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Signed-off-by: Oliver Koenig <okoenig@nvidia.com>
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# **NVIDIA NeMo Framework**

## Latest News
<!-- markdownlint-disable -->
<details open>
<summary><b>Large Language Models and Multimodal</b></summary>
<details>
<summary>
<a href="https://huggingface.co/models?sort=trending&search=nvidia%2Fnemotron-4-340B">
NVIDIA releases 340B base, instruct, and reward models pretrained on a total of 9T tokens.
</a> (2024-06-18)
</summary>
See documentation and tutorials for SFT, PEFT, and PTQ with
<a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/nemotron/index.html">
Nemotron 340B
</a>
in the NeMo Framework User Guide.
<br><br>
</details>

<details>
<summary>
<a href="https://developer.nvidia.com/blog/nvidia-sets-new-generative-ai-performance-and-scale-records-in-mlperf-training-v4-0/">
NVIDIA sets new generative AI performance and scale records in MLPerf Training v4.0
</a> (2024/06/12)
</summary>

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.
<br><br>
</details>

<details>
<summary>
<a href="https://cloud.google.com/blog/products/compute/gke-and-nvidia-nemo-framework-to-train-generative-ai-models">
Accelerate your generative AI journey with NVIDIA NeMo Framework on GKE
</a> (2024/03/16)
</summary>

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.
<br><br>
</details>

<details>
<summary>
<a href="https://blogs.nvidia.com/blog/bria-builds-responsible-generative-ai-using-nemo-picasso/">
Bria Builds Responsible Generative AI for Enterprises Using NVIDIA NeMo, Picasso
</a> (2024/03/06)
</summary>

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.
<br><br>
</details>

<details>
<summary>
<a href="https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/">
New NVIDIA NeMo Framework Features and NVIDIA H200
</a> (2023/12/06)
</summary>

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.
<br><br>
<a href="https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility">
<img src="https://github.com/sbhavani/TransformerEngine/blob/main/docs/examples/H200-NeMo-performance.png" alt="H200-NeMo-performance" style="width: 600px;"></a>
<br><br>
</details>

<details>
<summary>
<a href="https://blogs.nvidia.com/blog/nemo-amazon-titan/">
NVIDIA now powers training for Amazon Titan Foundation models
</a> (2023/11/28)
</summary>

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.
<br><br>
</details>

</details>

<details open>
<summary><b>Speech Recognition</b></summary>
<details>
<summary>
<a href="https://developer.nvidia.com/blog/new-standard-for-speech-recognition-and-translation-from-the-nvidia-nemo-canary-model/">
New Standard for Speech Recognition and Translation from the NVIDIA NeMo Canary Model
</a> (2024/04/18)
</summary>

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.
<br><br>
</details>

<details>
<summary>
<a href="https://developer.nvidia.com/blog/pushing-the-boundaries-of-speech-recognition-with-nemo-parakeet-asr-models/">
Pushing the Boundaries of Speech Recognition with NVIDIA NeMo Parakeet ASR Models
</a> (2024/04/18)
</summary>

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.
<br><br>
</details>

<details>
<summary>
<a href="https://developer.nvidia.com/blog/turbocharge-asr-accuracy-and-speed-with-nvidia-nemo-parakeet-tdt/">
Turbocharge ASR Accuracy and Speed with NVIDIA NeMo Parakeet-TDT
</a> (2024/04/18)
</summary>

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.
<br><br>
</details>

</details>
<!-- markdownlint-enable -->

## Introduction

NVIDIA NeMo Framework is a scalable and cloud-native generative AI
Expand Down Expand Up @@ -155,15 +285,15 @@ PyTorch container: nvcr.io/nvidia/pytorch:24.02-py3.**

Install NeMo in a fresh Conda environment:

``` bash
```bash
conda create --name nemo python==3.10.12
conda activate nemo
```

Install PyTorch using their
[configurator](https://pytorch.org/get-started/locally/):

``` bash
```bash
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
```

Expand All @@ -177,7 +307,7 @@ conda-forge or any other Conda channel.

To install the nemo_toolkit, use the following installation method:

``` bash
```bash
apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython packaging
pip install nemo_toolkit['all']
Expand All @@ -192,7 +322,7 @@ 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:

``` bash
```bash
pip install nemo_toolkit['asr']
pip install nemo_toolkit['nlp']
pip install nemo_toolkit['tts']
Expand All @@ -205,7 +335,7 @@ pip install nemo_toolkit['multimodal']
If you want to work with a specific version of NeMo from a particular
GitHub branch (e.g main), use the following installation method:

``` bash
```bash
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}#egg=nemo_toolkit[all]
Expand All @@ -216,7 +346,7 @@ python -m pip install git+https://github.com/NVIDIA/NeMo.git@{BRANCH}#egg=nemo_t
If you want to clone the NeMo GitHub repository and contribute to NeMo
open-source development work, use the following installation method:

``` bash
```bash
apt-get update && apt-get install -y libsndfile1 ffmpeg
git clone https://github.com/NVIDIA/NeMo
cd NeMo
Expand All @@ -237,7 +367,7 @@ then install the nemo_toolkit.

Run the following code:

``` shell
```shell
# [optional] install mecab using Homebrew, to use sacrebleu for NLP collection
# you can install Homebrew here: https://brew.sh
brew install mecab
Expand All @@ -261,7 +391,7 @@ pip install 'nemo_toolkit[all]'
To install the Windows Subsystem for Linux (WSL), run the following code
in PowerShell:

``` shell
```shell
wsl --install
# [note] If you run wsl --install and see the WSL help text, it means WSL is already installed.
```
Expand All @@ -283,7 +413,7 @@ installed.
Next, follow the instructions for Linux systems, as provided above. For
example:

``` bash
```bash
apt-get update && apt-get install -y libsndfile1 ffmpeg
git clone https://github.com/NVIDIA/NeMo
cd NeMo
Expand All @@ -297,7 +427,7 @@ install the Numba package from Conda.

Run the following code:

``` bash
```bash
conda remove numba
pip uninstall numba
conda install -c conda-forge numba
Expand All @@ -312,7 +442,7 @@ dependencies may require a recent commit.

The most recent working versions of these dependencies are here:

``` bash
```bash
export apex_commit=810ffae374a2b9cb4b5c5e28eaeca7d7998fca0c
export te_commit=bfe21c3d68b0a9951e5716fb520045db53419c5e
export mcore_commit=02871b4df8c69fac687ab6676c4246e936ce92d0
Expand All @@ -332,7 +462,7 @@ nvcr.io/nvidia/pytorch:24.02-py3.
If starting with a base NVIDIA PyTorch container, you must first launch
the container:

``` bash
```bash
docker run \
--gpus all \
-it \
Expand All @@ -353,7 +483,7 @@ to a newer version.

To install Apex, run the following code:

``` bash
```bash
git clone https://github.com/NVIDIA/apex.git
cd apex
git checkout $apex_commit
Expand All @@ -372,13 +502,13 @@ version that you are using.

To install cuda-nvprof, run the following code:

``` bash
```bash
conda install -c nvidia cuda-nvprof=11.8
```

Finally, install the packaging:

``` bash
```bash
pip install packaging
```

Expand All @@ -399,7 +529,7 @@ for information.

To install Transformer Engine, run the following code:

``` bash
```bash
git clone https://github.com/NVIDIA/TransformerEngine.git && \
cd TransformerEngine && \
git checkout $te_commit && \
Expand All @@ -418,7 +548,7 @@ and optimized PyTorch datasets.

To install Megatron Core, run the following code:

``` bash
```bash
git clone https://github.com/NVIDIA/Megatron-LM.git && \
cd Megatron-LM && \
git checkout $mcore_commit && \
Expand All @@ -443,21 +573,21 @@ page](https://github.com/NVIDIA/NeMo/releases).

To use a pre-built container, run the following code:

``` bash
```bash
docker pull nvcr.io/nvidia/nemo:24.05
```

To build a nemo container with Dockerfile from a branch, run the
following code:

``` bash
```bash
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.

``` bash
```bash
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
Expand Down

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