NVIDIA NeMo Framework
Latest News
Large Language Models and Multimodal Models
New Llama 3.1 Support
(2024-07-23)
The NeMo Framework now supports training and customizing the Llama 3.1 collection of LLMs from Meta.
Accelerate your Generative AI Distributed Training Workloads with the NVIDIA NeMo Framework on Amazon EKS
(2024-07-16)
NVIDIA NeMo Framework now runs distributed training workloads on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. For step-by-step instructions on creating an EKS cluster and running distributed training workloads with NeMo, see the GitHub repository here.
NVIDIA NeMo Accelerates LLM Innovation with Hybrid State Space Model Support
(2024/06/17)
NVIDIA NeMo and Megatron Core now support pre-training and fine-tuning of state space models (SSMs). NeMo also supports training models based on the Griffin architecture as described by Google DeepMind.
NVIDIA releases 340B base, instruct, and reward models pretrained on a total of 9T tokens.
(2024-06-18)
See documentation and tutorials for SFT, PEFT, and PTQ with
Nemotron 340B
in the NeMo Framework User Guide.
NVIDIA sets new generative AI performance and scale records in MLPerf Training v4.0
(2024/06/12)
Using NVIDIA NeMo Framework and NVIDIA Hopper GPUs NVIDIA was able to scale to 11,616 H100 GPUs and achieve near-linear performance scaling on LLM pretraining.
NVIDIA also achieved the highest LLM fine-tuning performance and raised the bar for text-to-image training.
Accelerate your generative AI journey with NVIDIA NeMo Framework on GKE
(2024/03/16)
An end-to-end walkthrough to train generative AI models on the Google Kubernetes Engine (GKE) using the NVIDIA NeMo Framework is available at https://github.com/GoogleCloudPlatform/nvidia-nemo-on-gke.
The walkthrough includes detailed instructions on how to set up a Google Cloud Project and pre-train a GPT model using the NeMo Framework.
Speech Recognition
New Standard for Speech Recognition and Translation from the NVIDIA NeMo Canary Model
(2024/04/18)
The NeMo team just released Canary, a multilingual model that transcribes speech in English, Spanish, German, and French with punctuation and capitalization.
Canary also provides bi-directional translation, between English and the three other supported languages.
Pushing the Boundaries of Speech Recognition with NVIDIA NeMo Parakeet ASR Models
(2024/04/18)
NVIDIA NeMo, an end-to-end platform for the development of multimodal generative AI models at scale anywhere—on any cloud and on-premises—released the Parakeet family of automatic speech recognition (ASR) models.
These state-of-the-art ASR models, developed in collaboration with Suno.ai, transcribe spoken English with exceptional accuracy.
Turbocharge ASR Accuracy and Speed with NVIDIA NeMo Parakeet-TDT
(2024/04/18)
NVIDIA NeMo, an end-to-end platform for developing multimodal generative AI models at scale anywhere—on any cloud and on-premises—recently released Parakeet-TDT.
This new addition to the NeMo ASR Parakeet model family boasts better accuracy and 64% greater speed over the previously best model, Parakeet-RNNT-1.1B.
Introduction
NVIDIA NeMo Framework is a scalable and cloud-native generative AI
framework built for researchers and PyTorch developers working on Large
Language Models (LLMs), Multimodal Models (MMs), Automatic Speech
Recognition (ASR), Text to Speech (TTS), and Computer Vision (CV)
domains. It is designed to help you efficiently create, customize, and
deploy new generative AI models by leveraging existing code and
pre-trained model checkpoints.
For technical documentation, please see the NeMo Framework User
Guide.
LLMs and MMs Training, Alignment, and Customization
All NeMo models are trained with
Lightning. Training is
automatically scalable to 1000s of GPUs.
When applicable, NeMo models leverage cutting-edge distributed training
techniques, incorporating parallelism
strategies
to enable efficient training of very large models. These techniques
include Tensor Parallelism (TP), Pipeline Parallelism (PP), Fully
Sharded Data Parallelism (FSDP), Mixture-of-Experts (MoE), and Mixed
Precision Training with BFloat16 and FP8, as well as others.
NeMo Transformer-based LLMs and MMs utilize NVIDIA Transformer
Engine for FP8 training on
NVIDIA Hopper GPUs, while leveraging NVIDIA Megatron
Core for
scaling Transformer model training.
NeMo LLMs can be aligned with state-of-the-art methods such as SteerLM,
Direct Preference Optimization (DPO), and Reinforcement Learning from
Human Feedback (RLHF). See NVIDIA NeMo
Aligner for more information.
In addition to supervised fine-tuning (SFT), NeMo also supports the
latest parameter efficient fine-tuning (PEFT) techniques such as LoRA,
P-Tuning, Adapters, and IA3. Refer to the NeMo Framework User
Guide
for the full list of supported models and techniques.
LLMs and MMs Deployment and Optimization
NeMo LLMs and MMs can be deployed and optimized with NVIDIA NeMo
Microservices.
Speech AI
NeMo ASR and TTS models can be optimized for inference and deployed for
production use cases with NVIDIA Riva.
NeMo Framework Launcher
NeMo Framework
Launcher is a
cloud-native tool that streamlines the NeMo Framework experience. It is
used for launching end-to-end NeMo Framework training jobs on CSPs and
Slurm clusters.
The NeMo Framework Launcher includes extensive recipes, scripts,
utilities, and documentation for training NeMo LLMs. It also includes
the NeMo Framework Autoconfigurator,
which is designed to find the optimal model parallel configuration for
training on a specific cluster.
To get started quickly with the NeMo Framework Launcher, please see the
NeMo Framework
Playbooks.
The NeMo Framework Launcher does not currently support ASR and TTS
training, but it will soon.
Get Started with NeMo Framework
Getting started with NeMo Framework is easy. State-of-the-art pretrained
NeMo models are freely available on Hugging Face
Hub
and NVIDIA
NGC.
These models can be used to generate text or images, transcribe audio,
and synthesize speech in just a few lines of code.
We have extensive
tutorials
that can be run on Google Colab or
with our NGC NeMo Framework
Container.
We also have
playbooks
for users who want to train NeMo models with the NeMo Framework
Launcher.
For advanced users who want to train NeMo models from scratch or
fine-tune existing NeMo models, we have a full suite of example
scripts that support
multi-GPU/multi-node training.
Key Features
Requirements
- Python 3.10 or above
- Pytorch 1.13.1 or above
- NVIDIA GPU (if you intend to do model training)
Developer Documentation
Install NeMo Framework
The NeMo Framework can be installed in a variety of ways, depending on
your needs. Depending on the domain, you may find one of the following
installation methods more suitable.
- Conda / Pip - Refer to Conda and Pip for
installation instructions.
- This is the recommended method for ASR and TTS domains.
- When using a Nvidia PyTorch container as the base, this is the
recommended method for all domains.
- Docker Containers - Refer to Docker containers
for installation instructions.
- NeMo Framework container -
[nvcr.io/nvidia/nemo:24.05]{.title-ref}
- LLMs and MMs Dependencies - Refer to LLMs and MMs
Dependencies for installation
instructions.
Important: We strongly recommended that you start with a base NVIDIA
PyTorch container: nvcr.io/nvidia/pytorch:24.02-py3.
Conda
Install NeMo in a fresh Conda environment:
conda create --name nemo python==3.10.12
conda activate nemo
Install PyTorch using their
configurator:
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
The command to install PyTorch may depend on your system. Use the
configurator linked above to find the right command for your system.
Then, install NeMo via Pip or from Source. We do not provide NeMo on the
conda-forge or any other Conda channel.
Pip
To install the nemo_toolkit, use the following installation method:
apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython packaging
pip install nemo_toolkit['all']
Depending on the shell used, you may need to use the
"nemo_toolkit[all]"
specifier instead in the above command.
Pip from a Specific Domain
To install a specific domain of NeMo, you must first install the
nemo_toolkit using the instructions listed above. Then, you run the
following domain-specific commands:
pip install nemo_toolkit['asr']
pip install nemo_toolkit['nlp']
pip install nemo_toolkit['tts']
pip install nemo_toolkit['vision']
pip install nemo_toolkit['multimodal']
Pip from a Source Branch
If you want to work with a specific version of NeMo from a particular
GitHub branch (e.g main), use the following installation method:
apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython packaging
python -m pip install git+https://github.com/NVIDIA/NeMo.git@{BRANCH}
Build from Source
If you want to clone the NeMo GitHub repository and contribute to NeMo
open-source development work, use the following installation method:
apt-get update && apt-get install -y libsndfile1 ffmpeg
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh
If you only want the toolkit without the additional Conda-based
dependencies, you can replace reinstall.sh
with pip install -e .
when your PWD is the root of the NeMo repository.
Mac Computers with Apple Silicon
To install NeMo on Mac computers with the Apple M-Series GPU, you need
to create a new Conda environment, install PyTorch 2.0 or higher, and
then install the nemo_toolkit.
Important: This method is only applicable to the ASR domain.
Run the following code:
# [optional] install mecab using Homebrew, to use sacrebleu for NLP collection
# you can install Homebrew here: https://brew.sh
brew install mecab
# [optional] install pynini using Conda, to use text normalization
conda install -c conda-forge pynini
# install Cython manually
pip install cython packaging
# clone the repo and install in development mode
git clone https://github.com/NVIDIA/NeMo
cd NeMo
pip install 'nemo_toolkit[all]'
# Note that only the ASR toolkit is guaranteed to work on MacBook - so for MacBook use pip install 'nemo_toolkit[asr]'
Windows Computers
To install the Windows Subsystem for Linux (WSL), run the following code
in PowerShell:
wsl --install
# [note] If you run wsl --install and see the WSL help text, it means WSL is already installed.
To learn more about installing WSL, refer to Microsoft's official
documentation.
After installing your Linux distribution with WSL, two options are
available:
Option 1: Open the distribution (Ubuntu by default) from the Start
menu and follow the instructions.
Option 2: Launch the Terminal application. Download it from
Microsoft's Windows Terminal
page if not
installed.
Next, follow the instructions for Linux systems, as provided above. For
example:
apt-get update && apt-get install -y libsndfile1 ffmpeg
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh
RNNT
For optimal performance of a Recurrent Neural Network Transducer (RNNT),
install the Numba package from Conda.
Run the following code:
conda remove numba
pip uninstall numba
conda install -c conda-forge numba
Install LLMs and MMs Dependencies
If you work with the LLM and MM domains, three additional dependencies
are required: NVIDIA Apex, NVIDIA Transformer Engine, and NVIDIA
Megatron Core. When working with the [main]{.title-ref} branch, these
dependencies may require a recent commit.
The most recent working versions of these dependencies are here:
export apex_commit=810ffae374a2b9cb4b5c5e28eaeca7d7998fca0c
export te_commit=bfe21c3d68b0a9951e5716fb520045db53419c5e
export mcore_commit=02871b4df8c69fac687ab6676c4246e936ce92d0
export nv_pytorch_tag=24.02-py3
When using a released version of NeMo, please refer to the Software
Component
Versions
for the correct versions.
PyTorch Container
We recommended that you start with a base NVIDIA PyTorch container:
nvcr.io/nvidia/pytorch:24.02-py3.
If starting with a base NVIDIA PyTorch container, you must first launch
the container:
docker run \
--gpus all \
-it \
--rm \
--shm-size=16g \
--ulimit memlock=-1 \
--ulimit stack=67108864 \
nvcr.io/nvidia/pytorch:$nv_pytorch_tag
Next, you need to install the dependencies.
Apex
NVIDIA Apex is required for LLM and MM domains. Although Apex is
pre-installed in the NVIDIA PyTorch container, you may need to update it
to a newer version.
To install Apex, run the following code:
git clone https://github.com/NVIDIA/apex.git
cd apex
git checkout $apex_commit
pip install . -v --no-build-isolation --disable-pip-version-check --no-cache-dir --config-settings "--build-option=--cpp_ext --cuda_ext --fast_layer_norm --distributed_adam --deprecated_fused_adam --group_norm"
When attempting to install Apex separately from the NVIDIA PyTorch
container, you might encounter an error if the CUDA version on your
system is different from the one used to compile PyTorch. To bypass this
error, you can comment out the relevant line in the setup file located
in the Apex repository on GitHub here:
https://github.com/NVIDIA/apex/blob/master/setup.py#L32.
cuda-nvprof is needed to install Apex. The version should match the CUDA
version that you are using.
To install cuda-nvprof, run the following code:
conda install -c nvidia cuda-nvprof=11.8
Finally, install the packaging:
pip install packaging
To install the most recent versions of Apex locally, it might be
necessary to remove the [pyproject.toml]{.title-ref} file from the Apex
directory.
Transformer Engine
NVIDIA Transformer Engine is required for LLM and MM domains. Although
the Transformer Engine is pre-installed in the NVIDIA PyTorch container,
you may need to update it to a newer version.
The Transformer Engine facilitates training with FP8 precision on NVIDIA
Hopper GPUs and introduces many enhancements for the training of
Transformer-based models. Refer to Transformer Engine
for information.
To install Transformer Engine, run the following code:
git clone https://github.com/NVIDIA/TransformerEngine.git && \
cd TransformerEngine && \
git checkout $te_commit && \
git submodule init && git submodule update && \
NVTE_FRAMEWORK=pytorch NVTE_WITH_USERBUFFERS=1 MPI_HOME=/usr/local/mpi pip install .
Transformer Engine requires PyTorch to be built with at least CUDA 11.8.
Megatron Core
Megatron Core is required for LLM and MM domains. Megatron Core is a
library for scaling large Transformer-based models. NeMo LLMs and MMs
leverage Megatron Core for model parallelism, transformer architectures,
and optimized PyTorch datasets.
To install Megatron Core, run the following code:
git clone https://github.com/NVIDIA/Megatron-LM.git && \
cd Megatron-LM && \
git checkout $mcore_commit && \
pip install . && \
cd megatron/core/datasets && \
make
NeMo Text Processing
NeMo Text Processing, specifically Inverse Text Normalization, is now a
separate repository. It is located here:
https://github.com/NVIDIA/NeMo-text-processing.
Docker Containers
NeMo containers are launched concurrently with NeMo version updates.
NeMo Framework now supports LLMs, MMs, ASR, and TTS in a single
consolidated Docker container. You can find additional information about
released containers on the NeMo releases
page.
To use a pre-built container, run the following code:
docker pull nvcr.io/nvidia/nemo:24.05
To build a nemo container with Dockerfile from a branch, run the
following code:
DOCKER_BUILDKIT=1 docker build -f Dockerfile -t nemo:latest
If you choose to work with the main branch, we recommend using NVIDIA's
PyTorch container version 23.10-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:23.10-py3
Future Work
The NeMo Framework Launcher does not currently support ASR and TTS
training, but it will soon.
Discussions Board
FAQ can be found on the NeMo Discussions
board. You are welcome to
ask questions or start discussions on the board.
Contribute to NeMo
We welcome community contributions! Please refer to
CONTRIBUTING.md
for the process.
Publications
We provide an ever-growing list of
publications that utilize
the NeMo Framework.
To contribute an article to the collection, please submit a pull request
to the gh-pages-src
branch of this repository. For detailed
information, please consult the README located at the gh-pages-src
branch.
Blogs
Large Language Models and Multimodal Models
Bria Builds Responsible Generative AI for Enterprises Using NVIDIA NeMo, Picasso
(2024/03/06)
Bria, a Tel Aviv startup at the forefront of visual generative AI for enterprises now leverages the NVIDIA NeMo Framework.
The Bria.ai platform uses reference implementations from the NeMo Multimodal collection, trained on NVIDIA Tensor Core GPUs, to enable high-throughput and low-latency image generation.
Bria has also adopted NVIDIA Picasso, a foundry for visual generative AI models, to run inference.
New NVIDIA NeMo Framework Features and NVIDIA H200
(2023/12/06)
NVIDIA NeMo Framework now includes several optimizations and enhancements,
including:
1) Fully Sharded Data Parallelism (FSDP) to improve the efficiency of training large-scale AI models,
2) Mix of Experts (MoE)-based LLM architectures with expert parallelism for efficient LLM training at scale,
3) Reinforcement Learning from Human Feedback (RLHF) with TensorRT-LLM for inference stage acceleration, and
4) up to 4.2x speedups for Llama 2 pre-training on NVIDIA H200 Tensor Core GPUs.
NVIDIA now powers training for Amazon Titan Foundation models
(2023/11/28)
NVIDIA NeMo Framework now empowers the Amazon Titan foundation models (FM) with efficient training of large language models (LLMs).
The Titan FMs form the basis of Amazon’s generative AI service, Amazon Bedrock.
The NeMo Framework provides a versatile framework for building, customizing, and running LLMs.
Licenses