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update base image to 2306 (#6741)
Fixes #6740 ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Wenqi Li <[email protected]>
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.github/workflows/cron.yml

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environment:
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- "PT191+CUDA113"
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- "PT110+CUDA113"
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- "PT112+CUDA113"
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- "PTLATEST+CUDA118"
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- "PT113+CUDA113"
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- "PTLATEST+CUDA121"
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include:
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
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- environment: PT191+CUDA113
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- environment: PT110+CUDA113
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pytorch: "torch==1.10.2 torchvision==0.11.3 --extra-index-url https://download.pytorch.org/whl/cu113"
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base: "nvcr.io/nvidia/pytorch:21.06-py3" # CUDA 11.3
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- environment: PT112+CUDA113
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pytorch: "torch==1.12.1 torchvision==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu113"
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- environment: PT113+CUDA113
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pytorch: "torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu113"
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base: "nvcr.io/nvidia/pytorch:21.06-py3" # CUDA 11.3
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- environment: PTLATEST+CUDA118
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- environment: PTLATEST+CUDA121
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pytorch: "-U torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118"
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base: "nvcr.io/nvidia/pytorch:23.03-py3" # CUDA 11.8
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base: "nvcr.io/nvidia/pytorch:23.06-py3" # CUDA 12.1
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container:
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image: ${{ matrix.base }}
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options: "--gpus all"
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if: github.repository == 'Project-MONAI/MONAI'
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strategy:
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matrix:
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container: ["pytorch:22.09", "pytorch:22.11", "pytorch:23.03"]
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container: ["pytorch:22.10", "pytorch:23.06"]
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container:
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image: nvcr.io/nvidia/${{ matrix.container }}-py3 # testing with the latest pytorch base image
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options: "--gpus all"
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if: github.repository == 'Project-MONAI/MONAI'
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strategy:
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matrix:
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container: ["pytorch:22.09", "pytorch:22.11", "pytorch:23.03"]
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container: ["pytorch:23.06"]
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container:
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image: nvcr.io/nvidia/${{ matrix.container }}-py3 # testing with the latest pytorch base image
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options: "--gpus all"
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if: github.repository == 'Project-MONAI/MONAI'
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needs: cron-gpu # so that monai itself is verified first
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container:
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image: nvcr.io/nvidia/pytorch:23.03-py3 # testing with the latest pytorch base image
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image: nvcr.io/nvidia/pytorch:23.06-py3 # testing with the latest pytorch base image
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options: "--gpus all --ipc=host"
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runs-on: [self-hosted, linux, x64, integration]
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steps:

.github/workflows/pythonapp-gpu.yml

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- "PT110+CUDA111"
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- "PT112+CUDA118DOCKER"
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- "PT113+CUDA116"
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- "PT114+CUDA120DOCKER"
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- "PT210+CUDA121DOCKER"
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include:
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
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- environment: PT19+CUDA114DOCKER
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- environment: PT113+CUDA116
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pytorch: "torch==1.13.1 torchvision==0.14.1"
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base: "nvcr.io/nvidia/cuda:11.6.1-devel-ubuntu18.04"
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- environment: PT114+CUDA120DOCKER
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# 23.03: 2.0.0a0+1767026
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- environment: PT210+CUDA121DOCKER
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# 23.06: 2.1.0a0+4136153
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pytorch: "-h" # we explicitly set pytorch to -h to avoid pip install error
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base: "nvcr.io/nvidia/pytorch:23.03-py3"
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base: "nvcr.io/nvidia/pytorch:23.06-py3"
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container:
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image: ${{ matrix.base }}
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options: --gpus all --env NVIDIA_DISABLE_REQUIRE=true # workaround for unsatisfied condition: cuda>=11.6

Dockerfile

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# To build with a different base image
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# please run `docker build` using the `--build-arg PYTORCH_IMAGE=...` flag.
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ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.03-py3
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ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.06-py3
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FROM ${PYTORCH_IMAGE}
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LABEL maintainer="[email protected]"

tests/test_grid_distortion.py

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)
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TESTS.append(
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[
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dict(num_cells=2, distort_steps=[(1.25,) * 3] * 3, mode="nearest", padding_mode="zeros"),
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dict(num_cells=2, distort_steps=[(1.26,) * 3] * 3, mode="nearest", padding_mode="zeros"),
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p(np.indices([3, 3, 3])[:1].astype(np.float32)),
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p(
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np.array(

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