https://github.com/dusty-nv/jetson-containers/packages/rag/llama-index
2.3K
llamaindex containerjetson-containers run $(autotag llama-index)
This will start the ollama server as well as Jupyter Lab server inside the container.
This is based on the official tutorial for local models.
When you run start the llama-index container, you should see lines like this on the terminal.
JupyterLab URL: http://192.168.1.10:8888 (password "nvidia")
JupyterLab logs: /data/logs/jupyter.log
On your Jetson desktop GUI, or on a PC on the same network as Jetson, open your web browser and access the address. When prompted, type the password nvidia and log in.
Jupyter Lab UI should show up, with LlamaIndex_Local-Models.ipynb listed in the left navigator pane - open it, and follow the guide in the Jupyter notebook.
After starting the llamaindex container, you should be on root@<hostname> console. First, download the Llama2 model using ollama
ollama pull llama2
This downloads the default 7-billion parameter Llama2 model - you can optionally specify ollma2:13b and ollma2:70b for other variations, and change the Python script (line 13) accordingly. Then type the following to start the sample Python script:
python3 /opt/llama-index/llamaindex_starter.py
llama-index | |
|---|---|
| Requires | L4T ['>=34.1.0'] |
| Dependencies | build-essential cuda cudnn python numpy cmake onnx pytorch ollama rust jupyterlab |
| Dockerfile | Dockerfile |
| Images | dustynv/llama-index:r35.4.1 (2024-04-30, 6.4GB)dustynv/llama-index:r36.2.0 (2024-04-30, 6.2GB) |
| Repository/Tag | Date | Arch | Size |
|---|---|---|---|
dustynv/llama-index:r35.4.1 | 2024-04-30 | arm64 | 6.4GB |
dustynv/llama-index:r36.2.0 | 2024-04-30 | arm64 | 6.2GB |
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+)
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 llama-index)
# or explicitly specify one of the container images above
jetson-containers run dustynv/llama-index:r35.4.1
# or if using 'docker run' (specify image and mounts/ect)
sudo docker run --runtime nvidia -it --rm --network=host dustynv/llama-index:r35.4.1
jetson-containers run forwards arguments todocker run with some defaults added (like--runtime nvidia, mounts a/datacache, 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 llama-index)
To launch the container running a command, as opposed to an interactive shell:
jetson-containers run $(autotag llama-index) 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.
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 llama-index
The dependencies from above will be built into the container, and it'll be tested during. Run it with --help for build options.
Content type
Image
Digest
sha256:aaa611822…
Size
6.4 GB
Last updated
over 2 years ago
docker pull dustynv/llama-index:samples-r35.4.1