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python

CONTAINERS IMAGES RUN BUILD

CONTAINERS
python:3.6
   Requires L4T ['==32.*']
   Dependencies build-essential
   Dockerfile Dockerfile
   Notes installs core python3 packages and pip
python:3.8
   Aliases python
   Requires L4T ['<36']
   Dependencies build-essential
   Dependants arrow:12.0.1 arrow:14.0.1 arrow:5.0.0 audiocraft auto_awq:0.2.4 auto_gptq:0.7.1 awq:0.1.0 bitsandbytes bitsandbytes:builder cmake:pip ctranslate2 cuda-python:11.4 cuda:11.4-samples cuda:11.8-samples cuda:12.2-samples cuda:12.4-samples cudf:21.10.02 cudf:23.10.03 cuml cupy deepstream docker efficientvit exllama:0.0.14 exllama:0.0.15 faiss:1.7.3 faiss:1.7.3-builder faiss:1.7.4 faiss:1.7.4-builder faiss_lite faster-whisper flash-attention gptq-for-llama gstreamer huggingface_hub jetson-inference jetson-utils jupyterlab l4t-diffusion l4t-ml l4t-pytorch l4t-tensorflow:tf1 l4t-tensorflow:tf2 l4t-text-generation langchain langchain:samples llama_cpp:0.2.57 llamaspeak llava local_llm minigpt4 mlc:51fb0f4 mlc:607dc5a nano_llm:24.4 nano_llm:main nanodb nanoowl nanosam nemo numba numpy onnx onnxruntime:1.11 onnxruntime:1.11-builder onnxruntime:1.16.3 onnxruntime:1.16.3-builder onnxruntime:1.17 onnxruntime:1.17-builder openai openai-triton openai-triton:builder opencv:4.5.0 opencv:4.5.0-builder opencv:4.8.1 opencv:4.8.1-builder opencv:4.9.0 optimum piper-tts protobuf:apt protobuf:cpp pycuda pytorch:1.10 pytorch:1.9 pytorch:2.0 pytorch:2.1 pytorch:2.2 pytorch:2.3 raft realsense riva-client:python ros:foxy-desktop ros:foxy-ros-base ros:foxy-ros-core ros:galactic-desktop ros:galactic-ros-base ros:galactic-ros-core ros:humble-desktop ros:humble-ros-base ros:humble-ros-core ros:iron-desktop ros:iron-ros-base ros:iron-ros-core ros:melodic-desktop ros:melodic-ros-base ros:melodic-ros-core ros:noetic-desktop ros:noetic-ros-base ros:noetic-ros-core rust sam stable-diffusion stable-diffusion-webui tam tensorflow tensorflow2 tensorrt tensorrt:10.0 tensorrt:8.6 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 torchaudio:0.10.0 torchaudio:0.9.0 torchaudio:2.0.1 torchaudio:2.1.0 torchaudio:2.2.2 torchaudio:2.3.0 torchvision:0.10.0 torchvision:0.11.1 torchvision:0.15.1 torchvision:0.16.2 torchvision:0.17.2 transformers transformers:git transformers:nvgpt tritonserver tvm whisper whisperx xformers xtts zed
   Dockerfile Dockerfile
   Notes installs core python3 packages and pip
python:3.10
   Requires L4T ['>=34']
   Dependencies build-essential
   Dockerfile Dockerfile
   Notes installs core python3 packages and pip
python:3.11
   Requires L4T ['>=34']
   Dependencies build-essential
   Dependants wyoming-openwakeword:latest
   Dockerfile Dockerfile
   Notes installs core python3 packages and pip
python:3.12
   Requires L4T ['>=34']
   Dependencies build-essential
   Dependants homeassistant-core:2024.4.2 homeassistant-core:latest
   Dockerfile Dockerfile
   Notes installs core python3 packages and pip
CONTAINER IMAGES
Repository/Tag Date Arch Size
  dustynv/python:r32.7.1 2023-12-05 arm64 0.4GB
  dustynv/python:r35.2.1 2023-12-05 arm64 5.0GB
  dustynv/python:r35.3.1 2023-09-07 arm64 5.0GB
  dustynv/python:r35.4.1 2023-10-07 arm64 4.9GB

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 python)

# or explicitly specify one of the container images above
jetson-containers run dustynv/python:r32.7.1

# or if using 'docker run' (specify image and mounts/ect)
sudo docker run --runtime nvidia -it --rm --network=host dustynv/python:r32.7.1

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 python)

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

jetson-containers run $(autotag python) 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 python

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