nvflare-autofl-report
Skill1k starsChanged 12 days agoApache-2.0View on GitHub
Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.
1 other project uses a version of this.
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- NVIDIA/skillsword for word, first seen Oct 1, 2026
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- Kaggle/kaggle-sdk-python“kagglesdk 0.1.31 and 0.1.32 import a missing competitions.legacy package”GitHub · Jun 30, 2026 · 1 reactions
- londonaicentre/FLIP“docs: call the NVIDIA framework FLARE, its short official name”GitHub · Oct 17, 2026
- NVIDIA/SkillEvaluator“[BUG]: Generated BENCHMARK.md does not faithfully explain a passing gate and splits canonical case IDs”GitHub · Oct 9, 2026
- Denimworld12/Hospital-Chain“feat: run hospital nodes on separate machines with provisioning kits and a cross-address verifier”GitHub · Oct 2, 2026
- ai4os/ai4os-nvflare“Update NVFlare version to 2.8”GitHub · Jul 16, 2026
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On GitHub · 25
- Jun 30, 2026kagglesdk 0.1.31 and 0.1.32 import a missing competitions.legacy package — Kaggle/kaggle-sdk-python1 reaction · 2 comments
- Oct 17, 2026docs: call the NVIDIA framework FLARE, its short official name — londonaicentre/FLIP0 reactions · 1 comment
- Oct 9, 2026[BUG]: Generated BENCHMARK.md does not faithfully explain a passing gate and splits canonical case IDs — NVIDIA/SkillEvaluatorsays it tested it0 reactions · 1 comment
- Oct 2, 2026feat: run hospital nodes on separate machines with provisioning kits and a cross-address verifier — Denimworld12/Hospital-Chain0 reactions
- Jul 16, 2026Update NVFlare version to 2.8 — ai4os/ai4os-nvflare0 reactions
20 more on GitHub
- Jul 6, 2026Upgrade NVFlare backbone 2.7.2 → 2.8.0 (deferred until a clean 2.7.2 run) — KatherLab/MediSwarm0 reactions · 3 comments
- Jun 26, 2026Upgrade NVFLARE backend 2.7.2 → 2.8.0 — londonaicentre/FLIP0 reactions
- Jun 22, 2026Add step-ca ephemeral admin certs — pcnudde/NVFlare0 reactions · 1 comment
- May 7, 2026feat(privacy): federated learning architecture-ready (Horizon Europe / EU4Health alignment) — Yanstart/VarunaPoC0 reactions · 1 comment
- Jan 7, 2026T3: Model fitting (deploying GWAS tool / model on every client) [Enamul, ++] — collaborativebioinformatics/FedGen0 reactions
- Sep 10, 2025[Feature]: From Vertical FL feasibility (#363) to first example — londonaicentre/FLIP0 reactions · 1 comment
- Jun 23, 2025Secure xgboost example — RhinoHealth/user-resources0 reactions · 3 comments
- May 14, 2025NVFlare backbone upgrade for better connection performance — KatherLab/MediSwarm0 reactions · 2 comments
- Mar 24, 2025NVFlare Integration — KCL-BMEIS/UltraFlwr0 reactions
- Jul 14, 2024Try to fix experiment already exist issue in `MLFlowHandler` — Project-MONAI/MONAI0 reactions · 11 comments
- Sep 13, 2023Add stats_sender to MonaiAlgo for FL stats — Project-MONAI/MONAI0 reactions · 1 comment
- Aug 9, 2023【CS-part3】New submissions for Wed, 9 Aug 23 — Yukeaaa/arxiv-daily0 reactions
- Jul 12, 2023New learner cifar10 — yanchengnv/NVFlare0 reactions
- May 6, 2023Bump nvflare from 2.0.10 to 2.3.0 in /federated_learning — NVIDIA/fsi-samples0 reactions · 1 comment
- Mar 22, 2023[WIP] add `FLStatsHandler` — Project-MONAI/MONAI0 reactions · 3 comments
- Mar 21, 2023Try NVFlare cifar10 example — KatherLab/swarm-learning-hpe0 reactions
- Mar 21, 2023Apply the new data split strategies — KatherLab/swarm-learning-hpe0 reactions
- Dec 6, 2022How to use bst.eval_set() and bst.update() with xgboost_ray — ray-project/xgboost_ray0 reactions · 3 comments
- May 18, 2022713 Update nvflare example for 2.1 — Project-MONAI/tutorials0 reactions · 2 comments
- Mar 22, 2022Bump nvflare from 2.0.2 to 2.0.16 in /federated_learning/nvflare/nvflare_spleen_example — Project-MONAI/tutorials0 reactions · 4 comments
Other versions1 project uses a version of this.
Word for word · 1
- NVIDIA/skills★ 3.9k100% same
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How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository16 other files it gives its agents.
- AGENTS.mdAGENTS.md
- examples/tutorials/self-paced-training/AGENTS.mdAGENTS.md
- research/auto-fl-research/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- research/auto-fl-research/CLAUDE.mdCLAUDE.md
- autofl-nvflare-reportSkill
- autofl-nvflareSkill
- nvflare-autoflSkill
- nvflare-convert-huggingfaceSkill
- nvflare-convert-lightningSkill
- nvflare-convert-pytorchSkill
- nvflare-diagnose-jobSkill
- nvflare-fed-statsSkill
- nvflare-orientSkill
- nvflare-sharedSkill
- nvflare-example-skillSkill
The file
We split this skill into the rules it gives an agent
11 rules. About 0 are common, 5 are distinctive, and 6 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
6
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 26
Use this skill after an NVFLARE Auto-FL campaign has stopped, reached an explicit cap, hit a hard blocker, or was manually interrupted.
- Very specific: line 33
Do not use this skill to start or continue optimization, invent missing results, or finalize a campaign that is still running.
- Very specific: line 44
Run this bundled CLI directly with Python.
- Very specific: line 68
Read both autofl_final_report.md and autofl_report_summary.json.
- Very specific: line 82
All relative helper path options, including --plotter, resolve from the campaign job directory rather than the shell's current working …
- Distinctive: line 137
every recorded literature checkpoint, its event ID and source markers, explicitly linked candidates, and whether measured evidence helped, …
- Distinctive: line 161
Read report-contract.md when interpreting lineage, literature outcomes, budget warnings, or interrupted state.
- Very specific: line 178
Treat results.tsv as recorded evidence.
- Distinctive: line 181
Never bypass campaign-lock contention or output-path collision checks.
- Distinctive: line 182
Use candidate manifests when available, but still report partial provenance when copied artifacts make old absolute manifest paths …
- Distinctive: line 184
Keep conclusions proportional to the evidence.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
NVFLARE Auto-FL Report
Purpose
Turn the recorded evidence from a stopped NVFLARE Auto-FL campaign into a reproducible Markdown report, machine-readable JSON summary, and refreshed progress plot without changing the campaign or its results.
Use When
Use this skill after an NVFLARE Auto-FL campaign has stopped, reached an explicit cap, hit a hard blocker, or was manually interrupted. Use it when the user asks for the final report, achieved improvement, literature findings, failed ideas, reproduction details, or a refreshed progress plot.
Do Not Use When
Do not use this skill to start or continue optimization, invent missing
results, or finalize a campaign that is still running. Use nvflare-autofl for
the active candidate loop. Do not stop an active campaign merely because the
user asks for a status snapshot.
Available Scripts
| Script | Purpose | Arguments |
scripts/generate_report.py | Validate stopped campaign evidence and generate the report, JSON summary, and optional progress plot. | Campaign job directory; optional evidence/output paths, interruption confirmation, plot settings, and agent context. Run python scripts/generate_report.py --help for the complete CLI. |
Run this bundled CLI directly with Python. This skill has no NVFLARE or agent
run_script() helper; do not invent or call one. Resolve the script from the
directory containing this SKILL.md, as shown in the workflow.
Workflow
- Locate the job directory containing
results.tsv,autofl.yaml,.nvflare/autofl/campaign_state.json, and candidate manifests. - Confirm execution has stopped. Prefer campaign state with
final_response_allowed=true. When a process was abruptly interrupted and state is stale, independently confirm no campaign or job process remains, then use--confirm-interrupted. Before finalizing, confirm that campaign state,results.tsv, and available candidate manifests contain no pending candidate. Finalize or abandon pending work throughnvflare-autoflfirst. Generate the deterministic report artifacts:
python "$REPORTER" <job-dir>Resolve
REPORTERonce to the absolute path ofscripts/generate_report.pyrelative to thisSKILL.md. Do not assume a provider-specific skill installation directory or$CODEX_HOME.- Read both autofl_final_report.md and
autofl_report_summary.json. Check warnings about metric use, executed budget changes, campaign-state/ledger disagreement, missing provenance, and incomplete interruption state. - Give the user the baseline, best score, delta, strongest candidate lineage, concise "what helped" and "what did not help" findings, literature ideas that helped or failed, selection rationale, reliability caveats, and absolute artifact paths.
The helper attempts to refresh progress.png by reusing the product Auto-FL
plotter. Plotting is optional evidence: if plotting dependencies are missing or
the artifact is not a valid PNG, the helper preserves the artifact, records a
warning and artifacts.progress_plot_available=false, and still writes the
Markdown and JSON reports without embedding the broken image. It does not
modify source, candidate manifests, results.tsv, or campaign state.
All relative helper path options, including --plotter, resolve from the
campaign job directory rather than the shell's current working directory.
The helper holds the campaign lifecycle lock from evidence loading through
artifact writes. It refuses a busy campaign and rejects writable output paths
that alias campaign evidence, job.py, trust-contract source paths, or another
output, including filesystem aliases. Outputs must not match the trust
contract's allowed source-creation patterns.
A read-only campaign archive is reportable only when its persisted
campaign.lock already exists and writable output paths are supplied; the
persisted lock target alone does not imply live contention.
Interrupted Campaigns
The report helper must refuse state with final_response_allowed=false unless
the human has said the campaign was stopped/interrupted and the agent confirms
that execution is no longer active. Only then run:
python "$REPORTER" <job-dir> --confirm-interruptedThis records a reporting-time interruption assertion; it does not rewrite the
campaign state or pretend the runner finalized cleanly. It bypasses only stale
stop state. A candidate ledger row or pending-candidate state always blocks
finalization. Any available candidate manifest whose status is not a recognized
terminal value (keep, discard, crash, or abandoned) also blocks. Missing,
unknown, or unreadable status is unfinished evidence because completion cannot
be established. The agent must finalize or abandon that candidate first.
Troubleshooting
| Error or symptom | Cause | Solution |
Reporting is refused because final_response_allowed=false. | The campaign may still be active, or persisted state may be stale after an interruption. | Confirm no campaign or job process remains. If a human confirmed the interruption, rerun with --confirm-interrupted; otherwise continue or stop the campaign through nvflare-autofl. |
| Reporting is refused for a pending candidate or unknown manifest status. | The recorded evidence cannot establish that candidate execution finished. | Finalize or abandon the candidate through nvflare-autofl, then regenerate the report. |
| The campaign lock is busy. | Another campaign or reporting process holds the lifecycle lock. | Identify and wait for the active process. Never bypass or delete a live lock. |
The JSON says progress_plot_available=false. | Plotting dependencies are unavailable or the generated artifact is not a valid PNG. | Use the completed Markdown and JSON reports, review their warning, and install the campaign's plotting dependencies before retrying if a plot is required. |
| An output path is rejected. | The path aliases protected campaign evidence, source, or another output. | Choose distinct writable output paths outside protected campaign inputs and rerun. |
Report Contract
The final report must include:
- campaign termination reason, objective, metric source, direction, environment, cap, abandoned-candidate count, and declared fixed budget;
- baseline, best retained result, score delta, runtime, failures, and status counts;
- selected-candidate rationale, strict retained improvements, representative non-improvements, grouped failures, and outcomes by recorded algorithm family;
- running-best trajectory selected by first, final, and largest objective improvements,
plus a refreshed
progress.pngwhen plotting is available, with explicit plot availability in the JSON summary otherwise; - best-candidate manifest, patch hash, base-candidate lineage, inherited code changes, artifacts, and exact baseline/best commands;
- every recorded literature checkpoint, its event ID and source markers, explicitly linked candidates, and whether measured evidence helped, matched, failed, or did not confirm the idea;
- discarded/crashed ideas and deterministic comparability warnings;
- optional agent model, reasoning effort, cost, or tooling notes when supplied;
- absolute paths to
autofl.yaml,results.tsv, campaign state,progress.png,autofl_report_summary.json, and autofl_final_report.md.
The report must distinguish imported/declared budget from executed command
arguments. It must warn when the best candidate changed training compute or
comparison population, when authoritative state disagrees with ledger-derived
accounting, or when repeated selection used a test-like metric. Product Auto-FL
campaigns retain the imported min or max direction; the report rejects
legacy minimization evidence without direction provenance. It must not add PR-specific sections such as "Product Findings"
unless the user explicitly requests them.
best means a scored retained baseline or keep row; an unretained scored
discard may appear only as best_observed. Candidate and crash rows never
become retained best results, milestones, or literature improvements.
Baseline identity is determined strictly by status=baseline, matching the
campaign guard. The report preserves per-run metric name, extraction source,
artifact, candidate kind, algorithm family, and literature event linkage.
It does not infer algorithm families or mechanisms from candidate names.
Read report-contract.md when interpreting lineage, literature outcomes, budget warnings, or interrupted state.
Limitations
- The report is only as complete as the persisted ledger, campaign state, and candidate manifests; it does not validate or reconstruct unrecorded claims.
- Results from a single campaign do not establish robustness or generalization.
- The helper does not start, stop, resume, or resubmit campaign jobs.
- Progress plotting remains optional, so Markdown and JSON may be produced without an embeddable PNG.
- Copied campaign archives may retain only partial provenance when recorded absolute manifest paths are no longer available.
Requirements
- Treat
results.tsvas recorded evidence; never repair scores by guessing. - Work without Git. Do not commit or push unless the user separately asks.
- Preserve the campaign and job sources exactly as found.
- Never bypass campaign-lock contention or output-path collision checks.
- Use candidate manifests when available, but still report partial provenance when copied artifacts make old absolute manifest paths unavailable.
- Keep conclusions proportional to the evidence. A single run is a candidate, not a robustness claim.
- For POC/production, report standard NVFLARE job IDs and downloaded artifacts already present in the ledger; do not resubmit jobs during reporting.
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