Installation Guide for NeMo Curator

Install NeMo Curator for text, image, video, and audio workflows, then verify the modules and GPU support your pipeline needs. For a single-modality path, start with a modality quickstart.

Prerequisites

Before installing, prepare a supported Python environment and the hardware required by your selected extras:

1. Use Ubuntu 20.04, 22.04, or 24.04 for the recommended Linux setup.
2. Install Python 3.11, 3.12, or 3.13.
3. Provide at least 16 GB RAM for basic text processing; GPU acceleration is optional and benefits from an NVIDIA GPU with 16 GB or more VRAM.
4. Install CUDA 12 and a compatible NVIDIA driver when using a GPU extra.

Development and Production Environments

Use the deployment guides to plan resources for production or multi-node clusters:

Deployment Paths

Installation Methods

For most workflows, install NeMo Curator from PyPI when the package version you need is available. Use a source checkout for development or an unpublished release. Audio and video workflows are easiest to reproduce in a locally built container, where you can include the required FFmpeg system packages on every worker.

Choose an installation method based on your environment:

Package Extras

Use installation extras to add only the components your pipeline needs. The following table lists the available extras and install commands.

Available Package Extras

Optional OpenCV Support

The base NeMo Curator installation does not install OpenCV. The cv2 extra installs opencv-python-headless for features that decode or transform images with OpenCV. Install it in the same environment as Curator when you use one of these features:

• interleaved image decoding, InterleavedBlurFilterStage, InterleavedQRCodeFilterStage, or InterleavedCLIPScoreFilterStage;
• Nemotron-Parse PDF postprocessing; or
• video target-resolution frame resizing or motion-vector filtering.

For a PyPI installation, run:

uv pip install "nemo-curator[cv2]"

For a source checkout, run:

uv sync --extra cv2

The cv2 extra is intentionally separate from the all extra. This keeps OpenCV’s bundled media dependencies out of installations that do not use OpenCV-gated features. The extra does not install or configure the ffmpeg command-line tool. Video and audio workflows that call FFmpeg still require the FFmpeg setup described above.

Most OpenCV-gated features raise an ImportError that identifies the missing opencv-python-headless dependency and suggests installing nemo_curator[cv2]. Nemotron-Parse PDF processing instead logs a warning and skips the affected PDF when OpenCV is unavailable. To verify the active environment, run:

python -c "import cv2; print(cv2.__version__)"

When you install inference_server or an extra that includes it with pip, add the public PyTorch and vLLM CUDA 12.9 wheel indexes:

python -m pip install \
  --extra-index-url https://download.pytorch.org/whl/cu129 \
  --extra-index-url https://wheels.vllm.ai/0.22.0/cu129 \
  "nemo-curator[inference_server]"

All remaining dependencies, including Dynamo, NIXL, and RAPIDS packages, are resolved from public PyPI.

For text_cuda12, download Curator’s override file and use uv pip install with the CUDA 12.9 wheel source:

curl -O https://raw.githubusercontent.com/NVIDIA-NeMo/Curator/main/requirements/text_cuda12-overrides.txt
uv pip install \
  --override text_cuda12-overrides.txt \
  --torch-backend cu129 \
  --extra-index-url https://wheels.vllm.ai/0.22.0/cu129 \
  "nemo-curator[text_cuda12]"

Additional Setup

Install external tools that your selected stages require.

Install FFmpeg for Python Package Installs

Some Curator audio and video stages use the ffmpeg executable for resampling, decoding, encoding, or metadata extraction. Python extras do not install it. For audio and video workflows, the container guide shows how to package FFmpeg in an image used by every worker. For Python package installs, verify that ffmpeg is on PATH on every executor node, or install it using one of these options.

conda install -c conda-forge ffmpeg
command -v ffmpeg
ffmpeg -version

Advanced Software Codec Support

Curator’s strict FFmpeg build routes H.264/HEVC/AV1 decode through NVDEC and excludes software H.264 encoders by default. You can add software codec support when CPU-only stages run ffprobe on H.264/HEVC/AV1 inputs or when you use --transcode-encoder=libopenh264.

Inside a container, run:

# Add software h264/hevc/av1 decoders only
bash /opt/Curator/docker/common/install_h264_support.sh
# Add those decoders plus the libopenh264 software h264 encoder
bash /opt/Curator/docker/common/install_h264_support.sh --with-libopenh264

Installation Verification

After installation, run an import check. A successful check prints the installed version and confirms core modules can be imported:

1. Import NeMo Curator and core pipeline modules.

   import nemo_curator
   print(f"NeMo Curator version: {nemo_curator.__version__}")
   from nemo_curator.pipeline import Pipeline
   from nemo_curator.tasks import DocumentBatch
   print("Core modules imported successfully")

2. If you installed GPU support, check that the driver and GPU modules are available. A GPU-enabled environment prints the device and cuDF confirmation. A CPU-only environment can report that no GPU was detected.

   try:
       import torch
       if torch.cuda.is_available():
           print(f"GPU available: {torch.cuda.get_device_name(0)}")
           print(f"GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
       else:
           print("No GPU detected")
       import cudf
       print("cuDF available for GPU-accelerated deduplication")
   except ImportError as e:
       print(f"Some GPU modules not available: {e}")

Next Steps

Run a modality quickstart or review the deployment guidance for your target environment: