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Project Status: Active – The project has reached a stable, usable state and is being actively developed. Documentation NeMo core license and license for collections in this repo Language grade: Python Total alerts Code style: black

NVIDIA NeMo

Introduction

NVIDIA NeMo is a conversational AI toolkit built for researchers working on automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech synthesis (TTS). The primary objective of NeMo is to help researchers from industry and academia to reuse prior work (code and pretrained models) and make it easier to create new conversational AI models.

Pre-trained NeMo models.

Introductory video.

Key Features

Built for speed, NeMo can utilize NVIDIA's Tensor Cores and scale out training to multiple GPUs and multiple nodes.

Requirements

  1. Python 3.8 or above
  2. Pytorch 1.10.0 or above
  3. NVIDIA GPU for training

Documentation

Version Status Description
Latest Documentation Status Documentation of the latest (i.e. main) branch.
Stable Documentation Status Documentation of the stable (i.e. most recent release) branch.

Tutorials

A great way to start with NeMo is by checking one of our tutorials.

Getting help with NeMo

FAQ can be found on NeMo's Discussions board. You are welcome to ask questions or start discussions there.

Installation

Conda

We recommend installing NeMo in a fresh Conda environment.

conda create --name nemo python==3.8
conda activate nemo

Install PyTorch using their configurator.

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

Note

The command used to install PyTorch may depend on your system.

Pip

Use this installation mode if you want the latest released version.

apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython
pip install nemo_toolkit['all']

Note

Depending on the shell used, you may need to use "nemo_toolkit[all]" instead in the above command.

Pip from source

Use this installation mode if you want the a version from particular GitHub branch (e.g main).

apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython
python -m pip install git+https://github.com/NVIDIA/NeMo.git@{BRANCH}#egg=nemo_toolkit[all]

From source

Use this installation mode if you are contributing to NeMo.

apt-get update && apt-get install -y libsndfile1 ffmpeg
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh

Note

If you only want the toolkit without additional conda-based dependencies, you may replace reinstall.sh with pip install -e . when your PWD is the root of the NeMo repository.

RNNT

Note that RNNT requires numba to be installed from conda.

conda remove numba
pip uninstall numba
conda install -c conda-forge numba

Megatron GPT

Megatron GPT training requires NVIDIA Apex to be installed.

git clone https://github.com/NVIDIA/apex
cd apex
git checkout 3c19f1061879394f28272a99a7ea26d58f72dace
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" ./

Note

You may need to modify [setup.py](https://github.com/NVIDIA/apex/blob/3c19f1061879394f28272a99a7ea26d58f72dace/setup.py) if your version of CUDA does not match the version used to compile Pytorch binaries, comment lines 33-41 in the above link before installing.

Docker containers:

To build a nemo container with Dockerfile from a branch, please run

DOCKER_BUILDKIT=1 docker build -f Dockerfile -t nemo:latest .

If you chose to work with main branch, we recommend using NVIDIA's PyTorch container version 22.07-py3 and then installing from GitHub.

docker run --gpus all -it --rm -v <nemo_github_folder>:/NeMo --shm-size=8g \
-p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \
stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:22.07-py3

Examples

Many examples can be found under "Examples" folder.

Contributing

We welcome community contributions! Please refer to the CONTRIBUTING.md CONTRIBUTING.md for the process.

Publications

We provide an ever growing list of publications that utilize the NeMo framework. Please refer to PUBLICATIONS.md. We welcome the addition of your own articles to this list !

Citation

@article{kuchaiev2019nemo,
  title={Nemo: a toolkit for building ai applications using neural modules},
  author={Kuchaiev, Oleksii and Li, Jason and Nguyen, Huyen and Hrinchuk, Oleksii and Leary, Ryan and Ginsburg, Boris and Kriman, Samuel and Beliaev, Stanislav and Lavrukhin, Vitaly and Cook, Jack and others},
  journal={arXiv preprint arXiv:1909.09577},
  year={2019}
}

License

NeMo is under Apache 2.0 license.

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