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…n the slim Docker image, all of which appear to be real errors. Signed-off-by: Eric Kerfoot <eric.kerfoot@gmail.com>
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Nine tests in the image currently fail. The first 4 are related to auto3dseg and mention a value "image_stats" being missing from a config file, these tests pass when run in isolation however. The others relate to the GMM module and not being able to compile it since nvcc is missing from image, which is true since the CUDA toolkit is omitted for size reasons. Output of the errors
It's a simple matter of forcing the GMM module to build when building the image, this also fails if used as a RUN command: |
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Actionable comments posted: 1
🧹 Nitpick comments (4)
.github/workflows/pythonapp.yml (1)
31-37: Consolidate repetitive cleanup logic.The "Clean unused tools" step is identical across three jobs. Consider using a GitHub Actions composite action or reusable workflow to avoid duplication. Additionally, verify whether the duplicate cleanup commands in the subsequent "Install dependencies" steps (e.g., line 56, line 169) are still necessary now that cleanup runs upfront.
Also applies to: 140-146, 232-238
Dockerfile.slim (1)
88-88: CUDA_HOME set in non-CUDA runtime image.Line 88 sets
ENV CUDA_HOME=/usr/local/cuda, but the CUDA toolkit is not present in the final image (intentionally omitted for size). While MONAI can degrade gracefully, this may cause confusing behavior or spurious build attempts. Consider removing this env var or documenting why it's needed.requirements.txt (1)
1-2: Add inline comments explaining version constraints.The
<2.9upper bound (ONNX/TorchScript incompatibility) and Windows!=2.7.0exclusion (known CUDA/XPU wheel issues) should be documented with comments to help future maintainers understand these constraints.tests/networks/test_convert_to_onnx.py (1)
73-104: Fix minor docstring typo in SegResNet testDocstring says “SetResNet” while the model is
SegResNet. Consider aligning the wording.- """Test converting SetResNet to ONNX.""" + """Test converting SegResNet to ONNX."""
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**/*.py
⚙️ CodeRabbit configuration file
Review the Python code for quality and correctness. Ensure variable names adhere to PEP8 style guides, are sensible and informative in regards to their function, though permitting simple names for loop and comprehension variables. Ensure routine names are meaningful in regards to their function and use verbs, adjectives, and nouns in a semantically appropriate way. Docstrings should be present for all definition which describe each variable, return value, and raised exception in the appropriate section of the Google-style of docstrings. Examine code for logical error or inconsistencies, and suggest what may be changed to addressed these. Suggest any enhancements for code improving efficiency, maintainability, comprehensibility, and correctness. Ensure new or modified definitions will be covered by existing or new unit tests.
Files:
tests/losses/test_multi_scale.pymonai/networks/nets/vista3d.pytests/data/meta_tensor/test_meta_tensor.pymonai/apps/vista3d/inferer.pymonai/networks/utils.pytests/bundle/test_bundle_download.pytests/networks/test_convert_to_onnx.py
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tests/losses/test_multi_scale.py (1)
tests/test_utils.py (1)
assert_allclose(119-159)
monai/networks/nets/vista3d.py (1)
monai/data/meta_tensor.py (1)
astype(434-461)
tests/data/meta_tensor/test_meta_tensor.py (2)
monai/bundle/scripts.py (1)
load(630-770)monai/networks/nets/transchex.py (1)
load(96-103)
🔇 Additional comments (15)
tests/bundle/test_bundle_download.py (2)
18-18: ImportingskipIfalongsideskipUnlessis appropriateMatches existing usage pattern and keeps decorators local; no issues here.
222-222: NGC private test skip condition is correctSkipping when
NGC_API_KEYis not set is a precise guard for this private-source test and avoids spurious failures in environments without credentials.tests/data/meta_tensor/test_meta_tensor.py (1)
248-248: Verify that production code loading MetaTensor objects accounts for custom class requirements.The
weights_only=Falseparameter at line 248 is necessary for MetaTensor to preserve its metadata and custom attributes during deserialization. However, ensure that any production code paths deserializing MetaTensor objects also useweights_only=False, asweights_only=Truerestricts loading to basic types and will fail for custom classes. For untrusted external model files, consider using safer formats like safetensors or ensure PyTorch ≥ 2.6.0 is used to mitigate known deserialization vulnerabilities..dockerignore (1)
6-10: LGTM.The additions align with Docker build context optimization and complement the Dockerfile.slim copying strategy.
Dockerfile.slim (3)
56-56: Confirm extension build succeeds despite known GMM failures.Per PR notes, GMM extension build fails in the slim image due to missing CUDA compiler. This build command (line 56) succeeds in the build stage, but GMM tests fail later when run inside the slim image. Clarify whether this is expected behavior or whether the slim image is intended to support GMM-dependent tests.
If the slim image is not meant to support GMM tests, document this limitation or skip GMM tests conditionally in the image.
16-89: Multi-stage build is well-designed for size optimization.The separation of build (with CUDA) and runtime (without CUDA) stages correctly achieves the slim image goal. Artifact cleanup (line 75-76) appropriately reduces bloat.
38-42: NGC CLI validation is thorough; verify it's used.The NGC CLI is downloaded and validated via md5sum in the build, but there's no evidence it's invoked in MONAI workflows. If unused, consider removing to reduce image bloat. Verify NGC CLI is actually used by searching the codebase for invocations (e.g.,
rg -i 'ngc\s+'in monai/ and tests/ directories).tests/losses/test_multi_scale.py (1)
58-58: Justify the tolerance relaxation.Loosening
rtolfrom1e-5to1e-4may mask numerical regressions. Add a comment explaining the reason (e.g., slim image, different CUDA runtime).monai/apps/vista3d/inferer.py (1)
89-95: LGTM!Using tuple for immutable slice specification is idiomatic.
requirements-dev.txt (2)
3-3: Unpinning pytorch-ignite may introduce breaking changes.Consider adding a minimum version constraint if specific features are relied upon.
55-55: LGTM!Making
onnxruntimeunconditional is appropriate since it now supports Python 3.11+.monai/networks/nets/vista3d.py (1)
246-249: LGTM!Explicit
int()cast avoids NumPy bool typing issues. Logic is clearer with intermediate variables.monai/networks/utils.py (1)
714-745: ONNX export target variable rename is correct and consistent
model_to_exportis now set appropriately in both the trace and script branches and passed totorch.onnx.export, resolving the previous naming inconsistency without changing external behavior. Looks good.tests/networks/test_convert_to_onnx.py (2)
25-32: CPU‑only device options and ONNX import handling are reasonableUsing
onnx, _ = optional_import("onnx")together with@SkipIfNoModule("onnx")is safe, and constrainingTORCH_DEVICE_OPTIONSto["cpu"]with a clear FIXME matches the current nondeterministic CUDA behavior and slim-image constraints.
48-70: UNet ONNX export test now robustly exercises both trace and script pathsThe refactored
test_unetalways callsconvert_to_onnx, parameterizesuse_traceanduse_ort, and validates the return type againstonnx.ModelProto, giving good coverage of the updatedconvert_to_onnxbehavior on CPU.
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
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📝 WalkthroughWalkthroughAdds/updates CI and build infra: expanded .dockerignore and a new multi-stage Dockerfile.slim for CUDA builds with NGC CLI and layered runtime. Code changes: unravel_slice built as a tuple; connected_components_combine now appends int(np.any(...)) instead of bool; convert_to_onnx uses model_to_export consistently and adjusts verify handling when tracing with dynamo. Dependency change: unpinned pytorch-ignite. Tests: gate NGC test with skipIf, load MetaTensor with weights_only=False, relax numeric tolerance, and simplify ONNX conversion tests to CPU paths and always run export. Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes 🚥 Pre-merge checks | ✅ 1 | ❌ 2❌ Failed checks (1 warning, 1 inconclusive)
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Actionable comments posted: 3
🧹 Nitpick comments (4)
tests/losses/test_multi_scale.py (1)
57-58: Relaxed tolerance is reasonable for numeric stabilityBumping
rtolto1e-4here is still strict for a scalar loss value and should help avoid backend‑dependent noise causing spurious failures. No functional concerns from this change alone. You might optionally consider using the sharedassert_allclosehelper fromtests.test_utilsfor consistency across tests, but it’s not required for correctness here..github/workflows/pythonapp.yml (1)
31-37: Cleanup step placement is ineffectual; consider moving or consolidating.Running this cleanup before
checkoutprovides minimal value since no artifacts exist yet. Cleanup is most useful after build steps produce large intermediate files. Additionally, the same 5-line block repeats across three jobs—consider extracting to a reusable workflow or composite action to reduce duplication.Also applies to: 140-146, 232-238
Dockerfile.slim (2)
90-90: BUILD_MONAI flag is build-time only; unnecessary in runtime image.Line 90 sets
BUILD_MONAI=1in the final image, but this flag is only meaningful duringsetup.py develop(line 56 of the build stage). Carrying it into the runtime image serves no purpose and may confuse future maintainers.Apply this diff to remove the unnecessary flag:
ENV PATH=${PATH}:/opt/tools:/opt/tools/ngc-cli ENV POLYGRAPHY_AUTOINSTALL_DEPS=1 -ENV BUILD_MONAI=1 ENV CUDA_HOME=/usr/local/cuda
76-77: Restrict pycache cleanup to MONAI-specific paths for efficiency.Line 77 uses
find /to remove all__pycache__directories, which is inefficient and targets too broadly (system libraries, /proc, /sys, etc.). Scope the cleanup to MONAI artifacts and Python site-packages.Apply this diff:
RUN rm -rf /opt/monai/build /opt/monai/monai.egg-info && \ - find / -name __pycache__ | xargs rm -rf + find /opt/monai /usr/local/lib -name __pycache__ -type d -exec rm -rf {} + 2>/dev/null || true
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**/*.py
⚙️ CodeRabbit configuration file
Review the Python code for quality and correctness. Ensure variable names adhere to PEP8 style guides, are sensible and informative in regards to their function, though permitting simple names for loop and comprehension variables. Ensure routine names are meaningful in regards to their function and use verbs, adjectives, and nouns in a semantically appropriate way. Docstrings should be present for all definition which describe each variable, return value, and raised exception in the appropriate section of the Google-style of docstrings. Examine code for logical error or inconsistencies, and suggest what may be changed to addressed these. Suggest any enhancements for code improving efficiency, maintainability, comprehensibility, and correctness. Ensure new or modified definitions will be covered by existing or new unit tests.
Files:
monai/networks/nets/vista3d.pymonai/networks/utils.pytests/networks/test_convert_to_onnx.pytests/data/meta_tensor/test_meta_tensor.pytests/bundle/test_bundle_download.pytests/losses/test_multi_scale.pymonai/apps/vista3d/inferer.py
🧬 Code graph analysis (2)
tests/networks/test_convert_to_onnx.py (3)
monai/utils/module.py (1)
optional_import(315-445)monai/networks/nets/unet.py (1)
UNet(28-299)monai/networks/utils.py (1)
convert_to_onnx(661-785)
tests/losses/test_multi_scale.py (1)
tests/test_utils.py (1)
assert_allclose(119-159)
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🔇 Additional comments (14)
tests/bundle/test_bundle_download.py (2)
18-18: ImportingskipIfis appropriate and consistentAdding
skipIfhere is correct for the new test gating and keeps skip-related utilities together withskipUnless. No issues.
220-223: Environment‑gated NGC private test looks correctSkipping
test_ngc_private_source_download_bundlewhenNGC_API_KEYis unset is a sensible way to avoid hard failures in environments without credentials, while still running the test when properly configured.monai/networks/utils.py (1)
716-736: LGTM - Typo fix in variable naming.Variable renamed from
mode_to_exporttomodel_to_exportin both tracing and scripting branches. Semantically correct and consistent.monai/apps/vista3d/inferer.py (1)
89-95: LGTM - List to tuple for slice construction.Changed from list to tuple for the slice indexing object. Both work equivalently for tensor indexing; tuple is more idiomatic for immutable sequences.
.dockerignore (1)
6-10: LGTM - Added development artifact ignores.Good practice to exclude .vscode, .git, and various tool caches from Docker builds. Reduces image size.
monai/networks/nets/vista3d.py (1)
246-249: LGTM - Explicit type conversion for numpy compatibility.Refactored to build per-point boolean list, apply
np.any(), and explicitly cast toint. Comment clarifies this avoids numpy typing issues. Functionally equivalent to previous logic.tests/networks/test_convert_to_onnx.py (3)
25-32: LGTM - Simplified to CPU-only ONNX testing.Removed CUDA device options, restricting tests to CPU. Aligns with slim Docker image without CUDA toolkit. Comment at line 29-31 notes CUDA produces different outputs during testing.
48-69: LGTM - Added docstring and type assertion.Added docstring for test clarity. Now always calls
convert_to_onnxand asserts return type isonnx.ModelProto, improving test coverage.
73-104: LGTM - Added docstring for SegResNet test.Documents that this tests SegResNet ONNX conversion with
use_trace=True. Existing tolerance and runtime checks preserved.tests/data/meta_tensor/test_meta_tensor.py (1)
248-248: Verify necessity ofweights_only=Falsechange.Changed from
weights_only=Truetoweights_only=Falsewhen loading the pickled MetaTensor. This allows loading arbitrary Python objects, which is less secure. Ensure this is required for proper MetaTensor deserialization with metadata.requirements.txt (1)
1-2: Reconsider Windows exclusion of PyTorch 2.7.0 — documented issues are environmental, not version-critical.PyTorch 2.7.0 (latest stable as of December 2025) has known Windows complications: CUDA/toolkit mismatches (especially CUDA 12.x builds), XPU wheel failures, and dependency conflicts with torchvision/torchaudio. However, these are configuration and environment issues, not version-specific breakages. Excluding 2.7.0 entirely on Windows prevents users from installing the latest stable release. Instead, document the required environment setup (CUDA version matching, wheel source verification, system DLL/Windows 10+ requirement) or keep 2.7.0 available if your tested environment passes.
The upper bound
<2.9is appropriate; MONAI 1.5.x supports PyTorch up to 2.8, and PyTorch 2.9 is not yet released.Dockerfile.slim (3)
72-73: ✓ Python version handling addressed prior feedback.The use of
ARG PYTHON_VERSION=3.11with variable substitution in the COPY command (line 73) makes the build maintainable and configurable across Python versions. This directly addresses the prior review concern about hardcoded paths.
38-42: Verify NGC CLI MD5 validation file exists in build context.Line 41 attempts to validate the NGC CLI download using
ngc-cli.md5, but this file must be available in the build context when the RUN command executes. Confirm thatngc-cli.md5is either:
- Present in the repository root, or
- Copied into the Docker image before line 41 via a COPY or ADD statement
89-89: CUDA_HOME points to non-existent directory in runtime image.Line 89 sets
CUDA_HOME=/usr/local/cuda, but thebuild2stage (lines 64–68) explicitly does not install the CUDA toolkit. This means the directory doesn't exist at runtime, which could cause failures if code or precompiled extensions try to resolve CUDA libraries or executables.Either remove this environment variable from the runtime image or install minimal CUDA runtime libraries in the
build2stage if they're required for the precompiled extensions.
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Hi @KumoLiu I think we should push this one through to solve some of the issues we're seeing with actions running out of space. Some other PRs are stuck with failing checks but I think the changes I have here will fix them. |
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🧹 Nitpick comments (1)
tests/networks/test_convert_to_onnx.py (1)
29-31: FIXME left without a tracking issue.The CUDA path is silently disabled with no linked issue. This means CUDA ONNX export goes untested indefinitely.
Want me to draft a GitHub issue to track the CUDA vs. ONNX output discrepancy so this isn't forgotten?
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tests/networks/test_convert_to_onnx.py` around lines 29 - 31, Replace the naked FIXME comment that disables CUDA in the test with an explicit tracking reference: open a GitHub issue describing the CUDA vs ONNX output discrepancy, then update the commented block around TORCH_DEVICE_OPTIONS (the conditional that would append "cuda") to include the issue number/URL and a short TODO note, or alternatively add a pytest.skip/marker linked to that issue; ensure the comment clearly states the issue ID so the CUDA path isn't silently untested.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@tests/networks/test_convert_to_onnx.py`:
- Line 73: The docstring contains a typo: replace the text "SetResNet" with
"SegResNet" in the test docstring that currently reads "Test converting
SetResNet to ONNX." so the string becomes "Test converting SegResNet to ONNX.";
update the triple-quoted docstring in the test function (the one with that exact
text) to correct the model name.
---
Nitpick comments:
In `@tests/networks/test_convert_to_onnx.py`:
- Around line 29-31: Replace the naked FIXME comment that disables CUDA in the
test with an explicit tracking reference: open a GitHub issue describing the
CUDA vs ONNX output discrepancy, then update the commented block around
TORCH_DEVICE_OPTIONS (the conditional that would append "cuda") to include the
issue number/URL and a short TODO note, or alternatively add a
pytest.skip/marker linked to that issue; ensure the comment clearly states the
issue ID so the CUDA path isn't silently untested.
Signed-off-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@requirements-dev.txt`:
- Around line 52-54: The requirements list contains a duplicate onnxruntime
entry that nullifies the python_version guard: remove the unconditional
"onnxruntime" line and keep only the guarded entry 'onnxruntime; python_version
< "3.12"' so the package is installed conditionally (preserving the onnxscript
line as-is); ensure no other duplicate onnxruntime entries remain in
requirements-dev.txt.
---
Duplicate comments:
In `@requirements-dev.txt`:
- Line 3: Unpinning pytorch-ignite can break code that imports ignite.contrib;
inspect the codebase (and MONAI-related modules) for any imports of
ignite.contrib and either remove/replace those imports or pin pytorch-ignite
back to a pre-0.5.0 release (e.g., <=0.4.x) in requirements-dev.txt;
specifically search for "ignite.contrib" and references to MONAI integration
points and update the dependency or the import usage accordingly before merging.
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Excellent, Eric. I am still running the tests. I will let you know when it is done and provide some feedback on that as well.
Thank you.
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Is this supposed to be a temporary image to then replace the current Docker image, or will we maintain both? If we are keeping both, it may worth it adding some documentation around here |
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It's meant to be kept as a slimmed image for anyone that wants it, we had originally thought about replacing the existing image which was encountering memory issues, but this was solved in the Actions themselves by caching in a different way. There wasn't really documentation on this and it made more sense to add something to the README.md file which I have now done. |
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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 oftorch.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/bashto tests with GPUs 0 and 1.Types of changes
./runtests.sh -f -u --net --coverage../runtests.sh --quick --unittests --disttests.make htmlcommand in thedocs/folder.