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Tutorial - NanoDB

Let's run NanoDB 's interactive demo to witness the impact of Vector Database that handles multimodal data.

What you need

  1. One of the following Jetson devices:

    Jetson AGX Orin (64GB) Jetson AGX Orin (32GB) Jetson Orin NX (16GB) Jetson Orin Nano (8GB)

  2. Running one of the following versions of JetPack :

    JetPack 5 (L4T r35.x) JetPack 6 (L4T r36.x)

  3. NVMe SSD highly recommended for storage speed and space

    • 16GB for container image
    • 40GB for MS COCO dataset

  4. Clone and setup jetson-containers : 

    git clone https://github.com/dusty-nv/jetson-containers
bash jetson-containers/install.sh

How to start

Download COCO

Just for an example, let's use MS COCO dataset: 

cd jetson-containers
mkdir -p data/datasets/coco/2017
cd data/datasets/coco/2017

wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/zips/unlabeled2017.zip

unzip train2017.zip
unzip val2017.zip
unzip unlabeled2017.zip

Download Index

You can download a pre-indexed NanoDB that was already prepared over the COCO dataset from here : 

cd jetson-containers/data
wget https://nvidia.box.com/shared/static/icw8qhgioyj4qsk832r4nj2p9olsxoci.gz -O nanodb_coco_2017.tar.gz
tar -xzvf nanodb_coco_2017.tar.gz

This allow you to skip the indexing process in the next step, and jump to starting the Web UI .

Indexing Data

If you didn't download the NanoDB index for COCO from above, we need to build the index by scanning your dataset directory: 

jetson-containers run $(autotag nanodb) \
  python3 -m nanodb \
    --scan /data/datasets/coco/2017 \
    --path /data/nanodb/coco/2017 \
    --autosave --validate

This will take a few hours on AGX Orin. Once the database has loaded and completed any start-up operations , it will drop down to a > prompt from which the user can run search queries. You can quickly check the operation by typing your query on this prompt: 

> a girl riding a horse

* index=80110   /data/datasets/coco/2017/train2017/000000393735.jpg      similarity=0.29991915822029114
* index=158747  /data/datasets/coco/2017/unlabeled2017/000000189708.jpg  similarity=0.29254037141799927
* index=123846  /data/datasets/coco/2017/unlabeled2017/000000026239.jpg  similarity=0.292171448469162
* index=127338  /data/datasets/coco/2017/unlabeled2017/000000042508.jpg  similarity=0.29118549823760986
* index=77416   /data/datasets/coco/2017/train2017/000000380634.jpg      similarity=0.28964102268218994
* index=51992   /data/datasets/coco/2017/train2017/000000256290.jpg      similarity=0.28929752111434937
* index=228640  /data/datasets/coco/2017/unlabeled2017/000000520381.jpg  similarity=0.28642547130584717
* index=104819  /data/datasets/coco/2017/train2017/000000515895.jpg      similarity=0.285491943359375

You can press Ctrl + C to exit. For more info about the various options available, see the NanoDB container documentation.

Interactive Web UI

Spin up the Gradio server: 

jetson-containers run $(autotag nanodb) \
  python3 -m nanodb \
    --path /data/nanodb/coco/2017 \
    --server --port=7860

Then navigate your browser to http://<IP_ADDRESS>:7860 , and you can enter text search queries as well as drag/upload images:

To use the dark theme, navigate to http://<IP_ADDRESS>:7860/?__theme=dark instead