c75e9708ae
Tracker /v1/route now resolves HF model nodes (by hf_repo or short name) in addition to preset models, using the same greedy interval-cover logic. This allows distributed inference routing across two nodes each holding half the model. Endpoint dedup: re-registering the same endpoint atomically replaces the old entry so stale registrations don't accumulate across node restarts. Purge logging: tracker now prints when a node expires due to missed heartbeats so operators can see dead nodes being removed. Timing fix: heartbeat timeout raised from 30s to 90s (3 missed beats); node heartbeat interval lowered from 30s to 20s to maintain margin. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
375 lines
14 KiB
Python
375 lines
14 KiB
Python
"""Full node startup sequence — self-configuring, non-interactive."""
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from __future__ import annotations
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import json
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import socket
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import sys
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import threading
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import time
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import urllib.error
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import urllib.parse
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import urllib.request
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from pathlib import Path
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from typing import Any
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from .downloader import compute_shard_checksum, download_shard
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from .hardware import detect_hardware
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from .server import StubNodeServer
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from .torch_server import TorchNodeServer
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from .wallet import load_or_create_wallet
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def _post_json(url: str, payload: dict, timeout: float = 10.0) -> dict:
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data = json.dumps(payload).encode()
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req = urllib.request.Request(
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url, data=data, headers={"Content-Type": "application/json"}, method="POST"
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)
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with urllib.request.urlopen(req, timeout=timeout) as r:
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return json.loads(r.read())
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def _get_json(url: str, timeout: float = 10.0) -> dict:
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with urllib.request.urlopen(url, timeout=timeout) as r:
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return json.loads(r.read())
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def _start_heartbeat(tracker_url: str, node_id: str, interval: float = 20.0) -> threading.Thread:
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"""Daemon thread that sends periodic heartbeats to the tracker."""
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def _loop() -> None:
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hb_url = f"{tracker_url}/v1/nodes/{node_id}/heartbeat"
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while True:
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time.sleep(interval)
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try:
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_post_json(hb_url, {})
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print(f" [node] heartbeat sent → tracker (node {node_id[:8]})", flush=True)
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except Exception as exc:
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print(f" [node] WARNING: heartbeat failed: {exc}", flush=True)
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t = threading.Thread(target=_loop, daemon=True, name="heartbeat")
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t.start()
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return t
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def run_startup(
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tracker_url: str,
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port: int = 0,
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model: str = "stub-model",
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model_id: str | None = None,
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shard_start: int | None = None,
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shard_end: int | None = None,
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quantization: str = "bfloat16",
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wallet_path: Path | None = None,
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cache_dir: Path | None = None,
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host: str = "127.0.0.1",
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advertise_host: str | None = None,
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contracts: Any | None = None,
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) -> StubNodeServer | TorchNodeServer:
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"""Execute the full startup sequence and return a running node server.
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Steps (all non-interactive):
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1. Detect GPU / hardware profile
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2. Load or generate Solana wallet keypair
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3. Query tracker for optimal shard assignment
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4. Download (or stub) the assigned shard from peers, then HuggingFace
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5. Start local HTTP server
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6. Register with tracker
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Prints a compact status summary on completion.
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"""
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tracker_url = tracker_url.rstrip("/")
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# 1. Hardware detection
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print("Detecting hardware...", flush=True)
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hw = detect_hardware()
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device: str = hw["device"]
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gpu_name: str | None = hw.get("gpu_name")
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vram_mb: int = hw.get("vram_mb", 0)
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if device == "cpu":
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print(" WARNING: No CUDA GPU detected — running in CPU mode", flush=True)
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else:
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print(f" GPU: {gpu_name} ({vram_mb} MB VRAM)", flush=True)
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# 2. Wallet
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print("Loading wallet...", flush=True)
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wallet_kwargs: dict = {}
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if wallet_path is not None:
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wallet_kwargs["path"] = wallet_path
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_, _, address = load_or_create_wallet(**wallet_kwargs)
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print(f" Wallet: {address}", flush=True)
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probationary_line = _probationary_status_line(contracts, address)
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if probationary_line is not None:
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print(f" {probationary_line}", flush=True)
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if model_id: # treat "" the same as None — no explicit model given
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# Auto-detect shard range from model config if not explicitly provided
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if shard_start is None or shard_end is None:
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detected = _detect_num_layers(model_id)
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if detected is None:
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raise ValueError(
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f"Could not read num_hidden_layers from {model_id} config. "
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"Pass --shard-start and --shard-end explicitly."
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)
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# When no explicit shard range given, ask the tracker if there's a gap for this model.
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if shard_start is None and shard_end is None:
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try:
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qs = urllib.parse.urlencode({
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"device": device, "vram_mb": vram_mb, "hf_repo": model_id,
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})
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net_asgn = _get_json(f"{tracker_url}/v1/network/assign?{qs}", timeout=5.0)
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if net_asgn.get("hf_repo") == model_id and net_asgn.get("gap_found"):
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shard_start = net_asgn["shard_start"]
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shard_end = net_asgn["shard_end"]
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print(
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f" Tracker found uncovered shard: "
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f"layers {shard_start}–{shard_end} (of {detected})",
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flush=True,
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)
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except Exception:
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pass # No other nodes registered yet — default to full model below
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shard_start = shard_start if shard_start is not None else 0
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shard_end = shard_end if shard_end is not None else detected - 1
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print(f" Auto-detected {detected} layers → shard {shard_start}–{shard_end}", flush=True)
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print("Loading real PyTorch model shard...", flush=True)
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node = TorchNodeServer(
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host=host,
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port=port,
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model_id=model_id,
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shard_start=shard_start,
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shard_end=shard_end,
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quantization=quantization,
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tracker_url=tracker_url,
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)
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actual_port = node.start()
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total_layers = getattr(node.backend, "total_layers", None)
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if isinstance(total_layers, int) and total_layers > 0:
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layer_count = shard_end - shard_start + 1
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shard_label = f"layers {shard_start}–{shard_end}; {layer_count} of {total_layers}"
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else:
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shard_label = f"layers {shard_start}–{shard_end}"
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public_host = advertise_host or (socket.getfqdn() if host == "0.0.0.0" else host)
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endpoint = f"http://{public_host}:{actual_port}"
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# Register with tracker so other nodes can auto-join this model.
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total_layers = getattr(node.backend, "total_layers", None)
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try:
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reg_resp = _post_json(
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f"{tracker_url}/v1/nodes/register",
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{
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"endpoint": endpoint,
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"model": model_id.split("/")[-1],
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"hf_repo": model_id,
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"num_layers": total_layers,
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"shard_start": shard_start,
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"shard_end": shard_end,
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"hardware_profile": hw,
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"wallet_address": address,
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"quantization": quantization,
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"score": 1.0,
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"tracker_mode": (shard_start == 0),
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},
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)
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node_id = reg_resp.get("node_id", "?")
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print(f" Registered with tracker — node ID: {node_id}", flush=True)
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_start_heartbeat(tracker_url, node_id)
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except Exception as exc:
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print(f" Warning: tracker registration failed: {exc}", flush=True)
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print(
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f"\n{'=' * 32}\n"
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f"meshnet-node ready\n"
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f" Wallet: {address}\n"
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f" Model ID: {model_id}\n"
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f" Shard: {shard_label}\n"
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f" Quantization: {quantization}\n"
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f" Endpoint: {endpoint}\n"
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f" Hardware: {device.upper()}\n"
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f"{'=' * 32}",
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flush=True,
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)
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return node
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if shard_start is not None or shard_end is not None:
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raise ValueError("--shard-start / --shard-end require --model-id")
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# 3a. Auto-join: query tracker for network-wide HF model assignment.
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print("Querying tracker for network assignment...", flush=True)
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assign_qs = urllib.parse.urlencode({"device": device, "vram_mb": vram_mb})
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net_assignment: dict = {}
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try:
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net_assignment = _get_json(f"{tracker_url}/v1/network/assign?{assign_qs}")
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except Exception as exc:
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print(f" (auto-join unavailable: {exc})", flush=True)
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assigned_hf_repo: str | None = net_assignment.get("hf_repo")
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_gap_found: bool = bool(net_assignment.get("gap_found", False))
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if assigned_hf_repo and _gap_found:
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assigned_shard_start: int = net_assignment["shard_start"]
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assigned_shard_end: int = net_assignment["shard_end"]
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assigned_num_layers: int = net_assignment["num_layers"]
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print(
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f" Assigned: {assigned_hf_repo} "
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f"layers {assigned_shard_start}–{assigned_shard_end} "
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f"(of {assigned_num_layers})",
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flush=True,
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)
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print("Loading real PyTorch model shard...", flush=True)
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node = TorchNodeServer(
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host=host,
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port=port,
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model_id=assigned_hf_repo,
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shard_start=assigned_shard_start,
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shard_end=assigned_shard_end,
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quantization=quantization,
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tracker_url=tracker_url,
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)
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actual_port = node.start()
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public_host = advertise_host or (socket.getfqdn() if host == "0.0.0.0" else host)
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endpoint = f"http://{public_host}:{actual_port}"
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try:
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reg_resp = _post_json(
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f"{tracker_url}/v1/nodes/register",
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{
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"endpoint": endpoint,
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"model": assigned_hf_repo.split("/")[-1],
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"hf_repo": assigned_hf_repo,
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"num_layers": assigned_num_layers,
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"shard_start": assigned_shard_start,
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"shard_end": assigned_shard_end,
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"hardware_profile": hw,
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"wallet_address": address,
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"quantization": quantization,
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"score": 1.0,
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"tracker_mode": (assigned_shard_start == 0),
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},
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)
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node_id = reg_resp.get("node_id", "?")
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print(f" Registered with tracker — node ID: {node_id}", flush=True)
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_start_heartbeat(tracker_url, node_id)
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except Exception as exc:
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print(f" Warning: tracker registration failed: {exc}", flush=True)
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shard_count = assigned_shard_end - assigned_shard_start + 1
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print(
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f"\n{'=' * 32}\n"
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f"meshnet-node ready (auto-joined)\n"
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f" Wallet: {address}\n"
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f" Model ID: {assigned_hf_repo}\n"
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f" Shard: layers {assigned_shard_start}–{assigned_shard_end} "
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f"({shard_count} of {assigned_num_layers})\n"
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f" Quantization: {quantization}\n"
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f" Endpoint: {endpoint}\n"
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f" Hardware: {device.upper()}\n"
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f"{'=' * 32}",
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flush=True,
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)
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return node
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# 3b. Shard assignment from tracker (stub-model / preset-based path)
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print("Querying tracker for shard assignment...", flush=True)
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assign_qs = urllib.parse.urlencode({
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"model": model,
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"device": device,
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"vram_mb": vram_mb,
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})
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try:
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assignment = _get_json(f"{tracker_url}/v1/nodes/assign?{assign_qs}")
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except urllib.error.URLError as exc:
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print(f" ERROR: Cannot reach tracker at {tracker_url}: {exc}", file=sys.stderr, flush=True)
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raise
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shard_start: int = assignment["shard_start"]
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shard_end: int = assignment["shard_end"]
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assigned_model: str = assignment.get("model", model)
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hf_repo: str | None = assignment.get("hf_repo")
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peers: list[dict] = assignment.get("peers", [])
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print(f" Shard: layers {shard_start}-{shard_end} of {assigned_model}", flush=True)
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# 4. Download shard
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print("Downloading shard...", flush=True)
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dl_kwargs: dict = {}
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if cache_dir is not None:
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dl_kwargs["cache_dir"] = cache_dir
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if hf_repo is not None:
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dl_kwargs["hf_repo"] = hf_repo
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if peers:
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dl_kwargs["peers"] = peers
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shard_path = download_shard(assigned_model, shard_start, shard_end, **dl_kwargs)
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shard_checksum = compute_shard_checksum(shard_path)
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print(f" Cached at: {shard_path}", flush=True)
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# 5. Start HTTP server
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is_last = shard_end >= assignment.get("model_layers_end", shard_end)
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node = StubNodeServer(
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host=host,
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port=port,
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shard_start=shard_start,
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shard_end=shard_end,
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is_last_shard=is_last,
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model=assigned_model,
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shard_path=shard_path,
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)
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actual_port = node.start()
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public_host = advertise_host or (socket.getfqdn() if host == "0.0.0.0" else host)
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endpoint = f"http://{public_host}:{actual_port}"
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# 6. Register with tracker
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print("Registering with tracker...", flush=True)
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try:
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reg_resp = _post_json(
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f"{tracker_url}/v1/nodes/register",
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{
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"endpoint": endpoint,
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"model": assigned_model,
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"shard_start": shard_start,
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"shard_end": shard_end,
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"shard_checksum": shard_checksum,
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"hardware_profile": hw,
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"wallet_address": address,
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"score": 1.0,
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},
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)
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node_id: str = reg_resp["node_id"]
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except Exception:
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node.stop()
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raise
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# Status summary
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hw_str = device.upper()
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if gpu_name:
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hw_str += f" ({gpu_name}, {vram_mb} MB)"
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print(
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f"\n{'=' * 32}\n"
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f"meshnet-node ready\n"
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f" Wallet: {address}\n"
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f" Shard: layers {shard_start}-{shard_end} ({assigned_model})\n"
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f" Endpoint: {endpoint}\n"
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f" Node ID: {node_id}\n"
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f" Hardware: {hw_str}\n"
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f"{'=' * 32}",
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flush=True,
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)
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return node
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def _detect_num_layers(model_id: str) -> int | None:
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"""Fetch num_hidden_layers from HuggingFace model config (downloads ~1 KB config.json only)."""
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try:
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from transformers import AutoConfig # type: ignore[import]
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cfg = AutoConfig.from_pretrained(model_id)
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return int(cfg.num_hidden_layers)
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except Exception as exc:
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print(f" Warning: could not read model config from HF: {exc}", flush=True)
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return None
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def _probationary_status_line(contracts: Any | None, wallet_address: str) -> str | None:
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if contracts is None:
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return None
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remaining = contracts.registry.probationary_jobs_remaining(wallet_address)
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if remaining <= 0:
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return "Probationary period complete: earning enabled"
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suffix = "job" if remaining == 1 else "jobs"
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return f"Probationary period: {remaining} {suffix} remaining before earning"
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