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nano_llm

CONTAINERS IMAGES RUN BUILD

Note

NanoLLM is a lightweight, optimized library for LLM inference and multimodal agents. For more info, see these resources:

CONTAINERS
nano_llm:main
   Aliases nano_llm
   Requires L4T ['>=35']
   Dependencies build-essential cuda:12.2 cudnn:8.9 python numpy cmake onnx pytorch:2.2 torchvision huggingface_hub rust transformers awq mlc riva-client:python tensorrt opencv gstreamer jetson-inference torch2trt torchaudio onnxruntime piper-tts whisper_trt cuda-python faiss faiss_lite clip_trt nanodb
   Dockerfile Dockerfile
CONTAINER IMAGES
Repository/Tag Date Arch Size
  dustynv/nano_llm:24.4-r35.4.1 2024-04-15 arm64 8.5GB
  dustynv/nano_llm:24.4-r36.2.0 2024-04-15 arm64 9.7GB
  dustynv/nano_llm:24.4.1-r35.4.1 2024-04-19 arm64 10.2GB
  dustynv/nano_llm:24.4.1-r36.2.0 2024-04-19 arm64 11.1GB
  dustynv/nano_llm:24.5-r35.4.1 2024-05-03 arm64 10.2GB
  dustynv/nano_llm:24.5-r36.2.0 2024-05-03 arm64 11.2GB
  dustynv/nano_llm:24.5.1-r35.4.1 2024-05-19 arm64 10.0GB
  dustynv/nano_llm:24.5.1-r36.2.0 2024-05-19 arm64 10.6GB
  dustynv/nano_llm:24.6-r36.2.0 2024-06-09 arm64 10.8GB
  dustynv/nano_llm:r35.4.1 2024-05-15 arm64 10.0GB
  dustynv/nano_llm:r36.2.0 2024-06-29 arm64 10.8GB

Container images are compatible with other minor versions of JetPack/L4T:
    • L4T R32.7 containers can run on other versions of L4T R32.7 (JetPack 4.6+)
    • L4T R35.x containers can run on other versions of L4T R35.x (JetPack 5.1+)

RUN CONTAINER

To start the container, you can use jetson-containers run and autotag, or manually put together a docker run command:

# automatically pull or build a compatible container image
jetson-containers run $(autotag nano_llm)

# or explicitly specify one of the container images above
jetson-containers run dustynv/nano_llm:r36.2.0

# or if using 'docker run' (specify image and mounts/ect)
sudo docker run --runtime nvidia -it --rm --network=host dustynv/nano_llm:r36.2.0

jetson-containers run forwards arguments to docker run with some defaults added (like --runtime nvidia, mounts a /data cache, and detects devices)
autotag finds a container image that's compatible with your version of JetPack/L4T - either locally, pulled from a registry, or by building it.

To mount your own directories into the container, use the -v or --volume flags:

jetson-containers run -v /path/on/host:/path/in/container $(autotag nano_llm)

To launch the container running a command, as opposed to an interactive shell:

jetson-containers run $(autotag nano_llm) my_app --abc xyz

You can pass any options to it that you would to docker run, and it'll print out the full command that it constructs before executing it.

BUILD CONTAINER

If you use autotag as shown above, it'll ask to build the container for you if needed. To manually build it, first do the system setup, then run:

jetson-containers build nano_llm

The dependencies from above will be built into the container, and it'll be tested during. Run it with --help for build options.