url.path not in public_paths:
header_token = request.headers.get("x-compute-token", "")
auth_header = request.headers.get("authorization", "")
bearer_token = auth_header.removeprefix("Bearer ").strip() if auth_header.startswith("Bearer ") else ""
if header_token != token and bearer_token != token:
return JSONResponse({"detail": "invalid compute service token"}, status_code=401)
return await call_next(request)
def now() -> float:
return time.time()
@@ -25,6 +44,68 @@ def create_app() -> FastAPI:
def execution_mode() -> str:
return os.getenv("COMPUTE_EXECUTION_MODE", os.getenv("COMPUTE_MODE", "real")).lower()
def _int_env(name: str, default: int) -> int:
raw = os.getenv(name)
if raw is None or raw == "":
return default
return int(raw)
def _float_env(name: str, default: float) -> float:
raw = os.getenv(name)
if raw is None or raw == "":
return default
return float(raw)
def _path_inside(root: Path, candidate: Path) -> bool:
try:
candidate.resolve().relative_to(root.resolve())
return True
except ValueError:
return False
def _llama_factory_version() -> str:
for command in (["llamafactory-cli", "version"], ["llamafactory-cli", "--version"]):
try:
result = subprocess.run(command, capture_output=True, text=True, timeout=5)
except Exception:
continue
output = (result.stdout or result.stderr).strip()
if result.returncode == 0 and output:
return output.splitlines()[0][:120]
return ""
def _slice_log_content(
content: str,
tail_lines: int | None = None,
offset: int | None = None,
limit: int | None = None,
) -> dict[str, Any]:
lines = content.splitlines()
total = len(lines)
if offset is not None or limit is not None:
start = max(0, offset or 0)
end = start + limit if limit else total
selected = lines[start:end]
else:
tail = tail_lines or 200
start = max(0, total - tail)
selected = lines[start:]
next_offset = start + len(selected)
return {
"content": "\n".join(selected),
"total_lines": total,
"offset": start,
"limit": len(selected),
"has_more": next_offset < total,
"next_offset": next_offset if next_offset < total else None,
}
def _safe_float(value: Any, default: float = 0) -> float:
try:
return float(str(value).replace("[N/A]", "").strip() or default)
except (TypeError, ValueError):
return default
def job_status(job: dict[str, Any]) -> dict[str, Any]:
if execution_mode() != "simulator":
return job
@@ -74,9 +155,80 @@ def create_app() -> FastAPI:
)
return "\n".join(lines)
def real_gpu_resources() -> list[dict[str, Any]]:
query = (
"index,uuid,name,memory.total,memory.used,utilization.gpu,"
"temperature.gpu,power.draw,power.limit"
)
try:
result = subprocess.run(
["nvidia-smi", f"--query-gpu={query}", "--format=csv,noheader,nounits"],
check=True,
capture_output=True,
text=True,
timeout=5,
)
except Exception:
return fallback_gpu_resources()
items: list[dict[str, Any]] = []
for line in result.stdout.splitlines():
parts = [part.strip() for part in line.split(",")]
if len(parts) < 9:
continue
idx, uuid, name, mem_total, mem_used, util, temp, power, power_limit = parts[:9]
total_gb = round(_safe_float(mem_total) / 1024, 2)
used_gb = round(_safe_float(mem_used) / 1024, 2)
memory_percent = round(used_gb / total_gb * 100, 1) if total_gb else 0
gpu_percent = int(_safe_float(util))
items.append(
{
"id": int(idx),
"gpu_index": int(idx),
"uuid": uuid,
"name": name,
"status": "busy" if gpu_percent >= 5 or used_gb > 1 else "idle",
"gpu_percent": gpu_percent,
"memory_used_gb": used_gb,
"memory_total_gb": total_gb,
"memory_percent": memory_percent,
"temperature": int(_safe_float(temp)),
"power_w": round(_safe_float(power), 1),
"power_limit_w": round(_safe_float(power_limit), 1),
"processes": [],
}
)
return items
def fallback_gpu_resources() -> list[dict[str, Any]]:
count = _int_env("COMPUTE_GPU_COUNT", 0)
if count <= 0:
return []
name = os.getenv("COMPUTE_GPU_NAME", "Configured GPU")
memory_total = _float_env("COMPUTE_GPU_MEMORY_GB", 80.0)
power_limit = _float_env("COMPUTE_GPU_POWER_LIMIT_W", 300.0)
return [
{
"id": idx,
"gpu_index": idx,
"uuid": f"GPU-{host_id().upper()}-{idx}",
"name": name,
"status": "idle",
"gpu_percent": 0,
"memory_used_gb": 0,
"memory_total_gb": memory_total,
"memory_percent": 0,
"temperature": _int_env("COMPUTE_GPU_BASE_TEMPERATURE", 35),
"power_w": 0,
"power_limit_w": power_limit,
"processes": [],
}
for idx in range(count)
]
def gpu_resources() -> list[dict[str, Any]]:
if execution_mode() != "simulator":
return []
return real_gpu_resources()
active_jobs = [job_status(job) for job in jobs.values() if job["status"] in {"queued", "running"}]
gpus: list[dict[str, Any]] = []
for idx in range(4):
@@ -109,6 +261,101 @@ def create_app() -> FastAPI:
)
return gpus
def _check_path_item(item: dict[str, Any]) -> dict[str, Any]:
path = Path(str(item.get("path") or ""))
exists = path.exists()
expected_type = str(item.get("type") or "any")
ok = exists
if exists and expected_type == "dir":
ok = path.is_dir()
if exists and expected_type == "file":
ok = path.is_file()
return {
"name": item.get("name") or "",
"path": str(path),
"type": expected_type,
"required": bool(item.get("required", True)),
"exists": exists,
"is_dir": path.is_dir() if exists else False,
"is_file": path.is_file() if exists else False,
"ok": ok or not item.get("required", True),
}
def _job_preview(payload: dict[str, Any], check_paths: bool) -> dict[str, Any]:
warnings: list[str] = []
try:
command = build_command(payload, os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"))
except ValueError as exc:
return {
"valid": False,
"errors": [part.strip() for part in str(exc).split(";") if part.strip()],
"warnings": warnings,
"engine": str(payload.get("engine") or payload.get("training_engine") or "llama_factory"),
"command": [],
"command_text": "",
"work_dir": os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"),
"env": {},
"path_checks": [],
}
errors: list[str] = []
engine = str(payload.get("engine") or payload.get("training_engine") or "llama_factory")
path_checks: list[dict[str, Any]] = []
if check_paths and engine != "smoke":
path_checks = [
_check_path_item(
{
"name": "model_name_or_path",
"path": payload.get("model_name_or_path") or payload.get("base_model") or "",
"type": "any",
"required": True,
}
)
]
if payload.get("dataset_dir"):
path_checks.append(
_check_path_item(
{
"name": "dataset_dir",
"path": payload.get("dataset_dir"),
"type": "dir",
"required": True,
}
)
)
output_dir = Path(str(payload.get("output_dir") or "/data/yg-ft/outputs/training-job"))
path_checks.append(
_check_path_item(
{
"name": "output_parent",
"path": str(output_dir.parent),
"type": "dir",
"required": False,
}
)
)
errors.extend(
[f"{item['name']} path not available: {item['path']}" for item in path_checks if not item["ok"] and item["required"]]
)
if shutil.which(command.command[0]) is None:
errors.append(f"training command not found: {command.command[0]}")
if not Path(command.work_dir).exists():
errors.append(f"llama_factory_home not found: {command.work_dir}")
elif engine == "smoke":
warnings.append("smoke engine skips model and dataset path checks")
return {
"valid": not errors,
"errors": errors,
"warnings": warnings,
"engine": engine,
"command": command.command,
"command_text": " ".join(command.command),
"work_dir": command.work_dir,
"env": command.env,
"path_checks": path_checks,
}
@app.get(f"{route_prefix}/health")
async def health_check() -> dict[str, str]:
return {
@@ -116,41 +363,121 @@ def create_app() -> FastAPI:
"compute_host_id": os.getenv("COMPUTE_HOST_ID", "unknown"),
}
@app.get("/health")
async def health_check_root() -> dict[str, str]:
return await health_check()
@app.get(f"{route_prefix}/v1/compute/health")
async def compute_health_check() -> dict[str, str | bool]:
async def compute_health_check() -> dict[str, Any]:
data_root = Path(os.getenv("YG_FT_DATA_ROOT", "/data/yg-ft"))
dataset_root = Path(os.getenv("YG_FT_DATASET_ROOT", str(data_root / "datasets")))
output_root = Path(os.getenv("YG_FT_OUTPUT_ROOT", str(data_root / "outputs")))
llama_factory_home = Path(os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"))
return {
"status": "ok",
"api_version": "v1",
"compute_host_id": os.getenv("COMPUTE_HOST_ID", "unknown"),
"app_callback_enabled": os.getenv("ENABLE_APP_CALLBACK", "false").lower() == "true",
"data_root": str(data_root),
"data_root_exists": data_root.exists(),
"model_root": os.getenv("YG_FT_MODEL_ROOT", str(data_root / "models")),
"dataset_root": str(dataset_root),
"dataset_root_exists": dataset_root.exists(),
"output_root": str(output_root),
"output_root_exists": output_root.exists(),
"log_root": os.getenv("TRAINING_LOG_ROOT", "/opt/yg-ft/logs/training"),
"llama_factory_home": str(llama_factory_home),
"llama_factory_home_exists": llama_factory_home.exists(),
"llama_factory_version": os.getenv("LLAMA_FACTORY_VERSION", ""),
"execution_mode": execution_mode(),
"gpu_count": _int_env("COMPUTE_GPU_COUNT", 0),
"gpu_discovery_endpoint": f"{route_prefix}/compute/resources/gpus",
"capabilities": ["gpu_discovery", "llama_factory", "file_gateway", "job_polling"],
}
@app.get(f"{route_prefix}/v1/compute/jobs")
async def list_jobs_alias() -> dict[str, list[dict[str, Any]]]:
return {"items": [job_status(job) for job in jobs.values()]}
items = process_manager.list_jobs() if execution_mode() != "simulator" else [job_status(job) for job in jobs.values()]
return {"items": items}
@app.get(f"{route_prefix}/compute/resources/gpus")
async def list_gpus() -> dict[str, Any]:
return {"items": gpu_resources(), "compute_host_id": host_id()}
@app.get(f"{route_prefix}/v1/compute/resources/gpus")
async def list_gpus_v1() -> dict[str, Any]:
return {"items": gpu_resources(), "compute_host_id": host_id()}
@app.post(f"{route_prefix}/compute/jobs/preview")
async def preview_job(payload: dict[str, Any]) -> dict[str, Any]:
return _job_preview(payload, check_paths=False)
@app.post(f"{route_prefix}/compute/jobs/validate")
async def validate_job(payload: dict[str, Any]) -> dict[str, Any]:
return _job_preview(payload, check_paths=True)
@app.post(f"{route_prefix}/v1/compute/jobs/preview")
async def preview_job_v1(payload: dict[str, Any]) -> dict[str, Any]:
return await preview_job(payload)
@app.post(f"{route_prefix}/v1/compute/jobs/validate")
async def validate_job_v1(payload: dict[str, Any]) -> dict[str, Any]:
return await validate_job(payload)
@app.post(f"{route_prefix}/compute/files/check-paths")
async def check_paths(payload: dict[str, Any]) -> dict[str, Any]:
items = [_check_path_item(item) for item in payload.get("paths", []) if isinstance(item, dict)]
return {"valid": all(item["ok"] for item in items), "items": items}
@app.get(f"{route_prefix}/compute/files/list")
async def list_files(
root: str = Query(default="data"),
relative_path: str = Query(default=""),
directories_only: bool = Query(default=False),
) -> dict[str, Any]:
roots = {
"data": Path(os.getenv("YG_FT_DATA_ROOT", "/data/yg-ft")),
"models": Path(os.getenv("YG_FT_MODEL_ROOT", os.getenv("YG_FT_DATA_ROOT", "/data/yg-ft") + "/models")),
"datasets": Path(os.getenv("YG_FT_DATASET_ROOT", os.getenv("YG_FT_DATA_ROOT", "/data/yg-ft") + "/datasets")),
"outputs": Path(os.getenv("YG_FT_OUTPUT_ROOT", os.getenv("YG_FT_DATA_ROOT", "/data/yg-ft") + "/outputs")),
}
base = roots.get(root)
if base is None:
raise HTTPException(status_code=400, detail="invalid root")
target = (base / relative_path.lstrip("/\\")).resolve()
if not _path_inside(base, target):
raise HTTPException(status_code=400, detail="path must stay inside selected root")
if not target.exists():
return {"root": root, "base_path": str(base), "relative_path": relative_path, "items": []}
items = []
for child in sorted(target.iterdir(), key=lambda path: (not path.is_dir(), path.name.lower())):
if directories_only and not child.is_dir():
continue
items.append(
{
"name": child.name,
"path": str(child),
"relative_path": str(child.relative_to(base)).replace("\\", "/"),
"type": "directory" if child.is_dir() else "file",
"byte_size": child.stat().st_size if child.is_file() else 0,
}
)
return {"root": root, "base_path": str(base), "relative_path": relative_path, "items": items}
@app.post(f"{route_prefix}/compute/jobs")
async def create_job(payload: dict[str, Any]) -> dict[str, Any]:
try:
command = build_command(payload, os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"))
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc))
if execution_mode() != "simulator":
raise HTTPException(
status_code=501,
detail="real compute executor is not implemented yet; set COMPUTE_EXECUTION_MODE=simulator only for isolated development",
)
job_id = str(payload.get("id") or f"job_{int(now() * 1000)}")
if execution_mode() != "simulator":
try:
return process_manager.create_job({**payload, "id": job_id}, command.command, command.work_dir)
except FileNotFoundError as exc:
raise HTTPException(status_code=500, detail=f"training command not found: {exc.filename}")
except ValueError as exc:
raise HTTPException(status_code=409, detail=str(exc))
job = {
"id": job_id,
"name": payload.get("name", job_id),
@@ -169,17 +496,29 @@ def create_app() -> FastAPI:
@app.get(f"{route_prefix}/compute/jobs")
async def list_jobs() -> dict[str, Any]:
return {"items": [job_status(job) for job in jobs.values()]}
items = process_manager.list_jobs() if execution_mode() != "simulator" else [job_status(job) for job in jobs.values()]
return {"items": items}
@app.get(f"{route_prefix}/compute/jobs/{{job_id}}")
async def get_job(job_id: str) -> dict[str, Any]:
job = jobs.get(job_id)
if execution_mode() != "simulator":
job = process_manager.get_job(job_id)
if not job:
raise HTTPException(status_code=404, detail="job not found")
return job
job = jobs.get(job_id)
if not job:
raise HTTPException(status_code=404, detail="job not found")
return job_status(job)
@app.post(f"{route_prefix}/compute/jobs/{{job_id}}/stop")
async def stop_job(job_id: str) -> dict[str, Any]:
if execution_mode() != "simulator":
job = process_manager.stop_job(job_id)
if not job:
raise HTTPException(status_code=404, detail="job not found")
return job
job = jobs.get(job_id)
if not job:
raise HTTPException(status_code=404, detail="job not found")
@@ -188,22 +527,101 @@ def create_app() -> FastAPI:
return job
@app.get(f"{route_prefix}/compute/jobs/{{job_id}}/logs")
async def job_logs(job_id: str) -> dict[str, Any]:
job = jobs.get(job_id)
if not job:
raise HTTPException(status_code=404, detail="job not found")
job = job_status(job)
metrics = [parse_log_line(line) for line in job["logs"].splitlines()]
return {"job_id": job_id, "content": job["logs"], "metrics": [m for m in metrics if m]}
async def job_logs(
job_id: str,
tail_lines: int | None = Query(default=200, ge=1, le=5000),
offset: int | None = Query(default=None, ge=0),
limit: int | None = Query(default=None, ge=1, le=5000),
) -> dict[str, Any]:
if execution_mode() != "simulator":
job = process_manager.get_job(job_id)
if not job:
raise HTTPException(status_code=404, detail="job not found")
content = process_manager.logs(job_id)
else:
job = jobs.get(job_id)
if not job:
raise HTTPException(status_code=404, detail="job not found")
job = job_status(job)
content = job["logs"]
window = _slice_log_content(content, tail_lines, offset, limit)
metrics = [parse_log_line(line) for line in window["content"].splitlines()]
return {"job_id": job_id, **window, "metrics": [m for m in metrics if m]}
@app.post(f"{route_prefix}/compute/files/upload")
async def upload_file(payload: dict[str, Any]) -> dict[str, Any]:
file_id = str(payload.get("id") or f"file_{int(now() * 1000)}")
return {"id": file_id, "status": "available", "local_path": f"/data/yg-ft/uploads/{file_id}"}
async def upload_file(
file: UploadFile | None = File(default=None),
target_relative_path: str | None = Form(default=None),
resource_type: str | None = Form(default=None),
resource_id: str | None = Form(default=None),
) -> dict[str, Any]:
file_id = f"file_{int(now() * 1000)}"
data_root = Path(os.getenv("YG_FT_DATA_ROOT", "/data/yg-ft"))
data_root.mkdir(parents=True, exist_ok=True)
filename = Path(file.filename if file else file_id).name
if target_relative_path:
target = (data_root / target_relative_path.lstrip("/\\")).resolve()
if not _path_inside(data_root, target):
raise HTTPException(status_code=400, detail="target path must stay inside YG_FT_DATA_ROOT")
else:
target = data_root / "uploads" / f"{file_id}_{filename}"
if file:
target.parent.mkdir(parents=True, exist_ok=True)
with target.open("wb") as output:
while chunk := await file.read(1024 * 1024):
output.write(chunk)
else:
target.parent.mkdir(parents=True, exist_ok=True)
target.write_text("", encoding="utf-8")
return {
"id": file_id,
"resource_type": resource_type,
"resource_id": resource_id,
"status": "available",
"local_path": str(target),
"byte_size": target.stat().st_size,
"checksum_sha256": hashlib.sha256(target.read_bytes()).hexdigest() if target.is_file() else "",
}
@app.post(f"{route_prefix}/compute/files/import-local")
async def import_local_file(payload: dict[str, Any]) -> dict[str, Any]:
source = Path(str(payload.get("source_path") or ""))
if not source.exists():
raise HTTPException(status_code=404, detail="source path not found")
data_root = Path(os.getenv("YG_FT_DATA_ROOT", "/data/yg-ft"))
data_root.mkdir(parents=True, exist_ok=True)
relative = str(payload.get("target_relative_path") or f"imports/{source.name}").lstrip("/\\")
target = (data_root / relative).resolve()
if not _path_inside(data_root, target):
raise HTTPException(status_code=400, detail="target path must stay inside YG_FT_DATA_ROOT")
target.parent.mkdir(parents=True, exist_ok=True)
if source.is_dir():
if target.exists():
shutil.rmtree(target)
shutil.copytree(source, target)
byte_size = sum(path.stat().st_size for path in target.rglob("*") if path.is_file())
checksum = ""
else:
shutil.copy2(source, target)
byte_size = target.stat().st_size
checksum = hashlib.sha256(target.read_bytes()).hexdigest()
return {
"id": str(payload.get("id") or f"file_{int(now() * 1000)}"),
"resource_type": payload.get("resource_type"),
"resource_id": payload.get("resource_id"),
"status": "available",
"local_path": str(target),
"byte_size": byte_size,
"checksum_sha256": checksum,
}
@app.get(f"{route_prefix}/compute/files/{{file_id}}/download")
async def download_file(file_id: str) -> dict[str, Any]:
return {"id": file_id, "status": "ready", "download_url": f"{route_prefix}/compute/files/{file_id}/download"}
async def download_file(file_id: str) -> FileResponse:
upload_root = Path(os.getenv("YG_FT_DATA_ROOT", "/data/yg-ft")) / "uploads"
matches = list(upload_root.glob(f"{file_id}_*"))
if not matches:
raise HTTPException(status_code=404, detail="file not found")
return FileResponse(matches[0])
return app

58
compute/engines/llama_factory/adapter.py
View File

@@ -19,22 +19,57 @@ def validate_config(config: dict[str, Any]) -> list[str]:
errors.append("base_model or model_name_or_path is required")
if not config.get("dataset") and not config.get("dataset_dir"):
errors.append("dataset or dataset_dir is required")
learning_rate = float(config.get("learning_rate", 0.0002))
try:
learning_rate = float(config.get("learning_rate", 0.0002))
except (TypeError, ValueError):
learning_rate = 0
if learning_rate <= 0:
errors.append("learning_rate must be greater than zero")
epochs = int(config.get("n_epochs", config.get("num_train_epochs", 1)))
try:
epochs = int(config.get("n_epochs", config.get("num_train_epochs", 1)))
except (TypeError, ValueError):
epochs = 0
if epochs <= 0:
errors.append("n_epochs must be greater than zero")
return errors
def _optional_arg(config: dict[str, Any], command: list[str], option: str, *keys: str) -> None:
for key in keys:
value = config.get(key)
if value is not None and value != "":
command.extend([option, str(value)])
return
def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-Factory") -> LlamaFactoryCommand:
errors = validate_config(config)
if errors:
raise ValueError("; ".join(errors))
engine = str(config.get("engine") or config.get("training_engine") or "llama_factory")
if engine == "smoke":
output_dir = config.get("output_dir") or f"/data/yg-ft/outputs/{config.get('name', 'training-smoke')}"
script = (
"import json, os, time; "
f"out={str(output_dir)!r}; "
"os.makedirs(out, exist_ok=True); "
"print('[INFO] smoke training started', flush=True); "
"\nfor step in range(1, 7):\n"
" loss=round(1.8/(step+1), 4)\n"
" lr=round(0.0002*(1-step/10), 8)\n"
" print({'loss': loss, 'grad_norm': round(0.4 + step*0.03, 4), 'learning_rate': lr, 'epoch': round(step/6, 4)}, flush=True)\n"
" time.sleep(0.4)\n"
"\nopen(os.path.join(out, 'adapter_config.json'), 'w', encoding='utf-8').write(json.dumps({'engine':'smoke','status':'completed'})); "
"print('***** train metrics *****', flush=True); "
"print('train_loss = 0.12', flush=True); "
"print('***** train metrics end *****', flush=True)"
)
return LlamaFactoryCommand(command=["python", "-u", "-c", script], work_dir="/app", env={})
model_path = config.get("base_model") or config.get("model_name_or_path")
dataset = config.get("dataset") or config.get("dataset_dir")
dataset = config.get("dataset") or config.get("dataset_name")
dataset_dir = config.get("dataset_dir")
output_dir = config.get("output_dir") or f"/data/yg-ft/outputs/{config.get('name', 'training-job')}"
command = [
"llamafactory-cli",
@@ -46,7 +81,7 @@ def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-
"--model_name_or_path",
str(model_path),
"--dataset",
str(dataset),
str(dataset or "default"),
"--template",
str(config.get("template", "qwen")),
"--finetuning_type",
@@ -61,7 +96,22 @@ def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-
str(config.get("n_epochs", 3)),
"--save_steps",
str(config.get("save_steps", 50)),
"--logging_steps",
str(config.get("logging_steps", 10)),
"--overwrite_output_dir",
"true",
"--plot_loss",
"true",
]
if dataset_dir:
command.extend(["--dataset_dir", str(dataset_dir)])
_optional_arg(config, command, "--cutoff_len", "max_length", "cutoff_len")
_optional_arg(config, command, "--lr_scheduler_type", "lr_scheduler_type")
_optional_arg(config, command, "--warmup_ratio", "warmup_ratio")
_optional_arg(config, command, "--weight_decay", "weight_decay")
_optional_arg(config, command, "--lora_rank", "lora_rank", "rank")
_optional_arg(config, command, "--lora_alpha", "lora_alpha")
_optional_arg(config, command, "--lora_dropout", "lora_dropout")
quantization_bit = int(config.get("quantization_bit", 0) or 0)
if quantization_bit in {4, 8}:
command.extend(["--quantization_bit", str(quantization_bit)])

1
compute/requirements.txt
View File

@@ -1,5 +1,6 @@
fastapi>=0.111.0
uvicorn[standard]>=0.30.0
python-multipart>=0.0.9
pydantic>=2.7.0
python-dotenv>=1.0.1
httpx>=0.27.0

40
docker/README.md
View File

@@ -56,7 +56,7 @@ $images | ForEach-Object { docker pull $_ }
| PostgreSQL | `15432` | `5432` | 开发阶段内置数据库 |
| Redis | `16379` | `6379` | 开发阶段内置缓存 |
| Compute API | `19100` | `9100` | 算力服务器 API |
| File Gateway | `19101` | 后续服务端口 | 当前预留，后续拆出文件网关服务时使用 |
| File Gateway | `19101` | `9100` | 当前由 Compute API 暴露文件网关契约，后续可拆为独立服务 |
注意：`8000` 是后端容器内部端口，不作为宿主机对外访问端口。宿主机或浏览器应访问 `http://<app-server-ip>:17861/modelTF/health`；前端 Nginx 容器在 Docker 网络内部访问 `http://backend-api:8000/modelTF/...`。
@@ -142,6 +142,16 @@ docker compose logs --tail=80 frontend
如果使用企业统一 PostgreSQL/Redis，修改 `docker/app/.env`：
如果前端 Nginx 日志出现 `open() "/usr/share/nginx/html/modelTF/login" failed` 或 `open() "/usr/share/nginx/html/login" failed`，说明当前容器没有加载项目的 Nginx 代理配置，`/modelTF/*` 被当成静态文件查找。处理方式：
```bash
cd <repo-root>/docker/app
docker compose up -d --force-recreate frontend
docker compose exec frontend nginx -T | grep -n "location.*modelTF" -A12
```
正常配置中应存在 `location ^~ /modelTF/`，并代理到 `BACKEND_PROXY_PASS`，默认是 `http://backend-api:8000`。
```env
DATABASE_URL=postgresql+psycopg://<user>:<password>@<postgres-host>:15432/<db>
REDIS_URL=redis://<redis-host>:16379/0
@@ -192,10 +202,28 @@ GET http://<compute-server-ip>:19100/modelTF/v1/compute/health
```text
../../compute -> /app/compute
${YG_FT_DATA_ROOT_HOST} -> /data/yg-ft
../../runtime/compute/logs -> /opt/yg-ft/logs/compute
../../runtime/compute/training-logs -> /opt/yg-ft/logs/training
${YG_FT_MODEL_ROOT_HOST} -> /data/yg-ft/models
${YG_FT_DATASET_ROOT_HOST} -> /data/yg-ft/datasets
${YG_FT_OUTPUT_ROOT_HOST} -> /data/yg-ft/outputs
${COMPUTE_LOG_ROOT_HOST} -> /opt/yg-ft/logs/compute
${TRAINING_LOG_ROOT_HOST} -> /opt/yg-ft/logs/training
```
算力服务器启动前必须先在宿主机创建持久化目录，基座模型、训练数据、训练产物和训练日志都应落在宿主机磁盘上，不能只写入容器层。推荐默认目录：
```bash
cd <repo-root>/docker/compute
mkdir -p data/yg-ft/models \
data/yg-ft/datasets \
data/yg-ft/outputs \
data/yg-ft/logs/compute \
data/yg-ft/logs/training
```
默认 `docker/compute/.env.example` 使用 `./data/yg-ft`，该相对路径以 `docker/compute/docker-compose.yml` 所在目录为基准，因此实际宿主机目录是 `<repo-root>/docker/compute/data/yg-ft`。如企业环境模型盘、数据盘、产物盘分盘挂载，可在 `docker/compute/.env` 中分别调整 `YG_FT_MODEL_ROOT_HOST`、`YG_FT_DATASET_ROOT_HOST`、`YG_FT_OUTPUT_ROOT_HOST`、`COMPUTE_LOG_ROOT_HOST`、`TRAINING_LOG_ROOT_HOST`，容器内路径建议保持 `/data/yg-ft/models`、`/data/yg-ft/datasets`、`/data/yg-ft/outputs`，避免训练参数和节点配置复杂化。
页面上传数据集时，文件先进入 Backend API，再由 Backend API 调用目标算力节点的 `POST /modelTF/compute/files/upload`，写入容器内 `/data/yg-ft/datasets/{dataset_id}/`。在默认开发配置下，宿主机可在 `<repo-root>/docker/compute/data/yg-ft/datasets/{dataset_id}/` 看到对应文件。仅创建 bind mount 不会自动让应用侧上传文件出现在算力目录，必须通过这条 File Gateway 链路同步。
## 应用与算力分离部署
应用服务器只需要主动访问算力服务器，不要求算力服务器回调应用服务器。
@@ -207,7 +235,7 @@ COMPUTE_API_BASE_URL=http://<compute-server-ip>:19100
FILE_GATEWAY_BASE_URL=http://<compute-server-ip>:19101
COMPUTE_SERVICE_TOKEN=change_me
COMPUTE_STATUS_SYNC_MODE=polling
COMPUTE_POLL_INTERVAL_SECONDS=10
COMPUTE_POLL_INTERVAL_SECONDS=3
COMPUTE_POLL_BATCH_SIZE=100
```
@@ -222,6 +250,8 @@ Frontend
<- Backend Worker 定时轮询 Compute API
```
算力服务默认开启服务间鉴权。`docker/compute/.env` 中保持 `COMPUTE_AUTH_ENABLED=true`，并确保 `COMPUTE_SERVICE_TOKEN` 与 `docker/app/.env` 一致；健康检查路径仍可用于容器探活。
## 多算力节点部署
多算力节点仍按“单机多 GPU 节点”部署。每台 GPU 服务器都独立部署一套 `docker/compute`：
@@ -234,6 +264,8 @@ gpu-node-03: docker/compute + /data/yg-ft + 19100/19101
节点之间默认不互访。应用平台主动访问每个节点的 Compute API/File Gateway，并通过 `compute_nodes`、`resource_replicas`、`resource_sync_jobs` 统一调度和同步。
节点地址、权重、标签、启用状态和本地路径在前端“算力节点”页面动态维护。新增或编辑节点后，点击“测试”会由 Backend API 主动访问该节点的 `GET /modelTF/v1/compute/health` 和 `GET /modelTF/compute/resources/gpus`，并把健康信息与 GPU 清单同步到 PostgreSQL。
## 常用命令
重新构建应用镜像：

3
docker/app/.env.example
View File

@@ -41,5 +41,6 @@ FILE_GATEWAY_BASE_URL=http://compute-api:9101
# The application side polls Compute API for job state to avoid opening reverse network access.
COMPUTE_MODE=real
COMPUTE_STATUS_SYNC_MODE=polling
COMPUTE_POLL_INTERVAL_SECONDS=10
COMPUTE_POLL_INTERVAL_SECONDS=3
COMPUTE_POLL_BATCH_SIZE=100
COMPUTE_REQUEST_TIMEOUT_SECONDS=5

5
docker/app/docker-compose.yml
View File

@@ -21,6 +21,8 @@ services:
ls -la /usr/share/nginx/html;
exit 1;
fi;
envsubst '$$BACKEND_PROXY_PASS' < /etc/nginx/templates/default.conf.template > /etc/nginx/conf.d/default.conf;
nginx -t;
nginx -g 'daemon off;'
networks:
- yg-ft-app
@@ -58,8 +60,9 @@ services:
FILE_GATEWAY_BASE_URL: ${FILE_GATEWAY_BASE_URL:-http://compute-api:9101}
COMPUTE_MODE: ${COMPUTE_MODE:-real}
COMPUTE_STATUS_SYNC_MODE: ${COMPUTE_STATUS_SYNC_MODE:-polling}
COMPUTE_POLL_INTERVAL_SECONDS: ${COMPUTE_POLL_INTERVAL_SECONDS:-10}
COMPUTE_POLL_INTERVAL_SECONDS: ${COMPUTE_POLL_INTERVAL_SECONDS:-3}
COMPUTE_POLL_BATCH_SIZE: ${COMPUTE_POLL_BATCH_SIZE:-100}
COMPUTE_REQUEST_TIMEOUT_SECONDS: ${COMPUTE_REQUEST_TIMEOUT_SECONDS:-5}
PYTHONPATH: /app
volumes:
- ../../backend:/app:ro

23
docker/compute/.env.example
View File

@@ -8,14 +8,35 @@ FILE_GATEWAY_PORT=19101
COMPUTE_API_IMAGE=yg-ft-compute-api:latest
# The application server actively polls Compute API; compute server does not need reverse access.
COMPUTE_AUTH_ENABLED=true
COMPUTE_SERVICE_TOKEN=change_me
ENABLE_APP_CALLBACK=false
# LLaMA-Factory is provided by the official hiyouga/llamafactory base image.
LLAMA_FACTORY_HOME=/app/LLaMA-Factory
# Persistent host directories on the compute server.
# Create these directories before starting docker compose. They are mounted into
# the container so base models, datasets, training outputs and logs survive
# container recreation or image upgrades.
YG_FT_DATA_ROOT=/data/yg-ft
YG_FT_DATA_ROOT_HOST=/data/yg-ft
YG_FT_DATA_ROOT_HOST=./data/yg-ft
YG_FT_MODEL_ROOT=/data/yg-ft/models
YG_FT_MODEL_ROOT_HOST=./data/yg-ft/models
YG_FT_DATASET_ROOT=/data/yg-ft/datasets
YG_FT_DATASET_ROOT_HOST=./data/yg-ft/datasets
YG_FT_OUTPUT_ROOT=/data/yg-ft/outputs
YG_FT_OUTPUT_ROOT_HOST=./data/yg-ft/outputs
TRAINING_LOG_ROOT=/opt/yg-ft/logs/training
TRAINING_LOG_ROOT_HOST=./data/yg-ft/logs/training
COMPUTE_LOG_ROOT_HOST=./data/yg-ft/logs/compute
# Optional fallback used when nvidia-smi is unavailable.
# Leave COMPUTE_GPU_COUNT=0 on real GPU servers with working NVIDIA runtime.
COMPUTE_GPU_COUNT=0
COMPUTE_GPU_NAME=NVIDIA A800-SXM4-80GB
COMPUTE_GPU_MEMORY_GB=80
COMPUTE_GPU_POWER_LIMIT_W=300
LOG_DIR=/opt/yg-ft/logs/compute
CUDA_VISIBLE_DEVICES=all

19
docker/compute/docker-compose.yml
View File

@@ -5,15 +5,25 @@ services:
gpus: all
ports:
- "${COMPUTE_API_PORT:-19100}:9100"
- "${FILE_GATEWAY_PORT:-19101}:9100"
environment:
COMPUTE_ENV: ${COMPUTE_ENV:-prod}
COMPUTE_HOST_ID: ${COMPUTE_HOST_ID:-gpu-node-01}
COMPUTE_EXECUTION_MODE: ${COMPUTE_EXECUTION_MODE:-real}
MODELTF_ROUTE_PREFIX: ${MODELTF_ROUTE_PREFIX:-/modelTF}
COMPUTE_AUTH_ENABLED: ${COMPUTE_AUTH_ENABLED:-true}
COMPUTE_SERVICE_TOKEN: ${COMPUTE_SERVICE_TOKEN:-change_me}
ENABLE_APP_CALLBACK: ${ENABLE_APP_CALLBACK:-false}
LLAMA_FACTORY_HOME: ${LLAMA_FACTORY_HOME:-/app/LLaMA-Factory}
YG_FT_DATA_ROOT: ${YG_FT_DATA_ROOT:-/data/yg-ft}
YG_FT_MODEL_ROOT: ${YG_FT_MODEL_ROOT:-/data/yg-ft/models}
YG_FT_DATASET_ROOT: ${YG_FT_DATASET_ROOT:-/data/yg-ft/datasets}
YG_FT_OUTPUT_ROOT: ${YG_FT_OUTPUT_ROOT:-/data/yg-ft/outputs}
TRAINING_LOG_ROOT: ${TRAINING_LOG_ROOT:-/opt/yg-ft/logs/training}
COMPUTE_GPU_COUNT: ${COMPUTE_GPU_COUNT:-0}
COMPUTE_GPU_NAME: ${COMPUTE_GPU_NAME:-NVIDIA A800-SXM4-80GB}
COMPUTE_GPU_MEMORY_GB: ${COMPUTE_GPU_MEMORY_GB:-80}
COMPUTE_GPU_POWER_LIMIT_W: ${COMPUTE_GPU_POWER_LIMIT_W:-300}
LOG_DIR: ${LOG_DIR:-/opt/yg-ft/logs/compute}
CUDA_VISIBLE_DEVICES: ${CUDA_VISIBLE_DEVICES:-all}
NVIDIA_VISIBLE_DEVICES: ${NVIDIA_VISIBLE_DEVICES:-all}
@@ -21,9 +31,12 @@ services:
PYTHONPATH: /app
volumes:
- ../../compute:/app/compute:ro
- ${YG_FT_DATA_ROOT_HOST:-/data/yg-ft}:${YG_FT_DATA_ROOT:-/data/yg-ft}
- ../../runtime/compute/logs:/opt/yg-ft/logs/compute
- ../../runtime/compute/training-logs:/opt/yg-ft/logs/training
- ${YG_FT_DATA_ROOT_HOST:-./data/yg-ft}:${YG_FT_DATA_ROOT:-/data/yg-ft}
- ${YG_FT_MODEL_ROOT_HOST:-./data/yg-ft/models}:${YG_FT_MODEL_ROOT:-/data/yg-ft/models}
- ${YG_FT_DATASET_ROOT_HOST:-./data/yg-ft/datasets}:${YG_FT_DATASET_ROOT:-/data/yg-ft/datasets}
- ${YG_FT_OUTPUT_ROOT_HOST:-./data/yg-ft/outputs}:${YG_FT_OUTPUT_ROOT:-/data/yg-ft/outputs}
- ${COMPUTE_LOG_ROOT_HOST:-./data/yg-ft/logs/compute}:${LOG_DIR:-/opt/yg-ft/logs/compute}
- ${TRAINING_LOG_ROOT_HOST:-./data/yg-ft/logs/training}:${TRAINING_LOG_ROOT:-/opt/yg-ft/logs/training}
networks:
- yg-ft-compute
healthcheck:

17
docker/nginx.conf.template
View File

@@ -7,11 +7,18 @@ server {
client_max_body_size 200m;
location / {
try_files $uri $uri/ /index.html;
location ^~ /modelTF/ {
proxy_pass ${BACKEND_PROXY_PASS};
proxy_http_version 1.1;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
proxy_read_timeout 300s;
proxy_send_timeout 300s;
}
location /modelTF {
location = /modelTF {
proxy_pass ${BACKEND_PROXY_PASS};
proxy_http_version 1.1;
proxy_set_header Host $host;
@@ -27,4 +34,8 @@ server {
expires 30d;
add_header Cache-Control "public, immutable";
}
location / {
try_files $uri $uri/ /index.html;
}
}

177
docs/backend-api-design.md
View File

@@ -271,7 +271,7 @@ page=1&page_size=20&keyword=xxx&sort=-created_at
| POST | `/modelTF/dataset-manage` | 创建数据集 |
| PUT | `/modelTF/dataset-manage/{id}` | 更新数据集 |
| DELETE | `/modelTF/dataset-manage/{id}` | 删除数据集 |
| POST | `/modelTF/dataset-manage/upload/{dataset_id}` | 上传文件，字段名 `files` |
| POST | `/modelTF/dataset-manage/upload/{dataset_id}` | 上传文件，字段名 `files`；默认同步到启用的算力节点 `/data/yg-ft/datasets/{dataset_id}/` |
| GET | `/modelTF/dataset-manage/download/{dataset_id}` | 打包下载数据集 |
| GET | `/modelTF/dataset-manage/download/{dataset_id}/{file_id}` | 下载单文件 |
@@ -451,7 +451,9 @@ page=1&page_size=20&keyword=xxx&sort=-created_at
| GET | `/modelTF/fine-tune/{id}` | 训练任务详情 |
| GET | `/modelTF/fine-tune/check-name?name=xxx` | 任务名查重 |
| POST | `/modelTF/fine-tune` | 创建训练任务记录 |
| POST | `/modelTF/fine-tune/start` | 启动训练 |
| POST | `/modelTF/fine-tune/{id}/command-preview` | 训练创建页/详情页命令预览，返回目标节点、Compute Job payload 和 LLaMA-Factory 命令 |
| POST | `/modelTF/fine-tune/{id}/preflight` | 训练创建页启动前预检，校验节点、模型路径、数据集路径、引擎命令和训练参数 |
| POST | `/modelTF/fine-tune/start` | 启动训练，应用侧选择算力节点并提交 Compute Job |
| PUT | `/modelTF/fine-tune/{id}` | 更新任务 |
| POST | `/modelTF/fine-tune/stop/{id}` | 停止任务 |
| DELETE | `/modelTF/fine-tune/{id}` | 删除任务 |
@@ -494,6 +496,49 @@ page=1&page_size=20&keyword=xxx&sort=-created_at
}
```
训练启动前检查和命令预览：
- 页面模块：`/fine-tune/create` 创建训练任务的“参数确认/启动训练”区域；`/training-log/:id` 训练详情页的“任务配置/命令查看”区域。
- `POST /modelTF/fine-tune/{id}/command-preview`：不做远端路径强校验，只返回应用侧调度出的算力节点、标准 Compute Job payload、训练引擎命令和工作目录，供前端展示最终 LLaMA-Factory 启动命令。
- `POST /modelTF/fine-tune/{id}/preflight`：启动前强校验，真实 `llama_factory` 会检查目标节点连通性、模型路径、数据集目录、LLaMA-Factory HOME、训练命令是否可用；`smoke` 引擎用于自动化闭环验收，会跳过模型/数据集路径检查。
- `POST /modelTF/fine-tune/start`：内部先执行 preflight，预检失败返回 `409` 且任务保持 `pending`，预检通过后再写入 `syncing/queued/running` 运行态并提交 Compute Job。
请求体可传启动覆盖参数：
```json
{
"requested_node_id": "node_xxx",
"gpus": [0],
"batch_size": 1,
"learning_rate": 0.0002,
"n_epochs": 1
}
```
响应结构：
```json
{
"valid": true,
"errors": [],
"warnings": [],
"node": {
"id": "node_xxx",
"code": "gpu-node-01",
"scheduler_status": "online",
"gpu_count": 1
},
"job_payload": {},
"preview": {
"engine": "llama_factory",
"command": ["llamafactory-cli", "train", "..."],
"command_text": "llamafactory-cli train ...",
"work_dir": "/app/LLaMA-Factory",
"path_checks": []
}
}
```
### 7.2 训练日志详情页
训练日志页还会联合调用：
@@ -841,7 +886,7 @@ page=1&page_size=20&keyword=xxx&sort=-created_at
| POST | `/modelTF/compute/nodes` | 新增算力节点 |
| GET | `/modelTF/compute/nodes/{id}` | 算力节点详情 |
| PUT | `/modelTF/compute/nodes/{id}` | 编辑节点地址、权重、标签、路径和启用状态 |
| POST | `/modelTF/compute/nodes/{id}/test-connection` | 测试 Compute API/File Gateway 连通性 |
| POST | `/modelTF/compute/nodes/{id}/test-connection` | 测试 Compute API/File Gateway 连通性，并同步节点健康信息和 GPU 清单 |
| POST | `/modelTF/compute/nodes/{id}/enable` | 启用节点 |
| POST | `/modelTF/compute/nodes/{id}/disable` | 禁用节点，不接收新任务 |
| POST | `/modelTF/compute/nodes/{id}/drain` | 进入维护模式，已有任务跑完后下线 |
@@ -853,6 +898,7 @@ page=1&page_size=20&keyword=xxx&sort=-created_at
| GET | `/modelTF/compute/jobs/{id}` | 算力任务详情 |
| POST | `/modelTF/compute/jobs/{id}/retry` | 重试任务 |
| POST | `/modelTF/compute/jobs/{id}/priority` | 调整优先级 |
| GET | `/modelTF/compute/jobs/{id}/logs` | 拉取算力任务训练日志，支持 tail/分页 |
| POST | `/modelTF/internal/compute-sync/jobs/poll` | 应用平台主动轮询并同步算力任务状态 |
| POST | `/modelTF/internal/compute-sync/resources` | 调度前同步数据集/模型到目标节点 |
@@ -862,6 +908,82 @@ page=1&page_size=20&keyword=xxx&sort=-created_at
- 每个可执行训练的节点都需要部署 `Compute API`、`Compute Agent`、`File Gateway` 和宿主机挂载的 LLaMA-Factory。
- 节点之间默认不互相访问，应用平台主动访问所有节点的 Compute API/File Gateway。
- 调度支持 `auto` 和 `manual`：普通用户默认自动调度，管理员或高级用户可手动指定节点。
- 节点地址、权重、标签、启用状态、最大并发和本地路径都由 `/compute` 算力节点页面维护。
- 连接测试由应用后端发起，依次探测算力侧 `GET /modelTF/v1/compute/health` 和 `GET /modelTF/compute/resources/gpus`；返回包可为裸 JSON，也可为 `{code,message,data}` 包装结构。
新增/编辑节点请求：
```json
{
"code": "gpu-node-01",
"name": "A800 Node 01",
"api_base_url": "http://10.10.20.31:19100",
"file_gateway_url": "http://10.10.20.31:19101",
"enabled": true,
"scheduler_status": "offline",
"scheduler_weight": 100,
"tags": ["A800", "80GB", "llama_factory"],
"max_parallel_jobs": 4,
"data_root": "/data/yg-ft",
"model_root": "/data/yg-ft/models",
"log_root": "/opt/yg-ft/logs/training",
"description": "北京机房训练节点"
}
```
启动成功后，响应中的训练任务会包含 `compute_node_id`、`compute_job_id`、`process_id`、`status`、`progress`、`output_dir`、`log_file` 等字段。应用侧后台 worker 会按 `COMPUTE_POLL_INTERVAL_SECONDS` 定时调用目标算力节点查询 Compute Job，并回写训练任务状态。
算力任务日志查询参数：
| 参数 | 类型 | 必填 | 说明 |
| --- | --- | --- | --- |
| `tail_lines` | int | 否 | 默认 `200`，返回最后 N 行，范围 `1-5000` |
| `offset` | int | 否 | 从第 N 行开始读取；当传入 `offset` 或 `limit` 时分页优先，忽略默认 tail 行数 |
| `limit` | int | 否 | 分页读取行数，范围 `1-5000` |
响应字段包括 `content`、`metrics`、`total_lines`、`offset`、`limit`、`has_more`、`next_offset`。前端训练详情页、训练日志页和算力队列页可以用该接口增量读取日志，避免一次性拉取大文件。
任务维度实时日志接口：`GET /modelTF/fine-tune/{task_id}/logs?tail_lines=500`。该接口由应用后端按任务绑定的 `compute_node_id` 和 `compute_job_id` 转发到目标算力节点日志接口；如果训练尚未创建 Compute Job 或远端日志暂时不可达，则返回任务 `failure_reason`，用于页面展示启动失败、预检失败和远端训练失败原因。
算力任务重试：
```json
{
"force": false,
"priority": "high",
"requested_node_id": "node_xxx",
"gpus": [0]
}
```
默认只允许 `failed`、`stopped` 任务重试；如确需重新执行已完成任务，需要显式传 `force=true`。重试会清空旧的运行时字段，重新调度节点并创建新的 Compute Job。
算力任务优先级：
```json
{
"priority": "low|normal|high|urgent"
}
```
第一版优先级写入任务 payload，并影响 `/modelTF/compute/queue` 的展示排序；后续如接入独立队列调度器，可保持接口不变，将该字段映射到调度器优先级。
连接测试响应：
```json
{
"node_id": "node_xxx",
"success": true,
"latency_ms": 35,
"gpu_count": 8,
"health": {
"status": "ok",
"api_version": "v1",
"execution_mode": "real",
"capabilities": ["gpu_discovery", "llama_factory", "file_gateway", "job_polling"]
}
}
```
算力节点响应字段：
@@ -882,6 +1004,9 @@ page=1&page_size=20&keyword=xxx&sort=-created_at
"data_root": "/data/yg-ft",
"model_root": "/data/yg-ft/models",
"log_root": "/opt/yg-ft/logs/compute",
"api_version": "v1",
"capabilities": ["gpu_discovery", "llama_factory"],
"description": "北京机房训练节点",
"last_health_check_at": "2026-07-20T12:00:00+08:00",
"health_detail": {
"compute_api": "ok",
@@ -916,11 +1041,15 @@ GPU 响应字段：
| 方法 | 路径 | 说明 |
| --- | --- | --- |
| POST | `/modelTF/compute/jobs` | 创建训练/评测/数据处理/推理任务 |
| POST | `/modelTF/compute/jobs/preview` | 算力节点训练命令预览，不启动进程 |
| POST | `/modelTF/compute/jobs/validate` | 算力节点训练启动前预检，校验参数、路径和引擎命令 |
| GET | `/modelTF/compute/jobs/{id}` | 查询任务 |
| POST | `/modelTF/compute/jobs/{id}/stop` | 停止任务 |
| GET | `/modelTF/compute/jobs/{id}/logs` | 拉取日志 |
| POST | `/modelTF/compute/files/check-paths` | 算力节点本地路径可用性检查 |
| GET | `/modelTF/compute/resources/gpus` | 查询 GPU |
| POST | `/modelTF/compute/files/upload` | 上传到算力本地磁盘 |
| POST | `/modelTF/compute/files/import-local` | 从算力服务器本地路径导入到 `YG_FT_DATA_ROOT` |
| GET | `/modelTF/compute/files/{id}/download` | 下载文件 |
创建算力任务：
@@ -957,6 +1086,48 @@ GPU 响应字段：
}
```
当前 LLaMA-Factory 训练作业最小 payload：
```json
{
"id": "ft_xxx",
"name": "finance-sft-001",
"engine": "llama_factory",
"base_model": "/data/yg-ft/models/Qwen2.5-7B",
"model_name_or_path": "/data/yg-ft/models/Qwen2.5-7B",
"dataset": "finance_train",
"dataset_dir": "/data/yg-ft/datasets",
"output_dir": "/data/yg-ft/outputs/finance-sft-001",
"template": "qwen",
"train_method": "lora",
"gpus": [0],
"batch_size": 2,
"learning_rate": 0.0002,
"n_epochs": 3,
"save_steps": 50
}
```
应用侧轮询同步响应：
```json
{
"synced": 1,
"failed": [],
"items": [
{
"id": "ft_xxx",
"status": "running",
"progress": 35,
"compute_job_id": "ft_xxx",
"process_id": 52341,
"output_dir": "/data/yg-ft/outputs/finance-sft-001",
"log_file": "/opt/yg-ft/logs/training/ft_xxx.log"
}
]
}
```
手动指定节点时：
```json

29
docs/deployment-plan.md
View File

@@ -228,6 +228,20 @@ GPU 算力服务器部署：
多节点任务调度由应用平台统一完成。应用平台从 `compute_nodes` 读取节点地址、权重、标签、启用状态、维护状态和健康检查结果；从 `resource_replicas` 判断目标节点是否已有所需数据集/模型副本；缺失时创建 `resource_sync_jobs`，通过目标节点 File Gateway 同步资源。
当前实现已支持在 `/compute` 算力节点页面新增和编辑节点。运维人员维护 `Compute API` 地址、`File Gateway` 地址、权重、标签、启用状态、最大并发和本地路径后，点击连接测试会由应用后端主动访问目标节点健康检查和 GPU 清单接口，并将 `health_detail`、`gpu_count`、`gpu_devices/gpus` 同步到 PostgreSQL。真实 GPU 服务器优先通过 `nvidia-smi` 发现 GPU；特殊环境可用 `COMPUTE_GPU_COUNT` 等环境变量声明兼容清单。
训练运行闭环：
- 前端启动训练后，Backend API 按 `compute_nodes` 的启用状态、调度状态、权重和并行任务数选择节点。
- Backend API 向目标节点 `POST /modelTF/compute/jobs` 提交 LLaMA-Factory 训练作业，并在 `fine_tune_tasks.compute_job_id` 记录算力任务 ID。
- Compute API 在真实模式下启动 `llamafactory-cli train` 子进程，训练日志写入 `TRAINING_LOG_ROOT/{job_id}.log`。
- Backend API 启动后会运行应用侧轮询 worker，按 `COMPUTE_POLL_INTERVAL_SECONDS` 主动查询目标节点 `GET /modelTF/compute/jobs/{id}`，同步任务状态、进度、PID、输出目录、日志路径和产物索引。
- 停止训练时，Backend API 优先调用目标节点 `POST /modelTF/compute/jobs/{id}/stop`，再回写应用任务状态。
- 失败或停止任务可以通过 `POST /modelTF/compute/jobs/{id}/retry` 重试；重试会清空旧运行态，重新调度节点并创建 Compute Job。
- 训练日志通过 `GET /modelTF/compute/jobs/{id}/logs` 读取，支持 `tail_lines`、`offset`、`limit`，用于训练详情页、训练日志页和日志平台采集。
- Compute API 使用 `COMPUTE_SERVICE_TOKEN` 做服务间鉴权，应用侧请求携带 `X-Compute-Token`；健康检查接口保持可公开探活。
- Compute API 会把本机训练作业登记到 `TRAINING_LOG_ROOT/compute-jobs.json`，服务重启后可恢复任务索引并继续暴露状态和日志。
调度策略：
- 默认自动调度，按节点健康、标签、GPU 空闲、队列长度、节点权重和资源副本命中率排序。
@@ -279,7 +293,7 @@ COMPUTE_API_BASE_URL=https://compute.internal:19100
COMPUTE_SERVICE_TOKEN=***
FILE_GATEWAY_BASE_URL=https://compute.internal:19101
COMPUTE_STATUS_SYNC_MODE=polling
COMPUTE_POLL_INTERVAL_SECONDS=10
COMPUTE_POLL_INTERVAL_SECONDS=3
COMPUTE_POLL_BATCH_SIZE=100
```
@@ -290,10 +304,21 @@ COMPUTE_ENV=prod
COMPUTE_HOST_ID=gpu-node-01
COMPUTE_API_PORT=19100
FILE_GATEWAY_PORT=19101
COMPUTE_AUTH_ENABLED=true
COMPUTE_SERVICE_TOKEN=***
ENABLE_APP_CALLBACK=false
LLAMA_FACTORY_HOME=/app/LLaMA-Factory
YG_FT_DATA_ROOT=/data/yg-ft
YG_FT_DATA_ROOT_HOST=./data/yg-ft
YG_FT_MODEL_ROOT=/data/yg-ft/models
YG_FT_MODEL_ROOT_HOST=./data/yg-ft/models
YG_FT_DATASET_ROOT=/data/yg-ft/datasets
YG_FT_DATASET_ROOT_HOST=./data/yg-ft/datasets
YG_FT_OUTPUT_ROOT=/data/yg-ft/outputs
YG_FT_OUTPUT_ROOT_HOST=./data/yg-ft/outputs
TRAINING_LOG_ROOT=/opt/yg-ft/logs/training
TRAINING_LOG_ROOT_HOST=./data/yg-ft/logs/training
COMPUTE_LOG_ROOT_HOST=./data/yg-ft/logs/compute
LOG_DIR=/opt/yg-ft/logs/compute
CUDA_VISIBLE_DEVICES=0,1,2,3
```
@@ -393,7 +418,7 @@ gpu-node-03 -> http://10.10.20.33:19100 / http://10.10.20.33:19101
```env
ENABLE_APP_CALLBACK=false
COMPUTE_SERVICE_TOKEN=change_me
YG_FT_DATA_ROOT_HOST=/data/yg-ft
YG_FT_DATA_ROOT_HOST=./data/yg-ft
```
## 12. 仍需确认的问题

17
docs/postgres-schema.sql
View File

@@ -1061,8 +1061,20 @@ CREATE TABLE IF NOT EXISTS compute_nodes (
name varchar(150) NOT NULL,
host varchar(200) NOT NULL,
api_base_url text NOT NULL,
file_gateway_url text NOT NULL DEFAULT '',
storage_node_id uuid REFERENCES storage_nodes(id) ON DELETE SET NULL,
status varchar(40) NOT NULL DEFAULT 'online',
scheduler_status varchar(40) NOT NULL DEFAULT 'online',
scheduler_weight integer NOT NULL DEFAULT 100,
enabled boolean NOT NULL DEFAULT true,
max_parallel_jobs integer NOT NULL DEFAULT 1,
data_root text NOT NULL DEFAULT '/data/yg-ft',
model_root text NOT NULL DEFAULT '/data/yg-ft/models',
log_root text NOT NULL DEFAULT '/opt/yg-ft/logs/training',
api_version varchar(40) NOT NULL DEFAULT 'v1',
capabilities jsonb NOT NULL DEFAULT '[]'::jsonb,
description text,
health_detail jsonb NOT NULL DEFAULT '{}'::jsonb,
agent_version varchar(80),
gpu_count integer NOT NULL DEFAULT 0,
last_heartbeat_at timestamptz,
@@ -1434,10 +1446,15 @@ ALTER TABLE fine_tune_tasks ADD COLUMN IF NOT EXISTS tenant_id uuid REFERENCES t
ALTER TABLE fine_tune_tasks ADD COLUMN IF NOT EXISTS project_id uuid REFERENCES projects(id) ON DELETE SET NULL;
ALTER TABLE fine_tune_tasks ADD COLUMN IF NOT EXISTS owner_id uuid REFERENCES users(id) ON DELETE SET NULL;
ALTER TABLE fine_tune_tasks ADD COLUMN IF NOT EXISTS approval_status approval_status NOT NULL DEFAULT 'not_required';
ALTER TABLE fine_tune_tasks ADD COLUMN IF NOT EXISTS compute_node_id uuid REFERENCES compute_nodes(id) ON DELETE SET NULL;
ALTER TABLE fine_tune_tasks ADD COLUMN IF NOT EXISTS compute_job_id uuid REFERENCES compute_jobs(id) ON DELETE SET NULL;
ALTER TABLE fine_tune_tasks ADD COLUMN IF NOT EXISTS resume_checkpoint_id uuid REFERENCES fine_tune_checkpoints(id) ON DELETE SET NULL;
CREATE INDEX IF NOT EXISTS idx_fine_tune_tasks_scope_status
ON fine_tune_tasks(tenant_id, project_id, status, created_at DESC) WHERE deleted_at IS NULL;
CREATE INDEX IF NOT EXISTS idx_fine_tune_tasks_compute_job
ON fine_tune_tasks(compute_job_id) WHERE deleted_at IS NULL;
CREATE INDEX IF NOT EXISTS idx_fine_tune_tasks_node_status
ON fine_tune_tasks(compute_node_id, status, created_at DESC) WHERE deleted_at IS NULL;
ALTER TABLE inference_tasks ADD COLUMN IF NOT EXISTS tenant_id uuid REFERENCES tenants(id) ON DELETE SET NULL;
ALTER TABLE inference_tasks ADD COLUMN IF NOT EXISTS project_id uuid REFERENCES projects(id) ON DELETE SET NULL;

2
frontend/dist/assets/AppConfirmDialog-g7rxg-F7.js → frontend/dist/assets/AppConfirmDialog-DssSJ2jv.js vendored
View File

@@ -1 +1 @@
import{d as C,bn as g,D as E,H as O,o as _,e as B,s as D,Z as $,w as A,c as I,aL as K,q as a,n as v,aa as r,g as q,bm as M,y as u,z as N,P as k}from"./index-BKKvzUDD.js";import{_ as R}from"./_plugin-vue_export-helper-DlAUqK2U.js";const V={class:"app-confirm-header"},z={class:"app-confirm-heading"},H={class:"app-confirm-icon","aria-hidden":"true"},L={class:"app-confirm-body"},P={class:"app-confirm-actions"},S=C({__name:"AppConfirmDialog",setup(j,{expose:x}){const c=u(!1),m=u(),p=u(),d=`app-confirm-title-${g()}`,y=`app-confirm-message-${g()}`,n=N({title:"请确认操作",message:"",confirmText:"确定",cancelText:"取消",tone:"warning",closeOnOverlay:!1});let o=null,i=null;function s(t){c.value=!1;const e=o;o=null,e==null||e(t)}function h(t){return o&&s(!1),Object.assign(n,{confirmText:"确定",cancelText:"取消",tone:"warning",closeOnOverlay:!1,...t}),c.value=!0,new Promise(e=>{o=e})}function w(){n.closeOnOverlay&&s(!1)}function T(t){var b;if(t.key==="Escape"){t.preventDefault(),s(!1);return}if(t.key!=="Tab")return;const e=Array.from(((b=m.value)==null?void 0:b.querySelectorAll("button:not([disabled])"))??[]),l=e[0],f=e[e.length-1];!l||!f||(t.shiftKey&&document.activeElement===l?(t.preventDefault(),f.focus()):!t.shiftKey&&document.activeElement===f&&(t.preventDefault(),l.focus()))}return E(c,async t=>{var e;if(t){i=document.activeElement instanceof HTMLElement?document.activeElement:null,await k(),(e=p.value)==null||e.focus();return}await k(),i==null||i.focus(),i=null}),O(()=>{o==null||o(!1),o=null}),x({open:h}),(t,e)=>(_(),B(M,{to:"body"},[D($,{name:"app-confirm"},{default:A(()=>[c.value?(_(),I("div",{key:0,class:"app-confirm-overlay",onMousedown:K(w,["self"])},[a("section",{ref_key:"dialogRef",ref:m,class:v(["app-confirm-dialog",`is-${n.tone}`]),role:"alertdialog","aria-modal":!0,"aria-labelledby":d,"aria-describedby":y,onKeydown:T},[a("header",V,[a("div",z,[a("span",H,[a("i",{class:v(n.tone==="primary"?"fa fa-question-circle":"fa fa-exclamation-triangle")},null,2)]),a("h2",{id:d},r(n.title),1)]),a("button",{class:"app-confirm-close",type:"button","aria-label":"关闭确认弹窗",onClick:e[0]||(e[0]=l=>s(!1))},[...e[3]||(e[3]=[a("i",{class:"fa fa-times","aria-hidden":"true"},null,-1)])])]),a("div",L,[a("p",{id:y},r(n.message),1)]),a("footer",P,[a("button",{ref_key:"cancelButtonRef",ref:p,class:"app-confirm-button is-cancel",type:"button",onClick:e[1]||(e[1]=l=>s(!1))},r(n.cancelText),513),a("button",{class:"app-confirm-button is-confirm",type:"button",onClick:e[2]||(e[2]=l=>s(!0))},r(n.confirmText),1)])],34)],32)):q("",!0)]),_:1})]))}}),G=R(S,[["__scopeId","data-v-398df98e"]]);export{G as A};
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2
frontend/dist/assets/CompareChatView-BZJACmmb.js → frontend/dist/assets/CompareChatView-BVdMCKbe.js vendored
View File

@@ -1 +1 @@
import{a as N,E as B}from"./el-form-item-D5kF3B90.js";import{E as K}from"./index-TFUf94PZ.js";import{E as M}from"./index-BjEW7-SA.js";import{E as h}from"./index-BDEF353-.js";import{E as I}from"./el-divider-DjByQoml.js";import{E as R}from"./el-slider-DYENF1-i.js";import{d as F,G as z,e as V,w as l,ac as D,y as L,o as f,s as a,x as p,q as $,c as k,ad as j,aa as E,M as A,g as G,f as J,v as O,z as H,j as _,A as P}from"./index-BKKvzUDD.js";import"./el-popper-D6_hxRbQ.js";import"./el-tooltip-l0sNRNKZ.js";import"./el-input-number-BFR4pu1i.js";/* empty css */import{P as Q}from"./PageCard-BoKXOzst.js";import{u as W}from"./usePolling-C6448AR2.js";import{a as X}from"./compare-CZ4TIoIW.js";import{_ as Y}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-5uErZEc3.js";import"./_baseClone-B5RBzbh5.js";import"./raf-C-x62Pcl.js";import"./index-DLzof2Fz.js";import"./index-GAnQrJsQ.js";import"./debounce-ByXPNh5F.js";import"./toNumber-Dkj3QRv9.js";import"./clamp-CbbY8h6F.js";import"./index-CWUnzf90.js";import"./index-BENk7lZo.js";import"./el-card-CApHJ1Gj.js";const Z={class:"model-list"},tt={key:0,class:"empty-hint"},et=F({__name:"CompareChatView",setup(ot){const b=D(),y=O(),x=b.params.id,n=L(null),e=H({systemPrompt:"",question:"",temperature:.7,topP:.9,topK:40,maxTokens:2048}),m=_(()=>{var s;if(!((s=n.value)!=null&&s.load_status))return[];try{return(typeof n.value.load_status=="string"?JSON.parse(n.value.load_status):n.value.load_status).loaded_models||[]}catch{return[]}}),T=_(()=>m.value.length>0&&m.value.every(s=>s.status==="ready"||s.status==="running")),g=_(()=>m.value.some(s=>s.status==="starting"));async function c(){try{n.value=await X(x)}catch{}}function C(){var u,i;if(!e.question.trim()){P.warning("请输入问题");return}if(g.value){P.warning("模型仍在启动中，请稍候");return}const s=new URLSearchParams({taskId:x,taskName:((u=n.value)==null?void 0:u.model_name)||((i=n.value)==null?void 0:i.name)||"",question:e.question,systemPrompt:e.systemPrompt,temperature:String(e.temperature),topP:String(e.topP),topK:String(e.topK),maxTokens:String(e.maxTokens)}),t=y.resolve(`/model-compare/result?${s.toString()}`).href;window.open(t,"_blank")}const{start:S}=W(c,5e3,{immediate:!1});return z(async()=>{await c(),S()}),(s,t)=>{const u=I,i=h,v=K,r=N,d=R,w=M,q=B;return f(),V(Q,{title:"模型对比配置"},{default:l(()=>[a(u,{"content-position":"left"},{default:l(()=>[...t[7]||(t[7]=[p("已启动模型",-1)])]),_:1}),$("div",Z,[(f(!0),k(A,null,j(m.value,(o,U)=>(f(),V(i,{key:U,type:o.status==="ready"||o.status==="running"?"success":o.status==="starting"?"warning":"danger",size:"large"},{default:l(()=>[p(E(o.model_name)+" ("+E(o.status)+") ",1)]),_:2},1032,["type"]))),128)),m.value.length?G("",!0):(f(),k("span",tt,"暂无已启动模型"))]),a(u,{"content-position":"left"},{default:l(()=>[...t[8]||(t[8]=[p("对话配置",-1)])]),_:1}),a(q,{"label-width":"120px",style:{"max-width":"700px"}},{default:l(()=>[a(r,{label:"系统提示词"},{default:l(()=>[a(v,{modelValue:e.systemPrompt,"onUpdate:modelValue":t[0]||(t[0]=o=>e.systemPrompt=o),type:"textarea",rows:3,placeholder:"可选"},null,8,["modelValue"])]),_:1}),a(r,{label:"问题"},{default:l(()=>[a(v,{modelValue:e.question,"onUpdate:modelValue":t[1]||(t[1]=o=>e.question=o),type:"textarea",rows:4,placeholder:"请输入要对比的问题"},null,8,["modelValue"])]),_:1}),a(r,{label:"Temperature"},{default:l(()=>[a(d,{modelValue:e.temperature,"onUpdate:modelValue":t[2]||(t[2]=o=>e.temperature=o),min:0,max:2,step:.1,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,{label:"Top-p"},{default:l(()=>[a(d,{modelValue:e.topP,"onUpdate:modelValue":t[3]||(t[3]=o=>e.topP=o),min:0,max:1,step:.05,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,{label:"Top-k"},{default:l(()=>[a(d,{modelValue:e.topK,"onUpdate:modelValue":t[4]||(t[4]=o=>e.topK=o),min:1,max:100,step:1,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,{label:"Max Tokens"},{default:l(()=>[a(d,{modelValue:e.maxTokens,"onUpdate:modelValue":t[5]||(t[5]=o=>e.maxTokens=o),min:256,max:4096,step:128,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,null,{default:l(()=>[a(w,{type:"primary",disabled:!T.value||g.value,onClick:C},{default:l(()=>[...t[9]||(t[9]=[p(" 开始对比 ",-1)])]),_:1},8,["disabled"]),a(w,{onClick:t[6]||(t[6]=o=>J(y).back())},{default:l(()=>[...t[10]||(t[10]=[p("返回",-1)])]),_:1})]),_:1})]),_:1})]),_:1})}}}),qt=Y(et,[["__scopeId","data-v-5da55d96"]]);export{qt as default};
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2
frontend/dist/assets/CompareResultView-yTJ0EdKT.js → frontend/dist/assets/CompareResultView-DOPqATtj.js vendored
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1
frontend/dist/assets/ComputeNodesView-BGkUyXxx.js vendored Normal file
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frontend/dist/assets/ComputeNodesView-BKuDW-um.css vendored
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frontend/dist/assets/DataProcessListView-Ds7sKAJx.js → frontend/dist/assets/DataProcessListView-C3R_ds-j.js vendored
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2
frontend/dist/assets/DataTablePage-C-EM-7-s.js → frontend/dist/assets/DataTablePage-DzEUb2tf.js vendored
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2
frontend/dist/assets/DatasetCreateView-ChBCun2m.js → frontend/dist/assets/DatasetCreateView-CCIPqE-L.js vendored
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frontend/dist/assets/DatasetListView-BD0YX55E.js vendored
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frontend/dist/assets/DatasetListView-CF1w9JzC.js vendored Normal file
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frontend/dist/assets/FineTuneCreateView-B4Xgm6l7.js → frontend/dist/assets/FineTuneCreateView-CJLb90K1.js vendored
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frontend/dist/assets/FineTuneListView-DB12Hfwf.css → frontend/dist/assets/FineTuneListView-CYVd4cuQ.css vendored
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.el-popover{--el-popover-bg-color:var(--el-bg-color-overlay);--el-popover-font-size:var(--el-font-size-base);--el-popover-border-color:var(--el-border-color-lighter);--el-popover-padding:12px;--el-popover-padding-large:18px 20px;--el-popover-title-font-size:16px;--el-popover-title-text-color:var(--el-text-color-primary);--el-popover-border-radius:4px}.el-popover.el-popper{background:var(--el-popover-bg-color);border-radius:var(--el-popover-border-radius);border:1px solid var(--el-popover-border-color);min-width:150px;padding:var(--el-popover-padding);z-index:var(--el-index-popper);color:var(--el-text-color-regular);line-height:1.4;font-size:var(--el-popover-font-size);box-shadow:var(--el-box-shadow-light);overflow-wrap:break-word;box-sizing:border-box}.el-popover.el-popper--plain{padding:var(--el-popover-padding-large)}.el-popover__title{color:var(--el-popover-title-text-color);font-size:var(--el-popover-title-font-size);margin-bottom:12px;line-height:1}.el-popover__reference:focus:not(.focusing),.el-popover__reference:focus:hover{outline-width:0}.el-popover.el-popper.is-dark{--el-popover-bg-color:var(--el-text-color-primary);--el-popover-border-color:var(--el-text-color-primary);--el-popover-title-text-color:var(--el-bg-color);color:var(--el-bg-color)}.el-popover.el-popper:focus:active,.el-popover.el-popper:focus{outline-width:0}.progress-value[data-v-5c76183f]{color:var(--primary-color);font-weight:600}.filter-header[data-v-5c76183f]{display:inline-flex;align-items:center;gap:6px}.filter-badge[data-v-5c76183f]{line-height:1}.filter-icon[data-v-5c76183f]{cursor:pointer;font-size:12px;color:#c0c4cc;transition:color .2s}.filter-icon[data-v-5c76183f]:hover,.filter-icon.active[data-v-5c76183f]{color:#1890ff}.filter-options[data-v-5c76183f]{display:flex;flex-direction:column;gap:8px;max-height:240px;overflow-y:auto}.filter-actions[data-v-5c76183f]{text-align:right;margin-top:8px;border-top:1px solid #ebeef5;padding-top:8px}
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frontend/dist/assets/GuideView-B0JJuoOs.js → frontend/dist/assets/GuideView-CiALCc3O.js vendored
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import{_ as s}from"./_plugin-vue_export-helper-DlAUqK2U.js";import{o as t,c,q as o}from"./index-BKKvzUDD.js";const n={},r={class:"simple-page"};function a(_,e){return t(),c("section",r,[...e[0]||(e[0]=[o("h1",null,"使用文档",-1),o("p",null,"第一版系统已接入后端、数据集、模型、微调任务、算力节点和训练日志主链路。",-1)])])}const p=s(n,[["render",a],["__scopeId","data-v-87551c13"]]);export{p as default};
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frontend/dist/assets/HardwareView-B7yq-27k.js → frontend/dist/assets/HardwareView-liD1i5KX.js vendored
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frontend/dist/assets/InferenceChatView-BP3Wudle.js → frontend/dist/assets/InferenceChatView-nI4RM9OF.js vendored
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frontend/dist/assets/InferenceCreateView-DrIqOCaE.js → frontend/dist/assets/InferenceCreateView-D9JfMwNe.js vendored
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@@ -1 +1 @@
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frontend/dist/assets/LoginView-BkI86ygw.js → frontend/dist/assets/LoginView-DxdhlDPY.js vendored
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frontend/dist/assets/ModelStatusTag.vue_vue_type_script_setup_true_lang-VFIQNBU2.js → frontend/dist/assets/ModelStatusTag.vue_vue_type_script_setup_true_lang-CHgevs5i.js vendored
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import{E as r}from"./index-BDEF353-.js";import{d as o,e as c,w as p,j as u,o as i,x as d,aa as l}from"./index-BKKvzUDD.js";/* empty css */const m=o({__name:"ModelStatusTag",props:{status:{}},setup(a){const s=a,t=u(()=>{const e=s.status||"";switch(e){case"running":case"ready":case"loaded":return{text:e==="ready"||e==="loaded"?"已就绪":"运行中",type:"success"};case"pending":return{text:"等待中",type:"info"};case"failed":return{text:"失败",type:"danger"};case"starting":case"loading":return{text:"加载中",type:"warning"};case"completed":return{text:"已完成",type:"success"};case"stopped":return{text:"已停止",type:"info"};default:return{text:e||"未知",type:"info"}}});return(e,f)=>{const n=r;return i(),c(n,{type:t.value.type,size:"small",effect:"light"},{default:p(()=>[d(l(t.value.text),1)]),_:1},8,["type"])}}});export{m as _};
import{E as r}from"./index-ByhS8A1d.js";import{d as o,e as c,w as p,j as u,o as i,x as d,aa as l}from"./index-Ds9AETjS.js";/* empty css */const m=o({__name:"ModelStatusTag",props:{status:{}},setup(a){const s=a,t=u(()=>{const e=s.status||"";switch(e){case"running":case"ready":case"loaded":return{text:e==="ready"||e==="loaded"?"已就绪":"运行中",type:"success"};case"pending":return{text:"等待中",type:"info"};case"failed":return{text:"失败",type:"danger"};case"starting":case"loading":return{text:"加载中",type:"warning"};case"completed":return{text:"已完成",type:"success"};case"stopped":return{text:"已停止",type:"info"};default:return{text:e||"未知",type:"info"}}});return(e,f)=>{const n=r;return i(),c(n,{type:t.value.type,size:"small",effect:"light"},{default:p(()=>[d(l(t.value.text),1)]),_:1},8,["type"])}}});export{m as _};

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frontend/dist/assets/PageCard-BoKXOzst.js → frontend/dist/assets/PageCard-CV3p14-x.js vendored
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import{E as i}from"./el-card-CApHJ1Gj.js";import{d as n,e as u,w as p,o as a,c as s,h as o,g as r,q as d,aa as l}from"./index-BKKvzUDD.js";import{_}from"./_plugin-vue_export-helper-DlAUqK2U.js";const h={key:0,class:"page-card-header"},m={class:"page-card-title"},f={key:0,class:"page-card-subtitle"},g={key:0,class:"page-card-extra"},v={class:"page-card-body"},b=n({__name:"PageCard",props:{title:{},subtitle:{},bordered:{type:Boolean,default:!1}},setup(t){return(e,k)=>{const c=i;return a(),u(c,{shadow:"never",class:"page-card"},{default:p(()=>[t.title||e.$slots.header?(a(),s("div",h,[o(e.$slots,"header",{},()=>[d("div",null,[d("h2",m,l(t.title),1),t.subtitle?(a(),s("p",f,l(t.subtitle),1)):r("",!0)])],!0),e.$slots.extra?(a(),s("div",g,[o(e.$slots,"extra",{},void 0,!0)])):r("",!0)])):r("",!0),d("div",v,[o(e.$slots,"default",{},void 0,!0)])]),_:3})}}}),$=_(b,[["__scopeId","data-v-05c37e8a"]]);export{$ as P};
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2
frontend/dist/assets/PermissionDeniedView-BtkZoqYQ.js → frontend/dist/assets/PermissionDeniedView-Caq1gqLf.js vendored
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import{_ as o}from"./_plugin-vue_export-helper-DlAUqK2U.js";import{o as c,c as t,q as s}from"./index-BKKvzUDD.js";const n={},r={class:"simple-page"};function a(i,e){return c(),t("section",r,[...e[0]||(e[0]=[s("h1",null,"无权访问",-1),s("p",null,"当前账号没有访问该页面的权限，请联系管理员调整角色或页面权限。",-1)])])}const p=o(n,[["render",a],["__scopeId","data-v-cc370c43"]]);export{p as default};
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frontend/dist/assets/ToolCreateView-BGgtRypT.js vendored Normal file
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import{a as y,E as h}from"./el-form-item-Bq0n_p5a.js";import{E as B}from"./index-h5FkzAP_.js";import{E as R}from"./index-BNsCyG4A.js";import{d as S,G as I,e as N,w as l,j as g,z as O,o as m,s as o,q as v,c as x,ad as U,n as V,f as q,bx as F,M as L,x as b,aa as M,A as w,y as j,v as z,ac as D}from"./index-Ds9AETjS.js";import{P}from"./PageCard-CV3p14-x.js";import{u as A}from"./tools-s3Et6p-2.js";import{_ as G}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-BWI4UxBy.js";import"./_baseClone-CZxxnGCv.js";import"./raf-zQ00nuMI.js";import"./index-B0if-AHo.js";import"./index-BSbCLbaz.js";import"./el-card-D86TXgMv.js";const $={class:"icon-grid"},H=["onClick"],J=S({__name:"ToolCreateView",setup(K){const d=D(),p=z(),n=A(),i=j(),s=g(()=>d.params.id!=null),c=g(()=>s.value?n.getTool(d.params.id):void 0),e=O({id:"",name:"",description:"",url:"",icon:"fa-cog"}),C={name:[{required:!0,message:"请输入工具名称",trigger:"blur"},{max:30,message:"不超过 30 字符",trigger:"blur"}],url:[{required:!0,message:"请输入跳转地址",trigger:"blur"}]};function k(){i.value&&i.value.validate(f=>{f&&(s.value?(n.updateTool(e.id,{...e}),w.success("更新成功")):(n.addTool({...e,id:"custom_"+Date.now()}),w.success("添加成功")),p.push("/tools"))})}function E(){p.back()}return I(()=>{c.value&&Object.assign(e,c.value)}),(f,a)=>{const u=B,r=y,_=R,T=h;return m(),N(P,{title:s.value?"编辑自定义工具":"添加自定义工具"},{default:l(()=>[o(T,{ref_key:"formRef",ref:i,model:e,rules:C,"label-width":"100px",style:{"max-width":"600px"}},{default:l(()=>[o(r,{label:"工具名称",prop:"name"},{default:l(()=>[o(u,{modelValue:e.name,"onUpdate:modelValue":a[0]||(a[0]=t=>e.name=t),placeholder:"请输入工具名称",maxlength:"30","show-word-limit":""},null,8,["modelValue"])]),_:1}),o(r,{label:"工具描述"},{default:l(()=>[o(u,{modelValue:e.description,"onUpdate:modelValue":a[1]||(a[1]=t=>e.description=t),type:"textarea",rows:2,maxlength:"100","show-word-limit":""},null,8,["modelValue"])]),_:1}),o(r,{label:"跳转地址",prop:"url"},{default:l(()=>[o(u,{modelValue:e.url,"onUpdate:modelValue":a[2]||(a[2]=t=>e.url=t),placeholder:"相对路径或完整 URL"},null,8,["modelValue"])]),_:1}),o(r,{label:"图标"},{default:l(()=>[v("div",$,[(m(!0),x(L,null,U(q(F),t=>(m(),x("div",{key:t,class:V(["icon-option",{selected:e.icon===t}]),onClick:Q=>e.icon=t},[v("i",{class:V(["fa",t])},null,2)],10,H))),128))])]),_:1}),o(r,null,{default:l(()=>[o(_,{type:"primary",onClick:k},{default:l(()=>[b(M(s.value?"保存":"添加"),1)]),_:1}),o(_,{onClick:E},{default:l(()=>[...a[3]||(a[3]=[b("取消",-1)])]),_:1})]),_:1})]),_:1},8,["model"])]),_:1},8,["title"])}}}),ue=G(J,[["__scopeId","data-v-9116c5de"]]);export{ue as default};

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frontend/dist/assets/ToolCreateView-Dyu4KgOC.js vendored
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import{a as y,E as h}from"./el-form-item-D5kF3B90.js";import{E as B}from"./index-TFUf94PZ.js";import{E as R}from"./index-BjEW7-SA.js";import{d as S,G as I,e as N,w as l,j as g,z as O,o as m,s as o,q as v,c as V,ad as U,n as b,f as q,bE as F,M as L,x,aa as M,A as w,y as j,v as z,ac as D}from"./index-BKKvzUDD.js";import{P}from"./PageCard-BoKXOzst.js";import{u as A}from"./tools-BSjcVinb.js";import{_ as G}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-5uErZEc3.js";import"./_baseClone-B5RBzbh5.js";import"./raf-C-x62Pcl.js";import"./index-DLzof2Fz.js";import"./index-GAnQrJsQ.js";import"./el-card-CApHJ1Gj.js";const $={class:"icon-grid"},H=["onClick"],J=S({__name:"ToolCreateView",setup(K){const d=D(),p=z(),n=A(),i=j(),s=g(()=>d.params.id!=null),c=g(()=>s.value?n.getTool(d.params.id):void 0),e=O({id:"",name:"",description:"",url:"",icon:"fa-cog"}),C={name:[{required:!0,message:"请输入工具名称",trigger:"blur"},{max:30,message:"不超过 30 字符",trigger:"blur"}],url:[{required:!0,message:"请输入跳转地址",trigger:"blur"}]};function E(){i.value&&i.value.validate(f=>{f&&(s.value?(n.updateTool(e.id,{...e}),w.success("更新成功")):(n.addTool({...e,id:"custom_"+Date.now()}),w.success("添加成功")),p.push("/tools"))})}function k(){p.back()}return I(()=>{c.value&&Object.assign(e,c.value)}),(f,a)=>{const u=B,r=y,_=R,T=h;return m(),N(P,{title:s.value?"编辑自定义工具":"添加自定义工具"},{default:l(()=>[o(T,{ref_key:"formRef",ref:i,model:e,rules:C,"label-width":"100px",style:{"max-width":"600px"}},{default:l(()=>[o(r,{label:"工具名称",prop:"name"},{default:l(()=>[o(u,{modelValue:e.name,"onUpdate:modelValue":a[0]||(a[0]=t=>e.name=t),placeholder:"请输入工具名称",maxlength:"30","show-word-limit":""},null,8,["modelValue"])]),_:1}),o(r,{label:"工具描述"},{default:l(()=>[o(u,{modelValue:e.description,"onUpdate:modelValue":a[1]||(a[1]=t=>e.description=t),type:"textarea",rows:2,maxlength:"100","show-word-limit":""},null,8,["modelValue"])]),_:1}),o(r,{label:"跳转地址",prop:"url"},{default:l(()=>[o(u,{modelValue:e.url,"onUpdate:modelValue":a[2]||(a[2]=t=>e.url=t),placeholder:"相对路径或完整 URL"},null,8,["modelValue"])]),_:1}),o(r,{label:"图标"},{default:l(()=>[v("div",$,[(m(!0),V(L,null,U(q(F),t=>(m(),V("div",{key:t,class:b(["icon-option",{selected:e.icon===t}]),onClick:Q=>e.icon=t},[v("i",{class:b(["fa",t])},null,2)],10,H))),128))])]),_:1}),o(r,null,{default:l(()=>[o(_,{type:"primary",onClick:E},{default:l(()=>[x(M(s.value?"保存":"添加"),1)]),_:1}),o(_,{onClick:k},{default:l(()=>[...a[3]||(a[3]=[x("取消",-1)])]),_:1})]),_:1})]),_:1},8,["model"])]),_:1},8,["title"])}}}),ue=G(J,[["__scopeId","data-v-9116c5de"]]);export{ue as default};

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frontend/dist/assets/ToolsView-CTjLaJbw.js → frontend/dist/assets/ToolsView-D4cqXALF.js vendored
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frontend/dist/assets/TrainingLogView-C5lCdvrq.js vendored
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frontend/dist/assets/TrainingLogView-DGSpikbN.css vendored Normal file
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frontend/dist/assets/TrainingLogView-k6dQRm27.css vendored
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frontend/dist/assets/UserCreateView-y4uo9Eyv.js → frontend/dist/assets/UserCreateView-H9YbwuR2.js vendored
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import{E as y,a as x}from"./el-form-item-D5kF3B90.js";import{E as k}from"./index-TFUf94PZ.js";import{E as U}from"./index-BjEW7-SA.js";import{E as C,a as B}from"./el-select-Dl-FRZQd.js";import{a as F,E as I}from"./el-checkbox-qGZ2-RM2.js";import{E as N,b as R}from"./el-radio-group-BkCoTeQL.js";import{d as G,c as _,q as M,s as o,w as l,o as f,x as r,M as O,ad as S,aa as q,f as z,v as A,y as D,z as L,db as T,A as $}from"./index-BKKvzUDD.js";/* empty css */import"./el-scrollbar-ClJnvz-9.js";import"./el-popper-D6_hxRbQ.js";/* empty css */import{_ as j}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-5uErZEc3.js";import"./_baseClone-B5RBzbh5.js";import"./raf-C-x62Pcl.js";import"./index-DLzof2Fz.js";import"./index-GAnQrJsQ.js";import"./index-CWUnzf90.js";import"./vnode-78qeDweP.js";import"./index-D5ryD3I5.js";import"./index-BDEF353-.js";import"./scroll-BkzZKETR.js";import"./clamp-CbbY8h6F.js";import"./toNumber-Dkj3QRv9.js";import"./_baseIteratee-BAilevJ_.js";import"./omit-BTSq4AYh.js";const H={class:"user-create"},J=G({__name:"UserCreateView",setup(K){const d=A(),n=D(!1),a=L({username:"",display_name:"",password:"platform123",role:"viewer",status:"active",permissions:["dashboard"]}),b=["dashboard","fine-tune","model-eval","model-inference","model-manage","dataset","data-process","data-convert","compute","hardware","logs","user-settings"];async function V(){n.value=!0;try{await T(a),$.success("用户已创建"),d.push("/user-settings")}finally{n.value=!1}}return(P,e)=>{const m=k,s=x,u=B,v=C,p=R,c=N,E=I,w=F,i=U,g=y;return f(),_("section",H,[e[11]||(e[11]=M("h1",null,"创建用户",-1)),o(g,{model:a,"label-width":"110px",class:"user-form"},{default:l(()=>[o(s,{label:"账号"},{default:l(()=>[o(m,{modelValue:a.username,"onUpdate:modelValue":e[0]||(e[0]=t=>a.username=t)},null,8,["modelValue"])]),_:1}),o(s,{label:"显示名称"},{default:l(()=>[o(m,{modelValue:a.display_name,"onUpdate:modelValue":e[1]||(e[1]=t=>a.display_name=t)},null,8,["modelValue"])]),_:1}),o(s,{label:"初始密码"},{default:l(()=>[o(m,{modelValue:a.password,"onUpdate:modelValue":e[2]||(e[2]=t=>a.password=t),type:"password","show-password":""},null,8,["modelValue"])]),_:1}),o(s,{label:"角色"},{default:l(()=>[o(v,{modelValue:a.role,"onUpdate:modelValue":e[3]||(e[3]=t=>a.role=t)},{default:l(()=>[o(u,{label:"管理员",value:"admin"}),o(u,{label:"操作员",value:"operator"}),o(u,{label:"观察员",value:"viewer"})]),_:1},8,["modelValue"])]),_:1}),o(s,{label:"状态"},{default:l(()=>[o(c,{modelValue:a.status,"onUpdate:modelValue":e[4]||(e[4]=t=>a.status=t)},{default:l(()=>[o(p,{value:"active"},{default:l(()=>[...e[7]||(e[7]=[r("启用",-1)])]),_:1}),o(p,{value:"disabled"},{default:l(()=>[...e[8]||(e[8]=[r("禁用",-1)])]),_:1})]),_:1},8,["modelValue"])]),_:1}),o(s,{label:"页面权限"},{default:l(()=>[o(w,{modelValue:a.permissions,"onUpdate:modelValue":e[5]||(e[5]=t=>a.permissions=t)},{default:l(()=>[(f(),_(O,null,S(b,t=>o(E,{key:t,value:t},{default:l(()=>[r(q(t),1)]),_:2},1032,["value"])),64))]),_:1},8,["modelValue"])]),_:1}),o(s,null,{default:l(()=>[o(i,{onClick:e[6]||(e[6]=t=>z(d).back())},{default:l(()=>[...e[9]||(e[9]=[r("返回",-1)])]),_:1}),o(i,{type:"primary",loading:n.value,onClick:V},{default:l(()=>[...e[10]||(e[10]=[r("保存",-1)])]),_:1},8,["loading"])]),_:1})]),_:1},8,["model"])])}}}),we=j(J,[["__scopeId","data-v-57777381"]]);export{we as default};
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2
frontend/dist/assets/UserPermissionView-B3oCc2mK.js → frontend/dist/assets/UserPermissionView-BQ5QgCsL.js vendored
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@@ -1 +1 @@
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import{_ as o}from"./_plugin-vue_export-helper-DlAUqK2U.js";import{o as t,c as r,q as s}from"./index-Ds9AETjS.js";const c={},n={class:"simple-page"};function a(_,e){return t(),r("section",n,[...e[0]||(e[0]=[s("h1",null,"权限设置",-1),s("p",null,"第一版已支持账号页面权限读取与保存，精细化项目/模型/数据集权限将在后续版本补齐。",-1)])])}const p=o(c,[["render",a],["__scopeId","data-v-608c6e7e"]]);export{p as default};

2
frontend/dist/assets/UserSettingsView-B2Ro6wyQ.js → frontend/dist/assets/UserSettingsView-CF5QiqZ2.js vendored
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@@ -1 +1 @@
import{E as v}from"./index-BjEW7-SA.js";import{a as w,E as b}from"./el-table-D8K5zLyz.js";import{v as g}from"./directive-DsZckUTJ.js";import{d as h,G as y,K as E,c as x,q as a,s as t,w as r,y as p,da as V,o as B,x as m,aa as C}from"./index-BKKvzUDD.js";import"./el-scrollbar-ClJnvz-9.js";import"./el-popper-D6_hxRbQ.js";import"./el-tooltip-l0sNRNKZ.js";import"./el-checkbox-qGZ2-RM2.js";/* empty css */import{_ as U}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./_baseClone-B5RBzbh5.js";import"./index-GAnQrJsQ.js";import"./_baseIteratee-BAilevJ_.js";import"./castArray-5uErZEc3.js";import"./debounce-ByXPNh5F.js";import"./toNumber-Dkj3QRv9.js";import"./index-CWUnzf90.js";import"./raf-C-x62Pcl.js";import"./omit-BTSq4AYh.js";const k={class:"user-settings"},N={class:"page-header"},S=h({__name:"UserSettingsView",setup(T){const s=p(!1),i=p([]);async function d(){s.value=!0;try{i.value=await V()}finally{s.value=!1}}return y(d),(u,e)=>{const _=v,o=b,c=w,f=g;return E((B(),x("section",k,[a("header",N,[e[2]||(e[2]=a("div",null,[a("h1",null,"用户设置"),a("p",null,"管理平台账号、角色状态和页面权限。")],-1)),t(_,{type:"primary",onClick:e[0]||(e[0]=l=>u.$router.push("/user-settings/create"))},{default:r(()=>[...e[1]||(e[1]=[m("创建用户",-1)])]),_:1})]),t(c,{data:i.value},{default:r(()=>[t(o,{prop:"username",label:"账号","min-width":"140"}),t(o,{prop:"display_name",label:"显示名称","min-width":"160"}),t(o,{prop:"role",label:"角色",width:"120"}),t(o,{prop:"status",label:"状态",width:"120"}),t(o,{label:"权限数",width:"120"},{default:r(({row:l})=>{var n;return[m(C(((n=l.permissions)==null?void 0:n.length)||0),1)]}),_:1}),t(o,{prop:"create_time",label:"创建时间","min-width":"180"})]),_:1},8,["data"])])),[[f,s.value]])}}}),X=U(S,[["__scopeId","data-v-16caa8c5"]]);export{X as default};
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2
frontend/dist/assets/_baseClone-B5RBzbh5.js → frontend/dist/assets/_baseClone-CZxxnGCv.js vendored
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@@ -1 +1 @@
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2
frontend/dist/assets/_baseIteratee-BAilevJ_.js → frontend/dist/assets/_baseIteratee-82gB5Yov.js vendored
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@@ -1 +1 @@
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2
frontend/dist/assets/castArray-5uErZEc3.js → frontend/dist/assets/castArray-BWI4UxBy.js vendored
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2
frontend/dist/assets/clamp-CbbY8h6F.js → frontend/dist/assets/clamp-CyhADbdq.js vendored
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@@ -1 +1 @@
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2
frontend/dist/assets/compare-CZ4TIoIW.js → frontend/dist/assets/compare-fmy9bbtE.js vendored
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2
frontend/dist/assets/createSeriesDataSimply-DVjwvHot.js → frontend/dist/assets/createSeriesDataSimply-DRfvkeEt.js vendored
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2
frontend/dist/assets/dataset-BJ2ikehQ.js → frontend/dist/assets/dataset-BWM6p-mU.js vendored
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@@ -1 +1 @@
import{aF as n,aH as d,aG as o,aI as r}from"./index-BKKvzUDD.js";const c=()=>n("/dataset-manage"),m=e=>n(`/dataset-manage/${e}`),l=e=>o("/dataset-manage",e),p=(e,a)=>d(`/dataset-manage/${e}`,a),g=e=>r(`/dataset-manage/${e}`),$=(e,a)=>{const t=new FormData;return a.forEach(s=>t.append("files",s)),o(`/dataset-manage/upload/${e}`,t,{headers:{"Content-Type":"multipart/form-data"}})},D=e=>n(`/dataset-manage/versions/${encodeURIComponent(e)}`),_=(e,a)=>n(`/dataset-manage/versions/${encodeURIComponent(e)}/${encodeURIComponent(a)}`),u=(e,a,t)=>o(`/dataset-manage/versions/${encodeURIComponent(e)}`,{content:a,description:t.description||"在线编辑",base_version_id:t.baseVersionId,expected_current_version_id:t.expectedCurrentVersionId}),v=(e,a,t)=>d(`/dataset-manage/versions/${encodeURIComponent(e)}/active`,{version_id:a,expected_current_version_id:t}),C=(e,a,t)=>r(`/dataset-manage/versions/${encodeURIComponent(e)}/${encodeURIComponent(a)}`,{expected_current_version_id:t}),F=(e,a,t)=>{const s=`/modelTF/dataset-manage/download/${e}/${a}`;return t?`${s}?version_id=${encodeURIComponent(t)}`:s},U=e=>`/modelTF/dataset-manage/download/${e}`;export{U as a,m as b,$ as c,g as d,l as e,D as f,c as g,_ as h,u as i,F as j,v as k,C as l,p as u};
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2
frontend/dist/assets/debounce-ByXPNh5F.js → frontend/dist/assets/debounce-D6mJVDYC.js vendored
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@@ -1 +1 @@
import{cz as S,cs as y}from"./index-BKKvzUDD.js";import{t as b}from"./toNumber-Dkj3QRv9.js";var h=function(){return S.Date.now()},R="Expected a function",A=Math.max,F=Math.min;function _(x,i,a){var u,d,m,f,n,r,o=0,E=!1,c=!1,g=!0;if(typeof x!="function")throw new TypeError(R);i=b(i)||0,y(a)&&(E=!!a.leading,c="maxWait"in a,m=c?A(b(a.maxWait)||0,i):m,g="trailing"in a?!!a.trailing:g);function v(e){var t=u,l=d;return u=d=void 0,o=e,f=x.apply(l,t),f}function p(e){return o=e,n=setTimeout(s,i),E?v(e):f}function C(e){var t=e-r,l=e-o,W=i-t;return c?F(W,m-l):W}function k(e){var t=e-r,l=e-o;return r===void 0||t>=i||t<0||c&&l>=m}function s(){var e=h();if(k(e))return I(e);n=setTimeout(s,C(e))}function I(e){return n=void 0,g&&u?v(e):(u=d=void 0,f)}function L(){n!==void 0&&clearTimeout(n),o=0,u=r=d=n=void 0}function M(){return n===void 0?f:I(h())}function T(){var e=h(),t=k(e);if(u=arguments,d=this,r=e,t){if(n===void 0)return p(r);if(c)return clearTimeout(n),n=setTimeout(s,i),v(r)}return n===void 0&&(n=setTimeout(s,i)),f}return T.cancel=L,T.flush=M,T}export{_ as d};
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2
frontend/dist/assets/directive-DsZckUTJ.js → frontend/dist/assets/directive-B1cwmVOz.js vendored
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@@ -1 +1 @@
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import{z as E,d7 as P,d as O,bp as z,I as g,w as j,K as R,s as K,L as Z,Z as q,aV as D,y as B,a0 as y,bt as F,P as I,J as S,bL as m,_ as k,a3 as _,aB as G,d8 as H}from"./index-Ds9AETjS.js";function J(e,s){let t;const o=B(!1),n=E({...e,originalPosition:"",originalOverflow:"",visible:!1});function r(l){n.text=l}function c(){const l=n.parent,u=f.ns;if(!l.vLoadingAddClassList){let i=l.getAttribute("loading-number");i=Number.parseInt(i)-1,i?l.setAttribute("loading-number",i.toString()):(y(l,u.bm("parent","relative")),l.removeAttribute("loading-number")),y(l,u.bm("parent","hidden"))}v(),C.unmount()}function v(){var l,u;(u=(l=f.$el)==null?void 0:l.parentNode)==null||u.removeChild(f.$el)}function x(){var l;e.beforeClose&&!e.beforeClose()||(o.value=!0,clearTimeout(t),t=setTimeout(a,400),n.visible=!1,(l=e.closed)==null||l.call(e))}function a(){if(!o.value)return;const l=n.parent;o.value=!1,l.vLoadingAddClassList=void 0,c()}const C=P(O({name:"ElLoading",setup(l,{expose:u}){const{ns:i,zIndex:N}=z("loading");return u({ns:i,zIndex:N}),()=>{const A=n.spinner||n.svg,T=g("svg",{class:"circular",viewBox:n.svgViewBox?n.svgViewBox:"0 0 50 50",...A?{innerHTML:A}:{}},[g("circle",{class:"path",cx:"25",cy:"25",r:"20",fill:"none"})]),V=n.text?g("p",{class:i.b("text")},[n.text]):void 0;return g(q,{name:i.b("fade"),onAfterLeave:a},{default:j(()=>[R(K("div",{style:{backgroundColor:n.background||""},class:[i.b("mask"),n.customClass,i.is("fullscreen",n.fullscreen)]},[g("div",{class:i.b("spinner")},[T,V])]),[[Z,n.visible]])])})}}}));Object.assign(C._context,s??{});const f=C.mount(document.createElement("div"));return{...D(n),setText:r,removeElLoadingChild:v,close:x,handleAfterLeave:a,vm:f,get $el(){return f.$el}}}let p;const L=function(e={},s){if(!F)return;const t=M(e);if(t.fullscreen&&p)return p;const o=J({...t,closed:()=>{var r;(r=t.closed)==null||r.call(t),t.fullscreen&&(p=void 0)}},s??L._context);Y(t,t.parent,o),w(t,t.parent,o),t.parent.vLoadingAddClassList=()=>w(t,t.parent,o);let n=t.parent.getAttribute("loading-number");return n?n=`${Number.parseInt(n)+1}`:n="1",t.parent.setAttribute("loading-number",n),t.parent.appendChild(o.$el),I(()=>o.visible.value=t.visible),t.fullscreen&&(p=o),o},M=e=>{let s;return S(e.target)?s=document.querySelector(e.target)??document.body:s=e.target||document.body,{parent:s===document.body||e.body?document.body:s,background:e.background||"",svg:e.svg||"",svgViewBox:e.svgViewBox||"",spinner:e.spinner||!1,text:e.text||"",fullscreen:s===document.body&&(e.fullscreen??!0),lock:e.lock??!1,customClass:e.customClass||"",visible:e.visible??!0,beforeClose:e.beforeClose,closed:e.closed,target:s}},Y=async(e,s,t)=>{const{nextZIndex:o}=t.vm.zIndex||t.vm._.exposed.zIndex,n={};if(e.fullscreen)t.originalPosition.value=m(document.body,"position"),t.originalOverflow.value=m(document.body,"overflow"),n.zIndex=o();else if(e.parent===document.body){t.originalPosition.value=m(document.body,"position"),await I();for(const r of["top","left"]){const c=r==="top"?"scrollTop":"scrollLeft";n[r]=`${e.target.getBoundingClientRect()[r]+document.body[c]+document.documentElement[c]-Number.parseInt(m(document.body,`margin-${r}`),10)}px`}for(const r of["height","width"])n[r]=`${e.target.getBoundingClientRect()[r]}px`}else t.originalPosition.value=m(s,"position");for(const[r,c]of Object.entries(n))t.$el.style[r]=c},w=(e,s,t)=>{const o=t.vm.ns||t.vm._.exposed.ns;["absolute","fixed","sticky"].includes(t.originalPosition.value)?y(s,o.bm("parent","relative")):k(s,o.bm("parent","relative")),e.fullscreen&&e.lock?k(s,o.bm("parent","hidden")):y(s,o.bm("parent","hidden"))};L._context=null;const b=Symbol("ElLoading"),d=e=>`element-loading-${H(e)}`,h=(e,s)=>{const t=s.instance,o=a=>_(s.value)?s.value[a]:void 0,n=a=>B(S(a)&&(t==null?void 0:t[a])||a),r=a=>n(o(a)||e.getAttribute(d(a))),c=o("fullscreen")??s.modifiers.fullscreen,v={text:r("text"),svg:r("svg"),svgViewBox:r("svgViewBox"),spinner:r("spinner"),background:r("background"),customClass:r("customClass"),fullscreen:c,target:o("target")??(c?void 0:e),body:o("body")??s.modifiers.body,lock:o("lock")??s.modifiers.lock},x=L(v);x._context=$._context,e[b]={options:v,instance:x}},Q=(e,s)=>{for(const t of Object.keys(e))G(e[t])&&(e[t].value=s[t])},$={mounted(e,s){s.value&&h(e,s)},updated(e,s){const t=e[b];if(!s.value){t==null||t.instance.close(),e[b]=null;return}t?Q(t.options,_(s.value)?s.value:{text:e.getAttribute(d("text")),svg:e.getAttribute(d("svg")),svgViewBox:e.getAttribute(d("svgViewBox")),spinner:e.getAttribute(d("spinner")),background:e.getAttribute(d("background")),customClass:e.getAttribute(d("customClass"))}):h(e,s)},unmounted(e){var s;(s=e[b])==null||s.instance.close(),e[b]=null}};$._context=null;export{$ as v};

2
frontend/dist/assets/dropdown-Cy5g1tjO.js → frontend/dist/assets/dropdown-CtOpTBjN.js vendored
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@@ -1 +1 @@
import{b as p,W as o,R as e,i as a}from"./index-BKKvzUDD.js";import{u as t,r as n,b as r}from"./el-popper-D6_hxRbQ.js";const d=p({trigger:{...r.trigger,type:e([String,Array])},triggerKeys:{type:e(Array),default:()=>[o.enter,o.numpadEnter,o.space,o.down]},virtualTriggering:r.virtualTriggering,virtualRef:r.virtualRef,effect:{...t.effect,default:"light"},type:{type:e(String)},placement:{type:e(String),default:"bottom"},popperOptions:{type:e(Object),default:()=>({})},id:String,size:{type:String,default:""},splitButton:Boolean,hideOnClick:{type:Boolean,default:!0},loop:{type:Boolean,default:!0},showArrow:{type:Boolean,default:!0},showTimeout:{type:Number,default:150},hideTimeout:{type:Number,default:150},tabindex:{type:e([Number,String]),default:0},maxHeight:{type:e([Number,String]),default:""},popperClass:t.popperClass,popperStyle:t.popperStyle,disabled:Boolean,role:{type:String,values:n,default:"menu"},buttonProps:{type:e(Object)},teleported:t.teleported,appendTo:t.appendTo,persistent:{type:Boolean,default:!0}}),u=p({command:{type:[Object,String,Number],default:()=>({})},disabled:Boolean,divided:Boolean,textValue:String,icon:{type:a}}),s=p({onKeydown:{type:e(Function)}});export{u as a,s as b,d};
import{b as p,W as o,R as e,i as a}from"./index-Ds9AETjS.js";import{u as t,r as n,b as r}from"./el-popper-D1tByNLb.js";const d=p({trigger:{...r.trigger,type:e([String,Array])},triggerKeys:{type:e(Array),default:()=>[o.enter,o.numpadEnter,o.space,o.down]},virtualTriggering:r.virtualTriggering,virtualRef:r.virtualRef,effect:{...t.effect,default:"light"},type:{type:e(String)},placement:{type:e(String),default:"bottom"},popperOptions:{type:e(Object),default:()=>({})},id:String,size:{type:String,default:""},splitButton:Boolean,hideOnClick:{type:Boolean,default:!0},loop:{type:Boolean,default:!0},showArrow:{type:Boolean,default:!0},showTimeout:{type:Number,default:150},hideTimeout:{type:Number,default:150},tabindex:{type:e([Number,String]),default:0},maxHeight:{type:e([Number,String]),default:""},popperClass:t.popperClass,popperStyle:t.popperStyle,disabled:Boolean,role:{type:String,values:n,default:"menu"},buttonProps:{type:e(Object)},teleported:t.teleported,appendTo:t.appendTo,persistent:{type:Boolean,default:!0}}),u=p({command:{type:[Object,String,Number],default:()=>({})},disabled:Boolean,divided:Boolean,textValue:String,icon:{type:a}}),s=p({onKeydown:{type:e(Function)}});export{u as a,s as b,d};

2
frontend/dist/assets/el-alert-xVLeNUgJ.js → frontend/dist/assets/el-alert-CoSdHkeK.js vendored
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@@ -1 +1 @@
import{b as $,bJ as D,bz as S,d as I,bl as V,a as x,o as l,e as n,w as d,K as M,q as h,n as o,f as e,E as k,h as m,r as z,g as i,c,x as g,aa as v,M as A,s as P,bv as j,L as q,Z as F,y as J,j as E,l as K}from"./index-BKKvzUDD.js";import{f as L,b as O}from"./vnode-78qeDweP.js";const Z=["light","dark"],G=$({title:{type:String,default:""},description:{type:String,default:""},type:{type:String,values:D(S),default:"info"},closable:{type:Boolean,default:!0},closeText:{type:String,default:""},showIcon:Boolean,center:Boolean,effect:{type:String,values:Z,default:"light"}}),H={close:t=>t instanceof MouseEvent};var Q=I({name:"ElAlert",__name:"alert",props:G,emits:H,setup(t,{emit:w}){const{Close:B}=j,p=t,T=w,r=V(),s=x("alert"),y=J(!0),b=E(()=>S[p.type]),u=E(()=>{var f;if(p.description)return!0;const a=(f=r.default)==null?void 0:f.call(r);return a?L(a).some(N=>!O(N)):!1}),C=a=>{y.value=!1,T("close",a)};return(a,f)=>(l(),n(F,{name:e(s).b("fade"),persisted:""},{default:d(()=>[M(h("div",{class:o([e(s).b(),e(s).m(t.type),e(s).is("center",t.center),e(s).is(t.effect)]),role:"alert"},[t.showIcon&&(a.$slots.icon||b.value)?(l(),n(e(k),{key:0,class:o([e(s).e("icon"),e(s).is("big",u.value)])},{default:d(()=>[m(a.$slots,"icon",{},()=>[(l(),n(z(b.value)))])]),_:3},8,["class"])):i("v-if",!0),h("div",{class:o(e(s).e("content"))},[t.title||a.$slots.title?(l(),c("span",{key:0,class:o([e(s).e("title"),{"with-description":u.value}])},[m(a.$slots,"title",{},()=>[g(v(t.title),1)])],2)):i("v-if",!0),u.value?(l(),c("p",{key:1,class:o(e(s).e("description"))},[m(a.$slots,"default",{},()=>[g(v(t.description),1)])],2)):i("v-if",!0),t.closable?(l(),c(A,{key:2},[t.closeText?(l(),c("div",{key:0,class:o([e(s).e("close-btn"),e(s).is("customed")]),onClick:C},v(t.closeText),3)):(l(),n(e(k),{key:1,class:o(e(s).e("close-btn")),onClick:C},{default:d(()=>[P(e(B))]),_:1},8,["class"]))],64)):i("v-if",!0)],2)],2),[[q,y.value]])]),_:3},8,["name"]))}}),R=Q;const X=K(R);export{X as E};
import{b as N,bI as $,bs as S,d as D,bF as V,a as x,o as l,e as n,w as d,K as M,q as h,n as o,f as e,E as k,h as m,r as A,g as i,c,x as g,aa as v,M as F,s as P,bo as j,L as q,Z as z,y as K,j as E,l as L}from"./index-Ds9AETjS.js";import{f as O,b as Z}from"./vnode-CBiWHFw7.js";const G=["light","dark"],H=N({title:{type:String,default:""},description:{type:String,default:""},type:{type:String,values:$(S),default:"info"},closable:{type:Boolean,default:!0},closeText:{type:String,default:""},showIcon:Boolean,center:Boolean,effect:{type:String,values:G,default:"light"}}),J={close:t=>t instanceof MouseEvent};var Q=D({name:"ElAlert",__name:"alert",props:H,emits:J,setup(t,{emit:w}){const{Close:B}=j,p=t,T=w,r=V(),s=x("alert"),y=K(!0),b=E(()=>S[p.type]),u=E(()=>{var f;if(p.description)return!0;const a=(f=r.default)==null?void 0:f.call(r);return a?O(a).some(I=>!Z(I)):!1}),C=a=>{y.value=!1,T("close",a)};return(a,f)=>(l(),n(z,{name:e(s).b("fade"),persisted:""},{default:d(()=>[M(h("div",{class:o([e(s).b(),e(s).m(t.type),e(s).is("center",t.center),e(s).is(t.effect)]),role:"alert"},[t.showIcon&&(a.$slots.icon||b.value)?(l(),n(e(k),{key:0,class:o([e(s).e("icon"),e(s).is("big",u.value)])},{default:d(()=>[m(a.$slots,"icon",{},()=>[(l(),n(A(b.value)))])]),_:3},8,["class"])):i("v-if",!0),h("div",{class:o(e(s).e("content"))},[t.title||a.$slots.title?(l(),c("span",{key:0,class:o([e(s).e("title"),{"with-description":u.value}])},[m(a.$slots,"title",{},()=>[g(v(t.title),1)])],2)):i("v-if",!0),u.value?(l(),c("p",{key:1,class:o(e(s).e("description"))},[m(a.$slots,"default",{},()=>[g(v(t.description),1)])],2)):i("v-if",!0),t.closable?(l(),c(F,{key:2},[t.closeText?(l(),c("div",{key:0,class:o([e(s).e("close-btn"),e(s).is("customed")]),onClick:C},v(t.closeText),3)):(l(),n(e(k),{key:1,class:o(e(s).e("close-btn")),onClick:C},{default:d(()=>[P(e(B))]),_:1},8,["class"]))],64)):i("v-if",!0)],2)],2),[[q,y.value]])]),_:3},8,["name"]))}}),R=Q;const X=L(R);export{X as E};

2
frontend/dist/assets/el-avatar-l9IBuq4_.js → frontend/dist/assets/el-avatar-CNBfYcqk.js vendored
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@@ -1 +1 @@
import{b as j,i as w,aj as z,R as c,ak as m,d as A,B as V,a as E,D as I,o as f,c as y,al as S,e as h,w as _,r as q,f as D,E as F,h as G,n as K,y as L,j as p,J as R,am as J,s as d,an as O,ao as U,x as $,F as H,z as M,a9 as g,l as Q,ab as W}from"./index-BKKvzUDD.js";import{f as X}from"./vnode-78qeDweP.js";import{u as C,a as Y,E as Z}from"./el-popper-D6_hxRbQ.js";const aa=j({size:{type:[Number,String],values:z,validator:a=>m(a)},shape:{type:String,values:["circle","square"]},icon:{type:w},src:{type:String,default:""},alt:String,srcSet:String,fit:{type:c(String),default:"cover"}}),ea={error:a=>a instanceof Event},b=Symbol("avatarGroupContextKey"),ta={size:{type:c([Number,String]),values:z,validator:a=>m(a)},shape:{type:c(String),values:["circle","square"]},collapseAvatars:Boolean,collapseAvatarsTooltip:Boolean,maxCollapseAvatars:{type:Number,default:1},effect:{type:c(String),default:"light"},placement:{type:c(String),values:Y,default:"top"},popperClass:C.popperClass,popperStyle:C.popperStyle,collapseClass:String,collapseStyle:{type:c([String,Array,Object,Boolean]),default:void 0}},sa=["src","alt","srcset"];var la=A({name:"ElAvatar",__name:"avatar",props:aa,emits:ea,setup(a,{emit:n}){const t=a,r=n,e=V(b,void 0),l=E("avatar"),o=L(!1),s=p(()=>t.size??(e==null?void 0:e.size)),v=p(()=>t.shape??(e==null?void 0:e.shape)??"circle"),B=p(()=>{const{icon:i}=t,u=[l.b()];return R(s.value)&&u.push(l.m(s.value)),i&&u.push(l.m("icon")),v.value&&u.push(l.m(v.value)),u}),x=p(()=>m(s.value)?l.cssVarBlock({size:J(s.value)}):void 0),T=p(()=>({objectFit:t.fit}));I(()=>[t.src,t.srcSet],()=>o.value=!1);function P(i){o.value=!0,r("error",i)}return(i,u)=>(f(),y("span",{class:K(B.value),style:S(x.value)},[(a.src||a.srcSet)&&!o.value?(f(),y("img",{key:0,src:a.src,alt:a.alt,srcset:a.srcSet,style:S(T.value),onError:P},null,44,sa)):a.icon?(f(),h(D(F),{key:1},{default:_(()=>[(f(),h(q(a.icon)))]),_:1})):G(i.$slots,"default",{key:2})],6))}}),k=la,N=A({name:"ElAvatarGroup",props:ta,setup(a,{slots:n}){const t=E("avatar-group");return H(b,M({size:g(a,"size"),shape:g(a,"shape")})),()=>{var l;const r=X(((l=n.default)==null?void 0:l.call(n))??[]);let e=r;if(a.collapseAvatars&&r.length>a.maxCollapseAvatars){e=r.slice(0,a.maxCollapseAvatars);const o=r.slice(a.maxCollapseAvatars);e.push(d(Z,{popperClass:a.popperClass,popperStyle:a.popperStyle,placement:a.placement,effect:a.effect,disabled:!a.collapseAvatarsTooltip},{default:()=>d(k,{size:a.size,shape:a.shape,class:a.collapseClass,style:a.collapseStyle},{default:()=>[$("+ "),o.length]}),content:()=>d("div",{class:t.e("collapse-avatars")},[o.map((s,v)=>O(s)?U(s,{key:s.key??v}):s)])}))}return d("div",{class:t.b()},[e])}}});const na=Q(k,{AvatarGroup:N});W(N);export{na as E};
import{b as j,i as w,aj as z,R as c,ak as m,d as A,B as V,a as E,D as I,o as f,c as y,al as S,e as h,w as _,r as q,f as D,E as F,h as G,n as K,y as L,j as p,J as R,am as J,s as d,an as O,ao as U,x as $,F as H,z as M,a9 as g,l as Q,ab as W}from"./index-Ds9AETjS.js";import{f as X}from"./vnode-CBiWHFw7.js";import{u as C,a as Y,E as Z}from"./el-popper-D1tByNLb.js";const aa=j({size:{type:[Number,String],values:z,validator:a=>m(a)},shape:{type:String,values:["circle","square"]},icon:{type:w},src:{type:String,default:""},alt:String,srcSet:String,fit:{type:c(String),default:"cover"}}),ea={error:a=>a instanceof Event},b=Symbol("avatarGroupContextKey"),ta={size:{type:c([Number,String]),values:z,validator:a=>m(a)},shape:{type:c(String),values:["circle","square"]},collapseAvatars:Boolean,collapseAvatarsTooltip:Boolean,maxCollapseAvatars:{type:Number,default:1},effect:{type:c(String),default:"light"},placement:{type:c(String),values:Y,default:"top"},popperClass:C.popperClass,popperStyle:C.popperStyle,collapseClass:String,collapseStyle:{type:c([String,Array,Object,Boolean]),default:void 0}},sa=["src","alt","srcset"];var la=A({name:"ElAvatar",__name:"avatar",props:aa,emits:ea,setup(a,{emit:n}){const t=a,r=n,e=V(b,void 0),l=E("avatar"),o=L(!1),s=p(()=>t.size??(e==null?void 0:e.size)),v=p(()=>t.shape??(e==null?void 0:e.shape)??"circle"),B=p(()=>{const{icon:i}=t,u=[l.b()];return R(s.value)&&u.push(l.m(s.value)),i&&u.push(l.m("icon")),v.value&&u.push(l.m(v.value)),u}),x=p(()=>m(s.value)?l.cssVarBlock({size:J(s.value)}):void 0),T=p(()=>({objectFit:t.fit}));I(()=>[t.src,t.srcSet],()=>o.value=!1);function P(i){o.value=!0,r("error",i)}return(i,u)=>(f(),y("span",{class:K(B.value),style:S(x.value)},[(a.src||a.srcSet)&&!o.value?(f(),y("img",{key:0,src:a.src,alt:a.alt,srcset:a.srcSet,style:S(T.value),onError:P},null,44,sa)):a.icon?(f(),h(D(F),{key:1},{default:_(()=>[(f(),h(q(a.icon)))]),_:1})):G(i.$slots,"default",{key:2})],6))}}),k=la,N=A({name:"ElAvatarGroup",props:ta,setup(a,{slots:n}){const t=E("avatar-group");return H(b,M({size:g(a,"size"),shape:g(a,"shape")})),()=>{var l;const r=X(((l=n.default)==null?void 0:l.call(n))??[]);let e=r;if(a.collapseAvatars&&r.length>a.maxCollapseAvatars){e=r.slice(0,a.maxCollapseAvatars);const o=r.slice(a.maxCollapseAvatars);e.push(d(Z,{popperClass:a.popperClass,popperStyle:a.popperStyle,placement:a.placement,effect:a.effect,disabled:!a.collapseAvatarsTooltip},{default:()=>d(k,{size:a.size,shape:a.shape,class:a.collapseClass,style:a.collapseStyle},{default:()=>[$("+ "),o.length]}),content:()=>d("div",{class:t.e("collapse-avatars")},[o.map((s,v)=>O(s)?U(s,{key:s.key??v}):s)])}))}return d("div",{class:t.b()},[e])}}});const na=Q(k,{AvatarGroup:N});W(N);export{na as E};

2
frontend/dist/assets/el-card-CApHJ1Gj.js → frontend/dist/assets/el-card-D86TXgMv.js vendored
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@@ -1 +1 @@
import{b as y,R as h,d as v,u as g,a as C,o as r,c as l,n as o,f as a,h as d,x as i,aa as f,g as c,q as S,al as b,l as m}from"./index-BKKvzUDD.js";const w=y({header:{type:String,default:""},footer:{type:String,default:""},bodyStyle:{type:h([String,Object,Array,Boolean]),default:""},headerClass:String,bodyClass:String,footerClass:String,shadow:{type:String,values:["always","hover","never"],default:void 0}});var $=v({name:"ElCard",__name:"card",props:w,setup(e){const u=g("card"),s=C("card");return(t,p)=>{var n;return r(),l("div",{class:o([a(s).b(),a(s).is(`${e.shadow||((n=a(u))==null?void 0:n.shadow)||"always"}-shadow`)])},[t.$slots.header||e.header?(r(),l("div",{key:0,class:o([a(s).e("header"),e.headerClass])},[d(t.$slots,"header",{},()=>[i(f(e.header),1)])],2)):c("v-if",!0),S("div",{class:o([a(s).e("body"),e.bodyClass]),style:b(e.bodyStyle)},[d(t.$slots,"default")],6),t.$slots.footer||e.footer?(r(),l("div",{key:1,class:o([a(s).e("footer"),e.footerClass])},[d(t.$slots,"footer",{},()=>[i(f(e.footer),1)])],2)):c("v-if",!0)],2)}}}),k=$;const E=m(k);export{E};
import{b as y,R as h,d as v,u as g,a as C,o as r,c as l,n as o,f as a,h as d,x as i,aa as f,g as c,q as S,al as b,l as m}from"./index-Ds9AETjS.js";const w=y({header:{type:String,default:""},footer:{type:String,default:""},bodyStyle:{type:h([String,Object,Array,Boolean]),default:""},headerClass:String,bodyClass:String,footerClass:String,shadow:{type:String,values:["always","hover","never"],default:void 0}});var $=v({name:"ElCard",__name:"card",props:w,setup(e){const u=g("card"),s=C("card");return(t,p)=>{var n;return r(),l("div",{class:o([a(s).b(),a(s).is(`${e.shadow||((n=a(u))==null?void 0:n.shadow)||"always"}-shadow`)])},[t.$slots.header||e.header?(r(),l("div",{key:0,class:o([a(s).e("header"),e.headerClass])},[d(t.$slots,"header",{},()=>[i(f(e.header),1)])],2)):c("v-if",!0),S("div",{class:o([a(s).e("body"),e.bodyClass]),style:b(e.bodyStyle)},[d(t.$slots,"default")],6),t.$slots.footer||e.footer?(r(),l("div",{key:1,class:o([a(s).e("footer"),e.footerClass])},[d(t.$slots,"footer",{},()=>[i(f(e.footer),1)])],2)):c("v-if",!0)],2)}}}),k=$;const E=m(k);export{E};

2
frontend/dist/assets/el-checkbox-qGZ2-RM2.js → frontend/dist/assets/el-checkbox-pV3o_8iy.js vendored
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2
frontend/dist/assets/el-descriptions-3GOD-6nw.js → frontend/dist/assets/el-descriptions-CsapHleL.js vendored
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@@ -1 +1 @@
import{b as B,aR as H,d as P,R as J,am as A,a as L,K as x,I as D,a8 as Q,B as q,f as d,o as c,c as f,q as v,M as g,ad as z,e as I,s as j,bl as T,n as _,h as F,x as K,aa as M,g as X,j as Y,F as Z,ab as ee,l as te}from"./index-BKKvzUDD.js";import{g as se,f as le}from"./vnode-78qeDweP.js";import{e as ae}from"./index-BjEW7-SA.js";const O=["left","center","right"],re=B({border:Boolean,column:{type:Number,default:3},direction:{type:String,values:["horizontal","vertical"],default:"horizontal"},size:H,title:{type:String,default:""},extra:{type:String,default:""},labelWidth:{type:[String,Number]}}),U="ElDescriptionsItem",ne=B({label:{type:String,default:""},span:{type:Number,default:1},rowspan:{type:Number,default:1},width:{type:[String,Number],default:""},minWidth:{type:[String,Number],default:""},labelWidth:{type:[String,Number]},align:{type:String,values:O,default:"left"},labelAlign:{type:String,values:O},className:{type:String,default:""},labelClassName:{type:String,default:""}}),G=P({name:U,props:ne}),R=Symbol("elDescriptions"),ie=B({row:{type:J(Array),default:()=>[]}});var E=P({name:"ElDescriptionsCell",props:{cell:{type:Object},tag:{type:String,default:"td"},type:{type:String}},setup(){return{descriptions:q(R,{})}},render(){var y;const e=se(this.cell),u=(((y=this.cell)==null?void 0:y.dirs)||[]).map(n=>{const{dir:a,arg:l,modifiers:W,value:$}=n;return[a,$,l,W]}),{border:m,direction:C}=this.descriptions,r=C==="vertical",h=()=>{var n,a,l;return((l=(a=(n=this.cell)==null?void 0:n.children)==null?void 0:a.label)==null?void 0:l.call(a))||e.label},k=()=>{var n,a,l;return(l=(a=(n=this.cell)==null?void 0:n.children)==null?void 0:a.default)==null?void 0:l.call(a)},S=e.span,s=e.rowspan,b=e.align?`is-${e.align}`:"",p=e.labelAlign?`is-${e.labelAlign}`:b,i=e.className,w=e.labelClassName,o={width:A(this.type==="label"?e.labelWidth??this.descriptions.labelWidth??e.width:e.width),minWidth:A(e.minWidth)},t=L("descriptions");switch(this.type){case"label":return x(D(this.tag,{style:o,class:[t.e("cell"),t.e("label"),t.is("bordered-label",m),t.is("vertical-label",r),p,w],colSpan:r?S:1,rowspan:r?1:s},h()),u);case"content":return x(D(this.tag,{style:o,class:[t.e("cell"),t.e("content"),t.is("bordered-content",m),t.is("vertical-content",r),b,i],colSpan:r?S:S*2-1,rowspan:r?s*2-1:s},k()),u);default:{const n=h(),a={},l=A(e.labelWidth??this.descriptions.labelWidth);return l&&(a.width=l,a.display="inline-block"),x(D("td",{style:o,class:[t.e("cell"),b],colSpan:S,rowspan:s},[Q(n)?void 0:D("span",{style:a,class:[t.e("label"),w]},n),D("span",{class:[t.e("content"),i]},k())]),u)}}}});const oe={key:1};var ce=P({name:"ElDescriptionsRow",__name:"descriptions-row",props:ie,setup(e){const u=q(R,{});return(m,C)=>d(u).direction==="vertical"?(c(),f(g,{key:0},[v("tr",null,[(c(!0),f(g,null,z(e.row,(r,h)=>(c(),I(d(E),{key:`tr1-${h}`,cell:r,tag:"th",type:"label"},null,8,["cell"]))),128))]),v("tr",null,[(c(!0),f(g,null,z(e.row,(r,h)=>(c(),I(d(E),{key:`tr2-${h}`,cell:r,tag:"td",type:"content"},null,8,["cell"]))),128))])],64)):(c(),f("tr",oe,[(c(!0),f(g,null,z(e.row,(r,h)=>(c(),f(g,{key:`tr3-${h}`},[d(u).border?(c(),f(g,{key:0},[j(d(E),{cell:r,tag:"td",type:"label"},null,8,["cell"]),j(d(E),{cell:r,tag:"td",type:"content"},null,8,["cell"])],64)):(c(),I(d(E),{key:1,cell:r,tag:"td",type:"both"},null,8,["cell"]))],64))),128))]))}}),pe=ce,de=P({name:"ElDescriptions",__name:"description",props:re,setup(e){const u=e,m=L("descriptions"),C=ae(),r=T();Z(R,u);const h=Y(()=>[m.b(),m.m(C.value)]),k=(s,b,p,i=!1)=>(s.props||(s.props={}),b>p&&(s.props.span=p),i&&(s.props.span=b),s),S=()=>{if(!r.default)return[];const s=le(r.default()).filter(t=>{var y;return((y=t==null?void 0:t.type)==null?void 0:y.name)===U}),b=[];let p=[],i=u.column,w=0;const o=[];return s.forEach((t,y)=>{var W,$,V;const n=((W=t.props)==null?void 0:W.span)||1,a=(($=t.props)==null?void 0:$.rowspan)||1,l=b.length;if(o[l]||(o[l]=0),a>1)for(let N=1;N<a;N++)o[V=l+N]||(o[V]=0),o[l+N]++,w++;if(o[l]>0&&(i-=o[l],o[l]=0),y<s.length-1&&(w+=n>i?i:n),y===s.length-1){const N=u.column-w%u.column;p.push(k(t,N,i,!0)),b.push(p);return}n<i?(i-=n,p.push(t)):(p.push(k(t,n,i)),b.push(p),i=u.column,p=[])}),b};return(s,b)=>(c(),f("div",{class:_(h.value)},[e.title||e.extra||s.$slots.title||s.$slots.extra?(c(),f("div",{key:0,class:_(d(m).e("header"))},[v("div",{class:_(d(m).e("title"))},[F(s.$slots,"title",{},()=>[K(M(e.title),1)])],2),v("div",{class:_(d(m).e("extra"))},[F(s.$slots,"extra",{},()=>[K(M(e.extra),1)])],2)],2)):X("v-if",!0),v("div",{class:_(d(m).e("body"))},[v("table",{class:_([d(m).e("table"),d(m).is("bordered",e.border)])},[v("tbody",null,[(c(!0),f(g,null,z(S(),(p,i)=>(c(),I(pe,{key:i,row:p},null,8,["row"]))),128))])],2)],2)],2))}}),ue=de;const fe=te(ue,{DescriptionsItem:G}),ye=ee(G);export{ye as E,fe as a};
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2
frontend/dist/assets/el-dialog-X3Nt8ib9.js → frontend/dist/assets/el-dialog-C3T3IeSf.js vendored
View File

@@ -1 +1 @@
import{d as L,aW as H,B as q,o as u,c as A,q as R,h as i,n as t,f as e,aa as Q,s as $,w as r,e as B,r as X,bk as x,E as _,g as N,al as O,j as E,bl as ee,a as oe,K as se,Y as K,a$ as ae,L as le,Z as te,bm as ne,y as M,F as re,l as ie}from"./index-BKKvzUDD.js";import{u as de}from"./index-BjEW7-SA.js";import{E as ce,u as ue}from"./index-DfyOU94W.js";import{F as fe,f as ge}from"./index-CWUnzf90.js";import{d as me,a as ve,b as j,c as be,e as ye,u as Ce}from"./use-dialog-DmridEEy.js";import{u as he}from"./index-CQXVR1Ud.js";const pe=(...o)=>f=>{o.forEach(g=>{g.value=f})},ke=["aria-level"],we=["aria-label"],Re=["id"];var $e=L({name:"ElDialogContent",__name:"dialog-content",props:ve,emits:me,setup(o,{expose:f}){const{t:g}=H(),{Close:F}=x,a=o,{dialogRef:m,headerRef:y,bodyId:v,ns:s,style:C}=q(j),{focusTrapRef:h}=q(fe),D=pe(h,m),p=E(()=>!!a.draggable),{resetPosition:k,updatePosition:I,isDragging:P}=he(m,y,p,E(()=>!!a.overflow)),T=E(()=>[s.b(),s.is("fullscreen",a.fullscreen),s.is("draggable",p.value),s.is("dragging",P.value),s.is("align-center",!!a.alignCenter),{[s.m("center")]:a.center}]);return f({resetPosition:k,updatePosition:I}),(n,w)=>(u(),A("div",{ref:e(D),class:t(T.value),style:O(e(C)),tabindex:"-1"},[R("header",{ref_key:"headerRef",ref:y,class:t([e(s).e("header"),o.headerClass,{"show-close":o.showClose}])},[i(n.$slots,"header",{},()=>[R("span",{role:"heading","aria-level":o.ariaLevel,class:t(e(s).e("title"))},Q(o.title),11,ke)]),o.showClose?(u(),A("button",{key:0,"aria-label":e(g)("el.dialog.close"),class:t(e(s).e("headerbtn")),type:"button",onClick:w[0]||(w[0]=z=>n.$emit("close"))},[$(e(_),{class:t(e(s).e("close"))},{default:r(()=>[(u(),B(X(o.closeIcon||e(F))))]),_:1},8,["class"])],10,we)):N("v-if",!0)],2),R("div",{id:e(v),class:t([e(s).e("body"),o.bodyClass])},[i(n.$slots,"default")],10,Re),n.$slots.footer?(u(),A("footer",{key:0,class:t([e(s).e("footer"),o.footerClass])},[i(n.$slots,"footer")],2)):N("v-if",!0)],6))}}),Ee=$e;const Fe=["aria-label","aria-labelledby","aria-describedby"];var De=L({name:"ElDialog",inheritAttrs:!1,__name:"dialog",props:ye,emits:be,setup(o,{expose:f}){const g=o,F=ee();de({scope:"el-dialog",from:"the title slot",replacement:"the header slot",version:"3.0.0",ref:"https://element-plus.org/en-US/component/dialog.html#slots"},E(()=>!!F.title));const a=oe("dialog"),m=M(),y=M(),v=M(),{visible:s,titleId:C,bodyId:h,style:D,overlayDialogStyle:p,rendered:k,transitionConfig:I,zIndex:P,_draggable:T,_alignCenter:n,_overflow:w,penetrable:z,handleClose:S,onModalClick:V,onOpenAutoFocus:U,onCloseAutoFocus:Y,onCloseRequested:J,onFocusoutPrevented:W,bringToFront:Z,closing:G}=Ce(g,m);re(j,{dialogRef:m,headerRef:y,bodyId:h,ns:a,rendered:k,style:D});const d=ue(V);return f({visible:s,dialogContentRef:v,resetPosition:()=>{var l;(l=v.value)==null||l.resetPosition()},handleClose:S}),(l,c)=>(u(),B(ne,{to:o.appendTo,disabled:o.appendTo!=="body"?!1:!o.appendToBody},[$(te,K(e(I),{persisted:""}),{default:r(()=>[se($(e(ce),{"custom-mask-event":"",mask:o.modal,"overlay-class":[o.modalClass??"",`${e(a).namespace.value}-modal-dialog`,e(a).is("penetrable",e(z))],"z-index":e(P)},{default:r(()=>[R("div",{role:"dialog","aria-modal":"true","aria-label":o.title||void 0,"aria-labelledby":o.title?void 0:e(C),"aria-describedby":e(h),class:t([`${e(a).namespace.value}-overlay-dialog`,e(a).is("closing",e(G))]),style:O(e(p)),onClick:c[0]||(c[0]=(...b)=>e(d).onClick&&e(d).onClick(...b)),onMousedown:c[1]||(c[1]=(...b)=>e(d).onMousedown&&e(d).onMousedown(...b)),onMouseup:c[2]||(c[2]=(...b)=>e(d).onMouseup&&e(d).onMouseup(...b))},[$(e(ge),{loop:"",trapped:e(s),"focus-start-el":"container",onFocusAfterTrapped:e(U),onFocusAfterReleased:e(Y),onFocusoutPrevented:e(W),onReleaseRequested:e(J)},{default:r(()=>[e(k)?(u(),B(Ee,K({key:0,ref_key:"dialogContentRef",ref:v},l.$attrs,{center:o.center,"align-center":e(n),"close-icon":o.closeIcon,draggable:e(T),overflow:e(w),fullscreen:o.fullscreen,"header-class":o.headerClass,"body-class":o.bodyClass,"footer-class":o.footerClass,"show-close":o.showClose,title:o.title,"aria-level":o.headerAriaLevel,onClose:e(S),onMousedown:e(Z)}),ae({header:r(()=>[l.$slots.title?i(l.$slots,"title",{key:1}):i(l.$slots,"header",{key:0,close:e(S),titleId:e(C),titleClass:e(a).e("title")})]),default:r(()=>[i(l.$slots,"default")]),_:2},[l.$slots.footer?{name:"footer",fn:r(()=>[i(l.$slots,"footer")]),key:"0"}:void 0]),1040,["center","align-center","close-icon","draggable","overflow","fullscreen","header-class","body-class","footer-class","show-close","title","aria-level","onClose","onMousedown"])):N("v-if",!0)]),_:3},8,["trapped","onFocusAfterTrapped","onFocusAfterReleased","onFocusoutPrevented","onReleaseRequested"])],46,Fe)]),_:3},8,["mask","overlay-class","z-index"]),[[le,e(s)]])]),_:3},16)],8,["to","disabled"]))}}),Ie=De;const ze=ie(Ie);export{ze as E,pe as c};
import{d as L,aW as G,B as q,o as u,c as A,q as R,h as i,n as t,f as e,aa as H,s as $,w as r,e as B,r as Q,bE as x,E as _,g as N,al as O,j as E,bF as ee,a as oe,K as se,Y as K,aX as ae,L as le,Z as te,bg as ne,y as M,F as re,l as ie}from"./index-Ds9AETjS.js";import{u as de}from"./index-BNsCyG4A.js";import{E as ce,a as ue}from"./index-RtwaMutE.js";import{F as fe,f as ge}from"./index-sWKLi7bH.js";import{d as me,a as ve,b as j,c as be,e as ye,u as Ce}from"./use-dialog-gWD8AZr4.js";import{u as he}from"./index-jBnYrPdD.js";const pe=(...o)=>f=>{o.forEach(g=>{g.value=f})},we=["aria-level"],ke=["aria-label"],Re=["id"];var $e=L({name:"ElDialogContent",__name:"dialog-content",props:ve,emits:me,setup(o,{expose:f}){const{t:g}=G(),{Close:F}=x,a=o,{dialogRef:m,headerRef:y,bodyId:v,ns:s,style:C}=q(j),{focusTrapRef:h}=q(fe),D=pe(h,m),p=E(()=>!!a.draggable),{resetPosition:w,updatePosition:I,isDragging:P}=he(m,y,p,E(()=>!!a.overflow)),T=E(()=>[s.b(),s.is("fullscreen",a.fullscreen),s.is("draggable",p.value),s.is("dragging",P.value),s.is("align-center",!!a.alignCenter),{[s.m("center")]:a.center}]);return f({resetPosition:w,updatePosition:I}),(n,k)=>(u(),A("div",{ref:e(D),class:t(T.value),style:O(e(C)),tabindex:"-1"},[R("header",{ref_key:"headerRef",ref:y,class:t([e(s).e("header"),o.headerClass,{"show-close":o.showClose}])},[i(n.$slots,"header",{},()=>[R("span",{role:"heading","aria-level":o.ariaLevel,class:t(e(s).e("title"))},H(o.title),11,we)]),o.showClose?(u(),A("button",{key:0,"aria-label":e(g)("el.dialog.close"),class:t(e(s).e("headerbtn")),type:"button",onClick:k[0]||(k[0]=z=>n.$emit("close"))},[$(e(_),{class:t(e(s).e("close"))},{default:r(()=>[(u(),B(Q(o.closeIcon||e(F))))]),_:1},8,["class"])],10,ke)):N("v-if",!0)],2),R("div",{id:e(v),class:t([e(s).e("body"),o.bodyClass])},[i(n.$slots,"default")],10,Re),n.$slots.footer?(u(),A("footer",{key:0,class:t([e(s).e("footer"),o.footerClass])},[i(n.$slots,"footer")],2)):N("v-if",!0)],6))}}),Ee=$e;const Fe=["aria-label","aria-labelledby","aria-describedby"];var De=L({name:"ElDialog",inheritAttrs:!1,__name:"dialog",props:ye,emits:be,setup(o,{expose:f}){const g=o,F=ee();de({scope:"el-dialog",from:"the title slot",replacement:"the header slot",version:"3.0.0",ref:"https://element-plus.org/en-US/component/dialog.html#slots"},E(()=>!!F.title));const a=oe("dialog"),m=M(),y=M(),v=M(),{visible:s,titleId:C,bodyId:h,style:D,overlayDialogStyle:p,rendered:w,transitionConfig:I,zIndex:P,_draggable:T,_alignCenter:n,_overflow:k,penetrable:z,handleClose:S,onModalClick:V,onOpenAutoFocus:U,onCloseAutoFocus:Y,onCloseRequested:J,onFocusoutPrevented:W,bringToFront:X,closing:Z}=Ce(g,m);re(j,{dialogRef:m,headerRef:y,bodyId:h,ns:a,rendered:w,style:D});const d=ue(V);return f({visible:s,dialogContentRef:v,resetPosition:()=>{var l;(l=v.value)==null||l.resetPosition()},handleClose:S}),(l,c)=>(u(),B(ne,{to:o.appendTo,disabled:o.appendTo!=="body"?!1:!o.appendToBody},[$(te,K(e(I),{persisted:""}),{default:r(()=>[se($(e(ce),{"custom-mask-event":"",mask:o.modal,"overlay-class":[o.modalClass??"",`${e(a).namespace.value}-modal-dialog`,e(a).is("penetrable",e(z))],"z-index":e(P)},{default:r(()=>[R("div",{role:"dialog","aria-modal":"true","aria-label":o.title||void 0,"aria-labelledby":o.title?void 0:e(C),"aria-describedby":e(h),class:t([`${e(a).namespace.value}-overlay-dialog`,e(a).is("closing",e(Z))]),style:O(e(p)),onClick:c[0]||(c[0]=(...b)=>e(d).onClick&&e(d).onClick(...b)),onMousedown:c[1]||(c[1]=(...b)=>e(d).onMousedown&&e(d).onMousedown(...b)),onMouseup:c[2]||(c[2]=(...b)=>e(d).onMouseup&&e(d).onMouseup(...b))},[$(e(ge),{loop:"",trapped:e(s),"focus-start-el":"container",onFocusAfterTrapped:e(U),onFocusAfterReleased:e(Y),onFocusoutPrevented:e(W),onReleaseRequested:e(J)},{default:r(()=>[e(w)?(u(),B(Ee,K({key:0,ref_key:"dialogContentRef",ref:v},l.$attrs,{center:o.center,"align-center":e(n),"close-icon":o.closeIcon,draggable:e(T),overflow:e(k),fullscreen:o.fullscreen,"header-class":o.headerClass,"body-class":o.bodyClass,"footer-class":o.footerClass,"show-close":o.showClose,title:o.title,"aria-level":o.headerAriaLevel,onClose:e(S),onMousedown:e(X)}),ae({header:r(()=>[l.$slots.title?i(l.$slots,"title",{key:1}):i(l.$slots,"header",{key:0,close:e(S),titleId:e(C),titleClass:e(a).e("title")})]),default:r(()=>[i(l.$slots,"default")]),_:2},[l.$slots.footer?{name:"footer",fn:r(()=>[i(l.$slots,"footer")]),key:"0"}:void 0]),1040,["center","align-center","close-icon","draggable","overflow","fullscreen","header-class","body-class","footer-class","show-close","title","aria-level","onClose","onMousedown"])):N("v-if",!0)]),_:3},8,["trapped","onFocusAfterTrapped","onFocusAfterReleased","onFocusoutPrevented","onReleaseRequested"])],46,Fe)]),_:3},8,["mask","overlay-class","z-index"]),[[le,e(s)]])]),_:3},16)],8,["to","disabled"]))}}),Ie=De;const ze=ie(Ie);export{ze as E,pe as c};

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