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

- 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

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