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Install NeMo AutoModel

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This guide explains how to install NeMo AutoModel for LLM, VLM, and OMNI models on various platforms and environments. Depending on your use case, there are several ways to install it:

MethodDev ModeUse CaseRecommended For
📦 PyPI-Install stable release with minimal setupMost users, production usage
🐳 Docker-Use in isolated GPU environments, e.g., with NeMo containerMulti-node deployments
🐍 Git Repo✅Use the latest code without cloning or installing extras manuallyPower users, testers
🧪 Editable Install✅Contribute to the codebase or make local modificationsContributors, researchers
🐳 Docker + Mount✅Use in isolated GPU environments, e.g., with NeMo containerMulti-node deployments

Choose Your Installation Method

Pick the installation method that matches your needs and platform.

Decision Criteria

MethodBest ForProsCons
Docker ContainerProduction, multi-node, Debian-based systemsReproducible environment, pre-configured dependencies, GPU driver isolationLarger download size, container overhead
virtualenv (PyPI/Git)Local development, quick prototyping, macOSFast setup, lightweight, direct code accessManual dependency management, platform-specific issues

When to Use Docker Containers

Use Docker containers when you need:

  • Multi-node deployments: Containers ensure consistency across cluster nodes
  • Production environments: Reproducible builds with tested dependency versions
  • GPU driver compatibility: Isolates CUDA/driver versions from host system
  • Debian-based systems: Recommended for Ubuntu, Debian, and derivatives due to dependency complexity
  • Complex dependencies: Pre-configured environment with all optimizations (TransformerEngine, DeepEP, etc.)
  • Team consistency: Same environment across development, testing, and production

When to Use virtualenv

Use virtualenv (PyPI, Git, or editable install) when you need:

  • Local development: Fast iteration on code changes
  • Quick prototyping: Minimal setup for experimentation
  • macOS systems: Better native support without container overhead
  • Frequent code changes: Contributors working on the codebase (use editable install)
  • Compatible GPU drivers: System has correct CUDA toolkit and drivers installed
  • Lightweight setup: Minimal disk space and memory footprint

Platform-Specific Recommendations

Linux (Debian-based: Ubuntu, Debian)

Recommended: Docker Container

Debian-based systems can have dependency conflicts with system packages. Containers provide isolation and consistency.

$docker pull nvcr.io/nvidia/nemo-automodel:26.06.00
$docker run --gpus all -it --rm --shm-size=8g nvcr.io/nvidia/nemo-automodel:26.06.00

Alternative: virtualenv (if Docker is not available)

Ensure CUDA 11.8+ and compatible drivers are installed:

$# Check CUDA version
$nvidia-smi
$
$# Install via PyPI
$pip3 install nemo-automodel

Linux (RHEL, CentOS, Fedora)

Recommended: Docker Container

Containers avoid enterprise Linux package management complexity.

Follow the same Docker commands as Debian-based systems above.

macOS

Recommended: virtualenv

Docker on macOS has GPU limitations. Use native Python installation:

$# Using PyPI
$pip3 install nemo-automodel
$
$# Or using uv for reproducible environments
$uv pip install nemo-automodel

GPU training on macOS is not supported. Use macOS for CPU-based experimentation or remote cluster submission.

Windows

Recommended: WSL2 + Docker

Run NeMo AutoModel in WSL2 with Docker Desktop:

  1. Install WSL2 and Docker Desktop.
  2. Use Docker container within WSL2 (follow Linux instructions).

Alternative: WSL2 and virtualenv

Install directly in WSL2 Ubuntu environment (follow Debian instructions).

Common Issues and Solutions

GPU driver compatibility errors

  • Problem: CUDA version mismatch between host and application
  • Solution: Use Docker container to isolate driver versions

Dependency conflicts on Debian/Ubuntu

  • Problem: System packages conflict with Python packages
  • Solution: Use Docker container or create isolated virtualenv with uv

Out of memory during container startup

  • Problem: Insufficient shared memory for PyTorch data loading
  • Solution: Increase --shm-size parameter (e.g., --shm-size=16g)

TransformerEngine import failures

  • Problem: Incorrect CUDA toolkit or missing dependencies
  • Solution: Use pre-configured Docker container

Prerequisites

System Requirements

  • Python: 3.10 or higher
  • CUDA: 11.8 or higher (for GPU support)
  • Memory: Minimum 16GB RAM, 32GB+ recommended
  • Storage: At least 50GB free space for models and datasets

Hardware Requirements

  • GPU: NVIDIA GPU with 8GB+ VRAM (16GB+ recommended)
  • CPU: Multi-core processor (8+ cores recommended)
  • Network: Stable internet connection for downloading models

Installation Options for Non-Developers

This section explains the easiest installation options for non-developers, including using pip3 via PyPI or leveraging a preconfigured NVIDIA NeMo Docker container. Both methods offer quick access to the latest stable release of NeMo AutoModel with all required dependencies.

For most users, the easiest way to get started is using pip3.

$pip3 install nemo-automodel

This installs the latest stable release of NeMo AutoModel from PyPI.

To verify the install, run python -c "import nemo_automodel; print(nemo_automodel.__version__)". See nemo-automodel on PyPI.

Install with NeMo Docker Container

You can use NeMo AutoModel with the NeMo Docker container. Pull the container by running:

$docker pull nvcr.io/nvidia/nemo-automodel:26.06.00

The above docker command uses the 26.06.00 container. Use the most recent container version to ensure you get the latest version of AutoModel and its dependencies like PyTorch, Transformers, etc.

Then you can enter the container using:

$docker run --gpus all -it --rm \
>  --shm-size=8g \
>  -v "$(pwd)"/checkpoints:/opt/Automodel/checkpoints \
>  nvcr.io/nvidia/nemo-automodel:26.06.00

Persist your checkpoints. By default, checkpoints are written to checkpoints/ inside the container. Because --rm destroys the container on exit, any data stored only inside the container is lost. Always bind-mount a host directory for the checkpoint path (as shown with -v above) so that your trained weights survive after the container stops. You can also mount additional directories for datasets and Hugging Face cache:

$docker run --gpus all -it --rm \
>  --shm-size=8g \
>  -v /path/to/your/checkpoints:/opt/Automodel/checkpoints \
>  -v /path/to/your/datasets:/datasets \
>  -v /path/to/your/hf_cache:/root/.cache/huggingface \
>  nvcr.io/nvidia/nemo-automodel:26.06.00

Models that require CUDA-specific packages (e.g., Nemotron). Some model families—such as Nemotron Nano and Nemotron Flash—depend on packages like mamba-ssm and causal-conv1d that must be compiled against a matching CUDA toolkit. Installing these from source on a bare-metal host can be error-prone. The NeMo Automodel Docker container ships with these dependencies pre-built, so using the container is the recommended approach for fine-tuning Nemotron and other models with similar requirements.

Installation Options for Developers

This section provides installation options for developers, including pulling the latest source from GitHub, using editable mode, or mounting the repo inside a NeMo Docker container.

Install from GitHub (Source)

If you want the latest features from the main branch or want to contribute:

Option A – Use pip With Git Repo

$pip3 install git+https://github.com/NVIDIA-NeMo/Automodel.git

This installs the repo as a standard Python package (not editable).

Option B – Use uv With Git Repo

$uv pip install git+https://github.com/NVIDIA-NeMo/Automodel.git

uv handles virtual environment transparently and enables more reproducible installs.

Install in Developer Mode (Editable Install)

To contribute or modify the code:

$git clone https://github.com/NVIDIA-NeMo/Automodel.git
$cd Automodel
$pip3 install -e .

This installs AutoModel in editable mode, so changes to the code are immediately reflected in Python.

Mount the Repo into a NeMo Docker Container

To develop against your local checkout while reusing the container’s pre-built dependencies, bind-mount your local Automodel directory over /opt/Automodel, overriding the source installed in the image:

$# Step 1: Clone the AutoModel repository.
$git clone https://github.com/NVIDIA-NeMo/Automodel.git
$cd Automodel
$
$# Step 2: Pull a compatible container image (replace the tag as needed).
$docker pull nvcr.io/nvidia/nemo-automodel:26.06.00
$
$# Step 3: Run the container, sync the mounted checkout, and run a usage example.
$docker run --gpus all --network=host -it --rm \
>  --shm-size=32g \
>  -v "$(pwd)":/opt/Automodel \
>  nvcr.io/nvidia/nemo-automodel:26.06.00 /bin/bash -c "\
>    cd /opt/Automodel && \
>    bash docker/common/update_pyproject_pytorch.sh /opt/Automodel && \
>    uv sync --locked --extra all --all-groups && \
>    automodel examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml"

The update_pyproject_pytorch.sh step is required. Without it, uv sync tries to reinstall torch, which causes CUDA version mismatches and TransformerEngine import failures, because uv cannot recognize the torch baked into the PyTorch base container.

Install Profiles

NeMo AutoModel provides several install extras for different use cases.

Full Install (default)

Installs the core library with all LLM training dependencies (PyTorch, CUDA, etc.):

$pip3 install nemo-automodel

CLI-Only Install (Login Nodes)

If you only need to submit jobs from a login node or CI environment (SLURM, SkyPilot, NeMo-Run) and do not need to run training locally, use the lightweight CLI-only install:

$pip3 install nemo-automodel[cli]

This installs only pyyaml — no PyTorch, no CUDA. The automodel and am CLI commands will be available for SLURM and SkyPilot job submission. If you also need the NeMo-Run launcher, install it separately (pip install nemo-run). If you accidentally try to run a local/interactive job with this install, you will get a clear error with instructions to install the full package.

VLM Dependencies

For vision-language model training, add the VLM extras:

$pip3 install nemo-automodel[vlm]

CUDA-Specific Packages

For models requiring TransformerEngine, bitsandbytes, Mamba, or other CUDA-compiled packages:

$pip3 install nemo-automodel[cuda]

All Extras

Install everything (CUDA, VLM, NeMo-Run, etc.):

$pip3 install nemo-automodel[all]

You can combine extras: pip3 install nemo-automodel[vlm,cuda]

Summary

GoalCommand or Method
Stable install (PyPI)pip3 install nemo-automodel
CLI-only (login nodes)pip3 install nemo-automodel[cli]
Latest from GitHubpip3 install git+https://github.com/NVIDIA-NeMo/Automodel.git
Editable install (dev mode)pip install -e . after cloning
Run without installingUse PYTHONPATH=$(pwd) to run scripts
Use in Docker containerMount repo and pip install -e . inside container
Fast install (using uv)uv pip install ...