torchvision

torchvision

CONTAINERS IMAGES RUN BUILD

CONTAINERS

torchvision:0.15.1
Requires	L4T ['==35.*']
Dependencies	build-essential cuda cudnn python numpy cmake onnx pytorch:2.0
Dockerfile	Dockerfile

torchvision:0.16.2
Requires	L4T ['>=35']
Dependencies	build-essential cuda cudnn python numpy cmake onnx pytorch:2.1
Dockerfile	Dockerfile

torchvision:0.17.2
Aliases	torchvision
Requires	L4T ['>=35']
Dependencies	build-essential cuda cudnn python numpy cmake onnx pytorch:2.2
Dependants	audiocraft auto_awq:0.2.4 auto_gptq:0.7.1 awq:0.1.0 bitsandbytes bitsandbytes:builder efficientvit gptq-for-llama jetson-inference l4t-diffusion l4t-ml l4t-pytorch llava minigpt4 mlc:0.1.0 mlc:0.1.0-builder mlc:0.1.1 mlc:0.1.1-builder nanodb nanoowl nanosam nemo optimum sam stable-diffusion stable-diffusion-webui tam tensorrt_llm:0.10.dev0 tensorrt_llm:0.10.dev0-builder tensorrt_llm:0.5 tensorrt_llm:0.5-builder text-generation-inference text-generation-webui:1.7 text-generation-webui:6a7cd01 text-generation-webui:main torch2trt torch_tensorrt transformers transformers:git transformers:nvgpt voicecraft whisperx xtts
Dockerfile	Dockerfile

torchvision:0.18.0
Requires	L4T ['>=35']
Dependencies	build-essential cuda cudnn python numpy cmake onnx pytorch:2.3
Dockerfile	Dockerfile

torchvision:0.11.1
Requires	L4T ['==32.*']
Dependencies	build-essential cuda cudnn python numpy cmake onnx pytorch:1.10
Dockerfile	Dockerfile

torchvision:0.10.0
Requires	L4T ['==32.*']
Dependencies	build-essential cuda cudnn python numpy cmake onnx pytorch:1.9
Dockerfile	Dockerfile

CONTAINER IMAGES

Repository/Tag	Date	Arch	Size
dustynv/torchvision:r32.7.1	2023-12-14	arm64	1.1GB
dustynv/torchvision:r35.2.1	2023-12-11	arm64	5.5GB
dustynv/torchvision:r35.3.1	2023-12-14	arm64	5.5GB
dustynv/torchvision:r35.4.1	2023-11-05	arm64	5.4GB

_{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 torchvision)

# or explicitly specify one of the container images above
jetson-containers run dustynv/torchvision:r35.3.1

# or if using 'docker run' (specify image and mounts/ect)
sudo docker run --runtime nvidia -it --rm --network=host dustynv/torchvision:r35.3.1

^{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 torchvision)

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

jetson-containers run $(autotag torchvision) 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 torchvision

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