Docker Slim Image (#8640)

### Description

This is an attempt to create a slim Docker image which is smaller than
the current one to avoid running out of space during testing. Various
fixes have been included to account for test fails within the image.
These appear to be all real issues that need to be addressed (eg. ONNX
export) or fixes that should be integrated either way.

~~This excludes PyTorch 2.9 from the requirements for now to avoid
legacy issues with ONNX, Torchscript, and other things. MONAI needs to
be updated for PyTorch 2.9 support, specifically dropping the use of
Torchscript in places as it's becoming obsolete in place of
`torch.export`.~~

Some tests fail without enough shared memory, the command I'm using to
run with is `docker run -ti --rm --gpus '"device=0,1"' --shm-size=10gb
-v $(pwd)/tests:/opt/monai/tests monai_slim /bin/bash` to tests with
GPUs 0 and 1.

### 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.
- [x] 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: Eric Kerfoot <eric.kerfoot@kcl.ac.uk>
Signed-off-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
Signed-off-by: Eric Kerfoot <eric.kerfoot@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

Author: 3 people

.dockerignore

@@ -3,11 +3,15 @@
 __pycache__/
 docs/
 
+.vscode
+.git
+.mypy_cache
+.ruff_cache
+.pytype
 .coverage
 .coverage.*
 .coverage/
 coverage.xml
 .readthedocs.yml
-*.toml
 
 !README.md

CONTRIBUTING.md

@@ -380,7 +380,8 @@ All code review comments should be specific, constructive, and actionable.
 
 ### Release a new version
 
-The `dev` branch's `HEAD` always corresponds to MONAI docker image's latest tag: `projectmonai/monai:latest`.
+The `dev` branch's `HEAD` always corresponds to MONAI Docker image's latest tag: `projectmonai/monai:latest`. (No
+release is currently done for the slim MONAI image, this is built locally by users.)
 The `main` branch's `HEAD` always corresponds to the latest MONAI milestone release.
 
 When major features are ready for a milestone, to prepare for a new release:

Dockerfile.slim

@@ -0,0 +1,93 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This is a slimmed down version of the MONAI Docker image using a smaller base image and multi-stage building. Not all
+# NVIDIA tools will be present but all libraries and compiled code are included. This image isn't provided through
+# Dockerhub so users must build locally: `docker build -t monai_slim -f Dockerfile.slim .`
+# Containers may require more shared memory, eg.: `docker run -ti --rm --gpus all --shm-size=10gb monai_slim /bin/bash`
+
+ARG IMAGE=debian:12-slim
+
+FROM ${IMAGE} AS build
+
+ARG TORCH_CUDA_ARCH_LIST="7.5 8.0 8.6 8.9 9.0+PTX"
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV APT_INSTALL="apt install -y --no-install-recommends"
+
+RUN apt update && apt upgrade -y && \
+    ${APT_INSTALL} ca-certificates python3-pip python-is-python3 git wget libopenslide0 unzip python3-dev && \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb && \
+    dpkg -i cuda-keyring_1.1-1_all.deb && \
+    apt update && \
+    ${APT_INSTALL} cuda-toolkit-12 && \
+    rm -rf /usr/lib/python*/EXTERNALLY-MANAGED /var/lib/apt/lists/* && \
+    python -m pip install --upgrade --no-cache-dir --no-build-isolation pip
+
+# TODO: remark for issue [revise the dockerfile](https://github.com/zarr-developers/numcodecs/issues/431)
+RUN if [[ $(uname -m) =~ "aarch64" ]]; then \
+      CFLAGS="-O3" DISABLE_NUMCODECS_SSE2=true DISABLE_NUMCODECS_AVX2=true python -m pip install numcodecs; \
+    fi
+
+# NGC Client
+WORKDIR /opt/tools
+ARG NGC_CLI_URI="https://ngc.nvidia.com/downloads/ngccli_linux.zip"
+RUN wget -q ${NGC_CLI_URI} && unzip ngccli_linux.zip && chmod u+x ngc-cli/ngc && \
+    find ngc-cli/ -type f -exec md5sum {} + | LC_ALL=C sort | md5sum -c ngc-cli.md5 && \
+    rm -rf ngccli_linux.zip ngc-cli.md5
+
+WORKDIR /opt/monai
+
+# copy relevant parts of repo
+COPY requirements.txt requirements-min.txt requirements-dev.txt versioneer.py setup.py setup.cfg pyproject.toml ./
+COPY LICENSE CHANGELOG.md CODE_OF_CONDUCT.md CONTRIBUTING.md README.md MANIFEST.in runtests.sh ./
+COPY tests ./tests
+COPY monai ./monai
+
+# install full deps
+RUN python -m pip install --no-cache-dir --no-build-isolation -r requirements-dev.txt
+
+# compile ext
+RUN CUDA_HOME=/usr/local/cuda FORCE_CUDA=1 USE_COMPILED=1 BUILD_MONAI=1 python setup.py develop
+
+# recreate the image without the installed CUDA packages then copy the installed MONAI and Python directories
+FROM ${IMAGE} AS build2
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV APT_INSTALL="apt install -y --no-install-recommends"
+
+RUN apt update && apt upgrade -y && \
+    ${APT_INSTALL} ca-certificates python3-pip python-is-python3 git libopenslide0 && \
+    apt clean && \
+    rm -rf /usr/lib/python*/EXTERNALLY-MANAGED /var/lib/apt/lists/* && \
+    python -m pip install --upgrade --no-cache-dir --no-build-isolation pip
+
+COPY --from=build /opt/monai /opt/monai
+COPY --from=build /opt/tools /opt/tools
+ARG PYTHON_VERSION=3.11
+COPY --from=build /usr/local/lib/python${PYTHON_VERSION}/dist-packages /usr/local/lib/python${PYTHON_VERSION}/dist-packages
+COPY --from=build /usr/local/bin /usr/local/bin
+
+RUN rm -rf /opt/monai/build /opt/monai/monai.egg-info && \
+    find /opt /usr/local/lib -type d -name __pycache__ -exec rm -rf {} +
+
+# flatten all layers down to one
+FROM ${IMAGE}
+LABEL maintainer="monai.contact@gmail.com"
+
+COPY --from=build2 / /
+
+WORKDIR /opt/monai
+
+ENV PATH=${PATH}:/opt/tools:/opt/tools/ngc-cli
+ENV POLYGRAPHY_AUTOINSTALL_DEPS=1
+ENV CUDA_HOME=/usr/local/cuda
+ENV BUILD_MONAI=1

README.md

@@ -61,6 +61,14 @@ Examples and notebook tutorials are located at [Project-MONAI/tutorials](https:/
 
 Technical documentation is available at [docs.monai.io](https://docs.monai.io).
 
+## Docker
+
+The MONAI Docker image is available from [Dockerhub](https://hub.docker.com/r/projectmonai/monai),
+tagged as `latest` for the latest state of `dev` or with a release version. A slimmed down image can also be built
+locally using `Dockerfile.slim`, see that file for instructions.
+
+To get started with the latest MONAI, use `docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash`.
+
 ## Citation
 
 If you have used MONAI in your research, please cite us! The citation can be exported from: <https://arxiv.org/abs/2211.02701>.

monai/apps/vista3d/inferer.py

@@ -86,13 +86,13 @@ def point_based_window_inferer(
             for j in range(len(ly_)):
                 for k in range(len(lz_)):
                     lx, rx, ly, ry, lz, rz = (lx_[i], rx_[i], ly_[j], ry_[j], lz_[k], rz_[k])
-                    unravel_slice = [
+                    unravel_slice = (
                         slice(None),
                         slice(None),
                         slice(int(lx), int(rx)),
                         slice(int(ly), int(ry)),
                         slice(int(lz), int(rz)),
-                    ]
+                    )
                     batch_image = image[unravel_slice]
                     output = predictor(
                         batch_image,

monai/networks/nets/vista3d.py

@@ -244,14 +244,10 @@ def connected_components_combine(
         _logits = logits[mapping_index]
         inside = []
         for i in range(_logits.shape[0]):
-            inside.append(
-                np.any(
-                    [
-                        _logits[i, 0, p[0], p[1], p[2]].item() > 0
-                        for p in point_coords[i].cpu().numpy().round().astype(int)
-                    ]
-                )
-            )
+            p_coord = point_coords[i].cpu().numpy().round().astype(int)
+            inside_p = [_logits[i, 0, p[0], p[1], p[2]].item() > 0 for p in p_coord]
+            inside.append(int(np.any(inside_p)))  # convert to int to avoid typing problems with Numpy
+
         inside_tensor = torch.tensor(inside).to(logits.device)
         nan_mask = torch.isnan(_logits)
         # _logits are converted to binary [B1, 1, H, W, D]

monai/networks/utils.py

@@ -713,7 +713,7 @@ def convert_to_onnx(
         torch_versioned_kwargs = {}
         if use_trace:
             # let torch.onnx.export to trace the model.
-            mode_to_export = model
+            model_to_export = model
             torch_versioned_kwargs = kwargs
             if "dynamo" in kwargs and kwargs["dynamo"] and verify:
                 torch_versioned_kwargs["verify"] = verify
@@ -726,9 +726,9 @@ def convert_to_onnx(
             #   pass the raw nn.Module directly—the exporter handles it via torch.export.
             _pt_major_minor = tuple(int(x) for x in torch.__version__.split("+")[0].split(".")[:2])
             if _pt_major_minor >= (2, 9):
-                mode_to_export = model
+                model_to_export = model
             else:
-                mode_to_export = torch.jit.script(model, **kwargs)
+                model_to_export = torch.jit.script(model, **kwargs)
 
         if torch.is_tensor(inputs) or isinstance(inputs, dict):
             onnx_inputs = (inputs,)
@@ -741,7 +741,7 @@ def convert_to_onnx(
         else:
             f = filename
         torch.onnx.export(
-            mode_to_export,
+            model_to_export,
             onnx_inputs,
             f=f,
             input_names=input_names,

requirements-dev.txt

@@ -1,6 +1,6 @@
 # Full requirements for developments
 -r requirements-min.txt
-pytorch-ignite==0.4.11
+pytorch-ignite
 gdown>=4.7.3
 scipy>=1.12.0; python_version >= '3.9'
 itk>=5.2

tests/bundle/test_bundle_download.py

@@ -15,7 +15,7 @@
 import os
 import tempfile
 import unittest
-from unittest.case import skipUnless
+from unittest.case import skipIf, skipUnless
 from unittest.mock import patch
 
 import numpy as np
@@ -219,6 +219,7 @@ def test_monaihosting_url_download_bundle(self, bundle_files, bundle_name, url):
 
     @parameterized.expand([TEST_CASE_5])
     @skip_if_quick
+    @skipIf(os.getenv("NGC_API_KEY", None) is None, "NGC API key required for this test")
     def test_ngc_private_source_download_bundle(self, bundle_files, bundle_name, _url):
         with skip_if_downloading_fails():
             # download a single file from url, also use `args_file`

tests/data/meta_tensor/test_meta_tensor.py

@@ -245,7 +245,7 @@ def test_pickling(self):
         with tempfile.TemporaryDirectory() as tmp_dir:
             fname = os.path.join(tmp_dir, "im.pt")
             torch.save(m, fname)
-            m2 = torch.load(fname, weights_only=True)
+            m2 = torch.load(fname, weights_only=False)
         self.check(m2, m, ids=False)
 
     @skip_if_no_cuda