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
