SKILL.md

NeMo Curator - GPU-Accelerated Data Curation

NVIDIA's toolkit for preparing high-quality training data for LLMs.

When to use NeMo Curator

Use NeMo Curator when:

Preparing LLM training data from web scrapes (Common Crawl)

Need fast deduplication (16× faster than CPU)

Curating multi-modal datasets (text, images, video, audio)

Filtering low-quality or toxic content

Scaling data processing across GPU cluster

Performance:

16× faster fuzzy deduplication (8TB RedPajama v2)

40% lower TCO vs CPU alternatives

Near-linear scaling across GPU nodes

Use alternatives instead:

datatrove: CPU-based, open-source data processing

dolma: Allen AI's data toolkit

Ray Data: General ML data processing (no curation focus)

Quick start

Installation

# Text curation (CUDA 12)

uv pip install "nemo-curator[text_cuda12]"

# All modalities

uv pip install "nemo-curator[all_cuda12]"

# CPU-only (slower)

uv pip install "nemo-curator[cpu]"

Basic text curation pipeline

from nemo_curator import ScoreFilter, Modify

from nemo_curator.datasets import DocumentDataset

import pandas as pd

# Load data

df = pd.DataFrame({"text": ["Good document", "Bad doc", "Excellent text"]})

dataset = DocumentDataset(df)

# Quality filtering

def quality_score(doc):

    return len(doc["text"].split()) > 5  # Filter short docs

filtered = ScoreFilter(quality_score)(dataset)

# Deduplication

from nemo_curator.modules import ExactDuplicates

deduped = ExactDuplicates()(filtered)

# Save

deduped.to_parquet("curated_data/")

Data curation pipeline

Stage 1: Quality filtering

from nemo_curator.filters import (

    WordCountFilter,

    RepeatedLinesFilter,

    UrlRatioFilter,

    NonAlphaNumericFilter

)

# Apply 30+ heuristic filters

from nemo_curator import ScoreFilter

# Word count filter

dataset = dataset.filter(WordCountFilter(min_words=50, max_words=100000))

# Remove repetitive content

dataset = dataset.filter(RepeatedLinesFilter(max_repeated_line_fraction=0.3))

# URL ratio filter

dataset = dataset.filter(UrlRatioFilter(max_url_ratio=0.2))

Stage 2: Deduplication

Exact deduplication:

from nemo_curator.modules import ExactDuplicates

# Remove exact duplicates

deduped = ExactDuplicates(id_field="id", text_field="text")(dataset)

Fuzzy deduplication (16× faster on GPU):

from nemo_curator.modules import FuzzyDuplicates

# MinHash + LSH deduplication

fuzzy_dedup = FuzzyDuplicates(

    id_field="id",

    text_field="text",

    num_hashes=260,      # MinHash parameters

    num_buckets=20,

    hash_method="md5"

)

deduped = fuzzy_dedup(dataset)

Semantic deduplication:

from nemo_curator.modules import SemanticDuplicates

# Embedding-based deduplication

semantic_dedup = SemanticDuplicates(

    id_field="id",

    text_field="text",

    embedding_model="sentence-transformers/all-MiniLM-L6-v2",

    threshold=0.8  # Cosine similarity threshold

)

deduped = semantic_dedup(dataset)

Stage 3: PII redaction

from nemo_curator.modules import Modify

from nemo_curator.modifiers import PIIRedactor

# Redact personally identifiable information

pii_redactor = PIIRedactor(

    supported_entities=["EMAIL_ADDRESS", "PHONE_NUMBER", "PERSON", "LOCATION"],

    anonymize_action="replace"  # or "redact"

)

redacted = Modify(pii_redactor)(dataset)

Stage 4: Classifier filtering

from nemo_curator.classifiers import QualityClassifier

# Quality classification

quality_clf = QualityClassifier(

    model_path="nvidia/quality-classifier-deberta",

    batch_size=256,

    device="cuda"

)

# Filter low-quality documents

high_quality = dataset.filter(lambda doc: quality_clf(doc["text"]) > 0.5)

GPU acceleration

GPU vs CPU performance

Operation

CPU (16 cores)

GPU (A100)

Speedup

Fuzzy dedup (8TB)

120 hours

7.5 hours

16×

Exact dedup (1TB)

8 hours

0.5 hours

16×

Quality filtering

2 hours

0.2 hours

10×

Multi-GPU scaling

from nemo_curator import get_client

import dask_cuda

# Initialize GPU cluster

client = get_client(cluster_type="gpu", n_workers=8)

# Process with 8 GPUs

deduped = FuzzyDuplicates(...)(dataset)

Multi-modal curation

Image curation

from nemo_curator.image import (

    AestheticFilter,

    NSFWFilter,

    CLIPEmbedder

)

# Aesthetic scoring

aesthetic_filter = AestheticFilter(threshold=5.0)

filtered_images = aesthetic_filter(image_dataset)

# NSFW detection

nsfw_filter = NSFWFilter(threshold=0.9)

safe_images = nsfw_filter(filtered_images)

# Generate CLIP embeddings

clip_embedder = CLIPEmbedder(model="openai/clip-vit-base-patch32")

image_embeddings = clip_embedder(safe_images)

Video curation

from nemo_curator.video import (

    SceneDetector,

    ClipExtractor,

    InternVideo2Embedder

)

# Detect scenes

scene_detector = SceneDetector(threshold=27.0)

scenes = scene_detector(video_dataset)

# Extract clips

clip_extractor = ClipExtractor(min_duration=2.0, max_duration=10.0)

clips = clip_extractor(scenes)

# Generate embeddings

video_embedder = InternVideo2Embedder()

video_embeddings = video_embedder(clips)

Audio curation

from nemo_curator.audio import (

    ASRInference,

    WERFilter,

    DurationFilter

)

# ASR transcription

asr = ASRInference(model="nvidia/stt_en_fastconformer_hybrid_large_pc")

transcribed = asr(audio_dataset)

# Filter by WER (word error rate)

wer_filter = WERFilter(max_wer=0.3)

high_quality_audio = wer_filter(transcribed)

# Duration filtering

duration_filter = DurationFilter(min_duration=1.0, max_duration=30.0)

filtered_audio = duration_filter(high_quality_audio)

Common patterns

Web scrape curation (Common Crawl)

from nemo_curator import ScoreFilter, Modify

from nemo_curator.filters import *

from nemo_curator.modules import *

from nemo_curator.datasets import DocumentDataset

# Load Common Crawl data

dataset = DocumentDataset.read_parquet("common_crawl/*.parquet")

# Pipeline

pipeline = [

    # 1. Quality filtering

    WordCountFilter(min_words=100, max_words=50000),

    RepeatedLinesFilter(max_repeated_line_fraction=0.2),

    SymbolToWordRatioFilter(max_symbol_to_word_ratio=0.3),

    UrlRatioFilter(max_url_ratio=0.3),

    # 2. Language filtering

    LanguageIdentificationFilter(target_languages=["en"]),

    # 3. Deduplication

    ExactDuplicates(id_field="id", text_field="text"),

    FuzzyDuplicates(id_field="id", text_field="text", num_hashes=260),

    # 4. PII redaction

    PIIRedactor(),

    # 5. NSFW filtering

    NSFWClassifier(threshold=0.8)

]

# Execute

for stage in pipeline:

    dataset = stage(dataset)

# Save

dataset.to_parquet("curated_common_crawl/")

Distributed processing

from nemo_curator import get_client

from dask_cuda import LocalCUDACluster

# Multi-GPU cluster

cluster = LocalCUDACluster(n_workers=8)

client = get_client(cluster=cluster)

# Process large dataset

dataset = DocumentDataset.read_parquet("s3://large_dataset/*.parquet")

deduped = FuzzyDuplicates(...)(dataset)

# Cleanup

client.close()

cluster.close()

Performance benchmarks

Fuzzy deduplication (8TB RedPajama v2)

CPU (256 cores): 120 hours

GPU (8× A100): 7.5 hours

Speedup: 16×

Exact deduplication (1TB)

CPU (64 cores): 8 hours

GPU (4× A100): 0.5 hours

Speedup: 16×

Quality filtering (100GB)

CPU (32 cores): 2 hours

GPU (2× A100): 0.2 hours

Speedup: 10×

Cost comparison

CPU-based curation (AWS c5.18xlarge × 10):

Cost: $3.60/hour × 10 = $36/hour

Time for 8TB: 120 hours

Total: $4,320

GPU-based curation (AWS p4d.24xlarge × 2):

Cost: $32.77/hour × 2 = $65.54/hour

Time for 8TB: 7.5 hours

Total: $491.55

Savings: 89% reduction ($3,828 saved)

Supported data formats

Input: Parquet, JSONL, CSV

Output: Parquet (recommended), JSONL

WebDataset: TAR archives for multi-modal

Use cases

Production deployments:

NVIDIA used NeMo Curator to prepare Nemotron-4 training data

Open-source datasets curated: RedPajama v2, The Pile

References

Filtering Guide - 30+ quality filters, heuristics

Deduplication Guide - Exact, fuzzy, semantic methods

Resources

GitHub: https://github.com/NVIDIA/NeMo-Curator ⭐ 500+

Docs: https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/

Version: 0.4.0+

License: Apache 2.0

nemo-curator