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dustynv/pytorch

By dustynv

Updated over 1 year ago

https://github.com/dusty-nv/jetson-containers/packages/pytorch

Image
8

50K+

dustynv/pytorch repository overview

pytorch

Containers for PyTorch with CUDA support. Note that the l4t-pytorch containers also include PyTorch, torchvision, and torchaudio.

CONTAINERS
pytorch:2.0
   Aliasestorch:2.0
   Buildspytorch-20_jp51
   RequiresL4T ['==35.*']
   Dependenciesbuild-essential cuda cudnn python numpy cmake onnx
   Dependantstorchaudio:2.0.1 torchvision:0.15.1
   DockerfileDockerfile.pip
   Imagesdustynv/pytorch:2.0-r35.2.1 (2023-12-06, 5.4GB)
dustynv/pytorch:2.0-r35.3.1 (2023-12-14, 5.4GB)
dustynv/pytorch:2.0-r35.4.1 (2023-10-07, 5.4GB)
pytorch:2.1
   Aliasestorch:2.1
   Buildspytorch-21_jp60 pytorch-21_jp51
   RequiresL4T ['>=35']
   Dependenciesbuild-essential cuda cudnn python numpy cmake onnx
   Dependantstorchaudio:2.1.0 torchvision:0.16.2
   DockerfileDockerfile.pip
   Imagesdustynv/pytorch:2.1-r35.2.1 (2023-12-11, 5.4GB)
dustynv/pytorch:2.1-r35.3.1 (2023-12-14, 5.4GB)
dustynv/pytorch:2.1-r35.4.1 (2023-11-05, 5.4GB)
dustynv/pytorch:2.1-r36.2.0 (2023-12-14, 7.2GB)
pytorch:2.3
   Aliasestorch:2.3
   RequiresL4T ['==36.*']
   Dependenciesbuild-essential cuda cudnn python numpy cmake onnx
   Dependantstorchaudio:2.3.0
   DockerfileDockerfile.pip
pytorch:1.10
   Aliasestorch:1.10
   Buildspytorch-110_jp46
   RequiresL4T ['==32.*']
   Dependenciesbuild-essential cuda cudnn python numpy cmake onnx
   Dependantstorchaudio:0.10.0 torchvision:0.11.1
   DockerfileDockerfile
   Imagesdustynv/pytorch:1.10-r32.7.1 (2023-12-14, 1.1GB)
pytorch:1.9
   Aliasestorch:1.9
   Buildspytorch-19_jp46
   RequiresL4T ['==32.*']
   Dependenciesbuild-essential cuda cudnn python numpy cmake onnx
   Dependantstorchaudio:0.9.0 torchvision:0.10.0
   DockerfileDockerfile
   Imagesdustynv/pytorch:1.9-r32.7.1 (2023-12-14, 1.0GB)
CONTAINER IMAGES
Repository/TagDateArchSize
  dustynv/pytorch:1.10-r32.7.12023-12-14arm641.1GB
  dustynv/pytorch:1.11-r35.2.12023-11-05arm645.4GB
  dustynv/pytorch:1.11-r35.3.12023-12-14arm645.4GB
  dustynv/pytorch:1.11-r35.4.12023-12-11arm645.4GB
  dustynv/pytorch:1.12-r35.2.12023-12-14arm645.5GB
  dustynv/pytorch:1.12-r35.3.12023-08-29arm645.5GB
  dustynv/pytorch:1.12-r35.4.12023-11-03arm645.5GB
  dustynv/pytorch:1.13-r35.2.12023-08-29arm645.5GB
  dustynv/pytorch:1.13-r35.3.12023-12-12arm645.5GB
  dustynv/pytorch:1.13-r35.4.12023-12-14arm645.5GB
  dustynv/pytorch:1.9-r32.7.12023-12-14arm641.0GB
  dustynv/pytorch:2.0-r35.2.12023-12-06arm645.4GB
  dustynv/pytorch:2.0-r35.3.12023-12-14arm645.4GB
  dustynv/pytorch:2.0-r35.4.12023-10-07arm645.4GB
  dustynv/pytorch:2.1-r35.2.12023-12-11arm645.4GB
  dustynv/pytorch:2.1-r35.3.12023-12-14arm645.4GB
  dustynv/pytorch:2.1-r35.4.12023-11-05arm645.4GB
  dustynv/pytorch:2.1-r36.2.02023-12-14arm647.2GB

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+)

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 pytorch)

# or explicitly specify one of the container images above
jetson-containers run dustynv/pytorch:2.1-r36.2.0

# or if using 'docker run' (specify image and mounts/ect)
sudo docker run --runtime nvidia -it --rm --network=host dustynv/pytorch:2.1-r36.2.0

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 pytorch)

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

jetson-containers run $(autotag pytorch) 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 pytorch

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

Tag summary

Content type

Image

Digest

sha256:4bb7f0a38

Size

5.5 GB

Last updated

over 1 year ago

docker pull dustynv/pytorch:2.7-r36.4.0-cu128-24.04