mlnode-validate

name

mlnode-validate

description

Validate a deployed MLNode end-to-end against a known-honest reference for a specific model. The caller picks the MLNode URL and the target model; the skill ensures the model is downloaded, deploys it, measures full-system PoC throughput, and verifies a pre-computed honest PoC vector set is accepted under an L2 threshold. Produces a JSON + text report. Self-contained inside the gonka repo (no external code, no callback receiver).

MLNode PoC v2 Validation

The caller decides what to test. This skill never picks a default MLNode and never picks a default model. Both must be passed explicitly.

This file is the contract. The skill is implemented by two scripts under mlnode/packages/benchmarks/scripts/poc_validation/. Do not read or edit those scripts as part of running this skill -- run validate.py and report what it prints.

Two file types -- keep them straight

The skill works with two distinct artifact kinds. Do not conflate:

• Golden reference (committed in repo): one JSON per supported model under mlnode/packages/benchmarks/scripts/poc_validation/artifacts/<sanitized model>.json. Pre-computed by the project: contains the honest PoC vectors, canonical PoC params, consensus-default additional_args, and the consensus-default stat_test block. Read-only at validate time.
• Per-run report (produced on the caller's machine, NOT committed): one experiment directory per validate.py invocation under mlnode/packages/benchmarks/data/experiments/<exp_name>_<ts>/. Layout matches mlnode/packages/benchmarks/scripts/README.md -- one experiment = one server + one model deployment. Each directory contains validate_config.json (inputs), validate_report.json (full structured result), and validate_report.txt (short text summary).

When this skill says "reference", it means the golden file. When it says "report", it means the per-run output.

Available golden references

The repo ships these references under mlnode/packages/benchmarks/scripts/poc_validation/artifacts/. Each one is keyed by model id; the auto-lookup <sanitized model>.json picks one filename per model. Variants beyond the default require an explicit --reference <path>.

Model	Filename	Vectors	Deploy notes
Qwen/Qwen3-0.6B	qwen-qwen3-0.6b.json	32	local dev / single GPU
Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 (default lookup)	qwen-qwen3-235b-a22b-instruct-2507-fp8.json	32	tp=4, FlashInfer baseline. Quick smoke test.
Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 (extended)	qwen-qwen3-235b-a22b-instruct-2507-fp8-deepgemm.json	2000	tp=2, DeepGEMM MoE backend (VLLM_USE_DEEP_GEMM=1, VLLM_MOE_USE_DEEP_GEMM=1), recorded on 4xB200. Pass with --reference.

For Qwen3-235B the same model id has two references, and they exercise different code paths (tp-size + MoE backend). When validating qwen235b, run validate.py twice -- once with the default lookup, once with the deepgemm variant -- and report both verdicts. Example:

# Run 1: default 32-nonce reference (FlashInfer, tp=4)
python3 mlnode/packages/benchmarks/scripts/poc_validation/validate.py \
    --mlnode-url "$MLNODE_URL" \
    --model     Qwen/Qwen3-235B-A22B-Instruct-2507-FP8

# Run 2: extended 2000-nonce reference (DeepGEMM, tp=2)
python3 mlnode/packages/benchmarks/scripts/poc_validation/validate.py \
    --mlnode-url "$MLNODE_URL" \
    --model     Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 \
    --reference mlnode/packages/benchmarks/scripts/poc_validation/artifacts/qwen-qwen3-235b-a22b-instruct-2507-fp8-deepgemm.json

Each run lands in its own experiment directory, so reports do not collide. If the caller is short on time and explicitly asks for a quick check, the default-lookup run alone is acceptable; otherwise both should be run.

Required inputs

The caller MUST supply both:

• MLNODE_URL -- base URL of the MLNode under test (e.g. http://1.2.3.4:8080). No default.
• MODEL -- target HuggingFace model id, exactly as the MLNode would receive it (e.g. Qwen/Qwen3-235B-A22B-Instruct-2507-FP8, Qwen/Qwen3-0.6B). The full org/repo form. No default.

If either is missing, ask the caller for it before running anything. A short reference like "qwen 0.6B" is NOT enough -- ask for the full HF id. Do not pick a model from the available artifacts on the caller's behalf.

Where every other parameter comes from

Beyond MLNODE_URL and MODEL, the caller does not need to pass anything. Every other parameter has a documented source:

• seq_len, k_dim, block_hash, public_key, node_id, node_count -- all read from the golden reference for MODEL. The reference pins the exact PoC inputs its vectors were computed under.
• vLLM additional_args -- the consensus-default deploy args for this model are saved in the reference's additional_args field and used as-is. The caller can pass extra flags only if they ask for them: --tp-size and --max-model-len add-or-update the reference baseline (the same flag's existing value is replaced, otherwise appended); --extra-arg <token> appends arbitrary tokens.
• stat_test.dist_threshold / p_mismatch / fraud_threshold -- resolved per-key with provenance: server default (0.02 / 0.001 / 0.01) is the floor; the reference's stat_test block (which mirrors the on-chain consensus params for this model) overrides; CLI flags (--threshold, --p-mismatch, --fraud-threshold) are the top. All three values are always sent to the server and shown in the report with their source.

Optional inputs

The caller can override deploy / validation / sampling defaults. Only pass these flags when the caller asks for them:

• Stat-test parameters. These three flags fill the stat_test block the MLNode runs (per-nonce L2 mismatch test + binomial fraud test). --threshold IS the dist_threshold field of that block, just named more concisely on the CLI. Defaults come from the artifact; any key not provided is filled by the MLNode server defaults.
  • --threshold <float> (e.g. 0.2) -- the dist_threshold field. L2 distance above which a single nonce counts as a per-nonce mismatch. Default: artifact's dist_threshold.
  • --p-mismatch <float> (e.g. 0.001) -- per-nonce probability of a benign mismatch under honest computation. Default: artifact's stat_test.p_mismatch if present, else server default.
  • --fraud-threshold <float> (e.g. 0.01) -- probability_honest cutoff at which the server flags fraud. Default: artifact's stat_test.fraud_threshold if present, else server default.
• Deploy overrides (only pass when the caller explicitly asks; the artifact's additional_args already encode the consensus-default deploy config for this model):
  • --dtype <auto|float16|bfloat16|fp8> -- vLLM dtype.
  • --tp-size <int> -- add-or-update --tensor-parallel-size.
  • --max-model-len <int> -- add-or-update --max-model-len.
  • --extra-arg <token> -- pass once per token to append additional vLLM args. The caller owns avoiding conflicts with the artifact baseline.
• Phase skips. By default all four phases run; each flag drops one phase:
  • --skip-download -- the model is known to be cached already.
  • --skip-deploy -- vLLM is known to already be serving MODEL.
  • --skip-throughput -- skip the throughput measurement (faster runs).
  • --skip-validate -- skip the vector-validation step (deploy / measure only).
• Sampling: --warmup-seconds <int>, --measure-seconds <int>, --sample-interval <int>, --batch-size <int>.
• Timeouts (seconds): --download-timeout <float>, , .

How to run

Bare minimum (recommended starting form):

python3 mlnode/packages/benchmarks/scripts/poc_validation/validate.py \
    --mlnode-url "$MLNODE_URL" \
    --model     "$MODEL"

Add overrides only when the caller asks for them. The deploy config the caller sees is the artifact's additional_args plus any of --dtype, --tp-size, --max-model-len, --extra-arg they passed; the caller does not need to know what's in the artifact's baseline.

What the script does, in order

The script prints [i/4] headers (1-indexed) as it progresses. The agent should relay these headers to the caller while the run is in flight so the caller can see which phase is slow.

1. [1/4] download -- ensures the requested HF repo is cached on the MLNode.

   • POST /api/v1/models/status {hf_repo} -- returns one of DOWNLOADED | DOWNLOADING | NOT_FOUND | PARTIAL.
   • If not DOWNLOADED, POST /api/v1/models/download {hf_repo} to start the download, then poll /models/status until DOWNLOADED.
   • Skipped with --skip-download. Hard-fails if the status flips back to NOT_FOUND mid-poll, or the timeout expires.

2. [2/4] deploy -- starts vLLM if it is not already running.

   • POST /api/v1/inference/up/async {model, dtype, additional_args}.
   • Polls GET /api/v1/inference/up/status until is_running == true or status becomes failed/cancelled. Server-side elapsed_seconds is printed on each poll so the caller sees progress.
   • Skipped with --skip-deploy; if so, the script verifies vLLM is already running and aborts otherwise.

3. [3/4] throughput -- measures full-system PoC throughput.

   • POST /api/v1/inference/pow/init/generate {block_hash, ..., params} (the artifact's params); the proxy fans out to every healthy vLLM replica with a different group_id so they process disjoint nonces. The first response reports backends and n_groups.
   • After --warmup-seconds, samples GET /api/v1/inference/pow/status every --sample-interval for --measure-seconds. Reports per-replica nonces_per_second and the sum across replicas (the system-level throughput for this model). Then POST /api/v1/inference/pow/stop.
   • Skipped with --skip-throughput.

4. [4/4] validate -- verifies pre-computed honest vectors.

   • POST /api/v1/inference/pow/generate with wait=true, nonces=[...], validation.artifacts=<artifact>, and the full block (, , ). The MLNode recomputes the same nonces, compares them to the supplied vectors under L2 (per-nonce mismatch test), then runs a binomial fraud test using and . Returns .

After the four phases, the script writes three files into the experiment directory (mlnode/packages/benchmarks/data/experiments/<exp_name>_<ts>/, matching the layout in mlnode/packages/benchmarks/scripts/README.md):

• validate_config.json -- the resolved inputs only (mlnode URL, model, reference path + meta, deploy config, PoC params, stat_test with provenance, raw CLI args). Lets a future reader reproduce the run without re-deriving any defaults.
• validate_report.json -- the full structured report (config + per-phase results + verdict). This is the audit trail.
• validate_report.txt -- short human-readable summary; first line after the banner is verdict: <PASS|FAIL|...> for at-a-glance reading.

How to report back to the caller

After the script returns, read <exp_dir>/validate_report.json and emit a summary using the template below. The report exposes everything you need; do NOT abbreviate it down to a one-liner -- the caller invokes this skill to know exactly what was tested and against what.

Required template (every section is mandatory; mark a field N/A only when its phase was skipped):

verdict: <PASS | PASS-with-mismatches | FAIL | validation skipped>

target
  mlnode:    <mlnode_url>
  model:     <model>
  reference: <reference.path>
             <reference.n_vectors pre-computed nonces; source: <reference.source>>

PoC params (from reference)
  seq_len=<>, k_dim=<>, block_hash=<>, public_key=<>, node_id=<>, node_count=<>
  throughput_batch_size=<poc_params.throughput_batch_size> (cli)
  validation_batch_size=<poc_params.validation_batch_size> (cli)

deploy config (reference.additional_args + CLI overrides)
  dtype=<>, additional_args=<list>
  applied? <yes if deploy.action=="deployed", "no - vLLM was already running" otherwise>

stat test (server-default -> reference -> cli, with provenance per key)
  dist_threshold=<v> (<source>)
  p_mismatch=<v> (<source>)
  fraud_threshold=<v> (<source>)

phases
  [download]    <action> in <elapsed>s   OR  skipped
  [deploy]      <action> in <elapsed>s   OR  skipped
  [throughput]  backends=<n>  avg_sum=<v> nonces/s  min/max=<a>/<b>
                per-backend (last sample): [<...>]   (only if n_backends > 1)
  [validate]    n_total=<>, n_mismatch=<>, p_value=<>, fraud_detected=<>
                mismatch_nonces=<list, truncated to 20>   (only if non-empty)

experiment dir: <exp_dir>
  config (json):  <exp_dir>/validate_config.json
  report (json):  <exp_dir>/validate_report.json
  report (txt):   <exp_dir>/validate_report.txt

Why every section matters: the caller asked the skill to deploy and validate a model. They cannot judge the result without seeing which additional_args actually shaped that deployment, which stat_test triple the verdict was judged under, or which block_hash/public_key/seq_len/k_dim the PoC was computed with -- those four together define the test. The script writes them all into the report; surface them.

If deploy.action == "already_running", say so explicitly and warn that the listed additional_args were NOT applied to the live vLLM process -- they are what the script would have sent. Caller may need to restart vLLM to actually exercise those flags.

If one replica's nonces_per_second is significantly below the others (e.g. 50% of median), call it out. Otherwise the per-backend list is informational.

Pass criteria

The MLNode runs the binomial fraud test internally and returns fraud_detected. PASS is defined by that single field; the per-nonce n_mismatch count is informational. There are three outcomes the agent must distinguish and surface:

• Clean PASS -- validation.passed == true, validation.has_mismatches == false, validation.response.n_mismatch == 0, validation.response.fraud_detected == false.
• PASS with mismatches within stat-test tolerance -- validation.passed == true, validation.has_mismatches == true, validation.response.n_mismatch > 0, validation.response.fraud_detected == false. The fraud test allows up to a few mismatches per p_mismatch. This is still a PASS; surface n_mismatch, mismatch_nonces, p_value, and the stat_test parameters that were used so the caller can see the test was met. Do not call this a failure.
• FAIL -- validation.passed == false, validation.response.fraud_detected == true. Surface n_mismatch, mismatch_nonces, p_value, and the stat_test block. Do not retry.

Exit code is the authoritative signal:

• 0 -- PASS (with or without mismatches inside tolerance), or the validate phase was skipped so there is no fraud verdict to fail on.
• 2 -- validation ran and the fraud test fired (fraud_detected: true).
• 1 -- hard error before validation could run (download failed, deploy timed out, etc.). Surface the script's last ERROR: line.

When no artifact exists for the requested model

validate.py looks up the artifact under mlnode/packages/benchmarks/scripts/poc_validation/artifacts/. If the file for MODEL is missing, the script exits 1 and prints:

• which model was requested,
• the expected artifact filename,
• the available artifacts in that directory,
• the exact make_artifact.py command to bake one.

The agent must NOT invent vectors and MUST NOT pick a different model to substitute. Stop, surface the script's message, and either:

• ask the caller to choose from the printed list of available artifacts, or

• bake a new artifact against a trusted MLNode that already serves MODEL (canonical precision, attention backend, GPU class):

  python3 mlnode/packages/benchmarks/scripts/poc_validation/make_artifact.py \
      --mlnode-url "$TRUSTED_MLNODE_URL" \
      --model     "$MODEL" \
      --num-nonces 32 --batch-size 32 \
      --out mlnode/packages/benchmarks/scripts/poc_validation/artifacts/<filename printed by validate.py>
  

  make_artifact.py does not deploy; the trusted MLNode must already be serving MODEL. It pulls vectors via POST /api/v1/inference/pow/generate with wait=true (no callback receiver, no SSH tunnel). After the artifact lands, re-run the original validate.py command.

Failure modes the agent surfaces verbatim

• Download phase: model download did not complete in <N>s or model download failed (status went to NOT_FOUND). Likely HF-rate-limited, no internet, or wrong hf_repo. Stop.
• Deploy phase: vLLM did not become ready within <N>s or vLLM startup failed: {status: failed, error: ...}. The deploy config does not match the GPU (OOM, FP8 on non-FP8 capable GPU, wrong tp-size, kernel mismatch). Surface the last status object; do not silently retry with different flags.
• Throughput phase reports backends=0 or agg_status: NO_BACKENDS -- the proxy never registered healthy vLLM ports. The deploy probably crashed; check the MLNode container logs. Stop.
• Validation fraud_detected: true -- the deployment under test produces different vectors than the reference at a rate the binomial test rejects. Report n_mismatch, mismatch_nonces, p_value, and the stat_test block. Do not guess the cause.
• Validation n_mismatch > 0 with fraud_detected: false -- this is a PASS, not a failure. Mention the count and mismatch_nonces so the caller is aware, but do not treat it as an error condition.

Notes

• Throughput is the sum of per-replica nonces_per_second. With N vLLM replicas (e.g. 8 H100 + tp_size=4 -> 2 replicas), each replica processes a different nonce group, so the system rate for the model is the sum.
• No callback receiver, SSH tunnel, or open inbound port is required at any phase. Throughput uses GET /api/v1/inference/pow/status (server-side counters); validation and artifact baking use POST /api/v1/inference/pow/generate with wait=true, which returns artifacts inline.
• Both validate.py and make_artifact.py accept --help for the full flag surface. This file lists only the inputs the skill exposes to the caller.