Curate LLM training data: dedupe, filter, PII redaction.
Skill metadata
| Source | Optional — install with hermes skills install official/mlops/nemo-curator |
| Path | optional-skills/mlops/nemo-curator |
| Version | 1.0.1 |
| Author | Orchestra Research |
| License | MIT |
| Dependencies | nemo-curator, cudf, dask, rapids |
| Platforms | linux, macos |
| Tags | Data Processing, NeMo Curator, Data Curation, GPU Acceleration, Deduplication, Quality Filtering, NVIDIA, RAPIDS, PII Redaction, Multimodal, LLM Training Data |
Reference: full SKILL.md
info
The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
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
# NeMo Curator 1.x installs with uv. Extras use hyphens (PyPI-normalized):
# text-cuda12 / text-cpu (and image/video/audio/math variants), or `all`.
# Text curation (CUDA 12)
uv pip install "nemo-curator[text-cuda12]"
# All modalities
uv pip install "nemo-curator[all]"
# CPU-only text (slower)
uv pip install "nemo-curator[text-cpu]"
Basic text curation pipeline
Major version rewrite (1.x): NeMo Curator was rewritten around a Ray-based pipeline/stage architecture. The old
DocumentDataset+nemo_curator.modules.*/ScoreFilter/Modifycall-the-object-on-a-dataset API from 0.x is gone. In 1.x you composeProcessingStages into aPipelineand run it with an executor. The exact stage/import surface differs per modality — treat the examples in this skill below as conceptual (0.x-style) and follow the current quickstart and text guide for the exact 1.x APIs rather than copying imports verbatim.
Shape of a 1.x pipeline (from the upstream quickstart):
from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.base import ProcessingStage
from nemo_curator.stages.resources import Resources
from nemo_curator.backends.xenna import XennaExecutor
from nemo_curator.core.client import RayClient
# 1. Define/compose stages (load -> filter -> dedupe -> classify -> write).
# Each stage declares its own Resources (CPU cores, GPU memory, replicas).
pipeline = Pipeline(name="curation", stages=[...])
# 2. Run it with an executor (Ray-backed).
client = RayClient()
client.start()
pipeline.run(XennaExecutor())
client.stop()
The 0.x-style snippets in the sections that follow illustrate the concepts (quality filtering, exact/fuzzy/semantic dedup, PII redaction, classifier filtering). For runnable 1.x code, map each concept onto the corresponding stage from the modality guide.
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
- Docs: https://docs.nvidia.com/nemo/curator/latest/
- Version: 1.2.0 (1.x is a Ray-based pipeline rewrite — see the quickstart before copying 0.x snippets)
- License: Apache 2.0