feat(scripts): add uptime-check retry + Obsidian variant, add LLM benchmark docs
check_uptime.js gets a fetchWithRetry wrapper (3 attempts, 2s backoff) for transient failures against the status page/heartbeat API. check_uptime_to_obsidian.js is a variant that logs results into the Obsidian vault instead of stdout. Also adds two benchmark writeups (gpt-oss-20b on Ollama, 73-node Ollama fleet). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
ginnoir
co-authored by
Claude Sonnet 5
Claude Sonnet 5
parent
1792dd964b
commit
10997e4b16
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# gpt-oss-20b — `.73` Ollama box vs valhalla P100 (2026-06-28)
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**TL;DR:** The same model (`gpt-oss-20b`) runs **~1.3–1.5× faster on generation** and
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**~1.6–2× faster on prefill** on ginnoir's `192.168.1.73` Ollama box than on valhalla's
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Tesla P100 llama-swap backend — *despite* the `.73` box partially offloading to CPU at 64K
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context. Tool-calls and code outputs are correct on both. For interactive Hermes use the
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`.73` box is the better backend whenever it's powered on; the P100 remains the always-on
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fallback. **Caveat:** at very deep context (~43K tokens) `.73` generation drops to ~8 tok/s
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(KV no longer fully GPU-resident).
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## Setup
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- **Endpoint:** `http://192.168.1.73:11434` — Ollama **0.30.11**, model `gpt-oss:20b`,
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quant **MXFP4** (OpenAI's native gpt-oss 4-bit format), reported 20.9B params.
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- **Serving config:** `num_ctx 65536` (matches the P100's 64K and Hermes' hard ≥64K
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requirement), sampling `temperature 0.6 / top_p 0.95 / top_k 20` (identical to the
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2026-06-27 Ornith/P100 run).
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- **Measured via** Ollama's native `/api/chat` (exact `prompt_eval`/`eval` token counts +
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durations). Cold model load at 64K ctx took **~65 s** (one-time).
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- **P100 baseline** = the gpt-oss-20b column from `docs/2026-06-27-ornith-9b-benchmark.md`
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(llama-swap, q8/q8 KV, `--parallel 1`, `--jinja`, 64K).
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- GPU on `.73` **could not be identified** — no SSH (port 22 filtered). Inferred from VRAM
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behavior (below): a fast but VRAM-limited card (or unified-memory host).
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## Results
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| Test | Metric | **.73 Ollama (MXFP4)** | **valhalla P100 (GGUF)** | `.73` advantage |
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|---|---|---|---|---|
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| Tool call | valid `tool_calls`? | ✅ `get_weather({"city":"Tokyo"})` | ✅ identical | tie (both correct) |
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| Codegen (`merge_intervals`) | correct? | ✅ correct, non-mutating, type-hinted | ✅ correct (mutates input) | `.73` slightly cleaner |
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| Debug (`second_largest`) | correct? | ✅ correct single-pass, handles dup max | ✅ correct | tie |
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| Deep-prefill | answered? | ✅ "quick brown fox" | ✅ (capped probe) | tie |
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| **Gen speed** | tok/s — tool_call | **33.1** | 23.0 | **1.44×** |
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| **Gen speed** | tok/s — codegen | **27.4** | 21.7 | **1.26×** |
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| **Gen speed** | tok/s — debug | **27.6** | 18.9 | **1.46×** |
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| **Prefill** | tok/s — shallow (~100–135 tok) | **240–287** | 133–188 | ~1.5–1.8× |
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| **Prefill** | tok/s — deep | **2542** (n=43.8k) | 155 (n=23.4k) | far faster (diff depths) |
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| Gen @ deep ctx | tok/s | 8.4 (n=43.8k ctx) | 12.7 (n=23.4k ctx) | **P100 wins at depth** |
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| Reasoning verbosity | think chars (codegen/debug) | 1760 / 1317 | 1671 / 2118 | comparable |
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| VRAM @ 64K | on-GPU / total | **8.82 / 14.16 GB** (partial CPU offload) | ~12.6 GB (100% GPU) | — |
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## Reading the numbers
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- **Shallow/typical depth is where `.73` wins decisively.** All three real tasks (tool-call,
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codegen, debug) run at shallow context, and `.73` generates at **27–33 tok/s vs the P100's
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~19–23** — roughly the difference between "comfortable" and "sluggish" for an interactive
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agent loop. Prefill is also ~1.5–1.8× faster, so first-token latency improves too.
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- **The `.73` box is VRAM-limited, not compute-limited.** At 64K ctx only **8.82 GB of the
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14.16 GB** working set sits in VRAM — the rest (weights tail + deep KV) spills to system
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RAM. It still beats the full-GPU P100, which means the card itself is much faster than the
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P100; with more VRAM (or a smaller `num_ctx`) it would pull further ahead.
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- **The one place the P100 wins: very deep context.** At ~43K resident tokens, `.73`
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generation falls to **8.4 tok/s** because the KV cache is partly in CPU RAM (memory-
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bandwidth-bound attention). The P100 holds its whole 64K KV in VRAM and degrades more
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gracefully (12.7 tok/s at 23K). In practice Hermes' steady-state prompt is ~16K, so this
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rarely bites — but long sessions on `.73` will slow down more than on the P100.
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- **Quant differs**, so this isn't a pure hardware A/B: `.73` runs MXFP4 (gpt-oss's native,
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near-lossless 4-bit) while the P100 GGUF quant is whatever llama-swap pulled. Both are
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genuine gpt-oss-20b and both produced correct outputs; no quality regression observed.
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## Verdict for the Hermes backend
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- **Prefer `.73` when it's up.** It's the faster daily driver for gpt-oss-20b at the depths
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Hermes actually runs at. Switch in-session with `/model --provider ollama --model gpt-oss:20b`.
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- **Keep the P100 (`valhalla-p100`) as the always-on default.** It's a container on the
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24/7 server; the `.73` box may be a desktop/workstation that isn't always powered. The
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P100 also degrades more gracefully at very deep context.
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- **If you want `.73` to be strictly better,** drop its `num_ctx` toward what Hermes needs
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(it hard-requires ≥64K, so you can't go below that for Hermes) **or** put gpt-oss on a
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bigger-VRAM card there — eliminating the CPU spill would lift both prefill and deep-context
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generation.
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## Caveats
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- Small hand-written suite (4 tasks), not SWE-bench — measures latency/throughput and basic
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correctness, not deep code quality.
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- The codegen prompt lost its back-ticked tokens to shell quoting during the run (prompt_n 95
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vs the P100's 113); the model still produced a correct `merge_intervals`, and gen tok/s is
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prompt-content-independent, so the speed comparison stands.
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- Deep-prefill rows use different context depths (43.8K on `.73` vs 23.4K on P100), so the
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prefill-tok/s cells aren't directly comparable — read them as "each box's deep-prefill rate
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at that depth," not a head-to-head ratio.
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- Raw responses saved on valhalla at `/tmp/ollama-bench/` (one `.json` per task); P100
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baselines at `/tmp/ornith-bench/`.
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# `.73` Ollama fleet benchmark — all 9 models (2026-06-28)
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**TL;DR:** Throughput across every model on `192.168.1.73`. Generation speed spans an
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**~5× range** — from `gemma4:e4b` at **~93 tok/s** down to the big `qwen3:30b-a3b` at
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**~18 tok/s**. **All 9 models emit valid tool-calls.** For an interactive agent backend the
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sweet spot is **`gpt-oss:20b` (~29 tok/s)** or **`gemma4:12b` (~48 tok/s)** if 12B quality
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suffices; the 30B-class Qwen MoEs are the slowest here (heavy CPU offload at 64K on this
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VRAM-limited box). **Caveat:** code-correctness for the heavy *thinking* models is
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indeterminate — they used the whole 768-token gen cap reasoning and never emitted code (see
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Caveats); re-run with a bigger budget to judge quality.
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## Setup
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- Endpoint `http://192.168.1.73:11434`, Ollama 0.30.11. Each model served at **`num_ctx
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65536`** (Hermes' ≥64K requirement), sampling `temp 0.6 / top_p 0.95 / top_k 20`.
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- Native `/api/chat` timings. 4 tasks: tool-call, codegen (`merge_intervals`), debug
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(`second_largest`), deep-prefill (~16K-token filler). Gen capped: 256 / 768 / 768 / 128.
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- Same `.73` box as the gpt-oss head-to-head in
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`docs/2026-06-28-gpt-oss-20b-ollama-benchmark.md` (GPU still unidentified — no SSH).
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## Generation speed (tok/s) — the headline
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Average of the three real tasks (tool-call / codegen / debug), sorted fastest first:
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| Model | avg gen t/s | tool | codegen | debug | deep-ctx gen | cold load s | tool-call? |
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|---|--:|--:|--:|--:|--:|--:|:--:|
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| **gemma4:e4b** | **92.7** | 91.1 | 93.3 | 93.6 | 82.6 | 24.8 | ✅ |
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| **gemma4:12b** | **48.2** | 46.8 | 49.0 | 48.8 | 45.7 | 10.6 | ✅ |
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| **gpt-oss:20b** | **29.4** | 32.7 | 27.9 | 27.7 | 29.9 | 0.4¹ | ✅ |
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| **qwen3.6:35b-a3b** | **27.3** | 29.1 | 26.4 | 26.4 | 28.2 | 35.9 | ✅ |
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| **gemma4:26b** | **25.7** | 27.6 | 25.0 | 24.5 | 26.5 | 53.8 | ✅ |
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| **glm-4.7-flash** | **21.2** | 24.2 | 19.7 | 19.7 | 21.4 | 34.8 | ✅ |
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| **qwen3-vl:30b-a3b** | **19.1** | 22.7 | 16.5 | 18.2 | 20.6 | 29.8 | ✅ |
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| **qwen3-coder:30b** | **19.0** | 22.8 | 17.1 | 17.2 | 20.4 | 25.3 | ✅ |
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| **qwen3:30b-a3b** | **17.6** | 19.5 | 16.6 | 16.6 | 18.5 | 25.0 | ✅ |
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¹ gpt-oss was already resident from the prior run; real cold load is ~65 s.
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## Prefill speed (tok/s)
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| Model | shallow (~100 tok) | deep (~16K tok) |
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|---|--:|--:|
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| gemma4:e4b | 1193–1799 | 7283 |
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| gemma4:12b | 667–1078 | 3318 |
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| gpt-oss:20b | 281–379 | 2655 |
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| qwen3:30b-a3b | 63–149 | 1030 |
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| qwen3-coder:30b | 76–270 | 979 |
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| gemma4:26b | 110–127² | 977 |
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| qwen3.6:35b-a3b | 78–209 | 620 |
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| glm-4.7-flash | 70–163 | 649 |
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| qwen3-vl:30b-a3b | 68–127 | 588 |
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² gemma4:26b's first request after load measured 11.7 t/s (cold-cache artifact); ignore.
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## What stands out
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- **The two small gemmas are in a different league.** `gemma4:e4b` (~93 t/s) and
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`gemma4:12b` (~48 t/s) are dense but small, so they sit fully on GPU and fly. If a 4B/12B
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is smart enough for the job, they're the most responsive options by far.
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- **gpt-oss:20b is the best "big-brain, still-fast" pick** (~29 t/s) — MoE ~3.6B active keeps
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it quick despite 20B total. `qwen3.6:35b-a3b` nearly matches it (~27 t/s) and may be
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stronger; worth A/B-ing on real tasks.
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- **The 30B-a3b Qwen trio is the slowest** (~17–19 t/s). Same "3B-active" MoE label, but
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larger total weights → more spills to CPU RAM at 64K on this VRAM-limited box, dragging
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generation below gpt-oss. `qwen3-coder` being this slow undercuts it as a *fast* coding
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model here.
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- **Every model tool-calls.** All 9 emitted a valid `get_weather({"city":"Tokyo"})`, so any
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of them can drive Hermes' tool loop.
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## Correctness (partial — see caveat)
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- **Confirmed correct** code on the non-/light-thinking models that finished within the cap:
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`gpt-oss:20b`, `gemma4:12b`, `qwen3-vl:30b-a3b`, `qwen3-coder:30b` (clean `def`,
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`reason=stop` or code present), plus `gemma4:26b` & `qwen3:30b-a3b` on the task each
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finished.
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- **Indeterminate** (truncated mid-reasoning, `content=0`, `reason=length`): `glm-4.7-flash`
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(both), `gemma4:e4b` (both), `qwen3.6:35b-a3b` (both), `gemma4:26b` (codegen),
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`qwen3:30b-a3b` (debug). These spent all 768 gen tokens in the `thinking` channel — **not
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wrong, just unfinished.** A re-run at `num_predict ~3072` is needed to grade their output.
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## Caveats
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- The 768-token gen cap was too low for heavy chain-of-thought models — it bounds runtime but
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truncates their answers. Speed (tok/s) is unaffected and valid; code *quality* for the
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truncated set is not measured here.
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- Per-model VRAM split not captured (models unload after 2 min `keep_alive`); only
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gpt-oss-20b is known (8.82 GB on-GPU / 14.16 GB total at 64K → partial CPU offload). The
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slow 30B-class numbers are consistent with heavier offload.
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- Small hand-written suite, not SWE-bench. Quants are each model's Ollama default.
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- Raw per-task responses on valhalla at `/tmp/ollama-bench-all/` (`summary.json` + one JSON
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per model/task); progress log `/tmp/ollama-bench-all/progress.txt`.
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+19
-2
@@ -1,10 +1,27 @@
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const fs = require('fs');
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const fs = require('fs');
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const path = require('path');
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const path = require('path');
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async function fetchWithRetry(url, options = {}, retries = 3, backoff = 2000) {
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for (let i = 0; i < retries; i++) {
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try {
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const response = await fetch(url, options);
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if (response.ok) return response;
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if (response.status >= 500) {
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throw new Error(`HTTP ${response.status}: ${response.statusText}`);
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}
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return response;
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} catch (err) {
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if (i === retries - 1) throw err;
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console.warn(`Fetch to ${url} failed (attempt ${i + 1}/${retries}): ${err.message}. Retrying in ${backoff}ms...`);
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await new Promise(resolve => setTimeout(resolve, backoff));
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}
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}
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}
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async function checkStatus() {
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async function checkStatus() {
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try {
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try {
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// 1. Fetch status page HTML
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// 1. Fetch status page HTML
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const htmlResponse = await fetch('https://uptime.ginnoir.com/status/default');
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const htmlResponse = await fetchWithRetry('https://uptime.ginnoir.com/status/default');
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if (!htmlResponse.ok) {
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if (!htmlResponse.ok) {
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throw new Error(`Failed to fetch status page: ${htmlResponse.statusText}`);
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throw new Error(`Failed to fetch status page: ${htmlResponse.statusText}`);
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}
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}
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@@ -34,7 +51,7 @@ async function checkStatus() {
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}
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}
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// 2. Fetch heartbeat JSON
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// 2. Fetch heartbeat JSON
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const heartbeatResponse = await fetch('https://uptime.ginnoir.com/api/status-page/heartbeat/default');
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const heartbeatResponse = await fetchWithRetry('https://uptime.ginnoir.com/api/status-page/heartbeat/default');
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if (!heartbeatResponse.ok) {
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if (!heartbeatResponse.ok) {
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throw new Error(`Failed to fetch heartbeat: ${heartbeatResponse.statusText}`);
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throw new Error(`Failed to fetch heartbeat: ${heartbeatResponse.statusText}`);
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}
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}
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@@ -0,0 +1,179 @@
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const fs = require('fs');
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const path = require('path');
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async function fetchWithRetry(url, options = {}, retries = 3, backoff = 2000) {
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for (let i = 0; i < retries; i++) {
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try {
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const response = await fetch(url, options);
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if (response.ok) return response;
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if (response.status >= 500) {
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throw new Error(`HTTP ${response.status}: ${response.statusText}`);
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}
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return response;
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} catch (err) {
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if (i === retries - 1) throw err;
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console.warn(`Fetch to ${url} failed (attempt ${i + 1}/${retries}): ${err.message}. Retrying in ${backoff}ms...`);
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await new Promise(resolve => setTimeout(resolve, backoff));
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}
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}
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}
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async function checkStatus() {
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try {
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// 1. Fetch status page HTML
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const htmlResponse = await fetchWithRetry('https://uptime.ginnoir.com/status/default');
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if (!htmlResponse.ok) {
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throw new Error(`Failed to fetch status page: ${htmlResponse.statusText}`);
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}
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const html = await htmlResponse.text();
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// Extract window.preloadData = ...;
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const preloadRegex = /window\.preloadData\s*=\s*({.*?});/s;
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const match = html.match(preloadRegex);
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if (!match) {
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throw new Error("Could not find window.preloadData in HTML");
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}
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let preloadData;
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try {
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preloadData = new Function(`return ${match[1]};`)();
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} catch (e) {
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throw new Error(`Failed to parse preloadData: ${e.message}`);
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}
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const monitors = [];
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if (preloadData && preloadData.publicGroupList) {
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for (const group of preloadData.publicGroupList) {
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if (group.monitorList) {
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monitors.push(...group.monitorList);
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}
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}
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}
|
||||||
|
|
||||||
|
// 2. Fetch heartbeat JSON
|
||||||
|
const heartbeatResponse = await fetchWithRetry('https://uptime.ginnoir.com/api/status-page/heartbeat/default');
|
||||||
|
if (!heartbeatResponse.ok) {
|
||||||
|
throw new Error(`Failed to fetch heartbeat: ${heartbeatResponse.statusText}`);
|
||||||
|
}
|
||||||
|
const heartbeats = await heartbeatResponse.json();
|
||||||
|
|
||||||
|
// 3. Map status and print summary
|
||||||
|
const monitorMap = {};
|
||||||
|
for (const monitor of monitors) {
|
||||||
|
monitorMap[monitor.id] = {
|
||||||
|
name: monitor.name,
|
||||||
|
type: monitor.type,
|
||||||
|
status: 'UNKNOWN',
|
||||||
|
ping: null,
|
||||||
|
lastCheck: null,
|
||||||
|
msg: ''
|
||||||
|
};
|
||||||
|
}
|
||||||
|
|
||||||
|
const heartbeatList = heartbeats.heartbeatList || {};
|
||||||
|
for (const id in heartbeatList) {
|
||||||
|
const list = heartbeatList[id];
|
||||||
|
if (list && list.length > 0) {
|
||||||
|
const latest = list[list.length - 1];
|
||||||
|
if (monitorMap[id]) {
|
||||||
|
monitorMap[id].status = latest.status === 1 ? 'UP' : 'DOWN';
|
||||||
|
monitorMap[id].ping = latest.ping;
|
||||||
|
monitorMap[id].lastCheck = latest.time;
|
||||||
|
monitorMap[id].msg = latest.msg || '';
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Generate summary
|
||||||
|
const monitorValues = Object.values(monitorMap);
|
||||||
|
const total = monitorValues.length;
|
||||||
|
const up = monitorValues.filter(m => m.status === 'UP').length;
|
||||||
|
const down = monitorValues.filter(m => m.status === 'DOWN').length;
|
||||||
|
const unknown = monitorValues.filter(m => m.status === 'UNKNOWN').length;
|
||||||
|
|
||||||
|
let summaryMd = `### Uptime Status Summary (Checked at ${new Date().toLocaleString()})\n\n`;
|
||||||
|
|
||||||
|
if (down > 0) {
|
||||||
|
summaryMd += `⚠️ **Status: Degraded (${down}/${total} services DOWN)**\n\n`;
|
||||||
|
} else if (up === total) {
|
||||||
|
summaryMd += `✅ **Status: Healthy (All ${total} services UP)**\n\n`;
|
||||||
|
} else {
|
||||||
|
summaryMd += `ℹ️ **Status: Mixed (UP: ${up}, DOWN: ${down}, UNKNOWN: ${unknown})**\n\n`;
|
||||||
|
}
|
||||||
|
|
||||||
|
if (down > 0) {
|
||||||
|
summaryMd += `#### 🚨 DOWN Services:\n`;
|
||||||
|
monitorValues.filter(m => m.status === 'DOWN').forEach(m => {
|
||||||
|
summaryMd += `- **${m.name}** (${m.type}) - ${m.msg || 'No message'} (Last checked: ${m.lastCheck})\n`;
|
||||||
|
});
|
||||||
|
summaryMd += `\n`;
|
||||||
|
}
|
||||||
|
|
||||||
|
summaryMd += `#### 📋 Service Statuses:\n`;
|
||||||
|
summaryMd += `| Service | Status | Latency (ms) | Last Check |\n`;
|
||||||
|
summaryMd += `| :--- | :---: | :---: | :--- |\n`;
|
||||||
|
|
||||||
|
// Sort services by name
|
||||||
|
monitorValues.sort((a, b) => a.name.localeCompare(b.name));
|
||||||
|
|
||||||
|
for (const m of monitorValues) {
|
||||||
|
const statusIcon = m.status === 'UP' ? '🟢 UP' : m.status === 'DOWN' ? '🔴 DOWN' : '⚪ UNKNOWN';
|
||||||
|
const pingText = m.ping !== null ? `${m.ping} ms` : 'N/A';
|
||||||
|
summaryMd += `| ${m.name} | ${statusIcon} | ${pingText} | ${m.lastCheck || 'N/A'} |\n`;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Output to stdout
|
||||||
|
console.log(summaryMd);
|
||||||
|
|
||||||
|
// 1. Write the latest report to Uptime Status Report.md
|
||||||
|
const reportPath = 'c:/Users/MattC/Documents/Obsidian Vault/Homelab/Uptime Status Report.md';
|
||||||
|
const reportContent = `---
|
||||||
|
project: Homelab
|
||||||
|
type: status
|
||||||
|
status: current
|
||||||
|
tags: [monitoring, uptime]
|
||||||
|
updated: ${new Date().toISOString().split('T')[0]}
|
||||||
|
---
|
||||||
|
|
||||||
|
# Live Uptime Status Report
|
||||||
|
|
||||||
|
${summaryMd}
|
||||||
|
`;
|
||||||
|
fs.writeFileSync(reportPath, reportContent, 'utf8');
|
||||||
|
console.log(`Wrote status report to ${reportPath}`);
|
||||||
|
|
||||||
|
// 2. Append to today's session log
|
||||||
|
const todayStr = new Date().toISOString().split('T')[0];
|
||||||
|
const sessionLogDir = 'c:/Users/MattC/Documents/Obsidian Vault/Homelab/Sessions';
|
||||||
|
if (!fs.existsSync(sessionLogDir)) {
|
||||||
|
fs.mkdirSync(sessionLogDir, { recursive: true });
|
||||||
|
}
|
||||||
|
const sessionLogPath = path.join(sessionLogDir, `${todayStr}-uptime-status-monitoring-schedule.md`);
|
||||||
|
|
||||||
|
let sessionContent = '';
|
||||||
|
if (!fs.existsSync(sessionLogPath)) {
|
||||||
|
sessionContent = `# Session Log — ${todayStr} — Uptime Status Monitoring Schedule
|
||||||
|
|
||||||
|
## What was done
|
||||||
|
- Automatically checked uptime status page and updated reports.
|
||||||
|
|
||||||
|
## Daily Log of Checks
|
||||||
|
`;
|
||||||
|
} else {
|
||||||
|
sessionContent = fs.readFileSync(sessionLogPath, 'utf8');
|
||||||
|
}
|
||||||
|
|
||||||
|
const timeStr = new Date().toLocaleTimeString();
|
||||||
|
const statusText = down > 0 ? `🚨 DEGRADED (${down}/${total} services DOWN)` : `✅ Healthy (${total}/${total} services UP)`;
|
||||||
|
const logEntry = `\n### Check at ${timeStr}\n- **Status**: ${statusText}\n`;
|
||||||
|
|
||||||
|
fs.writeFileSync(sessionLogPath, sessionContent + logEntry, 'utf8');
|
||||||
|
console.log(`Appended check entry to ${sessionLogPath}`);
|
||||||
|
|
||||||
|
} catch (error) {
|
||||||
|
console.error(`Error checking status: ${error.message}`);
|
||||||
|
process.exit(1);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
checkStatus();
|
||||||
Reference in New Issue
Block a user