clip_trt

clip_trt

CONTAINERS IMAGES RUN BUILD

CONTAINERS

clip_trt
Requires	L4T ['>=32.6']
Dependencies	build-essential cuda:12.2 cudnn:8.9 python numpy cmake onnx pytorch:2.2 torchvision huggingface_hub rust transformers tensorrt torch2trt
Dependants	local_llm nano_llm:24.4 nano_llm:24.4.1 nano_llm:24.5 nano_llm:24.5.1 nano_llm:24.6 nano_llm:main nanodb
Dockerfile	Dockerfile
Notes	TensorRT optimized CLIP/SigLIP from https://github.com/dusty-nv/clip_trt

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 clip_trt)

# or if using 'docker run' (specify image and mounts/ect)
sudo docker run --runtime nvidia -it --rm --network=host clip_trt:36.3.0

^{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 clip_trt)

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

jetson-containers run $(autotag clip_trt) 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 clip_trt

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