# Build And Dependency > Dev environment setup for NeMo AutoModel — container-based development, uv package management, installation options, environment variables, and common build pitfalls. Use when this capability is needed. - Skill: `tomevault-io/build-and-dependency` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/build-and-dependency` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/build-and-dependency/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/build-and-dependency --- # Build and Dependency ## Quick Start Clone and install: ```bash git clone https://github.com/NVIDIA-NeMo/Automodel.git && cd Automodel uv sync --locked --all-groups --extra all ``` Or use the NeMo-AutoModel container from NVIDIA NGC (pick a published tag from [the NGC catalog](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo-automodel) — e.g. `26.04`): ```bash docker pull nvcr.io/nvidia/nemo-automodel:26.04 docker run --gpus all -it nvcr.io/nvidia/nemo-automodel:26.04 ``` ## Installation Options ### Option 1: NeMo-AutoModel Container (NGC) The container ships with all dependencies pre-installed at `/opt/Automodel` (WORKDIR) with the venv at `/opt/venv`. Run as-is: ```bash docker run --gpus all --network=host -it --rm --shm-size=32g \ nvcr.io/nvidia/nemo-automodel:26.04 /bin/bash ``` #### Mounting your local checkout into the container To develop against your host checkout, bind-mount it over `/opt/Automodel` to override the installed source: ```bash docker run --gpus all --network=host -it --rm --shm-size=32g \ -v :/opt/Automodel \ nvcr.io/nvidia/nemo-automodel:26.04 /bin/bash ``` Inside the container, patch `pyproject.toml` / `uv.lock` for the PyTorch base image, then re-sync: ```bash cd /opt/Automodel bash docker/common/update_pyproject_pytorch.sh /opt/Automodel uv sync --locked --all-groups --extra all ``` > **Warning:** the `update_pyproject_pytorch.sh` step is required. Without it, > `uv sync` will try to reinstall `torch`, which leads to CUDA version > mismatches and TE import failures — uv cannot recognize the torch baked into > the PyTorch base container. ### Option 2: uv (Recommended for Local Development) `--all-groups` pulls the `build`, `docs`, and `test` dev groups (defined in `pyproject.toml`); drop it for a runtime-only install. ```bash uv sync --locked --all-groups # base + dev groups uv sync --locked --all-groups --extra cuda # CUDA support uv sync --locked --all-groups --extra fa # flash-attention uv sync --locked --all-groups --extra moe # mixture-of-experts uv sync --locked --all-groups --extra vlm # vision-language models (core) uv sync --locked --all-groups --extra vlm-media # + video/Qwen/Mistral decode (opencv, decord, qwen-utils; FFmpeg-bearing) uv sync --locked --all-groups --extra diffusion # diffusion models uv sync --locked --all-groups --extra diffusion-media # + diffusion preprocessing/export (imageio-ffmpeg, opencv) uv sync --locked --all-groups --extra media # vlm-media + diffusion-media (union) uv sync --locked --all-groups --extra delta-databricks # Delta Lake / Databricks uv sync --locked --all-groups --extra all # all standard extras (EXCLUDES media — FFmpeg kept opt-in) ``` The media extras (`vlm-media`, `diffusion-media`, `media`) bundle FFmpeg and are deliberately **excluded from `all`** and from the container image — add them explicitly for video/image decode. ### Option 3: pip Full install (matches `uv sync --extra all`): ```bash pip install -e ".[all]" ``` Login-node / submitter-only install — lightweight package for SLURM, k8s, or NeMo-Run job submission without local CUDA deps: ```bash pip install nemo-automodel[cli] ``` ## Package Management Always use `uv`. Do not introduce `pip install` commands in scripts or docs. | Task | Command | |---|---| | Install from lockfile | `uv sync --locked` | | Add a new dependency | `uv add ` | | Add an optional dependency | `uv add --optional --extra ` | | Regenerate the lockfile | `uv lock` | ## Environment Variables ```bash export HF_TOKEN="hf_..." # Hugging Face token for gated models export WANDB_API_KEY="..." # Weights & Biases logging export HF_HOME="/path/to/hf_cache" # Hugging Face cache directory ``` ## CLI Usage The entry point is `automodel` (defined at `nemo_automodel._cli.app:main`). Pattern: `automodel -c ` ```bash # LLM automodel finetune llm -c examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml automodel pretrain llm -c config.yaml automodel kd llm -c config.yaml automodel benchmark llm -c config.yaml # VLM automodel finetune vlm -c config.yaml # Diffusion automodel finetune diffusion -c config.yaml # Retrieval automodel finetune retrieval -c config.yaml ``` Override any config value from the CLI: ```bash automodel finetune llm -c config.yaml --model.name_or_path meta-llama/Llama-3.2-1B ``` ## Common Pitfalls | Problem | Cause | Fix | |---|---|---| | Stale `.venv` after switching branches | Cached environment out of sync | Delete `.venv` and re-run `uv sync --locked` | | Import errors for optional features (TE, flash-attn, MoE) | Missing extras | Install the matching `uv` extra (`--extra fa`, `--extra moe`, etc.) | | Import errors for media (`cv2`, `decord`, `qwen_vl_utils`, `imageio_ffmpeg`) | Media extras are opt-in (not in `all`) | Install `--extra vlm-media` (VLM/Qwen/Mistral) or `--extra diffusion-media` (diffusion) | | TransformerEngine version mismatch | The TE installed by `uv sync` takes precedence over the version baked into the container | Set the desired TE version in `pyproject.toml` / `uv.lock` and re-run `uv sync` — the venv's TE wins, not the container's | --- > Source: [NVIDIA-NeMo/Automodel](https://github.com/NVIDIA-NeMo/Automodel) — distributed by [TomeVault](https://tomevault.io).