| from __future__ import annotations | |
| import json | |
| import os | |
| import signal | |
| import subprocess | |
| from typing import Any, Dict | |
| import requests | |
| from .registry import ToolRegistry | |
| VLLM_BASE_URL = os.getenv("VLLM_BASE_URL", "http://localhost:8000/v1") | |
| def t_serve_status(_: Dict[str, Any]) -> str: | |
| try: | |
| r = requests.get(f"{VLLM_BASE_URL.rstrip('/')}/health", timeout=5) | |
| return json.dumps({"status": r.status_code, "body": r.text}) | |
| except Exception as e: | |
| return json.dumps({"error": str(e)}) | |
| def t_vllm_reload(args: Dict[str, Any]) -> str: | |
| """Attempt to restart the vLLM server via optional script or PID. | |
| Args: script (path) OR pid (int) | |
| """ | |
| script = args.get("script") | |
| pid = args.get("pid") | |
| if script: | |
| try: | |
| proc = subprocess.run(["bash", script], capture_output=True, text=True, timeout=180) | |
| return json.dumps({"returncode": proc.returncode, "stdout": proc.stdout, "stderr": proc.stderr}) | |
| except Exception as e: | |
| return json.dumps({"error": str(e)}) | |
| if pid: | |
| try: | |
| os.kill(int(pid), signal.SIGHUP) | |
| return json.dumps({"status": "signaled", "pid": int(pid)}) | |
| except Exception as e: | |
| return json.dumps({"error": str(e)}) | |
| return json.dumps({"error": "script or pid required"}) | |
| def t_hf_pull_model(args: Dict[str, Any]) -> str: | |
| """Pull/refresh a HF repo into MODEL_PATH using hf CLI. | |
| Args: repo (org/name), dest (MODEL_PATH) | |
| """ | |
| repo = args.get("repo") | |
| dest = args.get("dest") or os.getenv("MODEL_PATH", "/data/adaptai/platform/aiml/checkpoints/qwen3-8b-elizabeth-sft") | |
| token = os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_API_KEY") | |
| if not repo: | |
| return json.dumps({"error": "repo required"}) | |
| if not token: | |
| return json.dumps({"error": "HF_TOKEN not set"}) | |
| try: | |
| proc = subprocess.run([ | |
| "hf", "download", str(repo), "--repo-type", "model", "--include", "**", "--local-dir", str(dest) | |
| ], capture_output=True, text=True, timeout=3600) | |
| return json.dumps({"returncode": proc.returncode, "stdout": proc.stdout[-4000:], "stderr": proc.stderr[-4000:], "dest": dest}) | |
| except Exception as e: | |
| return json.dumps({"error": str(e)}) | |
| def t_promote_checkpoint(args: Dict[str, Any]) -> str: | |
| """Promote a trained checkpoint to MODEL_PATH (rsync copy). | |
| Args: src (path), dest (optional overrides MODEL_PATH) | |
| """ | |
| src = args.get("src") | |
| dest = args.get("dest") or os.getenv("MODEL_PATH", "/data/adaptai/platform/aiml/checkpoints/qwen3-8b-elizabeth-sft") | |
| if not src: | |
| return json.dumps({"error": "src required"}) | |
| try: | |
| proc = subprocess.run(["rsync", "-aH", f"{src}/", f"{dest}/"], capture_output=True, text=True, timeout=3600) | |
| return json.dumps({"returncode": proc.returncode, "stdout": proc.stdout[-4000:], "stderr": proc.stderr[-4000:], "dest": dest}) | |
| except Exception as e: | |
| return json.dumps({"error": str(e)}) | |
| def t_self_train(args: Dict[str, Any]) -> str: | |
| """Launch a training process (unconstrained). Provide 'script' and 'args' list. | |
| Example: {"script": "./train_elizabeth.sh", "args": ["--lr", "2e-5"]} | |
| """ | |
| script = args.get("script") | |
| sargs = args.get("args") or [] | |
| if not script: | |
| return json.dumps({"error": "script required"}) | |
| try: | |
| cmd = ["bash", script] + list(map(str, sargs)) | |
| proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) | |
| return json.dumps({"status": "started", "pid": proc.pid, "cmd": cmd}) | |
| except Exception as e: | |
| return json.dumps({"error": str(e)}) | |
| def register_tools(reg: ToolRegistry) -> None: | |
| reg.register( | |
| name="serve_status", | |
| description="Check vLLM /health upstream.", | |
| parameters={"type": "object", "properties": {}}, | |
| handler=t_serve_status, | |
| ) | |
| reg.register( | |
| name="vllm_reload", | |
| description="Reload/restart vLLM via script or send SIGHUP to a PID.", | |
| parameters={"type": "object", "properties": {"script": {"type": "string"}, "pid": {"type": "integer"}}}, | |
| handler=t_vllm_reload, | |
| ) | |
| reg.register( | |
| name="hf_pull_model", | |
| description="Pull/refresh a Hugging Face model into MODEL_PATH.", | |
| parameters={"type": "object", "properties": {"repo": {"type": "string"}, "dest": {"type": "string"}}, "required": ["repo"]}, | |
| handler=t_hf_pull_model, | |
| ) | |
| reg.register( | |
| name="promote_checkpoint", | |
| description="Promote a trained checkpoint into serving MODEL_PATH using rsync.", | |
| parameters={"type": "object", "properties": {"src": {"type": "string"}, "dest": {"type": "string"}}, "required": ["src"]}, | |
| handler=t_promote_checkpoint, | |
| ) | |
| reg.register( | |
| name="self_train", | |
| description="Launch an unconstrained training job via provided script and args.", | |
| parameters={"type": "object", "properties": {"script": {"type": "string"}, "args": {"type": "array", "items": {"type": "string"}}}, "required": ["script"]}, | |
| handler=t_self_train, | |
| ) | |