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dustynv/pytorch

By dustynv

•Updated over 1 year ago

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

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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