Tao Finetune Kumo Anomaly

NVIDIAOfficialCreated by NVIDIA Corporation

Skill
Tool integration
Data and AI
Apache-2.0
Medium risk

From the author

Kumo-Anomaly — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune Kumo-Anomaly", "run Kumo-Anomaly inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune Kumo-Anomaly", "use perform_anomaly_analysis_with_diffusion", "automl Kumo-Anomaly", "hyperparameter search Kumo-Anomaly", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "kumo-anomaly", "sdk/anomaly_analysis", or "TSDiffuser_Generic".

— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Kumo-Anomaly is a diffusion-based framework designed for detecting anomalies in multivariate time series. It works by reconstructing masked segments of input data and calculating the Mean Absolute Error (MAE) between the reconstruction and the original signal. The tool supports both inference on new data and fine-tuning on domain-specific normal behavior. It features adaptive thresholding (SCS or MACS) to convert reconstruction scores into binary anomaly labels and integrates with AutoML for hyperparameter optimization.

What it does

Performs anomaly detection on multivariate time series using diffusion models.
Fine-tunes pretrained models on custom datasets representing normal behavior.
Applies adaptive thresholding strategies (SCS or MACS) for binary classification.
Generates PDF reports summarizing anomaly detection findings.
Supports hyperparameter optimization for both fine-tuning and inference parameters.
Handles data preprocessing including PCA reduction and normalization.

When to use it

When you need to detect anomalies in multivariate time series data.
When you want to adapt a pretrained diffusion model to your specific domain's normal behavior.
When you need to perform hyperparameter optimization to improve detection accuracy.
When you require automated report generation for anomaly analysis.

Advantages

Leverages diffusion models for robust reconstruction-based anomaly detection.
Supports flexible thresholding strategies to suit different data characteristics.
Provides clear paths for both inference and model fine-tuning.
Integrates with AutoML workflows for automated parameter tuning.
Includes built-in reporting capabilities for analysis results.

Usage examples

“Fine-tune Kumo-Anomaly on my normal-behavior data.”: The agent will execute the fine-tuning script using your provided CSV, training for the specified epochs and saving the resulting model checkpoints to your output directory.
“Run Kumo-Anomaly inference on my dataset.”: The agent will process your numeric CSV data, apply the diffusion model, and return a DataFrame with appended Anomaly labels and MAE scores.
“Generate an anomaly detection report for my time series data.”: The agent will run the analysis and produce a PDF report detailing the anomaly findings based on the configured threshold strategy.
“Perform hyperparameter search for Kumo-Anomaly fine-tuning.”: The agent will initiate an AutoML run to optimize training parameters like learning rate and batch size to minimize validation loss.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/tao-finetune-kumo-anomaly/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip tao-finetune-kumo-anomaly.zip -d .agents/skills

Documentation by NVIDIA Corporation

Kumo-Anomaly

Diffusion-based anomaly detection and fine-tuning for multivariate time series. The model reconstructs randomly masked segments and scores each timestep by MAE between reconstruction and original signal; adaptive thresholding (SCS or MACS) converts scores to binary labels.

Source code: https://github.com/NVIDIA/Kumo-TS Pretrained weights: https://huggingface.co/nvidia/Kumo-Anomaly

For the most up-to-date usage information, refer to the README files in the Kumo-TS repository:
Kumo-Anomaly/README.md — full SDK reference, model architecture, and API docs
Kumo-Anomaly/examples/datasets/README.md — dataset format, synthetic data generation, and CSV conventions

External dependencies

Dependency	Purpose	Install
Python 3.12+	Runtime	https://www.python.org/downloads/
uv	Package + environment manager	`pip install uv`
CUDA toolkit (optional)	GPU acceleration	https://developer.nvidia.com/cuda-downloads
huggingface_hub	Weight download from HF	Bundled via `uv sync`

Credentials

nvidia/Kumo-Anomaly is a public repo — no token required for downloading weights. If you ever hit a 401/403 (gated access or private fork) or a 504 on first download, see the Known pitfalls section.

Quick start

git clone --branch main --single-branch https://github.com/NVIDIA/Kumo-TS Kumo-TS
cd Kumo-TS/Kumo-Anomaly
uv sync                              # install dependencies (one-time)

# Inference — synthetic data, auto-downloads weights from HF on first run
uv run python examples/quick_example.py

# Inference — your own CSV
uv run python examples/quick_example.py \
  --model-path final_model.pth \
  --config-path curriculum_medium.yaml \
  --dataset-path /path/to/data.csv

# Pre-download weights only (warm the cache before going offline)
uv run python examples/quick_example.py --download-weights

# Fine-tune on your own normal-behavior data
uv run python examples/finetune_example.py \
  --csv /path/to/normal_training_data.csv \
  --timestamp-col timestamp \
  --label-col is_anomaly \
  --epochs 20 \
  --output-dir artifacts/finetune_my_data

Inference

Use perform_anomaly_analysis_with_diffusion in sdk/anomaly_analysis.py. It validates input, auto-dispatches across all visible GPUs, applies adaptive thresholding (SCS or MACS), and returns the original DataFrame with Anomaly (0/1) and MAE columns appended.

import sys, pandas as pd
sys.path.append("/path/to/Kumo-TS/Kumo-Anomaly")  # clone Kumo-TS with --branch main
from sdk.anomaly_analysis import perform_anomaly_analysis_with_diffusion

df = pd.read_csv("your_data.csv")

# The API raises ValueError on non-numeric columns — drop timestamp, IDs, and labels first.
df = df.select_dtypes(include="number")

results = perform_anomaly_analysis_with_diffusion(
    df=df,
    threshold_strategy="scs",       # "scs" (fast) or "macs" (adaptive)
    model_path=None,                 # None → auto-download final_model.pth from HF
    model_config_path=None,          # None → auto-download curriculum_medium.yaml from HF
    nsample=15,                      # diffusion samples per window; ↑ accuracy, ↑ latency
    preprocess_model_dir=None,       # optional preprocessing model directory
)
# results columns: Anomaly (0/1), MAE (float), plus all original columns
print(results[["Anomaly", "MAE"]].describe())

Reporting (`sdk_config`)

perform_anomaly_analysis_with_diffusion takes an optional sdk_config keyword — an ADDiffusionConfig, a path to a YAML file, or None for defaults. It scopes report-generation knobs only; inference-behavior args (threshold_strategy, nsample, model_path, model_config_path) stay as direct function arguments.

from sdk.anomaly_analysis import ADDiffusionConfig, perform_anomaly_analysis_with_diffusion

results = perform_anomaly_analysis_with_diffusion(
    df=df,
    threshold_strategy="scs",
    sdk_config=ADDiffusionConfig(
        report_path="artifacts/anomaly_report.pdf",
        report_title="Anomaly Detection Report",
    ),
)

Or pass a YAML path instead of the dataclass — load_sdk_config accepts a flat mapping or one nested under a top-level sdk: key (so the file can carry other sections alongside it), and rejects unknown keys:

sdk:
  report_path: artifacts/anomaly_report.pdf
  report_title: Anomaly Detection Report

results = perform_anomaly_analysis_with_diffusion(
    df=df,
    threshold_strategy="scs",
    sdk_config="sdk_config.yaml",
)

ADDiffusionConfig fields: report_path (str/Path, default None — set to enable the PDF report), timestamp_column (str, None), ground_truth_column (str, None), report_title (str, "Kumo-Anomaly Report"), report_max_pages (int, 10).

Inference CLI reference

Argument	Default	Description
`--dataset-path`	synthetic	CSV with numeric feature columns
`--model-path`	auto-download	Path to `.pth` checkpoint
`--config-path`	auto-download	Path to `curriculum_medium.yaml`
`--download-weights`	—	Fetch weights from HF and exit
`--skip-download`	false	Require local weights; skip HF fetch

Fine-tuning

Fine-tune on your own data. The training CSV should contain mostly normal behavior. Validate the pretrained model on your domain before fine-tuning.

uv run python examples/finetune_example.py \
  --csv /path/to/normal_data.csv \
  --val-csv /path/to/val_data.csv \   # optional; otherwise --val-ratio splits --csv
  --pretrained-model final_model.pth \
  --epochs 20 --batch-size 16 --lr 1e-5 \
  --output-dir artifacts/finetune_my_data

Fine-tuning arguments

Argument	Default	Description
`--run-config`	—	JSON/YAML config file generated by AutoMLRunner (`{config_path}`). All fields below can be set here; explicit CLI flags override the file.
`--csv`	required	Training CSV (ideally containing normal behavior). Required if not supplied via `--run-config`.
`--val-csv`	—	Separate validation CSV
`--val-ratio`	`0.3`	Validation fraction when `--val-csv` not used (temporal split)
`--timestamp-col`	`timestamp`	Column to drop from features
`--label-col`	—	Label column to drop
`--drop-cols`	—	Comma-separated extra columns to drop
`--pretrained-model`	`final_model.pth`	Warm-start checkpoint (auto-downloaded if missing)
`--config`	`curriculum_medium.yaml`	Model config YAML
`--repo-id`	`nvidia/Kumo-Anomaly`	HuggingFace repo for auto-download
`--no-download`	false	Fail if pretrained weights are not local
`--epochs`	`10`	Training epochs
`--batch-size`	`16`	Per-GPU batch size
`--lr`	`1e-5`	AdamW learning rate
`--weight-decay`	`1e-6`	AdamW weight decay
`--grad-clip`	`1.0`	Gradient norm clip
`--num-workers`	`0`	DataLoader workers
`--seed`	`42`	Random seed
`--output-dir`	`artifacts/finetune`	Output directory
`--window-length`	config (100)	Sliding window length in timesteps
`--window-stride`	`1`	Step between consecutive windows
`--split`	config (10)	Alternating mask segments per window
`--mask-ratio`	`0.7`	Fraction of each window masked during training
`--scale-factor`	config (1)	Scale multiplier after min-max normalization
`--num-gpus`	all available	Number of GPUs for DDP fine-tuning; set `1` to force single-GPU

Running inference with a fine-tuned checkpoint

results = perform_anomaly_analysis_with_diffusion(
    df=df,
    threshold_strategy="scs",
    model_path="artifacts/finetune_my_data/best_finetuned_model.pth",
    model_config_path="artifacts/finetune_my_data/finetune_config.yaml",
    nsample=15,
)

AutoML (HPO: hyperparameter optimization)

This skill supports AutoML for fine-tuning HPO and labeled inference HPO through tao-skill-bank:tao-run-automl with this model's skill_dir.

Read references/automl.md when the user asks for AutoML/HPO setup, tunable parameters, VirtualEnvSDK setup, config flow, window-length constraints, inference trial scripts, or AutoML result handoff details.

Data requirements

Property	Requirement
Rows	≥ `window_length` (default 100); ≥ `target_dim` (default 18) for PCA
Columns	Must be numeric — the API raises `ValueError` on non-numeric columns; drop `timestamp`, IDs, and labels before calling
Values	No NaN / ±Inf — fill before passing to the API
Feature count > `target_dim`	PCA reduction to `target_dim`; needs ≥ `target_dim` rows
Feature count < `target_dim`	Zero-padded to `target_dim`

target_dim 18 is the config-file default, not a hard constant — when fine-tuning warm-starts from a published checkpoint (--pretrained-model), the checkpoint's embedded config overrides it (the published final_model.pth was trained at target_dim=40). Check the checkpoint's config rather than assuming 18 when reasoning about PCA/padding behavior for warm-started fine-tuning.

timestamp,sensor_1,sensor_2,sensor_3
2024-01-01 00:00:00,0.42,1.10,-0.33
...

Pass only numeric feature columns to the inference API — it raises ValueError on non-numeric columns rather than dropping them. Use df.select_dtypes(include="number") or drop by name before calling. Fine-tuning handles this via --timestamp-col, --label-col, and --drop-cols CLI args.

Output structure

Inference (examples/quick_example.py):

examples/datasets/
└── kumo-anomaly-results.csv # original columns + Anomaly (0/1) + MAE

Fine-tuning (--output-dir artifacts/finetune_my_data):

artifacts/finetune_my_data/
├── best_finetuned_model.pth     # checkpoint with lowest validation loss
├── final_finetuned_model.pth    # checkpoint from last epoch
├── metrics.json                 # scalar for AutoML: {"val_loss": <best>}
├── epoch_metrics.json           # per-epoch log: [{"epoch": N, "train_loss": …, "val_loss": …}]
└── finetune_config.yaml         # config used during training (for reproducibility)

Model configuration (`curriculum_medium.yaml`)

Field	Default	Description
`model.target_dim`	`18`	Internal feature dim; data is PCA'd/padded to this
`dataset.window_length`	`100`	Sliding window size in timesteps
`dataset.split`	`10`	Alternating mask segments per window
`dataset.scale_factor`	`1`	Scale multiplier after min-max normalization
`diffusion.num_steps`	`500`	Full diffusion steps (overridden by DPM-Solver)
`diffusion.channels`	`128`	Model hidden dimension
`diffusion.layers`	`6`	Transformer encoder layers

Hardware

Tier	Setup	Notes
Minimum	1× CPU	Functional; DPM-Solver reduces steps 500 → 20
Recommended	1× NVIDIA GPU (≥8 GB VRAM)	Strongly recommended for fine-tuning
Multi-GPU inference	2–8× NVIDIA GPUs	auto-dispatched by `perform_anomaly_analysis_with_diffusion`
Multi-GPU fine-tuning	2+× NVIDIA GPUs	Auto DDP via `--num-gpus` (defaults to all visible GPUs)

Known pitfalls

Symptom	Cause	Fix
`HfHubHTTPError: 401`	Repo gated or token missing	`export HUGGINGFACE_HUB_TOKEN="hf_..."` or `huggingface-cli login`
`504` / timeout on first weight download	HF CDN throttles unauthenticated requests — public repos are still subject to this on first download	Set `export HUGGINGFACE_HUB_TOKEN="$HF_TOKEN"` before running; authenticated requests use a more reliable CDN path
`ValueError: No numeric columns`	All columns are strings/dates	Drop non-numeric columns before calling API
`ValueError: PCA needs at least target_dim rows`	Fewer rows than `target_dim` (18)	Provide a longer time series
`ValueError: Need at least N rows` (finetune)	Split shorter than `window_length`	Ensure each train/val split has ≥ 100 rows
`RuntimeError: CUDA out of memory`	Batch too large	Reduce `--batch-size` or `nsample`
All MAE scores identical	Constant-value columns	Drop zero-variance columns before calling API
`ModuleNotFoundError: sdk`	Wrong working directory	`cd Kumo-Anomaly/` before `uv run`, or add it to `sys.path`
Slow inference on CPU	Many diffusion windows	Reduce `nsample` to 5–10 for smoke tests

Files

ATTRIBUTION.mdAdded by AutoGineer945 B
BENCHMARK.md7.0 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md13.3 KiB
config/skillspector-baseline.yaml834 B
evals/evals.json1.2 KiB
references/automl.md11.1 KiB
references/skill_info.yaml2.3 KiB
references/spec_template_inference_hpo.yaml181 B
references/spec_template_train.yaml426 B
schemas/inference_hpo.schema.json4.0 KiB
schemas/train.schema.json9.3 KiB
skill-card.md4.3 KiB
skill.oms.sig6.2 KiB

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs a command-line tool, Needs Python

The author says: Requires Python 3.12+ and uv. CUDA GPU recommended; CPU-only supported.

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Attribution and licence

Created by

NVIDIA Corporation

Publisher

NVIDIA (Official)

Source

NVIDIA/skills · skills/tao-finetune-kumo-anomaly · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

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2026-11-08

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