NV-Tesseract AD Diffusion

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/NV-Tesseract Pretrained weights: https://huggingface.co/nvidia/nv-tesseract-ad-diffusion

For the most up-to-date usage information, refer to the README files in the NV-Tesseract repository:

• ad_diffusion/README.md — full SDK reference, model architecture, and API docs
• ad_diffusion/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/nv-tesseract-ad-diffusion 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/NV-Tesseract
cd NV-Tesseract/ad_diffusion
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/NV-Tesseract/ad_diffusion")  # clone NV-Tesseract 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
    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())

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/nv-tesseract-ad-diffusion	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",
    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

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/
└── 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 ad_diffusion/ before uv run, or add it to sys.path
Slow inference on CPU	Many diffusion windows	Reduce nsample to 5–10 for smoke tests