| Name | megatron-bridge JSON |
| Version |
0.1.0rc0
JSON |
| download |
| home_page | None |
| Summary | Megatron Bridge: Training Recipes for Megatron-based LLM and VLM models |
| upload_time | 2025-08-16 00:22:29 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
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| keywords |
nlp
nemo
deep
gpu
language
learning
machine
nvidia
pytorch
speech
torch
tts
|
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<div align="center">
# Megatron Bridge
<!-- [](https://codecov.io/github/NVIDIA-NeMo/Megatron-Hub) -->
[](https://github.com/NVIDIA-NeMo/Megatron-Bridge/actions/workflows/cicd-main.yml)
[](https://www.python.org/downloads/release/python-3100/)
[](https://github.com/NVIDIA-NeMo/Megatron-Bridge/stargazers/)
[Recipes](#supported-models) | [Examples](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/examples) | [Contributing](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/CONTRIBUTING.md)
</div>
## Overview
Megatron Bridge is a PyTorch native library under [NeMo Framework](https://github.com/NVIDIA-NeMo) that leverages [megatron-core](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core) to provide state-of-the-art training throughput for top models. It enables researchers and community developers to do both pre and post training using a performant and scalable training loop, with features like model parallelisms and mixed precisions (FP8, BF16, FP4 etc.). Megatron Bridge users can either leverage existing 🤗HuggingFace models or define their custom PyTorch model definitions for end-to-end workflows with flexibility.
## 🔧 Installation
### 🐳 NeMo-FW container
Best experience, highest performance and full feature support is guaranteed by the [NeMo Framework container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags). Please fetch the most recent $TAG and run the following command to start a container:
```bash
docker run --rm -it -w /workdir -v $(pwd):/workdir \
--entrypoint bash \
--gpus all \
nvcr.io/nvidia/nemo:${TAG}
```
### 📦 Bare metal install with TransformerEngine
TransformerEngine is a required dependency for Megatron Bridge. To install on bare metal (without any container), the following system requirements need to be fulfilled:
- PyTorch >= 2.7
- CUDA >= 12.8
- cuDNN >= 9.3
We recommend installing the same versions that are present in the latest NGC PyTorch containers. The versions of these components for each container release can be found in the [PyTorch](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html) and [CUDA](https://docs.nvidia.com/deeplearning/frameworks/cuda-dl-release-notes/index.html) container release notes.
Please see these [instructions](https://developer.nvidia.com/cudnn-downloads) for installing cuDNN for your target platform. You can check if CUDA toolkit and cuDNN are installed with:
```bash
dpkg -l | grep 'cuda-toolkit'
dpkg -l | grep 'cudnn.*cuda'
```
You can then run the following to install Megatron Bridge:
```bash
pip install torch setuptools pybind11 wheel_stub # Required for TE
pip install --no-build-isolation megatron-bridge
```
### uv
For installing Megatron Bridge with uv, please refer to our [Contribution guide](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/CONTRIBUTING.md)
## ⚡ Quickstart
To get started, first install Megatron Bridge or download a NeMo Framework container as described [above](#-installation).
Log in to HuggingFace Hub:
```sh
huggingface-cli login --token <your token>
```
You can then run the following to import a model from HuggingFace and start training with mock data:
```python
from megatron.bridge import AutoBridge
import megatron.bridge.recipes.llama.llama32_1b as llama32_1b
from megatron.bridge.training.gpt_step import forward_step
from megatron.bridge.training.pretrain import pretrain
if __name__ == "__main__":
# Load Llama from HuggingFace Hub and convert to Megatron
bridge = AutoBridge.from_hf_pretrained("meta-llama/Llama-3.2-1B")
model_provider = bridge.to_megatron_provider()
# Get defaults for other configuration from an existing Llama 3.2 recipe
cfg = llama32_1b.pretrain_config()
cfg.model = model_provider
cfg.train.train_iters = 10
cfg.dataset.sequence_length = cfg.model.seq_length
cfg.tokenizer.vocab_size = cfg.model.vocab_size
pretrain(cfg, forward_step)
```
You can launch the above script with:
```sh
torchrun --nproc-per-node=<num devices> /path/to/script.py
```
## 🚀 Key Features
- **Bridge with 🤗Hugging Face**: Seamless bidirectional conversion between 🤗Hugging Face and Megatron formats for interoperability ([model bridges](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/models), [auto bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/models/conversion/auto_bridge.py), [conversion examples](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/examples/models))
- **Flexible to Customize**: Lightweight custom training loop making it easy to configure custom logic in data loading, distributed training, checkpointing, evaluation and logging ([training framework](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/training), [training utilities](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/training/utils))
- **Supervised & Parameter-Efficient Finetuning**: SFT & PEFT implementation tailored for Megatron-based models that supports LoRA, DoRA, and user-defined PEFT methods ([PEFT implementations](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/peft), [finetune module](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/training/finetune.py), [SFT dataset](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/data/datasets/sft.py))
- **SoTA Training Recipes**: Pre-configured production-ready training recipes for popular models like Llama 3, with optimized hyperparameters and distributed training configuration ([Llama recipes](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/recipes/llama), [recipe examples](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/examples/recipes))
- **Performance Optimization**: Built-in support for FP8 training, model parallelisms, and memory-efficient techniques to offer high utilization and near linear scalability to thousands of nodes. ([mixed precision](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/training/mixed_precision.py), [communication overlap](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/training/comm_overlap.py), [optimizer utilities](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/utils/optimizer_utils.py))
## Supported Models
Megatron Bridge provides out-of-the-box recipes for a wide range of models, built on top of base model architectures from [megatron-core](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core):
### Large Language Models
| Model | Style | Sizes | Pretrain | SFT & LoRA |
|------------------------|-----------|-----------|--------------|--------------|
| Llama 3 | [GPT](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/models/gpt_provider.py) | [8b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama3_8b.py), [70b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama3_70b.py) | ✅ | APIs available, recipes upcoming|
| Llama 3.1 | [GPT](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/models/gpt_provider.py) | [8b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama31_8b.py), [70b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama31_70b.py), [405b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama31_405b.py) | ✅ | APIs available, recipes upcoming |
| Llama 3.2 | [GPT](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/models/gpt_provider.py) | [1b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama32_1b.py), [3b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama32_3b.py) | ✅ | APIs available, recipes upcoming |
#### Launching Recipes
All recipes are ready to train out of the box, using mock data by default. For an example of how to override the default configuration through YAML or Hydra-style CLI overrides, please have a look at this [script](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/recipes/llama/pretrain_llama3_8b.py). The script can then be launched with `torchrun`. For example, with the aforementioned script:
```sh
torchrun --nproc-per-node=2 pretrain_llama3_8b.py model.tensor_model_parallel_size=1 <additional overrides ...>
```
Optionally, Megatron Bridge also supports launching with [NeMo-Run](https://github.com/NVIDIA-NeMo/Run). See the following examples for reference on launching with NeMo-Run:
- [pretrain_llama3_8b_nemo_run_script.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/recipes/llama/pretrain_llama3_8b_nemo_run_script.py)
- [pretrain_llama3_8b_nemo_run_partial.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/recipes/llama/pretrain_llama3_8b_nemo_run_partial.py)
These examples can also be run as is with the Llama 3 8b recipe (with NeMo-Run installed).
Launch Llama 3 8b Pretraining with NeMo-Run's `run.Script`:
```sh
uv run python pretrain_llama3_8b_nemo_run_script.py \
--nproc-per-node=2 \
model.pipeline_model_parallel_size=1 \
train.train_iters=10 # this script passes Hydra-style overrides to the target script
```
Launch Llama 3 8b Pretraining with NeMo-Run's `run.Partial`
```sh
uv run python pretrain_llama3_8b_nemo_run_partial.py \
--nproc-per-node=2
```
<!-- ### Vision-Language Models -->
## Performance Benchmarks
Coming soon ...
## Project Structure
```
Megatron-Bridge/
├── examples/
│ ├── models/ # Bridge usage examples
│ └── recipes/ # Training examples
├── src/megatron/bridge/
│ ├── data/ # Dataloaders and iterators
│ ├── models/ # HuggingFace bridge infrastructure and model-specific implementations
│ │ ├── llama/ # Llama model providers
│ │ └── .../ # Other models (gpt, t5, etc.)
│ ├── peft/ # PEFT transformations and wrappers
│ ├── recipes/ # Complete training recipes
│ ├── training/ # Training loop components
│ │ ├── tokenizers/ # Tokenizer library
│ │ └── utils/ # Training-specific utilities
│ └── utils/ # Generic utilities for repo-wide usage
└── tests/ # Comprehensive test suite
```
## Contributing
We welcome community contributions! Please see our [Contributor Guidelines](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/CONTRIBUTING.md) for more information on how to get involved.
Raw data
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"description": "<div align=\"center\">\n\n# Megatron Bridge\n\n<!-- [](https://codecov.io/github/NVIDIA-NeMo/Megatron-Hub) -->\n[](https://github.com/NVIDIA-NeMo/Megatron-Bridge/actions/workflows/cicd-main.yml)\n[](https://www.python.org/downloads/release/python-3100/)\n[](https://github.com/NVIDIA-NeMo/Megatron-Bridge/stargazers/)\n\n[Recipes](#supported-models) | [Examples](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/examples) | [Contributing](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/CONTRIBUTING.md)\n</div>\n\n## Overview\n\nMegatron Bridge is a PyTorch native library under [NeMo Framework](https://github.com/NVIDIA-NeMo) that leverages [megatron-core](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core) to provide state-of-the-art training throughput for top models. It enables researchers and community developers to do both pre and post training using a performant and scalable training loop, with features like model parallelisms and mixed precisions (FP8, BF16, FP4 etc.). Megatron Bridge users can either leverage existing \ud83e\udd17HuggingFace models or define their custom PyTorch model definitions for end-to-end workflows with flexibility.\n\n\n## \ud83d\udd27 Installation\n\n### \ud83d\udc33 NeMo-FW container\n\nBest experience, highest performance and full feature support is guaranteed by the [NeMo Framework container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags). Please fetch the most recent $TAG and run the following command to start a container:\n\n```bash\ndocker run --rm -it -w /workdir -v $(pwd):/workdir \\\n --entrypoint bash \\\n --gpus all \\\n nvcr.io/nvidia/nemo:${TAG}\n```\n\n### \ud83d\udce6 Bare metal install with TransformerEngine\n\nTransformerEngine is a required dependency for Megatron Bridge. To install on bare metal (without any container), the following system requirements need to be fulfilled:\n\n- PyTorch >= 2.7\n- CUDA >= 12.8\n- cuDNN >= 9.3\n\nWe recommend installing the same versions that are present in the latest NGC PyTorch containers. The versions of these components for each container release can be found in the [PyTorch](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html) and [CUDA](https://docs.nvidia.com/deeplearning/frameworks/cuda-dl-release-notes/index.html) container release notes.\n\nPlease see these [instructions](https://developer.nvidia.com/cudnn-downloads) for installing cuDNN for your target platform. You can check if CUDA toolkit and cuDNN are installed with:\n\n```bash\ndpkg -l | grep 'cuda-toolkit'\ndpkg -l | grep 'cudnn.*cuda'\n```\n\nYou can then run the following to install Megatron Bridge:\n\n```bash\npip install torch setuptools pybind11 wheel_stub # Required for TE\npip install --no-build-isolation megatron-bridge\n```\n\n### uv\n\nFor installing Megatron Bridge with uv, please refer to our [Contribution guide](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/CONTRIBUTING.md)\n\n## \u26a1 Quickstart\n\nTo get started, first install Megatron Bridge or download a NeMo Framework container as described [above](#-installation).\n\nLog in to HuggingFace Hub:\n```sh\nhuggingface-cli login --token <your token>\n```\n\nYou can then run the following to import a model from HuggingFace and start training with mock data:\n```python\nfrom megatron.bridge import AutoBridge\n\nimport megatron.bridge.recipes.llama.llama32_1b as llama32_1b\nfrom megatron.bridge.training.gpt_step import forward_step\nfrom megatron.bridge.training.pretrain import pretrain\n\nif __name__ == \"__main__\":\n # Load Llama from HuggingFace Hub and convert to Megatron\n bridge = AutoBridge.from_hf_pretrained(\"meta-llama/Llama-3.2-1B\")\n model_provider = bridge.to_megatron_provider()\n\n # Get defaults for other configuration from an existing Llama 3.2 recipe\n cfg = llama32_1b.pretrain_config()\n cfg.model = model_provider\n cfg.train.train_iters = 10\n\n cfg.dataset.sequence_length = cfg.model.seq_length\n cfg.tokenizer.vocab_size = cfg.model.vocab_size\n\n pretrain(cfg, forward_step)\n```\n\nYou can launch the above script with:\n```sh\ntorchrun --nproc-per-node=<num devices> /path/to/script.py\n```\n\n## \ud83d\ude80 Key Features\n\n- **Bridge with \ud83e\udd17Hugging Face**: Seamless bidirectional conversion between \ud83e\udd17Hugging Face and Megatron formats for interoperability ([model bridges](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/models), [auto bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/models/conversion/auto_bridge.py), [conversion examples](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/examples/models))\n- **Flexible to Customize**: Lightweight custom training loop making it easy to configure custom logic in data loading, distributed training, checkpointing, evaluation and logging ([training framework](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/training), [training utilities](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/training/utils))\n- **Supervised & Parameter-Efficient Finetuning**: SFT & PEFT implementation tailored for Megatron-based models that supports LoRA, DoRA, and user-defined PEFT methods ([PEFT implementations](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/peft), [finetune module](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/training/finetune.py), [SFT dataset](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/data/datasets/sft.py))\n- **SoTA Training Recipes**: Pre-configured production-ready training recipes for popular models like Llama 3, with optimized hyperparameters and distributed training configuration ([Llama recipes](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/recipes/llama), [recipe examples](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/examples/recipes))\n- **Performance Optimization**: Built-in support for FP8 training, model parallelisms, and memory-efficient techniques to offer high utilization and near linear scalability to thousands of nodes. ([mixed precision](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/training/mixed_precision.py), [communication overlap](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/training/comm_overlap.py), [optimizer utilities](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/utils/optimizer_utils.py))\n\n## Supported Models\n\nMegatron Bridge provides out-of-the-box recipes for a wide range of models, built on top of base model architectures from [megatron-core](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core):\n\n### Large Language Models\n\n| Model | Style | Sizes | Pretrain | SFT & LoRA |\n|------------------------|-----------|-----------|--------------|--------------|\n| Llama 3 | [GPT](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/models/gpt_provider.py) | [8b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama3_8b.py), [70b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama3_70b.py) | \u2705 | APIs available, recipes upcoming|\n| Llama 3.1 | [GPT](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/models/gpt_provider.py) | [8b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama31_8b.py), [70b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama31_70b.py), [405b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama31_405b.py) | \u2705 | APIs available, recipes upcoming |\n| Llama 3.2 | [GPT](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/models/gpt_provider.py) | [1b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama32_1b.py), [3b](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/src/megatron/bridge/recipes/llama/llama32_3b.py) | \u2705 | APIs available, recipes upcoming |\n\n#### Launching Recipes\n\nAll recipes are ready to train out of the box, using mock data by default. For an example of how to override the default configuration through YAML or Hydra-style CLI overrides, please have a look at this [script](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/recipes/llama/pretrain_llama3_8b.py). The script can then be launched with `torchrun`. For example, with the aforementioned script:\n\n```sh\ntorchrun --nproc-per-node=2 pretrain_llama3_8b.py model.tensor_model_parallel_size=1 <additional overrides ...>\n```\n\nOptionally, Megatron Bridge also supports launching with [NeMo-Run](https://github.com/NVIDIA-NeMo/Run). See the following examples for reference on launching with NeMo-Run:\n\n- [pretrain_llama3_8b_nemo_run_script.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/recipes/llama/pretrain_llama3_8b_nemo_run_script.py)\n- [pretrain_llama3_8b_nemo_run_partial.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/recipes/llama/pretrain_llama3_8b_nemo_run_partial.py)\n\nThese examples can also be run as is with the Llama 3 8b recipe (with NeMo-Run installed).\n\nLaunch Llama 3 8b Pretraining with NeMo-Run's `run.Script`:\n\n```sh\nuv run python pretrain_llama3_8b_nemo_run_script.py \\\n --nproc-per-node=2 \\\n model.pipeline_model_parallel_size=1 \\\n train.train_iters=10 # this script passes Hydra-style overrides to the target script\n```\n\nLaunch Llama 3 8b Pretraining with NeMo-Run's `run.Partial`\n\n```sh\nuv run python pretrain_llama3_8b_nemo_run_partial.py \\\n --nproc-per-node=2\n```\n\n<!-- ### Vision-Language Models -->\n\n## Performance Benchmarks\n\nComing soon ...\n\n## Project Structure\n\n```\nMegatron-Bridge/\n\u251c\u2500\u2500 examples/\n\u2502 \u251c\u2500\u2500 models/ # Bridge usage examples\n\u2502 \u2514\u2500\u2500 recipes/ # Training examples\n\u251c\u2500\u2500 src/megatron/bridge/\n\u2502 \u251c\u2500\u2500 data/ # Dataloaders and iterators\n\u2502 \u251c\u2500\u2500 models/ # HuggingFace bridge infrastructure and model-specific implementations\n\u2502 \u2502 \u251c\u2500\u2500 llama/ # Llama model providers\n\u2502 \u2502 \u2514\u2500\u2500 .../ # Other models (gpt, t5, etc.)\n\u2502 \u251c\u2500\u2500 peft/ # PEFT transformations and wrappers\n\u2502 \u251c\u2500\u2500 recipes/ # Complete training recipes\n\u2502 \u251c\u2500\u2500 training/ # Training loop components\n\u2502 \u2502 \u251c\u2500\u2500 tokenizers/ # Tokenizer library\n\u2502 \u2502 \u2514\u2500\u2500 utils/ # Training-specific utilities\n\u2502 \u2514\u2500\u2500 utils/ # Generic utilities for repo-wide usage\n\u2514\u2500\u2500 tests/ # Comprehensive test suite\n```\n\n## Contributing\n\nWe welcome community contributions! Please see our [Contributor Guidelines](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/CONTRIBUTING.md) for more information on how to get involved.\n",
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